diff --git a/experiments/LASER-2heads0.5.ipynb b/experiments/LASER-2heads0.5.ipynb new file mode 100644 index 0000000..2e28038 --- /dev/null +++ b/experiments/LASER-2heads0.5.ipynb @@ -0,0 +1,2324 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "name": "stdout", + "text": [ + "/home/pczapla/workspace/ulmfit-multilingual\r\n" + ], + "output_type": "stream" + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "from fastai.text import *" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "# unzip task6_test.zip\n", + "# unzip task_6-1.zip -d Task6/task\\ 01\n", + "# unzip task_6-2.zip -d Task6/task\\ 02\n", + "# mv Task6/task\\ 01 task-1\n", + "# mv Task6/task\\ 02 task-2\n", + "# rmdir Task6" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "torch.cuda.set_device(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "[PosixPath(\u0027data/poleval19/task-1/test_set_clean_only_text.txt\u0027),\n PosixPath(\u0027data/poleval19/task-1/training_set_clean_only_text.tmp.tok\u0027),\n PosixPath(\u0027data/poleval19/task-1/training_set_clean_only_text.txt\u0027),\n PosixPath(\u0027data/poleval19/task-1/toxic-train-tags.csv\u0027),\n PosixPath(\u0027data/poleval19/task-1/test_set_clean_only_text.tmp.bpe\u0027),\n PosixPath(\u0027data/poleval19/task-1/test_set_clean_only_text.tmp.enc\u0027),\n PosixPath(\u0027data/poleval19/task-1/test_set_clean_only_text.tmp.split\u0027),\n PosixPath(\u0027data/poleval19/task-1/toxic-train-text.tmp.split.enc\u0027),\n PosixPath(\u0027data/poleval19/task-1/test_set_clean_only_text.tmp.sid\u0027),\n PosixPath(\u0027data/poleval19/task-1/toxic-train-text.tmp.sid\u0027),\n PosixPath(\u0027data/poleval19/task-1/training_set_clean_only_tags.txt\u0027),\n PosixPath(\u0027data/poleval19/task-1/training_set_clean_only_text.tmp.sid\u0027),\n PosixPath(\u0027data/poleval19/task-1/toxic-train-text.tmp.tok\u0027),\n PosixPath(\u0027data/poleval19/task-1/training_set_clean_only_text.tmp.split.enc\u0027),\n PosixPath(\u0027data/poleval19/task-1/models\u0027),\n PosixPath(\u0027data/poleval19/task-1/toxic-train-text.csv\u0027),\n PosixPath(\u0027data/poleval19/task-1/evaulate1.pl\u0027),\n PosixPath(\u0027data/poleval19/task-1/test_set_clean_only_text.tmp.tok\u0027),\n PosixPath(\u0027data/poleval19/task-1/toxic-train-text.tmp.bpe\u0027),\n PosixPath(\u0027data/poleval19/task-1/test_set_clean_only_tags.txt\u0027),\n PosixPath(\u0027data/poleval19/task-1/training_set_clean_only_text.tmp.enc\u0027),\n PosixPath(\u0027data/poleval19/task-1/toxic-train-text.tmp.enc\u0027),\n PosixPath(\u0027data/poleval19/task-1/toxic-train-text.tmp.split\u0027),\n PosixPath(\u0027data/poleval19/task-1/training_set_clean_only_text.tmp.split\u0027),\n PosixPath(\u0027data/poleval19/task-1/training_set_clean_only_text.tmp.bpe\u0027),\n PosixPath(\u0027data/poleval19/task-1/test_set_clean_only_text.tmp.split.enc\u0027)]" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 10 + } + ], + "source": "dataset\u003dPath(\u0027data\u0027)/\u0027poleval19\u0027/\u0027task-1\u0027\ndataset.ls()" + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": "fn \u003d \u0027data/toxic-comment/train.csv\u0027\ndf \u003d pd.read_csv(fn)\ncomment_text \u003d df.comment_text.str.replace(\"\\n\", \" \")\ndel df[\u0027comment_text\u0027]" + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "comment_text.to_csv(dataset/\"toxic-train-text.csv\", sep\u003d\u0027\\t\u0027, index\u003dFalse, header\u003dFalse, quoting\u003dcsv.QUOTE_NONE, quotechar\u003d\u0027|\u0027, escapechar\u003d\u0027\\\\\u0027)\n", + "df.to_csv(dataset/\"toxic-train-tags.csv\", index\u003dFalse, header\u003dFalse)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "name": "stdout", + "text": [ + "Explanation Why the edits made under my username Hardcore Metallica Fan were reverted? They weren\u0027t vandalisms, just closure on some GAs after I voted at New York Dolls FAC. And please don\u0027t remove the template from the talk page since I\u0027m retired now.89.205.38.27\r\nD\u0027aww! He matches this background colour I\u0027m seemingly stuck with. Thanks. (talk) 21:51, January 11, 2016 (UTC)\r\nHey man, I\u0027m really not trying to edit war. It\u0027s just that this guy is constantly removing relevant information and talking to me through edits instead of my talk page. He seems to care more about the formatting than the actual info.\r\n\" More I can\u0027t make any real suggestions on improvement - I wondered if the section statistics should be later on, or a subsection of \"\"types of accidents\"\" -I think the references may need tidying so that they are all in the exact same format ie date format etc. I can do that later on, if no-one else does first - if you have any preferences for formatting style on references or want to do it yourself please let me know. There appears to be a backlog on articles for review so I guess there may be a delay until a reviewer turns up. It\u0027s listed in the relevant form eg Wikipedia:Good_article_nominations#Transport \"\r\nYou, sir, are my hero. Any chance you remember what page that\u0027s on?\r\n\" Congratulations from me as well, use the tools well.  · talk \"\r\nCOCKSUCKER BEFORE YOU PISS AROUND ON MY WORK\r\nYour vandalism to the Matt Shirvington article has been reverted. Please don\u0027t do it again, or you will be banned.\r\nSorry if the word \u0027nonsense\u0027 was offensive to you. Anyway, I\u0027m not intending to write anything in the article(wow they would jump on me for vandalism), I\u0027m merely requesting that it be more encyclopedic so one can use it for school as a reference. I have been to the selective breeding page but it\u0027s almost a stub. It points to \u0027animal breeding\u0027 which is a short messy article that gives you no info. There must be someone around with expertise in eugenics? 93.161.107.169\r\nalignment on this subject and which are contrary to those of DuLithgow\r\n" + ], + "output_type": "stream" + } + ], + "source": [ + "!head {dataset/\"toxic-train-text.csv\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "LASER \u003d os.environ.get(\u0027LASER\u0027, \"/home/pczapla/workspace/_oss/LASER\")\n", + "os.environ[\u0027LASER\u0027] \u003d LASER\n", + "def cond_add_pypath(*paths):\n", + " for path in paths:\n", + " if path not in sys.path:\n", + " sys.path.append(path)\n", + "cond_add_pypath(LASER + \u0027/source\u0027,LASER + \u0027/source/tools\u0027)\n", + "\n", + "from embed import SentenceEncoder, EncodeLoad, EncodeFile\n", + "from text_processing import Token, BPEfastApply, SplitLines, JoinEmbed" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "from subprocess import run, check_output, DEVNULL\n", + "def tokenize(inp_fname, out_fname, lang\u003d\u0027en\u0027,\n", + " lower_case\u003dTrue, romanize\u003dFalse, descape\u003dFalse,\n", + " verbose\u003dFalse, over_write\u003dFalse, gzip\u003dFalse):\n", + " FASTBPE \u003d LASER + \u0027/tools-external/fastBPE/fast\u0027\n", + " MOSES_BDIR \u003d LASER + \u0027/tools-external/moses-tokenizer/tokenizer/\u0027\n", + " MOSES_TOKENIZER \u003d MOSES_BDIR + \u0027tokenizer.perl -q -no-escape -threads 20 -l \u0027\n", + " MOSES_LC \u003d MOSES_BDIR + \u0027lowercase.perl\u0027\n", + " NORM_PUNC \u003d MOSES_BDIR + \u0027normalize-punctuation.perl -l \u0027\n", + " DESCAPE \u003d MOSES_BDIR + \u0027deescape-special-chars.perl\u0027\n", + " REM_NON_PRINT_CHAR \u003d MOSES_BDIR + \u0027remove-non-printing-char.perl\u0027\n", + "\n", + " # Romanization (Greek only)\n", + " ROMAN_LC \u003d \u0027python3 \u0027 + LASER + \u0027/source/lib/romanize_lc.py -l \u0027\n", + "\n", + " # Mecab tokenizer for Japanese\n", + " MECAB \u003d LASER + \u0027/tools-external/mecab\u0027\n", + " assert lower_case, \u0027lower case is needed by all the models\u0027\n", + " if not out_fname.exists():\n", + " cat \u003d \u0027zcat \u0027 if gzip else \u0027cat \u0027\n", + " roman \u003d lang if romanize else \u0027none\u0027\n", + " # handle some iso3 langauge codes\n", + " if lang in (\u0027cmn\u0027, \u0027wuu\u0027, \u0027yue\u0027):\n", + " lang \u003d \u0027zh\u0027\n", + " if lang in (\u0027jpn\u0027):\n", + " lang \u003d \u0027ja\u0027\n", + " if verbose:\n", + " print(\u0027 - Tokenizer: {} in language {} {} {}\u0027\n", + " .format(os.path.basename(inp_fname), lang,\n", + " \u0027(gzip)\u0027 if gzip else \u0027\u0027,\n", + " \u0027(de-escaped)\u0027 if descape else \u0027\u0027,\n", + " \u0027(romanized)\u0027 if romanize else \u0027\u0027))\n", + " cmd \u003d (cat + str(inp_fname)\n", + " + \u0027|\u0027 + REM_NON_PRINT_CHAR\n", + " + \u0027| sed \"s/\\(@anonymized_account *\\)/account /gi\"\u0027\n", + "# + \u0027| iconv -f UTF-8 -t ASCII//TRANSLIT \u0027\n", + " + \u0027|\u0027 + NORM_PUNC + lang\n", + " + (\u0027|\u0027 + DESCAPE if descape else \u0027\u0027)\n", + " + \u0027|\u0027 + MOSES_TOKENIZER + lang\n", + " + (\u0027| python3 -m jieba -d \u0027 if lang \u003d\u003d \u0027zh\u0027 else \u0027\u0027)\n", + " + (\u0027|\u0027 + MECAB + \u0027/bin/mecab -O wakati -b 50000 \u0027 if lang \u003d\u003d \u0027ja\u0027 else \u0027\u0027)\n", + " + (\u0027|\u0027 + ROMAN_LC + roman if romanize else \u0027\u0027)\n", + " + (\u0027| tr \"[:upper:]\" \"[:lower:]\"\u0027 if lower_case else \u0027\u0027)\n", + " + \u0027\u003e\u0027 + str(out_fname))\n", + " print (\"Tokenziation CMD: \", cmd)\n", + " run(cmd,\n", + " env\u003ddict(os.environ, LD_LIBRARY_PATH\u003dMECAB + \u0027/lib\u0027),\n", + " shell\u003dTrue)\n", + " elif not over_write and verbose:\n", + " print(\u0027 - Tokenizer: {} exists already\u0027\n", + " .format(os.path.basename(out_fname), lang))\n", + "\n", + "\n", + "def split_lines(*args): return SplitLines(*list(map(str,args)))\n", + "def bpe_fast_apply(*args, **kwargs): return BPEfastApply(*list(map(str,args)), **kwargs)\n", + "def encode_file(enc, *args, **kwargs): return EncodeFile(enc, *list(map(str,args)), **kwargs)\n", + "def join_embed(*args): return JoinEmbed(*list(map(str,args)))\n", + " \n", + "def process_file(inputfn, lang\u003d\u0027pl\u0027):\n", + " inputfn \u003d Path(inputfn)\n", + " args \u003d SimpleNamespace(\n", + " encoder\u003dstr(Path(LASER)/\"models\"/\"bilstm.93langs.2018-12-26.pt\"), \n", + " bpe_codes\u003dstr(Path(LASER)/\"models\"/\"93langs.fcodes\"),\n", + " lang\u003dlang,\n", + " buffer_size\u003d10000,\n", + " max_tokens\u003d12000,\n", + " max_sentences\u003dNone,\n", + " cpu\u003dFalse,\n", + " verbose\u003dFalse,\n", + " stable\u003dTrue)\n", + " enc \u003d EncodeLoad(args)\n", + " def fn(suffix):\n", + " return inputfn.with_suffix(f\u0027.tmp{suffix}\u0027)\n", + " tokenize(inputfn,\n", + " fn(\u0027.tok\u0027),\n", + " lang\u003dlang,\n", + " romanize\u003d(True if lang \u003d\u003d \u0027el\u0027 else False),\n", + " lower_case\u003dTrue, gzip\u003dFalse,\n", + " verbose\u003dargs.verbose, over_write\u003dFalse)\n", + " bpe_fast_apply(fn(\u0027.tok\u0027),\n", + " fn(\u0027.bpe\u0027),\n", + " args.bpe_codes,\n", + " verbose\u003dargs.verbose, over_write\u003dFalse)\n", + " split_lines(fn(\u0027.bpe\u0027),\n", + " fn(\u0027.split\u0027),\n", + " fn(\u0027.sid\u0027))\n", + " encode_file(enc,\n", + " fn(\u0027.split\u0027),\n", + " fn(\u0027.split.enc\u0027),\n", + " verbose\u003dargs.verbose, over_write\u003dFalse,\n", + " buffer_size\u003dargs.buffer_size)\n", + " join_embed(fn(\u0027.split.enc\u0027),\n", + " fn(\u0027.sid\u0027),\n", + " fn(\u0027.enc\u0027))\n", + "# encode_file(enc,\n", + "# fn(\u0027.bpe\u0027),\n", + "# fn(\u0027.enc\u0027),\n", + "# verbose\u003dargs.verbose, over_write\u003dFalse,\n", + "# buffer_size\u003dargs.buffer_size)\n", + " return fn(\u0027.enc\u0027)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/toxic-train-text.csv|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl| sed \"s/\\(@anonymized_account *\\)/account /gi\"|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l en|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l en| tr \"[:upper:]\" \"[:lower:]\"\u003e../data/poleval19/task-1/toxic-train-text.tmp.tok\n", + " - Split sentences: ../data/poleval19/task-1/toxic-train-text.tmp.bpe\n", + " - lines/max words: 159571/4950 -\u003e 619938/4951\n", + " - Combine embeddings:\n", + " input: ../data/poleval19/task-1/toxic-train-text.tmp.split.enc 619938 sentences\n", + " IDs: ../data/poleval19/task-1/toxic-train-text.tmp.sid, 159571 sentences\n", + " output: ../data/poleval19/task-1/toxic-train-text.tmp.enc\n" + ] + } + ], + "source": [ + "!rm {dataset}/toxic-train-text.tmp.*\n", + "tx_trn_fn \u003d process_file(dataset / \u0027toxic-train-text.csv\u0027, lang\u003d\u0027en\u0027)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rm: cannot remove \u0027../data/poleval19/task-1/training_set_clean_only_text.tmp.*\u0027: No such file or directory\n", + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/training_set_clean_only_text.txt|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl| sed \"s/\\(@anonymized_account *\\)/account /gi\"|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l pl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l pl| tr \"[:upper:]\" \"[:lower:]\"\u003e../data/poleval19/task-1/training_set_clean_only_text.tmp.tok\n", + " - Split sentences: ../data/poleval19/task-1/training_set_clean_only_text.tmp.bpe\n", + " - lines/max words: 10041/82 -\u003e 12066/83\n", + " - Combine embeddings:\n", + " input: ../data/poleval19/task-1/training_set_clean_only_text.tmp.split.enc 12066 sentences\n", + " IDs: ../data/poleval19/task-1/training_set_clean_only_text.tmp.sid, 10041 sentences\n", + " output: ../data/poleval19/task-1/training_set_clean_only_text.tmp.enc\n" + ] + } + ], + "source": [ + "!rm {dataset}/training_set_clean_only_text.tmp.*\n", + "trn_fn \u003d process_file(dataset / \u0027training_set_clean_only_text.txt\u0027)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dla mnie faworytem do tytułu będzie Cracovia. Zobaczymy, czy typ się sprawdzi.\r", + "\r\n", + "account account Brawo ty Daria kibic ma być na dobre i złe\r", + "\r\n", + "account account Super, polski premier składa kwiaty na grobach kolaborantów. Ale doczekaliśmy czasów.\r", + "\r\n", + "account account Musi. Innej drogi nie mamy.\r", + "\r\n", + "Odrzut natychmiastowy, kwaśna mina, mam problem\r", + "\r\n", + "Jaki on był fajny xdd pamiętam, że spóźniłam się na jego pierwsze zajęcia i to sporo i za karę kazał mi usiąść w pierwszej ławce XD\r", + "\r\n", + "account No nie ma u nas szczęścia 😉\r", + "\r\n", + "account Dawno kogoś tak wrednego nie widziałam xd\r", + "\r\n", + "account account Zaległości były, ale ważne czy były wezwania do zapłaty z których się klub nie wywiązał.\r", + "\r\n", + "account account account Gdzie jest account . Brudziński jesteś kłamcą i marnym kutasem account \r", + "\r\n", + "sed: couldn\u0027t write 130 items to stdout: Broken pipe\r\n", + "cat: write error: Broken pipe\r\n" + ] + } + ], + "source": [ + "! cat ../data/poleval19/task-1/training_set_clean_only_text.txt| sed \u0027s/\\(@anonymized_account *\\)/account /gi\u0027 |head" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dla mnie faworytem do tytulu bedzie Cracovia. Zobaczymy, czy typ sie sprawdzi.\r", + "\r\n", + "@anonymized_account @anonymized_account Brawo ty Daria kibic ma byc na dobre i zle\r", + "\r\n", + "@anonymized_account @anonymized_account Super, polski premier sklada kwiaty na grobach kolaborantow. Ale doczekalismy czasow.\r", + "\r\n", + "@anonymized_account @anonymized_account Musi. Innej drogi nie mamy.\r", + "\r\n", + "Odrzut natychmiastowy, kwasna mina, mam problem\r", + "\r\n", + "Jaki on byl fajny xdd pamietam, ze spoznilam sie na jego pierwsze zajecia i to sporo i za kare kazal mi usiasc w pierwszej lawce XD\r", + "\r\n", + "@anonymized_account No nie ma u nas szczescia ?\r", + "\r\n", + "@anonymized_account Dawno kogos tak wrednego nie widzialam xd\r", + "\r\n", + "@anonymized_account @anonymized_account Zaleglosci byly, ale wazne czy byly wezwania do zaplaty z ktorych sie klub nie wywiazal.\r", + "\r\n", + "@anonymized_account @anonymized_account @anonymized_account Gdzie jest @anonymized_account . Brudzinski jestes klamca i marnym kutasem @anonymized_account\r", + "\r\n", + "iconv: conversion stopped due to problem in writing the output\r\n" + ] + } + ], + "source": [ + "! cat ../data/poleval19/task-1/training_set_clean_only_text.txt| iconv -f UTF-8 -t ASCII//TRANSLIT |head" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dla mnie faworytem do tytułu będzie cracovia . zobaczymy , czy typ się sprawdzi .\r\n", + "account account brawo ty daria kibic ma być na dobre i złe\r\n", + "account account super , polski premier składa kwiaty na grobach kolaborantów . ale doczekaliśmy czasów .\r\n", + "account account musi . innej drogi nie mamy .\r\n", + "odrzut natychmiastowy , kwaśna mina , mam problem\r\n", + "jaki on był fajny xdd pamiętam , że spóźniłam się na jego pierwsze zajęcia i to sporo i za karę kazał mi usiąść w pierwszej ławce xd\r\n", + "account no nie ma u nas szczęścia 😉\r\n", + "account dawno kogoś tak wrednego nie widziałam xd\r\n", + "account account zaległości były , ale ważne czy były wezwania do zapłaty z których się klub nie wywiązał .\r\n", + "account account account gdzie jest account . brudziński jesteś kłamcą i marnym kutasem account\r\n" + ] + } + ], + "source": [ + "! head ../data/poleval19/task-1/training_set_clean_only_text.tmp.tok" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/test_set_clean_only_text.txt|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl| sed \"s/\\(@anonymized_account *\\)/account /gi\"|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l pl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l pl| tr \"[:upper:]\" \"[:lower:]\"\u003e../data/poleval19/task-1/test_set_clean_only_text.tmp.tok\n", + " - Split sentences: ../data/poleval19/task-1/test_set_clean_only_text.tmp.bpe\n", + " - lines/max words: 1000/104 -\u003e 1237/105\n", + " - Combine embeddings:\n", + " input: ../data/poleval19/task-1/test_set_clean_only_text.tmp.split.enc 1237 sentences\n", + " IDs: ../data/poleval19/task-1/test_set_clean_only_text.tmp.sid, 1000 sentences\n", + " output: ../data/poleval19/task-1/test_set_clean_only_text.tmp.enc\n" + ] + } + ], + "source": [ + "!rm {dataset}/test_set_clean_only_text.tmp.*\n", + "tst_fn \u003d process_file(dataset / \u0027test_set_clean_only_text.txt\u0027)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": "tx_trn_fn\u003d\"data/poleval19/task-1/toxic-train-text.tmp.enc\"\ntrn_fn\u003d \"data/poleval19/task-1/training_set_clean_only_text.tmp.enc\"\ntst_fn\u003d \"data/poleval19/task-1/test_set_clean_only_text.tmp.enc\"" + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "(10041, 1024)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 20 + } + ], + "source": [ + "dim\u003d1024\n", + "def read_enc(fn):\n", + " em \u003d np.fromfile(str(fn),dtype\u003dnp.float32).reshape(-1, dim)\n", + " return em\n", + "read_enc(trn_fn).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "(1000,)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 21 + } + ], + "source": [ + "np.loadtxt(str(dataset/\u0027test_set_clean_only_text.txt\u0027), delimiter\u003d\u0027\\n\u0027, comments\u003dNone, dtype\u003dnp.str).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "tst_df \u003d pd.DataFrame({\n", + " \u0027y\u0027:np.loadtxt(str(dataset/\u0027test_set_clean_only_tags.txt\u0027),delimiter\u003d\u0027\\n\u0027, comments\u003dNone, dtype\u003dnp.int),\n", + " \u0027em\u0027:list(read_enc(tst_fn)), \n", + " \u0027txt\u0027:np.loadtxt(str(dataset/\u0027test_set_clean_only_text.tmp.bpe\u0027), delimiter\u003d\u0027\\n\u0027, comments\u003dNone, dtype\u003dnp.str),\n", + "})\n", + "\n", + "pl_trn_df \u003d pd.DataFrame({\n", + " \u0027y\u0027:np.loadtxt(str(dataset/\u0027training_set_clean_only_tags.txt\u0027),delimiter\u003d\u0027\\n\u0027, comments\u003dNone, dtype\u003dnp.int),\n", + " \u0027em\u0027:list(read_enc(trn_fn)), \n", + " \u0027txt\u0027:np.loadtxt(str(dataset/\u0027training_set_clean_only_text.tmp.bpe\u0027), delimiter\u003d\u0027\\n\u0027, comments\u003dNone, dtype\u003dnp.str),\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "txt \u003d np.loadtxt(str(dataset/\u0027toxic-train-text.tmp.bpe\u0027), delimiter\u003d\u0027\\n\u0027, comments\u003dNone, dtype\u003dnp.str)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "159571" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 25 + } + ], + "source": [ + "len (txt)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "159571" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 26 + } + ], + "source": [ + "enc\u003dread_enc(tx_trn_fn)\n", + "len(enc)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "tx_trn_df \u003d pd.DataFrame({\n", + " \u0027ey\u0027: df[\u0027toxic\u0027],\n", + " \u0027em\u0027:list(enc), \n", + " \u0027txt\u0027:txt,\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "trn_df \u003d pd.concat([pl_trn_df, tx_trn_df], ignore_index\u003dTrue, sort\u003dFalse)[[\u0027y\u0027, \u0027ey\u0027, \u0027em\u0027, \u0027txt\u0027]].fillna(0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": false, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": " y ey em \\\n0 0.0 0.5 [0.0047513233, 0.0040491223, 0.015147643, 0.01... \n1 0.0 0.5 [0.0037102806, -2.0261248e-05, 7.142721e-05, 0... \n2 0.0 0.5 [0.0065289, 0.0048259646, -0.00015079533, 0.02... \n3 0.0 0.5 [0.025331493, -0.0001753498, 0.0006774627, 0.0... \n4 0.0 0.5 [0.007905564, 0.0017290547, -0.0015668216, 0.0... \n\n txt \n0 dla mnie fa@@ wor@@ y@@ tem do ty@@ tu@@ łu bę... \n1 account account bra@@ wo ty dar@@ ia ki@@ bi@@... \n2 account account super , pol@@ ski premier sk@@... \n3 account account musi . in@@ nej dro@@ gi nie m... \n4 odr@@ zut nat@@ ych@@ mi@@ a@@ sto@@ wy , kwa@... ", + "text/html": "\u003cdiv\u003e\n\u003cstyle scoped\u003e\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n\u003c/style\u003e\n\u003ctable border\u003d\"1\" class\u003d\"dataframe\"\u003e\n \u003cthead\u003e\n \u003ctr style\u003d\"text-align: right;\"\u003e\n \u003cth\u003e\u003c/th\u003e\n \u003cth\u003ey\u003c/th\u003e\n \u003cth\u003eey\u003c/th\u003e\n \u003cth\u003eem\u003c/th\u003e\n \u003cth\u003etxt\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth\u003e0\u003c/th\u003e\n \u003ctd\u003e0.0\u003c/td\u003e\n \u003ctd\u003e0.5\u003c/td\u003e\n \u003ctd\u003e[0.0047513233, 0.0040491223, 0.015147643, 0.01...\u003c/td\u003e\n \u003ctd\u003edla mnie fa@@ wor@@ y@@ tem do ty@@ tu@@ łu bę...\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth\u003e1\u003c/th\u003e\n \u003ctd\u003e0.0\u003c/td\u003e\n \u003ctd\u003e0.5\u003c/td\u003e\n \u003ctd\u003e[0.0037102806, -2.0261248e-05, 7.142721e-05, 0...\u003c/td\u003e\n \u003ctd\u003eaccount account bra@@ wo ty dar@@ ia ki@@ bi@@...\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth\u003e2\u003c/th\u003e\n \u003ctd\u003e0.0\u003c/td\u003e\n \u003ctd\u003e0.5\u003c/td\u003e\n \u003ctd\u003e[0.0065289, 0.0048259646, -0.00015079533, 0.02...\u003c/td\u003e\n \u003ctd\u003eaccount account super , pol@@ ski premier sk@@...\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth\u003e3\u003c/th\u003e\n \u003ctd\u003e0.0\u003c/td\u003e\n \u003ctd\u003e0.5\u003c/td\u003e\n \u003ctd\u003e[0.025331493, -0.0001753498, 0.0006774627, 0.0...\u003c/td\u003e\n \u003ctd\u003eaccount account musi . in@@ nej dro@@ gi nie m...\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth\u003e4\u003c/th\u003e\n \u003ctd\u003e0.0\u003c/td\u003e\n \u003ctd\u003e0.5\u003c/td\u003e\n \u003ctd\u003e[0.007905564, 0.0017290547, -0.0015668216, 0.0...\u003c/td\u003e\n \u003ctd\u003eodr@@ zut nat@@ ych@@ mi@@ a@@ sto@@ wy , kwa@...\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 29 + } + ], + "source": [ + "trn_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "tst_df[\u0027ey\u0027] \u003d 0.5\n", + "tst_df\u003dtst_df[[\u0027y\u0027, \u0027ey\u0027, \u0027em\u0027, \u0027txt\u0027]]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": " y ey em \\\n0 0 0.5 [0.01599568, 0.0028042805, -0.0040145353, 0.01... \n1 0 0.5 [0.010937454, -4.452362e-05, 0.003249705, 0.01... \n2 0 0.5 [0.008327394, -3.972248e-05, -0.0011421117, 0.... \n3 0 0.5 [0.008076943, 0.0034733585, -0.002149525, 0.00... \n4 0 0.5 [0.0062172827, 0.0055052936, -0.00076900126, 0... \n\n txt \n0 account spo@@ ko , jak im duda z mor@@ awi@@ e... \n1 account account ale on tu nie miał sz@@ ans je... \n2 account no czy pre@@ zes nie miał ra@@ cji , m... \n3 account account przecież to nawet nie jest prz... \n4 account account ow@@ sz@@ em podat@@ ki tak . ... ", + "text/html": "\u003cdiv\u003e\n\u003cstyle scoped\u003e\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n\u003c/style\u003e\n\u003ctable border\u003d\"1\" class\u003d\"dataframe\"\u003e\n \u003cthead\u003e\n \u003ctr style\u003d\"text-align: right;\"\u003e\n \u003cth\u003e\u003c/th\u003e\n \u003cth\u003ey\u003c/th\u003e\n \u003cth\u003eey\u003c/th\u003e\n \u003cth\u003eem\u003c/th\u003e\n \u003cth\u003etxt\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth\u003e0\u003c/th\u003e\n \u003ctd\u003e0\u003c/td\u003e\n \u003ctd\u003e0.5\u003c/td\u003e\n \u003ctd\u003e[0.01599568, 0.0028042805, -0.0040145353, 0.01...\u003c/td\u003e\n \u003ctd\u003eaccount spo@@ ko , jak im duda z mor@@ awi@@ e...\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth\u003e1\u003c/th\u003e\n \u003ctd\u003e0\u003c/td\u003e\n 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0...\u003c/td\u003e\n \u003ctd\u003eaccount account ow@@ sz@@ em podat@@ ki tak . ...\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 31 + } + ], + "source": [ + "tst_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "y 0\ney 0.5\nem [0.0037102806, -2.0261248e-05, 7.142721e-05, 0...\ntxt account account bra@@ wo ty dar@@ ia ki@@ bi@@...\nName: 1, dtype: object" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 32 + } + ], + "source": [ + "trn_df.loc[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "array([ 0.015996, 0.002804, -0.004015, 0.018448, ..., 0.022837, 0.005017, 0.011453, 0.016846], dtype\u003dfloat32)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 33 + } + ], + "source": [ + "tst_df[\u0027em\u0027][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "class EmbeddingText(ItemBase):\n", + " \"Basic item for \u003ccode\u003etext\u003c/code\u003e data in numericalized `ids`.\"\n", + " def __init__(self, emb, text): self.data,self.text \u003d np.array(emb, dtype\u003dnp.float32),text\n", + " def __str__(self): return str(self.text)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "class EmbeddingList(ItemList):\n", + " \"Basic `ItemList` for embeding of text data.\"\n", + " _bunch \u003d DataBunch # TextDataBunch converts input to LongTensor !\n", + " _processor \u003d []\n", + "\n", + " def __init__(self, items:Iterator, **kwargs):\n", + " super().__init__(items, **kwargs)\n", + "\n", + " def find_text_for_em(self, em):\n", + " # todo find a way to reverse embedding\n", + " return self.inner_df[\u0027txt\u0027][1]\n", + " \n", + " def get(self, i):\n", + " o \u003d super().get(i)\n", + " return EmbeddingText(o, self.inner_df[\u0027txt\u0027][i])\n", + "\n", + " def reconstruct(self, t:Tensor):\n", + " return EmbeddingText(t, self.find_text_for_em(t))\n", + "\n", + " def show_xys(self, xs, ys, max_len:int\u003d70)-\u003eNone:\n", + " \"Show the `xs` (inputs) and `ys` (targets). `max_len` is the maximum number of tokens displayed.\"\n", + " from IPython.display import display, HTML\n", + " names \u003d [\u0027text\u0027,\u0027target\u0027]\n", + " items \u003d []\n", + " for i, (x,y) in enumerate(zip(xs,ys)):\n", + " txt_x \u003d \u0027 \u0027.join(x.text.split(\u0027 \u0027)[:max_len]) if max_len is not None else x.text\n", + " items.append([txt_x, y])\n", + " items \u003d np.array(items)\n", + " df \u003d pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns\u003dnames)\n", + " with pd.option_context(\u0027display.max_colwidth\u0027, -1):\n", + " display(HTML(df.to_html(index\u003dFalse)))\n", + "\n", + " def show_xyzs(self, xs, ys, zs, max_len:int\u003d70):\n", + " \"Show `xs` (inputs), `ys` (targets) and `zs` (predictions). `max_len` is the maximum number of tokens displayed.\"\n", + " from IPython.display import display, HTML\n", + " items,names \u003d [],[\u0027text\u0027,\u0027target\u0027,\u0027prediction\u0027]\n", + " for i, (x,y,z) in enumerate(zip(xs,ys,zs)):\n", + " txt_x \u003d \u0027 \u0027.join(x.text.split(\u0027 \u0027)[:max_len]) if max_len is not None else x.text\n", + " items.append([txt_x, y, z])\n", + " items \u003d np.array(items)\n", + " df \u003d pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns\u003dnames)\n", + " with pd.option_context(\u0027display.max_colwidth\u0027, -1):\n", + " display(HTML(df.to_html(index\u003dFalse)))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "path\u003ddataset/\"models\"\n", + "src \u003d ItemLists(path, \n", + " EmbeddingList.from_df(trn_df, cols\u003d2, path\u003dpath),\n", + " EmbeddingList.from_df(tst_df, cols\u003d2, path\u003dpath))\n", + "classes\u003dnp.unique(trn_df[\u0027y\u0027])\n", + "#src.label_from_lists(trn_df[[\u0027y\u0027, \u0027ey\u0027]], tst_df[[\u0027y\u0027, \u0027ey\u0027]], classes\u003dclasses, processor\u003d[]) # todo why it wasnt workign\n", + "src.label_from_df(cols\u003d[0,1])\n", + "data \u003d src.databunch(bs\u003d1000, num_workers\u003d0)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "\u003cIPython.core.display.HTML object\u003e", + "text/html": "\u003ctable border\u003d\"1\" class\u003d\"dataframe\"\u003e\n \u003cthead\u003e\n \u003ctr style\u003d\"text-align: right;\"\u003e\n \u003cth\u003etext\u003c/th\u003e\n \u003cth\u003etarget\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eaccount account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eaccount account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eaccount account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eaccount account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eaccount account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e\u003c/td\u003e\n \u003ctd\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.show_batch()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "class LaserSentCLS(nn.Module):\n", + " def __init__(self, idim\u003d1024, odim\u003d2, nhid\u003dNone,\n", + " dropout\u003d0.0, activation\u003d\u0027TANH\u0027):\n", + " super().__init__()\n", + " modules \u003d []\n", + "\n", + " modules \u003d []\n", + " print(\u0027 - mlp {:d}\u0027.format(idim), end\u003d\u0027\u0027)\n", + " if len(nhid) \u003e 0:\n", + " if dropout \u003e 0:\n", + " modules.append(nn.Dropout(p\u003ddropout))\n", + " nprev \u003d idim\n", + " for nh in nhid:\n", + " if nh \u003e 0:\n", + " modules.append(nn.Linear(nprev, nh))\n", + " nprev \u003d nh\n", + " if activation \u003d\u003d \u0027TANH\u0027:\n", + " modules.append(nn.Tanh())\n", + " print(\u0027-{:d}t\u0027.format(nh), end\u003d\u0027\u0027)\n", + " elif activation \u003d\u003d \u0027RELU\u0027:\n", + " modules.append(nn.ReLU())\n", + " print(\u0027-{:d}r\u0027.format(nh), end\u003d\u0027\u0027)\n", + " else:\n", + " raise Exception(\u0027Unrecognized activation {activation}\u0027)\n", + " if dropout \u003e 0:\n", + " modules.append(nn.Dropout(p\u003ddropout))\n", + " modules.append(nn.Linear(nprev, odim))\n", + " print(\u0027-{:d}, dropout\u003d{:.1f}\u0027.format(odim, dropout))\n", + " else:\n", + " modules.append(nn.Linear(idim, odim))\n", + " print(\u0027 - mlp %d-%d\u0027.format(idim, odim))\n", + " self.mlp \u003d nn.Sequential(*modules)\n", + " def forward(self, x):\n", + " return self.mlp(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "1000" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 41 + } + ], + "source": [ + "data.train_dl.dl.batch_size" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "# FBeta(beta\u003d1, average\u003d\u0027binary\u0027)\n", + "def accuracy_t(y_pred:Tensor, y_true:Tensor, thresh:float\u003d0.5, ignore_y_val\u003d0.5)-\u003eRank0Tensor:\n", + " \"Compute accuracy only for the first axis when `y_pred` and `y_true` are the same size.\"\n", + " y_pred \u003d y_pred.sigmoid() \u003e thresh\n", + " mask \u003d (y_true !\u003d ignore_y_val)\n", + " return ((y_pred)\u003d\u003dy_true.byte())[mask].float().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "scrolled": true, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "tensor(1.)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 43 + } + ], + "source": [ + "y_pred\u003dtorch.Tensor([[-1,10],[-1 , 10], [10,10], [10, 10]])\n", + "y_true\u003dtorch.Tensor([[0, 1],[0.5, 1.0],[1.0, 1.0], [1.0, 0.5]])\n", + "mask \u003d (y_true !\u003d 0.5)\n", + "accuracy_t(y_pred,y_true)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[1., 1.],\n", + " [0., 1.],\n", + " [1., 1.],\n", + " [1., 0.]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask.float()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": false, + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.2689, 1.0000],\n", + " [0.2689, 1.0000],\n", + " [1.0000, 1.0000],\n", + " [1.0000, 1.0000]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred.sigmoid()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0., 1.],\n", + " [0., 1.],\n", + " [1., 1.],\n", + " [1., 0.]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(y_pred.sigmoid()\u003e0.5).float()*mask.float()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0., 1.],\n", + " [0., 1.],\n", + " [1., 1.],\n", + " [1., 0.]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_true*mask.float()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float\u003d0.5, beta:float\u003d2, ignore_y_val\u003d0.5, eps:float\u003d1e-9, sigmoid:bool\u003dTrue)-\u003eRank0Tensor:\n", + " \"Computes the f_beta between `preds` and `targets`\"\n", + " beta2 \u003d beta ** 2\n", + " if sigmoid: y_pred \u003d y_pred.sigmoid()\n", + " mask \u003d (y_true !\u003d ignore_y_val)\n", + " y_pred \u003d (y_pred\u003ethresh).float()*mask.float()\n", + " y_true \u003d y_true.float()*mask.float()\n", + " TP \u003d (y_pred*y_true).sum(dim\u003d1)\n", + " prec \u003d TP/(y_pred.sum(dim\u003d1)+eps)\n", + " rec \u003d TP/(y_true.sum(dim\u003d1)+eps)\n", + " res \u003d (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)\n", + " return res.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "def fbeta1(y_pred, y_true):\n", + " return fbeta(y_pred, y_true, thresh\u003d0.5, beta\u003d1, ignore_y_val\u003d0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "tensor(1.)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 46 + } + ], + "source": [ + "fbeta1(y_pred, y_true)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "# def f1_loss(input, target, reduction\u003dNone):\n", + "# eps \u003d 1e-7\n", + "# logit \u003d F.softmax(input, dim\u003d-1) # [bs, 2]\n", + "# tp \u003d logit[target \u003d\u003d 1, 1].sum()\n", + "# tn \u003d logit[target \u003d\u003d 0, 0].sum()\n", + "# bs \u003d target.shape[0]\n", + "# return 1.0 - 2.0*tp / (tp - tn + bs +eps)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "#learn.loss_func\u003df1_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "def f1_loss(input, target, reduction\u003dNone, treshold\u003d0.5):\n", + " eps \u003d 1e-7\n", + " preds \u003d torch.sigmoid(input) # [bs, 2]\n", + " tp \u003d (preds[target \u003d\u003d 1]).sum()\n", + " tn \u003d (1-preds[target \u003d\u003d 0]).sum()\n", + " bs \u003d (target !\u003d 0.5).sum()\n", + " #print(tp, tn, bs)\n", + " return 1.0 - 2.0*tp / (tp - tn + bs +eps)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "scrolled": true, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "tensor(171.)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 48 + } + ], + "source": [ + "y_true \u003d (torch.empty(1000, 1).uniform_(0, 1) \u003e 0.8).float()\n", + "y_true.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "f1l \u003d[]\n", + "f1m \u003d[]\n", + "for prc in range(0, 101):\n", + " mask \u003d (torch.empty(1000, 1).uniform_(0, 100) \u003e\u003d prc).float()\n", + " y_pred \u003d ((1 - y_true) * (1 - mask) + y_true * mask) * torch.empty_like(y_true).uniform_(2, 100) \n", + " f1l.append(-f1_loss(y_pred, y_true).cpu())\n", + " f1m.append(fbeta1(y_pred, y_true).cpu())\n", + "f1l\u003dnp.array(f1l)\n", + "f1m\u003dnp.array(f1m)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "array([-0.548238, -0.551204, -0.557501, -0.558579, ..., -0.837871, -0.837386, -0.841939, -0.842278], dtype\u003dfloat32)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 50 + }, + { + "data": { + "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e", + "image/png": 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\u003d\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(f1l)\n", + "plt.plot(f1m)\n", + "f1l" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "tensor(0.)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 51 + } + ], + "source": [ + "fbeta1(y_pred, y_true)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "tensor(1.)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 52 + } + ], + "source": [ + "y_pred \u003d torch.Tensor([[-1,10],[-1 , 10], [10, 10], [10, 10]])\n", + "y_true \u003d torch.Tensor([[0, 1], [0.5, 1.0],[1.0, 1.0], [1.0, 0.5]])\n", + "mask \u003d (y_true !\u003d 0.5)\n", + "accuracy_t(y_pred,y_true)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "data": { + "text/plain": "tensor(0.0262)" + }, + "metadata": {}, + "output_type": "execute_result", + "execution_count": 53 + } + ], + "source": [ + "f1_loss(y_pred, y_true)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "name": "stdout", + "text": [ + " - mlp 1024-100r-50r-2, dropout\u003d0.0\n" + ], + "output_type": "stream" + } + ], + "source": [ + "m\u003dLaserSentCLS(nhid\u003d[100, 50], odim\u003d2, dropout\u003d0, activation\u003d\u0027RELU\u0027)\n", + "learn \u003d Learner(data, m, metrics\u003d[accuracy_thresh,accuracy_t,fbeta1])\n", + "#learn.loss_func\u003df1_loss\n", + "learn.save(\"rnd\")" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "\u003cIPython.core.display.HTML object\u003e" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 432x288 with 1 Axes\u003e" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "learn.lr_find();learn.recorder.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "def fbeta1(y_pred, y_true):\n", + " return fbeta(y_pred, y_true, thresh\u003d0.5, beta\u003d1, ignore_y_val\u003d0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": true, + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "name": "stderr", + "text": [ + "/home/pczapla/anaconda3/envs/fastaiv1/lib/python3.7/site-packages/torch/serialization.py:251: UserWarning: Couldn\u0027t retrieve source code for container of type LaserSentCLS. It won\u0027t be checked for correctness upon loading.\n \"type \" + obj.__name__ + \". It won\u0027t be checked \"\n" + ], + "output_type": "stream" + }, + { + "data": { + "text/plain": "\u003cIPython.core.display.HTML object\u003e", + "text/html": "\n \u003cdiv\u003e\n \u003cstyle\u003e\n /* Turns off some styling */\n progress {\n /* gets rid of default border in Firefox and Opera. */\n border: none;\n /* Needs to be in here for Safari polyfill so background images work as expected. */\n background-size: auto;\n }\n .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n background: #F44336;\n }\n \u003c/style\u003e\n \u003cprogress value\u003d\u00270\u0027 class\u003d\u0027\u0027 max\u003d\u00273\u0027, style\u003d\u0027width:300px; height:20px; vertical-align: middle;\u0027\u003e\u003c/progress\u003e\n \n \u003c/div\u003e\n \n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": "from fastai.callbacks import * \nruns\u003d[]\nfor i in range(0, 1):\n learn.load(\"rnd\")\n #cb\u003dSaveModelCallback(learn, every\u003d\u0027improvement\u0027,monitor\u003d\u0027fbeta1\u0027, name\u003d\u0027best\u0027)\n learn.fit_one_cycle(3, 0.08, wd\u003d0.06)" + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.53880954, tensor(0.7195), tensor(0.8660), tensor(0.)]" + ] + }, + "execution_count": 257, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learn.validate()" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "get_preds() got an unexpected keyword argument \u0027activ\u0027", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u003cipython-input-269-e97a4c9406e0\u003e\u001b[0m in \u001b[0;36m\u003cmodule\u003e\u001b[0;34m\u001b[0m\n\u001b[0;32m----\u003e 1\u001b[0;31m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_preds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactiv\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0maccuracy_thresh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maccuracy_t\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfbeta1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: get_preds() got an unexpected keyword argument \u0027activ\u0027" + ] + } + ], + "source": [ + "y_pred, y_true \u003d learn.get_preds(activ\u003dNone)\n", + "accuracy_thresh(y_pred, y_true),accuracy_t(y_pred, y_true), fbeta1(y_pred, y_true), " + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0, 0],\n", + " [0, 0],\n", + " [0, 0],\n", + " ...,\n", + " [0, 0],\n", + " [0, 0],\n", + " [0, 0]], dtype\u003dtorch.uint8)" + ] + }, + "execution_count": 267, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred \u003c0" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(134)" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((y_pred.sigmoid()\u003e0.5).float() \u003d\u003d y_true).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(134)" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((y_pred.sigmoid() \u003e 0.5).float() \u003d\u003d y_true)[y_true\u003d\u003d1.0].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.0670)" + ] + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "accuracy_thresh(y_pred, y_true)" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "# FBeta(beta\u003d1, average\u003d\u0027binary\u0027)\n", + "def accuracy_t(y_pred:Tensor, y_true:Tensor, thresh:float\u003d0.5, ignore_y_val\u003d0.5)-\u003eRank0Tensor:\n", + " \"Compute accuracy only for the first axis when `y_pred` and `y_true` are the same size.\"\n", + " y_pred \u003d y_pred.sigmoid() \u003e thresh\n", + " mask \u003d (y_true !\u003d ignore_y_val)\n", + " return ((y_pred)\u003d\u003dy_true.byte())[mask].float().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "def f_tst(y_pred:Tensor, y_true:Tensor, thresh:float\u003d0.5, beta:float\u003d1,\n", + " ignore_y_val\u003d0.5, eps:float\u003d1e-9, sigmoid:bool\u003dTrue)-\u003eRank0Tensor:\n", + " \"Computes the f_beta between `preds` and `targets`\"\n", + " \n", + " beta2 \u003d beta ** 2\n", + " if sigmoid: y_pred \u003d y_pred.sigmoid()\n", + " mask \u003d (y_true !\u003d ignore_y_val)\n", + " y_pred \u003d (y_pred\u003ethresh).float()*mask.float()\n", + " y_true \u003d y_true.float()*mask.float()\n", + " TP \u003d (y_pred*y_true).sum(dim\u003d1)\n", + " prec \u003d TP/(y_pred.sum(dim\u003d1)+eps)\n", + " rec \u003d TP/(y_true.sum(dim\u003d1)+eps)\n", + " res \u003d (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)\n", + " return res.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.0000, 0.5000],\n", + " [0.0000, 0.5000],\n", + " [0.0000, 0.5000],\n", + " ...,\n", + " [0.0000, 0.5000],\n", + " [1.0000, 0.5000],\n", + " [0.0000, 0.5000]])" + ] + }, + "execution_count": 233, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_true" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.1340)" + ] + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f_tst(y_pred, y_true)" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.134, 0.13400002, 1.4901161e-08)" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(runs), np.mean(runs), np.std(runs)" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "scrolled": false, + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/html": [ + "Total time: 00:49 \u003cp\u003e\u003ctable border\u003d\"1\" class\u003d\"dataframe\"\u003e\n", + " \u003cthead\u003e\n", + " \u003ctr style\u003d\"text-align: left;\"\u003e\n", + " \u003cth\u003eepoch\u003c/th\u003e\n", + " 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"outputs": [ + { + "data": { + "text/plain": [ + "[0.5057879, tensor(0.0330)]" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learn.validate(metrics\u003d[fbeta_1])" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u003e \u001b[0;32m\u003cipython-input-185-346e6c34bc60\u003e\u001b[0m(13)\u001b[0;36mfbeta_1\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 10 \u001b[0;31m \u001b[0mrec\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0mTP\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 11 \u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mprec\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprec\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 12 \u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m---\u003e 13 \u001b[0;31m \u001b[0;32mraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 14 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + "ipdb\u003e p y_pred.shape\n", + "torch.Size([200, 2])\n", + "ipdb\u003e quit\n" + ] + } + ], + "source": [ + "%debug" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "p, lbls \u003d learn.get_preds()" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "def fbeta_1(y_pred:Tensor, y_true:Tensor, thresh:float\u003d0.5, beta:float\u003d2, ignore_y_val\u003d0.5, eps:float\u003d1e-9, sigmoid:bool\u003dTrue)-\u003eRank0Tensor:\n", + " \"Computes the f_beta between `preds` and `targets`\"\n", + " beta2 \u003d beta ** 2\n", + " if sigmoid: y_pred \u003d y_pred.sigmoid()\n", + " mask \u003d (y_true !\u003d ignore_y_val)\n", + " y_pred \u003d (y_pred\u003ethresh).float()*mask.float()\n", + " y_true \u003d y_true.float()*mask.float()\n", + " TP \u003d (y_pred*y_true).sum(dim\u003d1)\n", + " prec \u003d TP/(y_pred.sum(dim\u003d1)+eps)\n", + " rec \u003d TP/(y_true.sum(dim\u003d1)+eps)\n", + " res \u003d (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)\n", + " return res.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n", + " 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n", + " 0., 0., 0., 1., 1., 0., 0., 1., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 0., 1.,\n", + " 0., 0., 0., 0., 0., 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0., 0., 0., 1., 0.])\n" + ] + }, + { + "data": { + "text/plain": [ + "tensor(0.1340)" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fbeta_1(p, lbls)" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "\u003cIPython.core.display.HTML object\u003e" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "\u003cFigure size 432x288 with 1 Axes\u003e" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "learn.recorder.plot_lr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "interp \u003d ClassificationInterpretation.from_learner(learn)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Expected object of scalar type Float but got scalar type Long for argument #2 \u0027other\u0027", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u003cipython-input-165-ab4f144f3c10\u003e\u001b[0m in \u001b[0;36m\u003cmodule\u003e\u001b[0;34m\u001b[0m\n\u001b[0;32m----\u003e 1\u001b[0;31m \u001b[0minterp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/anaconda3/envs/laser/lib/python3.6/site-packages/fastai/train.py\u001b[0m in \u001b[0;36mplot_confusion_matrix\u001b[0;34m(self, normalize, title, cmap, slice_size, norm_dec, plot_txt, return_fig, **kwargs)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;34m\"Plot the confusion matrix, with `title` and using `cmap`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;31m# This function is mainly copied from the sklearn docs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--\u003e 164\u001b[0;31m \u001b[0mcm\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslice_size\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0mslice_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 165\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcm\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u0027float\u0027\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/laser/lib/python3.6/site-packages/fastai/train.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(self, slice_size)\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 155\u001b[0m cm_slice \u003d ((self.pred_class[i:i+slice_size]\u003d\u003dx[:,None])\n\u001b[0;32m--\u003e 156\u001b[0;31m \u0026 (self.y_true[i:i+slice_size]\u003d\u003dx[:,None,None])).sum(2)\n\u001b[0m\u001b[1;32m 157\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcm_slice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mto_np\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Expected object of scalar type Float but got scalar type Long for argument #2 \u0027other\u0027" + ] + } + ], + "source": [ + "interp.plot_confusion_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "len() of a 0-d tensor", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u003cipython-input-174-eec5bea2d7ec\u003e\u001b[0m in \u001b[0;36m\u003cmodule\u003e\u001b[0;34m\u001b[0m\n\u001b[0;32m----\u003e 1\u001b[0;31m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0minterp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtop_losses\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/anaconda3/envs/laser/lib/python3.6/site-packages/fastai/train.py\u001b[0m in \u001b[0;36mtop_losses\u001b[0;34m(self, k, largest)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtop_losses\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlargest\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"`k` largest(/smallest) losses and indexes, defaulting to all losses (sorted by `largest`).\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--\u003e 195\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtopk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlargest\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0mlargest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_learner_interpret\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mLearner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_type\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mDatasetType\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0mDatasetType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/laser/lib/python3.6/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36m__len__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m\u003d\u003d\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--\u003e 411\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"len() of a 0-d tensor\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 412\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: len() of a 0-d tensor" + ] + } + ], + "source": [ + "l, i \u003d interp.top_losses()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "scrolled": false, + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 0, 86), (0, 1, 52)]" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interp.most_confused()" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name \u0027i\u0027 is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u003cipython-input-169-88d0e67a5958\u003e\u001b[0m in \u001b[0;36m\u003cmodule\u003e\u001b[0;34m\u001b[0m\n\u001b[0;32m----\u003e 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mix\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalid_ds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name \u0027i\u0027 is not defined" + ] + } + ], + "source": [ + "for ix in range(0,10): print(data.valid_ds[i[ix]])" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(EmbeddingText account wie@@ czna zdra@@ da , nie zdra@@ da , trzeba roz@@ mawi@@ ać , pier@@ dol@@ enie od rzeczy, Category 0)\n", + "(EmbeddingText account account to pani kry@@ sia jest two@@ ją oj@@ czy@@ zn@@ ą ?, Category 0)\n", + "(EmbeddingText account droga p.@@ kam@@ il@@ ko ! le@@ czyć się . le@@ czyć pó@@ ki czas 😁 😁, Category 0)\n", + "(EmbeddingText account du@@ baj w@@ zor@@ em , czy@@ li jednak islam , sal@@ am al@@ ej@@ kum ci@@ em@@ ki@@ e@@ wi@@ cz, Category 0)\n", + "(EmbeddingText account a ja za@@ łoż@@ ę fit@@ blo@@ ga 😂 😂 😂, Category 0)\n", + "(EmbeddingText account to tak jak ja prze@@ widział@@ em , że cię nie w@@ pusz@@ cza do g@@ b, Category 0)\n", + "(EmbeddingText prowad@@ zą@@ cy mówi ze nikt mu nie wy@@ s@@ ła@@ ł sz@@ ki@@ ców projekt@@ ów jak nie jak ja ci wy@@ sł@@ ałam im@@ be@@ cy@@ lu, Category 0)\n", + "(EmbeddingText ra@@ fa@@ ł br@@ zo@@ zo@@ w@@ ski ma nar@@ ze@@ cz@@ on@@ ą ? mam na@@ dzie@@ je m , że na co@@ dzień jej aż tak nie fa@@ ł@@ sz@@ uje jak w tym kol@@ e fortun@@ y a jeśli tak to big ri@@ p girl, Category 0)\n", + "(EmbeddingText account account to ta pani co ma roz@@ tro@@ jen@@ ie ja@@ ź@@ ni ?, Category 0)\n", + "(EmbeddingText account account account prawda o kar@@ cze@@ w@@ skim to nie prawda o pol@@ s@@ ce . coś się panu po@@ je@@ ba@@ ło account ., Category 0)\n", + "(EmbeddingText account zna@@ jąc ment@@ al@@ ność pis win@@ ny będzie ten kto wi@@ dzi nie ten kto czy@@ ni ., Category 0)\n", + "(EmbeddingText account prz@@ esta@@ ń@@ cie kur@@ wa cały czas p@@ ła@@ kać, Category 0)\n", + "(EmbeddingText account account ko@@ do@@ wa zar@@ aza nar@@ ód nasz dzi@@ eli , m@@ roz@@ ny de@@ sz@@ cz gro@@ zy po kar@@ ku prze@@ chodzi , \\ ndo@@ ść dy@@ kt@@ atu pej@@ sat@@ ej bru@@ k@@ sel@@ i, Category 0)\n", + "(EmbeddingText rt account account ku@@ ter ry@@ bac@@ ki przy niej , to dou@@ glas na wod@@ zie ., Category 0)\n", + "(EmbeddingText account i we@@ ź tu zosta@@ ń p@@ sem, Category 0)\n", + "(EmbeddingText account ty naj@@ pier@@ w prze@@ czy@@ taj te swoje ksi@@ ąż@@ ki z foto@@ ta@@ pet@@ y a potem po@@ gad@@ amy ., Category 0)\n", + "(EmbeddingText trzeba być patri@@ ot@@ ą swo@@ jego mi@@ asta , swo@@ jego region@@ u , swo@@ jej ziemi . pre@@ zes # pis j. # ka@@ czy@@ ń@@ ski w # zam@@ o@@ ść : # do@@ trzy@@ mu@@ jem@@ ys@@ ło@@ wa, Category 0)\n", + "(EmbeddingText rt account trzeba być patri@@ ot@@ ą swo@@ jego mi@@ asta , swo@@ jego region@@ u , swo@@ jej ziemi . pre@@ zes # pis j. # ka@@ czy@@ ń@@ ski w # zam@@ o@@ ść : # do@@ trzy@@ mu@@ jem@@ ys@@ ło@@ wa, Category 0)\n", + "(EmbeddingText account taki z niego patri@@ ota jak z ciebie dzi@@ enni@@ kar@@ z . nie po@@ gr@@ ąż@@ aj się ., Category 0)\n", + "(EmbeddingText rt account account taki z niego patri@@ ota jak z ciebie dzi@@ enni@@ kar@@ z . nie po@@ gr@@ ąż@@ aj się ., Category 0)\n", + "(EmbeddingText account account zanim się o@@ dez@@ wiesz to wy@@ trze@@ ź@@ wi@@ ej, Category 0)\n", + "(EmbeddingText account bier@@ z tego account razem jeste@@ ście moc@@ ni, Category 0)\n", + "(EmbeddingText account a br@@ am@@ kar@@ ze ? gli@@ k ? kry@@ cho@@ wi@@ ak ? ku@@ ba ? mili@@ k ? pis@@ z@@ cz@@ ek jak jest zdro@@ wy ?, Category 0)\n", + "(EmbeddingText kur@@ wa wszyscy w domu ka@@ sz@@ l@@ ą, Category 0)\n", + "(EmbeddingText account account account je@@ ba@@ ć sł@@ oni@@ c@@ ę@@ - mo@@ ty@@ li@@ cę ! ! ! ! 😁 😁 😁 . wro@@ c@@ ła@@ w nie daj się ! ! !, Category 0)\n", + "(EmbeddingText account jeśli to jest sz@@ rot to jak naz@@ wać was@@ ze transfer@@ y ?, Category 0)\n", + "(EmbeddingText no teraz kur@@ wa jak muszę wyj@@ ść to pada@@ ć zac@@ zy@@ na no ja pier@@ dole do dup@@ y z ta@@ kim ży@@ ciem, Category 0)\n", + "(EmbeddingText account account chyba ty , ch@@ ł@@ op jak by@@ k, Category 0)\n", + "(EmbeddingText account ale tu@@ sk nie jest świ@@ rem, Category 0)\n", + "(EmbeddingText account account account account ki@@ bi@@ ce wier@@ zą w zwy@@ ci@@ ę@@ stwo swo@@ jej dru@@ ży@@ ny@@ .@@ ty to nazy@@ was@@ z do@@ pis@@ y@@ wan@@ iem p@@ kt ? ha@@ ha, Category 0)\n", + "(EmbeddingText account account account historia lu@@ dz@@ ko@@ ści to historia gł@@ upo@@ ty # ja@@ j@@ co, Category 0)\n", + "(EmbeddingText account ten to już zu@@ peł@@ nie od@@ wi@@ esi@@ ł mó@@ z@@ g na ko@@ ł@@ ek , chor@@ y mó@@ z@@ g, Category 0)\n", + "(EmbeddingText account rzu@@ ć ż@@ on@@ ę , przej@@ dź na bud@@ dy@@ z@@ m , prze@@ prowad@@ ź sie z u@@ kochan@@ ą na bar@@ ba@@ dos , zac@@ zni@@ j s@@ sa@@ ć życie, Category 0)\n", + "(EmbeddingText h@@ ali@@ c@@ ki : ga@@ w@@ ło@@ w@@ ski nie może się br@@ oni@@ ć bo sied@@ zi . a po wciąż za ba@@ ł@@ ag@@ an w w-@@ wie ob@@ cią@@ ż@@ a m@@ .@@ i. nie@@ ży@@ ją@@ cego l.@@ ka@@ czy@@ ń@@ skiego \\ n # wor@@ oni@@ cza@@ 17, Category 0)\n", + "(EmbeddingText pis już się z@@ bliż@@ a już pu@@ ka do t@@ wych dr@@ z@@ wi@@ .@@ po@@ bieg@@ ni@@ j go przy@@ wit@@ ać z ra@@ do@@ ści ser@@ ce dr@@ ży . 😁 😁 😁, Category 0)\n", + "(EmbeddingText account pola@@ cy rat@@ uj@@ my pol@@ sk@@ Ę od zlo@@ dzie@@ ji po i lisa wol@@ j@@ s@@ do@@ j@@ cza, Category 0)\n", + "(EmbeddingText rt account account pola@@ cy rat@@ uj@@ my pol@@ sk@@ Ę od zlo@@ dzie@@ ji po i lisa wol@@ j@@ s@@ do@@ j@@ cza, Category 0)\n", + "(EmbeddingText account panie reda@@ ktor@@ ze , proszę u@@ ca@@ ło@@ wać pana account wró@@ ć . poz@@ dro@@ wić ., Category 0)\n", + "(EmbeddingText account ja się wiele cie@@ ka@@ wych rzeczy do@@ wiedzi@@ ałem n@@ p że ry@@ cz@@ kow@@ ski to 87 ro@@ cz@@ nik@@ .@@ spraw@@ dzić tekst przed dru@@ kiem to chyba za tru@@ dne ., Category 0)\n", + "(EmbeddingText account o in@@ wek@@ ty@@ wach proszę poroz@@ mawi@@ ać ze swo@@ im prez@@ es@@ em a nie pie@@ przy@@ ć mi tutaj o jak@@ ich@@ ś przy@@ kł@@ ada@@ ch ., Category 0)\n", + "(EmbeddingText daj@@ cie fa@@ v a ja wam jutro zrobi@@ ę gad@@ an@@ ego in@@ da pier@@ wsz@@ ego w ży@@ ciu !, Category 0)\n", + "(EmbeddingText wiek to ok@@ rop@@ ny z@@ łod@@ zi@@ ej@@ . kiedy zac@@ zy@@ nasz rozu@@ mieć , na czym po@@ lega życie , ono ś@@ cina cię z nó@@ g i przy@@ gar@@ bia ci ple@@ cy ., Category 0)\n", + "(EmbeddingText rt account wiek to ok@@ rop@@ ny z@@ łod@@ zi@@ ej@@ . kiedy zac@@ zy@@ nasz rozu@@ mieć , na czym po@@ lega życie , ono ś@@ cina cię z nó@@ g i przy@@ gar@@ bia ci ple@@ cy ., Category 0)\n", + "(EmbeddingText account account mo@@ wa była o fak@@ ta@@ ch a nie two@@ ich od@@ czu@@ ci@@ ach czy go uważ@@ asz za po@@ laka czy nie account, Category 0)\n", + "(EmbeddingText account ale ja uci@@ ek@@ nę tam gdzie pie@@ pr@@ z ro@@ śnie, Category 0)\n" + ] + } + ], + "source": [ + "neutral_as_offensive \u003d np.nonzero((interp.pred_class !\u003d interp.y_true) \u0026 (interp.y_true \u003d\u003d 0)).reshape(-1)\n", + "for ix in neutral_as_offensive: print(data.valid_ds[ix])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "name": "pycharm-9ea8f68f", + "language": "python", + "display_name": "PyCharm (ulmfit-multilingual)" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/experiments/LASER-lc-large.ipynb b/experiments/LASER-lc-large.ipynb new file mode 100644 index 0000000..3dac129 --- /dev/null +++ b/experiments/LASER-lc-large.ipynb @@ -0,0 +1,75644 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/pczapla/workspace/ulmfit-multilingual/experiments\r\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from fastai.text import *" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# unzip task6_test.zip\n", + "# unzip task_6-1.zip -d Task6/task\\ 01\n", + "# unzip task_6-2.zip -d Task6/task\\ 02\n", + "# mv Task6/task\\ 01 task-1\n", + "# mv Task6/task\\ 02 task-2\n", + "# rmdir Task6" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[PosixPath('../data/poleval19/task-1/test_set_clean_only_text.txt'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.tmp.tok'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.txt'),\n", + " PosixPath('../data/poleval19/task-1/test_set_clean_only_text.tmp.bpe'),\n", + " PosixPath('../data/poleval19/task-1/test_set_clean_only_text.tmp.enc'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_tags.txt'),\n", + " PosixPath('../data/poleval19/task-1/models'),\n", + " PosixPath('../data/poleval19/task-1/evaulate1.pl'),\n", + " PosixPath('../data/poleval19/task-1/test_set_clean_only_text.tmp.tok'),\n", + " PosixPath('../data/poleval19/task-1/test_set_clean_only_tags.txt'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.tmp.enc'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.tmp.bpe')]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset=Path('../data')/'poleval19'/'task-1'\n", + "dataset.ls()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "fn = '../data/toxic-comment/train.csv'\n", + "df = pd.read_csv(fn)\n", + "comment_text = df.comment_text.str.replace(\"\\n\", \" \")\n", + "del df['comment_text']" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "comment_text.to_csv(dataset/\"toxic-train-text.csv\", sep='\\t', index=False, header=False, quoting=csv.QUOTE_NONE, quotechar='|', escapechar='\\\\')\n", + "df.to_csv(dataset/\"toxic-train-tags.csv\", index=False, header=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explanation Why the edits made under my username Hardcore Metallica Fan were reverted? They weren't vandalisms, just closure on some GAs after I voted at New York Dolls FAC. And please don't remove the template from the talk page since I'm retired now.89.205.38.27\r\n", + "D'aww! He matches this background colour I'm seemingly stuck with. Thanks. (talk) 21:51, January 11, 2016 (UTC)\r\n", + "Hey man, I'm really not trying to edit war. It's just that this guy is constantly removing relevant information and talking to me through edits instead of my talk page. He seems to care more about the formatting than the actual info.\r\n", + "\" More I can't make any real suggestions on improvement - I wondered if the section statistics should be later on, or a subsection of \"\"types of accidents\"\" -I think the references may need tidying so that they are all in the exact same format ie date format etc. I can do that later on, if no-one else does first - if you have any preferences for formatting style on references or want to do it yourself please let me know. There appears to be a backlog on articles for review so I guess there may be a delay until a reviewer turns up. It's listed in the relevant form eg Wikipedia:Good_article_nominations#Transport \"\r\n", + "You, sir, are my hero. Any chance you remember what page that's on?\r\n", + "\" Congratulations from me as well, use the tools well.  · talk \"\r\n", + "COCKSUCKER BEFORE YOU PISS AROUND ON MY WORK\r\n", + "Your vandalism to the Matt Shirvington article has been reverted. Please don't do it again, or you will be banned.\r\n", + "Sorry if the word 'nonsense' was offensive to you. Anyway, I'm not intending to write anything in the article(wow they would jump on me for vandalism), I'm merely requesting that it be more encyclopedic so one can use it for school as a reference. I have been to the selective breeding page but it's almost a stub. It points to 'animal breeding' which is a short messy article that gives you no info. There must be someone around with expertise in eugenics? 93.161.107.169\r\n", + "alignment on this subject and which are contrary to those of DuLithgow\r\n" + ] + } + ], + "source": [ + "!head {dataset/\"toxic-train-text.csv\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "LASER = os.environ.get('LASER', \"/home/pczapla/workspace/_oss/LASER\")\n", + "os.environ['LASER'] = LASER\n", + "def cond_add_pypath(*paths):\n", + " for path in paths:\n", + " if path not in sys.path:\n", + " sys.path.append(path)\n", + "cond_add_pypath(LASER + '/source',LASER + '/source/tools')\n", + "\n", + "from embed import SentenceEncoder, EncodeLoad, EncodeFile\n", + "from text_processing import Token, BPEfastApply, SplitLines, JoinEmbed" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "from subprocess import run, check_output, DEVNULL\n", + "def tokenize(inp_fname, out_fname, lang='en',\n", + " lower_case=True, romanize=False, descape=False,\n", + " verbose=False, over_write=False, gzip=False):\n", + " FASTBPE = LASER + '/tools-external/fastBPE/fast'\n", + " MOSES_BDIR = LASER + '/tools-external/moses-tokenizer/tokenizer/'\n", + " MOSES_TOKENIZER = MOSES_BDIR + 'tokenizer.perl -q -no-escape -threads 20 -l '\n", + " MOSES_LC = MOSES_BDIR + 'lowercase.perl'\n", + " NORM_PUNC = MOSES_BDIR + 'normalize-punctuation.perl -l '\n", + " DESCAPE = MOSES_BDIR + 'deescape-special-chars.perl'\n", + " REM_NON_PRINT_CHAR = MOSES_BDIR + 'remove-non-printing-char.perl'\n", + "\n", + " # Romanization (Greek only)\n", + " ROMAN_LC = 'python3 ' + LASER + '/source/lib/romanize_lc.py -l '\n", + "\n", + " # Mecab tokenizer for Japanese\n", + " MECAB = LASER + '/tools-external/mecab'\n", + " assert lower_case, 'lower case is needed by all the models'\n", + " if not out_fname.exists():\n", + " cat = 'zcat ' if gzip else 'cat '\n", + " roman = lang if romanize else 'none'\n", + " # handle some iso3 langauge codes\n", + " if lang in ('cmn', 'wuu', 'yue'):\n", + " lang = 'zh'\n", + " if lang in ('jpn'):\n", + " lang = 'ja'\n", + " if verbose:\n", + " print(' - Tokenizer: {} in language {} {} {}'\n", + " .format(os.path.basename(inp_fname), lang,\n", + " '(gzip)' if gzip else '',\n", + " '(de-escaped)' if descape else '',\n", + " '(romanized)' if romanize else ''))\n", + " cmd = (cat + str(inp_fname)\n", + " + '|' + REM_NON_PRINT_CHAR\n", + " + '| sed \"s/\\(@anonymized_account *\\)/account /gi\"'\n", + "# + '| iconv -f UTF-8 -t ASCII//TRANSLIT '\n", + " + '|' + NORM_PUNC + lang\n", + " + ('|' + DESCAPE if descape else '')\n", + " + '|' + MOSES_TOKENIZER + lang\n", + " + ('| python3 -m jieba -d ' if lang == 'zh' else '')\n", + " + ('|' + MECAB + '/bin/mecab -O wakati -b 50000 ' if lang == 'ja' else '')\n", + " + ('|' + ROMAN_LC + roman if romanize else '')\n", + " + ('| tr \"[:upper:]\" \"[:lower:]\"' if lower_case else '')\n", + " + '>' + str(out_fname))\n", + " print (\"Tokenziation CMD: \", cmd)\n", + " run(cmd,\n", + " env=dict(os.environ, LD_LIBRARY_PATH=MECAB + '/lib'),\n", + " shell=True)\n", + " elif not over_write and verbose:\n", + " print(' - Tokenizer: {} exists already'\n", + " .format(os.path.basename(out_fname), lang))\n", + "\n", + "\n", + "def split_lines(*args): return SplitLines(*list(map(str,args)))\n", + "def bpe_fast_apply(*args, **kwargs): return BPEfastApply(*list(map(str,args)), **kwargs)\n", + "def encode_file(enc, *args, **kwargs): return EncodeFile(enc, *list(map(str,args)), **kwargs)\n", + "def join_embed(*args): return JoinEmbed(*list(map(str,args)))\n", + " \n", + "def process_file(inputfn, lang='pl'):\n", + " inputfn = Path(inputfn)\n", + " args = SimpleNamespace(\n", + " encoder=str(Path(LASER)/\"models\"/\"bilstm.93langs.2018-12-26.pt\"), \n", + " bpe_codes=str(Path(LASER)/\"models\"/\"93langs.fcodes\"),\n", + " lang=lang,\n", + " buffer_size=10000,\n", + " max_tokens=12000,\n", + " max_sentences=None,\n", + " cpu=False,\n", + " verbose=False,\n", + " stable=True)\n", + " enc = EncodeLoad(args)\n", + " def fn(suffix):\n", + " return inputfn.with_suffix(f'.tmp{suffix}')\n", + " tokenize(inputfn,\n", + " fn('.tok'),\n", + " lang=lang,\n", + " romanize=(True if lang == 'el' else False),\n", + " lower_case=True, gzip=False,\n", + " verbose=args.verbose, over_write=False)\n", + " bpe_fast_apply(fn('.tok'),\n", + " fn('.bpe'),\n", + " args.bpe_codes,\n", + " verbose=args.verbose, over_write=False)\n", + " split_lines(fn('.bpe'),\n", + " fn('.split'),\n", + " fn('.sid'))\n", + " encode_file(enc,\n", + " fn('.split'),\n", + " fn('.split.enc'),\n", + " verbose=args.verbose, over_write=False,\n", + " buffer_size=args.buffer_size)\n", + " join_embed(fn('.split.enc'),\n", + " fn('.sid'),\n", + " fn('.enc'))\n", + "# encode_file(enc,\n", + "# fn('.bpe'),\n", + "# fn('.enc'),\n", + "# verbose=args.verbose, over_write=False,\n", + "# buffer_size=args.buffer_size)\n", + " return fn('.enc')" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/toxic-train-text.csv|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl| sed \"s/\\(@anonymized_account *\\)/account /gi\"|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l en|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l en| tr \"[:upper:]\" \"[:lower:]\">../data/poleval19/task-1/toxic-train-text.tmp.tok\n", + " - Split sentences: ../data/poleval19/task-1/toxic-train-text.tmp.bpe\n", + " - lines/max words: 159571/4950 -> 619938/4951\n", + " - Combine embeddings:\n", + " input: ../data/poleval19/task-1/toxic-train-text.tmp.split.enc 619938 sentences\n", + " IDs: ../data/poleval19/task-1/toxic-train-text.tmp.sid, 159571 sentences\n", + " output: ../data/poleval19/task-1/toxic-train-text.tmp.enc\n" + ] + } + ], + "source": [ + "!rm {dataset}/toxic-train-text.tmp.*\n", + "tx_trn_fn = process_file(dataset / 'toxic-train-text.csv', lang='en')" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rm: cannot remove '../data/poleval19/task-1/training_set_clean_only_text.tmp.*': No such file or directory\n", + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/training_set_clean_only_text.txt|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl| sed \"s/\\(@anonymized_account *\\)/account /gi\"|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l pl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l pl| tr \"[:upper:]\" \"[:lower:]\">../data/poleval19/task-1/training_set_clean_only_text.tmp.tok\n", + " - Split sentences: ../data/poleval19/task-1/training_set_clean_only_text.tmp.bpe\n", + " - lines/max words: 10041/82 -> 12066/83\n", + " - Combine embeddings:\n", + " input: ../data/poleval19/task-1/training_set_clean_only_text.tmp.split.enc 12066 sentences\n", + " IDs: ../data/poleval19/task-1/training_set_clean_only_text.tmp.sid, 10041 sentences\n", + " output: ../data/poleval19/task-1/training_set_clean_only_text.tmp.enc\n" + ] + } + ], + "source": [ + "!rm {dataset}/training_set_clean_only_text.tmp.*\n", + "trn_fn = process_file(dataset / 'training_set_clean_only_text.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dla mnie faworytem do tytułu będzie Cracovia. Zobaczymy, czy typ się sprawdzi.\r", + "\r\n", + "account account Brawo ty Daria kibic ma być na dobre i złe\r", + "\r\n", + "account account Super, polski premier składa kwiaty na grobach kolaborantów. Ale doczekaliśmy czasów.\r", + "\r\n", + "account account Musi. Innej drogi nie mamy.\r", + "\r\n", + "Odrzut natychmiastowy, kwaśna mina, mam problem\r", + "\r\n", + "Jaki on był fajny xdd pamiętam, że spóźniłam się na jego pierwsze zajęcia i to sporo i za karę kazał mi usiąść w pierwszej ławce XD\r", + "\r\n", + "account No nie ma u nas szczęścia 😉\r", + "\r\n", + "account Dawno kogoś tak wrednego nie widziałam xd\r", + "\r\n", + "account account Zaległości były, ale ważne czy były wezwania do zapłaty z których się klub nie wywiązał.\r", + "\r\n", + "account account account Gdzie jest account . Brudziński jesteś kłamcą i marnym kutasem account \r", + "\r\n", + "sed: couldn't write 130 items to stdout: Broken pipe\r\n", + "cat: write error: Broken pipe\r\n" + ] + } + ], + "source": [ + "! cat ../data/poleval19/task-1/training_set_clean_only_text.txt| sed 's/\\(@anonymized_account *\\)/account /gi' |head" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dla mnie faworytem do tytulu bedzie Cracovia. Zobaczymy, czy typ sie sprawdzi.\r", + "\r\n", + "@anonymized_account @anonymized_account Brawo ty Daria kibic ma byc na dobre i zle\r", + "\r\n", + "@anonymized_account @anonymized_account Super, polski premier sklada kwiaty na grobach kolaborantow. Ale doczekalismy czasow.\r", + "\r\n", + "@anonymized_account @anonymized_account Musi. Innej drogi nie mamy.\r", + "\r\n", + "Odrzut natychmiastowy, kwasna mina, mam problem\r", + "\r\n", + "Jaki on byl fajny xdd pamietam, ze spoznilam sie na jego pierwsze zajecia i to sporo i za kare kazal mi usiasc w pierwszej lawce XD\r", + "\r\n", + "@anonymized_account No nie ma u nas szczescia ?\r", + "\r\n", + "@anonymized_account Dawno kogos tak wrednego nie widzialam xd\r", + "\r\n", + "@anonymized_account @anonymized_account Zaleglosci byly, ale wazne czy byly wezwania do zaplaty z ktorych sie klub nie wywiazal.\r", + "\r\n", + "@anonymized_account @anonymized_account @anonymized_account Gdzie jest @anonymized_account . Brudzinski jestes klamca i marnym kutasem @anonymized_account\r", + "\r\n", + "iconv: conversion stopped due to problem in writing the output\r\n" + ] + } + ], + "source": [ + "! cat ../data/poleval19/task-1/training_set_clean_only_text.txt| iconv -f UTF-8 -t ASCII//TRANSLIT |head" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dla mnie faworytem do tytułu będzie cracovia . zobaczymy , czy typ się sprawdzi .\r\n", + "account account brawo ty daria kibic ma być na dobre i złe\r\n", + "account account super , polski premier składa kwiaty na grobach kolaborantów . ale doczekaliśmy czasów .\r\n", + "account account musi . innej drogi nie mamy .\r\n", + "odrzut natychmiastowy , kwaśna mina , mam problem\r\n", + "jaki on był fajny xdd pamiętam , że spóźniłam się na jego pierwsze zajęcia i to sporo i za karę kazał mi usiąść w pierwszej ławce xd\r\n", + "account no nie ma u nas szczęścia 😉\r\n", + "account dawno kogoś tak wrednego nie widziałam xd\r\n", + "account account zaległości były , ale ważne czy były wezwania do zapłaty z których się klub nie wywiązał .\r\n", + "account account account gdzie jest account . brudziński jesteś kłamcą i marnym kutasem account\r\n" + ] + } + ], + "source": [ + "! head ../data/poleval19/task-1/training_set_clean_only_text.tmp.tok" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/test_set_clean_only_text.txt|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl| sed \"s/\\(@anonymized_account *\\)/account /gi\"|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l pl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l pl| tr \"[:upper:]\" \"[:lower:]\">../data/poleval19/task-1/test_set_clean_only_text.tmp.tok\n", + " - Split sentences: ../data/poleval19/task-1/test_set_clean_only_text.tmp.bpe\n", + " - lines/max words: 1000/104 -> 1237/105\n", + " - Combine embeddings:\n", + " input: ../data/poleval19/task-1/test_set_clean_only_text.tmp.split.enc 1237 sentences\n", + " IDs: ../data/poleval19/task-1/test_set_clean_only_text.tmp.sid, 1000 sentences\n", + " output: ../data/poleval19/task-1/test_set_clean_only_text.tmp.enc\n" + ] + } + ], + "source": [ + "!rm {dataset}/test_set_clean_only_text.tmp.*\n", + "tst_fn = process_file(dataset / 'test_set_clean_only_text.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10041, 1024)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dim=1024\n", + "def read_enc(fn):\n", + " em = np.fromfile(str(fn),dtype=np.float32).reshape(-1, dim)\n", + " return em\n", + "read_enc(trn_fn).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000,)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.loadtxt(str(dataset/'test_set_clean_only_text.txt'), delimiter='\\n', comments=None, dtype=np.str).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "tst_df = pd.DataFrame({\n", + " 'y':np.loadtxt(str(dataset/'test_set_clean_only_tags.txt'),delimiter='\\n', comments=None, dtype=np.int),\n", + " 'em':list(read_enc(tst_fn)), \n", + " 'txt':np.loadtxt(str(dataset/'test_set_clean_only_text.tmp.bpe'), delimiter='\\n', comments=None, dtype=np.str),\n", + "})\n", + "\n", + "trn_df = pd.DataFrame({\n", + " 'y':np.loadtxt(str(dataset/'training_set_clean_only_tags.txt'),delimiter='\\n', comments=None, dtype=np.int),\n", + " 'em':list(read_enc(trn_fn)), \n", + " 'txt':np.loadtxt(str(dataset/'training_set_clean_only_text.tmp.bpe'), delimiter='\\n', comments=None, dtype=np.str),\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "txt = np.loadtxt(str(dataset/'toxic-train-text.tmp.bpe'), delimiter='\\n', comments=None, dtype=np.str)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "159571" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len (txt)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "159571" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enc=read_enc(tx_trn_fn)\n", + "len(enc)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "tx_trn_df = pd.DataFrame({\n", + " 'y': df['toxic'],\n", + " 'em':list(enc), \n", + " 'txt':txt,\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "trn_df = pd.concat([trn_df, tx_trn_df], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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account account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e1
account account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e1
account account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e0
account account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e0
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0.01095 , ..., 0.001673, 0.00638 , 0.008416, 0.021459], dtype=float32)" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trn_df['em'][169611]" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "class LaserSentCLS(nn.Module):\n", + " def __init__(self, idim=1024, odim=2, nhid=None,\n", + " dropout=0.0, activation='TANH'):\n", + " super().__init__()\n", + " modules = []\n", + "\n", + " modules = []\n", + " print(' - mlp {:d}'.format(idim), end='')\n", + " if len(nhid) > 0:\n", + " if dropout > 0:\n", + " modules.append(nn.Dropout(p=dropout))\n", + " nprev = idim\n", + " for nh in nhid:\n", + " if nh > 0:\n", + " modules.append(nn.Linear(nprev, nh))\n", + " nprev = nh\n", + " if activation == 'TANH':\n", + " modules.append(nn.Tanh())\n", + " print('-{:d}t'.format(nh), end='')\n", + " elif activation == 'RELU':\n", + " modules.append(nn.ReLU())\n", + " print('-{:d}r'.format(nh), end='')\n", + " else:\n", + " raise Exception('Unrecognized activation {activation}')\n", + " if dropout > 0:\n", + " modules.append(nn.Dropout(p=dropout))\n", + " modules.append(nn.Linear(nprev, odim))\n", + " print('-{:d}, dropout={:.1f}'.format(odim, dropout))\n", + " else:\n", + " modules.append(nn.Linear(idim, odim))\n", + " print(' - mlp %d-%d'.format(idim, odim))\n", + " self.mlp = nn.Sequential(*modules)\n", + " def forward(self, x):\n", + " return self.mlp(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.train_dl.dl.batch_sampler.sampler" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "200" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.train_dl.dl.batch_size" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - mlp 1024-100r-50r-2, dropout=0.0\n" + ] + } + ], + "source": [ + "m=LaserSentCLS(nhid=[100, 50],dropout=0, activation='RELU')\n", + "learn = Learner(data, m, metrics=[accuracy, FBeta(beta=1, average='binary')])\n", + "learn.save(\"rnd\")" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [], + "source": [ + "def f1_loss(input, target, reduction=None):\n", + " eps = 1e-7\n", + " logit = F.softmax(input, dim=-1) # [bs, 2]\n", + " tp = logit[target == 1, 1].sum()\n", + " tn = logit[target == 0, 0].sum()\n", + " bs = target.shape[0]\n", + " return 1.0 - 2.0*tp / (tp - tn + bs +eps)" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "learn.loss_func=f1_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" + ] + }, + { + "data": { + "image/png": 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epochtrain_lossvalid_lossaccuracyf_betatime
00.1461820.3423340.8710000.40553000:07
10.1316660.3573620.8690000.23391800:07
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epochtrain_lossvalid_lossaccuracyf_betatime
00.1458150.3341620.8740000.33684200:07
10.1334300.3542050.8740000.31521700:07
20.1222710.3359700.8690000.25142900:07
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epochtrain_lossvalid_lossaccuracyf_betatime
00.1482210.3525760.8730000.39810400:07
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epochtrain_lossvalid_lossaccuracyf_betatime
00.1523820.3324550.8760000.39805800:07
10.1352320.3217780.8730000.30601100:07
20.1307730.3121520.8630000.15950900:07
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40.1114470.3154590.8670000.21301800:07
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Better model found at epoch 0 with f_beta value: 0.3980582654476166.\n", + "Run tensor(0.3981)\n", + "lr=0.09010000000000001,wd=0.09001 [0.45106384, 0.39067143, 0.039341398]\n" + ] + } + ], + "source": [ + "from fastai.callbacks import * \n", + "results = {}\n", + "def test_param(lr=1e-2, wd=None):\n", + " runs=[]\n", + " for i in range(0, 10):\n", + " learn.load(\"rnd\")\n", + " cb=SaveModelCallback(learn, every='improvement',monitor='f_beta', name='best')\n", + " learn.fit_one_cycle(5, 1e-2, wd=None, callbacks=[cb])\n", + " runs.append(cb.best)\n", + " print (\"Run\", cb.best)\n", + " return runs\n", + "\n", + "for lr in [annealing_linear(1e-3, 1e-1, pct=x/10) for x in range(0,10)]:\n", + " for wd in [annealing_linear(1e-4, 1e-1, pct=x/10) for x in range(0,10)]: # def wd=1e-2\n", + " runs = test_param(lr, wd)\n", + " print(f\"lr={lr},wd={wd}\", [np.max(runs), np.mean(runs), np.std(runs)])\n", + " results[f\"lr={lr},wd={wd}\"] = [np.max(runs), np.mean(runs), np.std(runs)]" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lr=0.001,wd=0.0001': [0.41880342, 0.3921749, 0.027337264],\n", + " 'lr=0.001,wd=0.01009': [0.44787642, 0.3755535, 0.039731324],\n", + " 'lr=0.001,wd=0.02008': [0.42105263, 0.38561115, 0.02851636],\n", + " 'lr=0.001,wd=0.03007': [0.43697476, 0.40554914, 0.020560784],\n", + " 'lr=0.001,wd=0.040060000000000005': [0.45106384, 0.39535522, 0.03635963],\n", + " 'lr=0.001,wd=0.050050000000000004': [0.42677823, 0.38121435, 0.0326602],\n", + " 'lr=0.001,wd=0.06004': [0.42105263, 0.39451385, 0.01950172],\n", + " 'lr=0.001,wd=0.07003': [0.42608693, 0.39931774, 0.030449877],\n", + " 'lr=0.001,wd=0.08002000000000001': [0.44541487, 0.38503286, 0.042738147],\n", + " 'lr=0.001,wd=0.09001': 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lrwdmaxmeanstd
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..................
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0.07003 0.459144 0.405058 0.036981\n", + "44 0.0406 0.04006 0.454148 0.404852 0.042110\n", + "56 0.0505 0.06004 0.429825 0.404416 0.014622\n", + "14 0.0109 0.04006 0.438356 0.404018 0.021250\n", + "98 0.0901 0.08002 0.431718 0.403507 0.014815\n", + "32 0.0307 0.02008 0.442478 0.402443 0.026062\n", + "89 0.0802 0.09001 0.443478 0.402329 0.020548\n", + "27 0.0208 0.07003 0.439560 0.402276 0.026513\n", + "64 0.0604 0.04006 0.447653 0.402202 0.034280\n", + "21 0.0208 0.01009 0.434783 0.401308 0.026177\n", + "34 0.0307 0.04006 0.429752 0.400795 0.017976\n", + "91 0.0901 0.01009 0.434043 0.399822 0.035164\n", + ".. ... ... ... ... ...\n", + "78 0.0703 0.08002 0.433628 0.385733 0.031496\n", + "2 0.0010 0.02008 0.421053 0.385611 0.028516\n", + "37 0.0307 0.07003 0.422907 0.385504 0.036051\n", + "15 0.0109 0.05005 0.424242 0.385440 0.021202\n", + "8 0.0010 0.08002 0.445415 0.385033 0.042738\n", + "68 0.0604 0.08002 0.430493 0.384949 0.042812\n", + "16 0.0109 0.06004 0.436975 0.384062 0.037100\n", + "55 0.0505 0.05005 0.465116 0.383600 0.053216\n", + "61 0.0604 0.01009 0.407583 0.383329 0.024603\n", + "42 0.0406 0.02008 0.427273 0.382561 0.034482\n", + "54 0.0505 0.04006 0.447368 0.382376 0.058316\n", + "53 0.0505 0.03007 0.462810 0.382262 0.045407\n", + "29 0.0208 0.09001 0.443478 0.381864 0.031170\n", + "50 0.0505 0.00010 0.442478 0.381238 0.041377\n", + "5 0.0010 0.05005 0.426778 0.381214 0.032660\n", + "67 0.0604 0.07003 0.435146 0.381203 0.050594\n", + "43 0.0406 0.03007 0.460377 0.379907 0.050612\n", + "31 0.0307 0.01009 0.435897 0.379321 0.041445\n", + "70 0.0703 0.00010 0.437500 0.378910 0.057549\n", + "85 0.0802 0.05005 0.420091 0.378893 0.036736\n", + "94 0.0901 0.04006 0.425339 0.378338 0.032446\n", + "84 0.0802 0.04006 0.426087 0.378334 0.049495\n", + "90 0.0901 0.00010 0.413462 0.377556 0.043032\n", + "35 0.0307 0.05005 0.427948 0.377122 0.044827\n", + "17 0.0109 0.07003 0.425926 0.376076 0.053430\n", + "1 0.0010 0.01009 0.447876 0.375553 0.039731\n", + "60 0.0604 0.00010 0.413462 0.375317 0.029319\n", + "39 0.0307 0.09001 0.425926 0.373799 0.051351\n", + "26 0.0208 0.06004 0.420091 0.373688 0.042182\n", + "62 0.0604 0.02008 0.437500 0.370917 0.048129\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame([(list(map(float, k.replace(\"wd=\",\"\").replace(\"lr=\",\"\").split(\",\")))\n", + " + list(v)) for k, v in results.items()],\n", + " columns=[\"lr\", \"wd\", \"max\", \"mean\", \"std\"])\n", + "df.to_csv(\"paramters-search.csv\")\n", + "df.sort_values(\"mean\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.4257028, 0.38777068, 0.0438628)" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(runs), np.mean(runs), np.std(runs) # lr = 1e-2" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.41628963, 0.37591398, 0.035294134)" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(runs), np.mean(runs), np.std(runs) # lr= 5e-2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" + ] + }, + { + "data": { + "image/png": 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WSEyPrjUgtEXJQlVfV9WJqvofznaGql7e3HEiMk9EdotImojc38D+20Rku4hsEZEvRGSs176JIrLGWaN7u4h0b80HM8YYX6qormXh4s307B7GHWeO9Hc4Ha6lHdyJIvKWiBx0Xm+KSGIzx4QCi4ALgLHAAu9k4FisqhNUdTLwGPC4c2wY8HfgNlUdB8wFqlvzwYwxxpd+tWwXuw4c4bdXTqRfr673t2tLm6FeBJYCA53XO05ZU2YAac5dSBWwBJjvXcHpNK8XBajz/jxgm6pudeoVqWptC2M1xhifWp6ax0tr9nLzaUmcNabrjIDy1tJkkaCqL6pqjfP6K9DcBO2DgGyv7Ryn7DgicoeIpOO5s7jTKR4NqIgsF5FNIvLfDV1ARG4VkRQRSSkoKGjhRzHGmJbLPXyM/35jGxMGxfDf88b4Oxy/aWmyKBKRa0Uk1HldCxT5IgBVXaSqI4D7gAec4jDgNOAa57+XisjZDRz7rKomq2pyQkLwLy5ijAksuYePcdOL66mtU55cMIXwsK4zVPZELf3kP8AzbDYPOABcAdzYzDG5gPdE7olOWWOWAJc473OAz1S1UFXLgWXA1BbGaowx7bY9p4RLFq3iQEkFz143jWHxUf4Oya9aOt3HXuBi7zIR+Qnw+yYO2wCMcuaRygWuAq4+4RyjVLV+CO6FfDMcdznw3yLSA6gCzgCeaEmsxhjTGiXl1dz/z20cq65l+rA4ZibFUXC0krtf20pcVDiLb57JqH7R/g7T79qzUt7dNJEsnCnNF+L5xR8KvKCqqSLyMJCiqkuBhSJyDp6RTofwzGqLqh5yphPZgKfTe5mqvteOWI0x5ltKyqu59i/r2J13lMFxkazc/U3f56TEGJ6/YToJ0RF+jDBwiKo2X6uhA0WyVTVg1gtMTk7WlJQUf4dhjAkSJceque4v69h14Ah/unYaZ5/cj6LSSlL2HiL/SAVXThtMZHjnn3pcRDaqanJz9dpzZ9G2LGOMMX7WUKIA6NMzgvPH9fdzdIGpyWQhIkdpOCkIEOlKRMYY46KK6lpueSmFXQeO8Mw13yQK07Qmk4WqWq+OMabTqKtT7nl9K+uzinlywRTO6UJTjLdX1x00bIzpcn71/i7e23aAn31nDBdNGujvcIKKJQtjTJfw4qpMnvs8kxtmD+WWOcP9HU7QaU8HtzHGBLzq2jr++NEenvokjfPG9uPBi8YhIv4OK+hYsjDGdFp7i8q4a8kWtmQf5oppiTwyfzyhIZYo2sKShTGmU3prcw4PvLWD0BDhqaun8N2J1kfRHpYsjDGdSlVNHT9/byd/W7OXGUlxPPH9yQyKtZH+7WXJwhjTaRw8WsEdr2xiQ9YhbpmTxH3zxhAWauN4fMGShTGmU0g7WMo1z6+l5Fg1f1wwhYttaKxPWbIwxnQKz32WQWlFDW/dfionD+jl73A6Hbs/M8YEveraOpbvzOPcsf0sUbjEkoUxJuitzSjicHk1F0wY4O9QOi1LFicor6rhQMkxf4dhjGmFZdsPEBUeyhmjbXllt1iy8FJUWskli1Zx3hOfUVJe3a5zfb6ngJteXE9Fda2PojPGNKSmto7lqfmcfXI/unfr/OtP+IslC8ehsiqueX4dWUXlHK2o4W9rstp1vpdW7+WT3QW8sTHHJ/EZYxq2LrOY4rIqvmNNUK5yNVmIyDwR2S0iaSJyfwP7bxOR7SKyRUS+EJGxJ+wfIiKlInKvm3HWL62YUVjGCzdM56wxfXlhVSblVTVtOl95VQ2f7/Esz/jMynSqa+t8Ga4xxst72w/QIzyUuSdZE5SbXEsWIhIKLAIuAMYCC05MBsBiVZ2gqpOBx4DHT9j/OPC+WzECHKmo5voX1rEnv5Rnr5vGaaPiuePMERwqr+bV9dltOufnewqprKnjh6clkXv4GP/ast/HURtjwGmC2pHHWWP6WhOUy9y8s5gBpKlqhqpWAUuA+d4VVPWI12YUXqvyicglQCaQ6mKMZBSUkVVUzjPXTmXuSX0BmDY0jplJcTz3WQaVNa3vc1ixM59e3cO4b94Yxg7oxdOfpFFbZ6vQGuNr6zOLKSqr4kJrgnKdm8liEOD9p3mOU3YcEblDRNLx3Fnc6ZT1BO4D/s/F+ACYPDiWz+8781tLK95x5kjyjlTw1qbcVp2vtk75+MuDnDmmL+FhIdxx5kgyCst4f8cBX4ZtjMHTBBXZLfTrP/SMe/zewa2qi1R1BJ7k8IBT/BDwhKqWNnWsiNwqIikiklJQUNDmGHp17/atsjmj4pkwKIZnPk2nphV9Dhv3HqK4rIpzneUa543vz4iEKJ76OA1Vu7swxldq65TlqZ4mqMhwa4Jym5vJIhcY7LWd6JQ1ZglwifN+JvCYiGQBPwF+JiILTzxAVZ9V1WRVTU5I8G3nlohwx5kj2FtUzrIdeS0+7sPUPMJDQ74e7x0aItw+dyRf5h1leWrLz2OMadrq9EIKS6u4cKI1QXUEN5PFBmCUiCSJSDhwFbDUu4KIjPLavBDYA6Cqc1R1mKoOA34P/FJVn3Ix1gadN7Y/faMj+HhXfovqqyorduUze0Qfor3uVi6ePJCk+Chuf2UT976+lezicsCzePyGrGL+563t/Or9Xa58BmM6q7c25RLdPYyzxlgTVEdwbSJBVa1x7gaWA6HAC6qaKiIPAymquhRYKCLnANXAIeAGt+Jpi5AQYdLgWLbllrSo/p6DpewtKv/W+r7dQkN447bZPLMynb+t3cvbm3M5f1x/tuYcJueQ52lxEfjR6SOIiwr3+ecwprMpr6rhg9Q85k8eaKOgOoirs86q6jJg2QllD3q9v6sF53jI95G13KTEGFbszOdIRXWDfRveVuz03IHU91d469Mzgge+O5ab5wxn0SdpvL05lylDe3P3uaOJ7xnB9S+sZ016kd1SG9MCH6bmU15VyyWTvzVmxrjEpihvxoTEWAB25JZwyoj4Jut+uDOfSYkx9OvVvdE6/WO688gl43nkkvFfl9XU1tEzIoxV6YWWLIxpgX9uzmVQbCTTh8X5O5Quw++joQLdhEExAGzPabop6uDRCrZmH27wrqI5YaEhzEyKY3VaYZtiNKYrOXi0gi/2FHDJlIGEhIi/w+kyLFk0Iy4qnMFxkWxrJlmsSS8CYM6oto3KOnVkPFlF5eQcKm/T8cZ0FUu37KdO4dIpif4OpUuxZNECEwfFsi33cJN11mYUEd09jHED27bwyqkjPU1cq9OK2nS8MV3FW5tzmZgYw8i+Pf0dSpdiyaIFJibGkF18jOKyqkbrrEkvYmZSXJsXhx/dryfxPSNYlW5NUcY05qv8o6TuP2Id235gyaIFJiQ6/RaNDKHdf/gYWUXlzBrep83XEBFOHdmHVWlF9qS3MY14a3MuoSHCRZMG+juULseSRQuMdzq5t2U33BRV31/R3Gip5pw6Ip7C0kq+ym9ylhNjuqTaOuWfm3I4fVQ8CdER/g6ny7Fk0QK9undjeEJUow/nrckoonePbozpH92u65wy0nNnsspGRRnzLZ/tKSD/SCXfnz64+crG5yxZtNDEQTFsy2n8zmJmUp92D+NL7N2DYX16sNqFfotdB47wv2/vIO3gUZ+f25iO8HpKNnFR4Zw1pvXD0037WbJooYmJseQfqST/SMVx5dnF5eQePsbsEW3vr/B2ysh41mYUt2qm26aUV9Xwq2W7+O6TX/Dy2r1c9OQqW+rVBJ2i0kpW7Mzn0imDCA+zX1v+YD/1FprodHKf+LxF/V3AKT5KFqeOiKe0soatzTzX0RKr0wo59/HP+PNnGVw5LZEP//N0Jg2O4d7Xt3L3a1soq2zbsrHGdLS3t+ynulb5XrI1QfmLJYsWGjcwhhCB7Sc0Ra1JLyK+Z4TPxnzPHtEHEc/a3W1dAxw8nYELX91MeFgIr/1oNr++fCKj+0Xzys2zuPPsUby1OZeLnvyCLY102hsTKFSV11OymTQ4lpPa2S9o2s6SRQtFhocyul/0cZ3cqsqajCJmDY9DxDfTDsRFhXP/vDF89GU+lz29mqzCsjadZ0u2ZxGmu88dzYykb+bPCQ0R7j53NK/8cCbHqmu5/JnV/O7D3VTV+KbZyxhf255bwpd5R/lesj2x7U+WLFphwqAYtuWUfP0cREZhGflHKts9ZPZEPzpjBC/eOJ0DJRVc9NQX/Htny9bT8PbJlwWEhginNzL9yCkj4/ngJ6czf/JAnvw4jUufXsXuPHc7v0vKq8krqWi+ojFeXkvJJiIsxJ6t8DNLFq0wcXAsxWVV3PxSCn9fu5elW/YD+Kxz29vck/ry7o9PY0hcD255OaXVkwx+/OVBpg3pTUyPxqdVj4nsxuPfm8yfr5tGnpOYnv88g7o63z4UWFunvLwmizmPfcz5v/+MgqOVPj2/6bwqqmv515b9fGfCgGaXCDDusmTRChdNHMB1s4byZd5RHnh7B3/4aA/9e3VnWJ8erlxvcFwPXr9tNsP6RPFfb2yjtIUd0nklFew8cIS5Y1o2qeH54/qz/D9P5/RR8fz8vV1c+5d17D98rD2hA55mupSsYi5+6gv+91+pjBnQi/KqGn65zFYFNC3z/o4DHK2o4UprgvI7W8+iFWJ7hPPIJeN5WJX0glJW7i5gRN+ePuuvaEiP8DB+e+VErvjTGn7x3i5+ddmEZo9ZufsgQKuWm4zvGcFz1yfzjw3ZPPzuTi784+d8dM/cVq/c98GOA6zYeZC0glIyDpZytLKG/r2689TVU7hwwgAeX/EVT36cxhXTEr+ePNGYxixet4+k+Chmt2MqHeMbdmfRBiLCyL7R3DxnOGee5P76v9OGxnHLnOG8un4fn35V0Gz9T3YfZEBMd07q17qRIyLCVTOG8OKN0zlUXs3ne5q/lrePduVz29838elXBfSMCOXSqYP4xaXj+eieM/juxIGICHecOZJhfXrwwNs7qKiubdX5TdfyVf5RNmQdYsGMwa7+QWZaxtVkISLzRGS3iKSJyP0N7L9NRLaLyBYR+UJExjrl54rIRmffRhE5y804g8Hd545mZN+e3PfGNkqOVTdar6qmji/2FHLmmL5t/geWPCyOXt3DWjVdekZBKT9ZsoUJg2L44r4zeeXmWTw8fzzXzBxKVMQ3N7Ddu4Xy80smkFlYxjMr09sUn+kaFq/bR3hoCFdMs2crAoFryUJEQoFFwAXAWGBBfTLwslhVJ6jqZOAx4HGnvBC4SFUnADcAL7sVZ7Do3i2U3105iYLSSn7VRJv/hqxiyqpq23XHExoizBrep8XTpZdW1nDryxvpFhbCn66bRvduoU3WP21UPPMnD+SZlemkF9ikiebbjlXV8uamHOaN79/qplDjDjfvLGYAaaqaoapVwBJgvncFVT3itRkFqFO+WVX3O+WpQKSIdPlpJicNjuXGU4bxWkp2o79kP/nyIOGhIe1+ovzUkfHkHDrGvqKmV+5TVe59bSuZhWU8dfUUBsVGtuj8D1w4lvCwEJ76OK1dcZrO6d1t+zlaUcPVM4f4OxTjcDNZDAKyvbZznLLjiMgdIpKO587izgbOczmwSVW/Nd5SRG4VkRQRSSkoaF37erC6fe4IuncL5Y8f7Wlw/8e7DzJzeNxxTT9tcWr9DLjN3F28sm4fH6Tm8dMLxrTqeZOE6AgumzqI97YdaHJRKdM1LV6/jxEJUcz0eqDU+JffO7hVdZGqjgDuAx7w3ici44BHgR81cuyzqpqsqskJCW1b+zrY9OkZwfWzh7F063725B//EN3eojIyCsp80uk+IqEnfaMjmpwuva5Oef7zDKYOieWHpyW1+hrXzhpKVW0dr6VkN1/ZdBk79x9h877DLJgxxDq2A4ibySIX8O6ZSnTKGrMEuKR+Q0QSgbeA61XVekK93Hr6cHp0C+X3XncXqsqLq7KA1g2ZbYxn5b541qQXNfqQ3qd7CsgqKufGU5Pa9I96dL9oZiTF8cq6vT5/ENAEr8Xr9xIeFsIV0+zZikDiZrLYAIwSkSQRCQeuApZ6VxCRUV6bFwJ7nPJY4D3gflVd5WKMQSkuKpybTk3ivW0H+DLvCDW1dfzsre38dXUW18wcwrD4KJ9c55QRfSgqq2J3fsPTgPxtdRYJ0RHMG9e/zde4btZQsouP8Wkrh+mazqm8qoZ5HsHhAAAUlUlEQVR/bd7PhRMGENvDOrYDiWvJQlVrgIXAcmAX8JqqporIwyJysVNtoYikisgW4G48I59wjhsJPOgMq90iIu4/0BBEbp6TRHREGL/5YDe3v7KJV9dns/DMkfz8kvE+u0b9Q3MNNUVlFZax8qsCrpk5pF3rC5w/rj/xPSN4Ze3eNp/DdB7vbjvA0coaFsywju1A4+oT3Kq6DFh2QtmDXu/vauS4nwM/dzO2YBfbI5wfnJbEHz7agwg8dNFYbjy19f0GTRkYG0lSfBSr04u4ec7w4/a9vHYvoSJc3c5/1OFhIVw1fTCLVqaRc6icxN7uTJ1igsMSp2N7+rDe/g7FnMDvHdym7X5wWhJnnpTAkwum+DxR1DtlRB/WZRRR7bVyX3lVDa+lZHPBhAH07dW93ddYMHMIAry6fl+7z2WC11f5R9m07zBXTbeO7UBkySKIxUR248WbZvDdie5N3XzqyHjKqmqPW3/87c2eMfA3zB7qk2sMio3krDF9+ceGbHJ9MIGhCU5L1mfTLVS4bOq3RtibAGDJwjRp9nDPyn3vbcsjJauY5al5vLAqk3EDezFtqO+aCv5j7kjKKms59/FPee6zDJ+tQW6CQ0V1Lf/cnMN5Y/vTp2eXf/42INmss6ZJvaPCGTewFy+syuSFVZlflz/+vUk+bSqYNrQ3K+4+nQf/lcovlu3irc25PHbFRMYPivHZNUzgWp6ax+Hyaq6aYfNABSqpX/Ut2CUnJ2tKSoq/w+iU0g4eZXtuCXFREcT1CCchOoL+Me3vq2iIqvLBjjweeieVqpo6lt01hwExLZtCxASvq59bS/ahcj6990xCQqy/oiOJyEZVTW6unjVDmWaN7BvNpVMSOWN0AhMSY1xLFOB5GPCCCQNYfMssKmvquOvVLdYk1cntLSpjdXoR308ebIkigFmyMAFpREJPfnnpBNZnFfOHRubBMp3D4vX7CA0Rrky2JqhAZsnCBKxLpgzie8mJPPVJGl/sad0a5CY4lFXW8Oq6fZw3th/9fDAM27jHkoUJaA9dPI6RCT35yT82k1lY5u9wjI+9uSmHIxU133ro0wQeSxYmoPUID2PRNVOprKnj/Cc+43cf7uZYlS3H2hnU1ikvfJHJ5MGxPh2GbdxhycIEvNH9ovno7jO4cOIAnvw4jXMe/5Rl2w/QWUbydVUf7conq6icm+e4M/uA8S17zsIEhb69uvPE9yezYMYQHvzXDm5/ZRPTh/XmZ985mSlDvv1X6bGqWrKKysgsLCOrqIy9heVkFpWRe+gYYwf24rIpgzjr5L5EhIVSVFrJ0q37eXvLfqYMjuWhi8f54RN2PX/5IpNBsZHtmrXYdBxLFiaozEiK490fn8brG3P43YdfcenTq7lo0kAmJcaQWVj29etAScVxxyVERzCsTw+mDIllXWYxK3bmExPZjbEDerEhq5iaOiW+Zzjbcg5z7awhjOwb7adP2DVszylhXWYxD1x4MmGh1sARDCxZmKATFhrCghlDuGjSQJ79NJ1nP8/gna37iYnsxvCEKGYP70NSfBRJCVEM6xPFsPgoenotM1tTW8eq9CLe2pTDjv1H+OFpSVw2NZGE6AhOe/Rjnvo4jd9fNcWPn7Dz+8sXGUSFh/K96TZcNlhYsjBBq2dEGHefdxI/nDOc2jolLqpli+WEhYZwxugEzhj97aV4r501lOc/z+Cuc0aT5KNFpMzxduSW8O62A1w/exi9unfzdzimhSxZmKAXE+m7Xzg3z0nipdVZLPokjd9eOenr8vSCUpZtO0B1bR21qtTWwdyTEpg1vI/Prt0VbM0+zHV/WUe/Xt350Rk2XDaYWLIwxkvf6O4smDGEl9fu5a6zRzE4rgf/3pnPXUs2U+YM2Q11pqT406fpXD97KPdfMIYe4fZPqTkb9x7ixhfWExvVjcU3z7KH8IKMq99wEZkH/AEIBZ5X1V+fsP824A6gFigFblXVnc6+nwI/dPbdqarL3YzVmHq3nTGCxev28fTKNBJ79+C3H+5m/MAYnr1+2teTGh6rquU3y3fz4upMPv2qgN9eOYnpw+L8HHng2pBVzI0vrCchOoLFt8xiYKxNDhlsXJt1VkRCga+Ac4EcYAOwoD4ZOHV6qeoR5/3FwO2qOk9ExgKvAjOAgcC/gdGq2ujTWDbrrPGlB97ezt/Xelbumz95II9ePpHu3UK/VW9dRhH3vrGVnEPHeO66ZM4Z26+jQw146zKKuOmvG+jfqzuLb5nl6kSUpvUCYdbZGUCaqmaoahWwBJjvXaE+UTiigPrMNR9YoqqVqpoJpDnnM6ZD/MfckQxPiOL+C8bw++9PbjBRAMwc3ocP7jqdcQN7cfdrW8guLu/gSANbfaIYENOdJbdaoghmbiaLQUC213aOU3YcEblDRNKBx4A7W3OsMW4ZFBvJx/fM5bYzRjS7yFNURBhPXz0NBW5/ZROVNTYdCcDajCJufNGTKF69dZZP1ms3/uP3p2FUdZGqjgDuAx5ozbEicquIpIhISkFBgTsBGtMCQ/r04HdXTmJ7bgmPvLuz+QM6ofKqGjbtO8Tidfv437d3cNOLG0jsHcmSW2fTN9oSRbBzs4M7F/B+4ibRKWvMEuCZ1hyrqs8Cz4Knz6I9wRrTXueN68+PTh/Onz/LYPqwOOZP7jo3wx/sOMC9r2+jtLIG8DwDM3N4HL+5YhIJ0bamdmfgZrLYAIwSkSQ8v+ivAq72riAio1S1fmWbC4H690uBxSLyOJ4O7lHAehdjNcYn7j3/JDbuPcRDS1M5f1z/Rvs6OovaOuWJFV/x1CdpTB4cy+1zR3DygF4k9o706Rrtxv9cSxaqWiMiC4HleIbOvqCqqSLyMJCiqkuBhSJyDlANHAJucI5NFZHXgJ1ADXBHUyOhjAkU3UJDuOe8k1jw3FqWbtnfaaazKK2s4Q///orXUnIYnhBF8tDeTBvam39syOaT3QVcNX0w/zd/HBFhnTs5dmWuDZ3taDZ01gQKVWXe7z8nJERYdudpQf0XtqqybHsej7y7k7wjFZw/rh9FpVVsyymhqraObqHC/7toHNfMHBLUn7Mra+nQWXvs1BgfExFuPHUYP/3ndjZkHWJGkm8e1vswNY8hfXowpn8vn5yvOWWVNdy1ZDP/3nWQsQN68fS1U5nqTAdfWVPLjtwSevcIZ3hCzw6Jx/iX30dDGdMZXTJ5EDGR3fjr6kyfnO+NjTnc+vJGLl20mhU7831yzqYcLq/imufX8cnuAh648GSWLjz160QBEBEWyrShcZYouhBLFsa4IDI8lKumD2Z5aj77Dx9r0TE5h8q589XNfPzl8clgVVoh97+5jdnD+zC6X09+9HIKf1uT5fugHQePVPD9P69l5/4jPH3NVG6eM9zWnDCWLIxxy7WzhqKq/H3t3mbr7jpwhMueXs3Srfv5wV9TuOe1rZSUV7M77yi3vbyREQk9+fP103j11lmcNaYfD/4rlV+8t5PaOt/2Oe7JP8oVf1pD9qFyXrxpOufbKnbGYX0WxrhkcFwPzjm5H6+u38edZ49qdBjtmvQibv1bClERYbz749NYnprH0yvT+XxPAaEhQmR4KC/eNP3rtR/+fN00Hn4nlec+z2R7bgl/uGpKi2dwzT18jP/8xxaOHKvm+9MHc9nURGIiu5FdXM4fP9rDm5ty6BXZjVduntngcrWm67LRUMa4aHV6IVc/t44HLjyZm+d8e/2G97cf4K4lWxjSpwd/+8GMr2dj3ZFbwr2vbyW7uJx//Gg24wfFHHecqvLmplz+9+0dRIaH8tsrJ3LWmKYnMdy4t5gfvbyRyuo6hidEsTWnhO7dQpiZ1IfV6YWICNfOHMrtZ44gvqc9SNdVtHQ0lCULY1ykqvzwpRRW7j7In69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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "learn.recorder.plot_lr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [], + "source": [ + "interp = ClassificationInterpretation.from_learner(learn)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\u001b[0;36mtop_losses\u001b[0;34m(self, k, largest)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtop_losses\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlargest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\"`k` largest(/smallest) losses and indexes, defaulting to all losses (sorted by `largest`).\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtopk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mifnone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlargest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlargest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_learner_interpret\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mLearner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_type\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mDatasetType\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDatasetType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/laser/lib/python3.6/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36m__len__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"len() of a 0-d tensor\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 412\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: len() of a 0-d tensor" + ] + } + ], + "source": [ + "l, i = interp.top_losses()" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 0, 86), (0, 1, 52)]" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interp.most_confused()" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'i' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mix\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalid_ds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'i' is not defined" + ] + } + ], + "source": [ + "for ix in range(0,10): print(data.valid_ds[i[ix]])" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(EmbeddingText account wie@@ czna zdra@@ da , nie zdra@@ da , trzeba roz@@ mawi@@ ać , pier@@ dol@@ enie od rzeczy, Category 0)\n", + "(EmbeddingText account account to pani kry@@ sia jest two@@ ją oj@@ czy@@ zn@@ ą ?, Category 0)\n", + "(EmbeddingText account droga p.@@ kam@@ il@@ ko ! le@@ czyć się . le@@ czyć pó@@ ki czas 😁 😁, Category 0)\n", + "(EmbeddingText account du@@ baj w@@ zor@@ em , czy@@ li jednak islam , sal@@ am al@@ ej@@ kum ci@@ em@@ ki@@ e@@ wi@@ cz, Category 0)\n", + "(EmbeddingText account a ja za@@ łoż@@ ę fit@@ blo@@ ga 😂 😂 😂, Category 0)\n", + "(EmbeddingText account to tak jak ja prze@@ widział@@ em , że cię nie w@@ pusz@@ cza do g@@ b, Category 0)\n", + "(EmbeddingText prowad@@ zą@@ cy mówi ze nikt mu nie wy@@ s@@ ła@@ ł sz@@ ki@@ ców projekt@@ ów jak nie jak ja ci wy@@ sł@@ ałam im@@ be@@ cy@@ lu, Category 0)\n", + "(EmbeddingText ra@@ fa@@ ł br@@ zo@@ zo@@ w@@ ski ma nar@@ ze@@ cz@@ on@@ ą ? mam na@@ dzie@@ je m , że na co@@ dzień jej aż tak nie fa@@ ł@@ sz@@ uje jak w tym kol@@ e fortun@@ y a jeśli tak to big ri@@ p girl, Category 0)\n", + "(EmbeddingText account account to ta pani co ma roz@@ tro@@ jen@@ ie ja@@ ź@@ ni ?, Category 0)\n", + "(EmbeddingText account account account prawda o kar@@ cze@@ w@@ skim to nie prawda o pol@@ s@@ ce . coś się panu po@@ je@@ ba@@ ło account ., Category 0)\n", + "(EmbeddingText account zna@@ jąc ment@@ al@@ ność pis win@@ ny będzie ten kto wi@@ dzi nie ten kto czy@@ ni ., Category 0)\n", + "(EmbeddingText account prz@@ esta@@ ń@@ cie kur@@ wa cały czas p@@ ła@@ kać, Category 0)\n", + "(EmbeddingText account account ko@@ do@@ wa zar@@ aza nar@@ ód nasz dzi@@ eli , m@@ roz@@ ny de@@ sz@@ cz gro@@ zy po kar@@ ku prze@@ chodzi , \\ ndo@@ ść dy@@ kt@@ atu pej@@ sat@@ ej bru@@ k@@ sel@@ i, Category 0)\n", + "(EmbeddingText rt account account ku@@ ter ry@@ bac@@ ki przy niej , to dou@@ glas na wod@@ zie ., Category 0)\n", + "(EmbeddingText account i we@@ ź tu zosta@@ ń p@@ sem, Category 0)\n", + "(EmbeddingText account ty naj@@ pier@@ w prze@@ czy@@ taj te swoje ksi@@ ąż@@ ki z foto@@ ta@@ pet@@ y a potem po@@ gad@@ amy ., Category 0)\n", + "(EmbeddingText trzeba być patri@@ ot@@ ą swo@@ jego mi@@ asta , swo@@ jego region@@ u , swo@@ jej ziemi . pre@@ zes # pis j. # ka@@ czy@@ ń@@ ski w # zam@@ o@@ ść : # do@@ trzy@@ mu@@ jem@@ ys@@ ło@@ wa, Category 0)\n", + "(EmbeddingText rt account trzeba być patri@@ ot@@ ą swo@@ jego mi@@ asta , swo@@ jego region@@ u , swo@@ jej ziemi . pre@@ zes # pis j. # ka@@ czy@@ ń@@ ski w # zam@@ o@@ ść : # do@@ trzy@@ mu@@ jem@@ ys@@ ło@@ wa, Category 0)\n", + "(EmbeddingText account taki z niego patri@@ ota jak z ciebie dzi@@ enni@@ kar@@ z . nie po@@ gr@@ ąż@@ aj się ., Category 0)\n", + "(EmbeddingText rt account account taki z niego patri@@ ota jak z ciebie dzi@@ enni@@ kar@@ z . nie po@@ gr@@ ąż@@ aj się ., Category 0)\n", + "(EmbeddingText account account zanim się o@@ dez@@ wiesz to wy@@ trze@@ ź@@ wi@@ ej, Category 0)\n", + "(EmbeddingText account bier@@ z tego account razem jeste@@ ście moc@@ ni, Category 0)\n", + "(EmbeddingText account a br@@ am@@ kar@@ ze ? gli@@ k ? kry@@ cho@@ wi@@ ak ? ku@@ ba ? mili@@ k ? pis@@ z@@ cz@@ ek jak jest zdro@@ wy ?, Category 0)\n", + "(EmbeddingText kur@@ wa wszyscy w domu ka@@ sz@@ l@@ ą, Category 0)\n", + "(EmbeddingText account account account je@@ ba@@ ć sł@@ oni@@ c@@ ę@@ - mo@@ ty@@ li@@ cę ! ! ! ! 😁 😁 😁 . wro@@ c@@ ła@@ w nie daj się ! ! !, Category 0)\n", + "(EmbeddingText account jeśli to jest sz@@ rot to jak naz@@ wać was@@ ze transfer@@ y ?, Category 0)\n", + "(EmbeddingText no teraz kur@@ wa jak muszę wyj@@ ść to pada@@ ć zac@@ zy@@ na no ja pier@@ dole do dup@@ y z ta@@ kim ży@@ ciem, Category 0)\n", + "(EmbeddingText account account chyba ty , ch@@ ł@@ op jak by@@ k, Category 0)\n", + "(EmbeddingText account ale tu@@ sk nie jest świ@@ rem, Category 0)\n", + "(EmbeddingText account account account account ki@@ bi@@ ce wier@@ zą w zwy@@ ci@@ ę@@ stwo swo@@ jej dru@@ ży@@ ny@@ .@@ ty to nazy@@ was@@ z do@@ pis@@ y@@ wan@@ iem p@@ kt ? ha@@ ha, Category 0)\n", + "(EmbeddingText account account account historia lu@@ dz@@ ko@@ ści to historia gł@@ upo@@ ty # ja@@ j@@ co, Category 0)\n", + "(EmbeddingText account ten to już zu@@ peł@@ nie od@@ wi@@ esi@@ ł mó@@ z@@ g na ko@@ ł@@ ek , chor@@ y mó@@ z@@ g, Category 0)\n", + "(EmbeddingText account rzu@@ ć ż@@ on@@ ę , przej@@ dź na bud@@ dy@@ z@@ m , prze@@ prowad@@ ź sie z u@@ kochan@@ ą na bar@@ ba@@ dos , zac@@ zni@@ j s@@ sa@@ ć życie, Category 0)\n", + "(EmbeddingText h@@ ali@@ c@@ ki : ga@@ w@@ ło@@ w@@ ski nie może się br@@ oni@@ ć bo sied@@ zi . a po wciąż za ba@@ ł@@ ag@@ an w w-@@ wie ob@@ cią@@ ż@@ a m@@ .@@ i. nie@@ ży@@ ją@@ cego l.@@ ka@@ czy@@ ń@@ skiego \\ n # wor@@ oni@@ cza@@ 17, Category 0)\n", + "(EmbeddingText pis już się z@@ bliż@@ a już pu@@ ka do t@@ wych dr@@ z@@ wi@@ .@@ po@@ bieg@@ ni@@ j go przy@@ wit@@ ać z ra@@ do@@ ści ser@@ ce dr@@ ży . 😁 😁 😁, Category 0)\n", + "(EmbeddingText account pola@@ cy rat@@ uj@@ my pol@@ sk@@ Ę od zlo@@ dzie@@ ji po i lisa wol@@ j@@ s@@ do@@ j@@ cza, Category 0)\n", + "(EmbeddingText rt account account pola@@ cy rat@@ uj@@ my pol@@ sk@@ Ę od zlo@@ dzie@@ ji po i lisa wol@@ j@@ s@@ do@@ j@@ cza, Category 0)\n", + "(EmbeddingText account panie reda@@ ktor@@ ze , proszę u@@ ca@@ ło@@ wać pana account wró@@ ć . poz@@ dro@@ wić ., Category 0)\n", + "(EmbeddingText account ja się wiele cie@@ ka@@ wych rzeczy do@@ wiedzi@@ ałem n@@ p że ry@@ cz@@ kow@@ ski to 87 ro@@ cz@@ nik@@ .@@ spraw@@ dzić tekst przed dru@@ kiem to chyba za tru@@ dne ., Category 0)\n", + "(EmbeddingText account o in@@ wek@@ ty@@ wach proszę poroz@@ mawi@@ ać ze swo@@ im prez@@ es@@ em a nie pie@@ przy@@ ć mi tutaj o jak@@ ich@@ ś przy@@ kł@@ ada@@ ch ., Category 0)\n", + "(EmbeddingText daj@@ cie fa@@ v a ja wam jutro zrobi@@ ę gad@@ an@@ ego in@@ da pier@@ wsz@@ ego w ży@@ ciu !, Category 0)\n", + "(EmbeddingText wiek to ok@@ rop@@ ny z@@ łod@@ zi@@ ej@@ . kiedy zac@@ zy@@ nasz rozu@@ mieć , na czym po@@ lega życie , ono ś@@ cina cię z nó@@ g i przy@@ gar@@ bia ci ple@@ cy ., Category 0)\n", + "(EmbeddingText rt account wiek to ok@@ rop@@ ny z@@ łod@@ zi@@ ej@@ . kiedy zac@@ zy@@ nasz rozu@@ mieć , na czym po@@ lega życie , ono ś@@ cina cię z nó@@ g i przy@@ gar@@ bia ci ple@@ cy ., Category 0)\n", + "(EmbeddingText account account mo@@ wa była o fak@@ ta@@ ch a nie two@@ ich od@@ czu@@ ci@@ ach czy go uważ@@ asz za po@@ laka czy nie account, Category 0)\n", + "(EmbeddingText account ale ja uci@@ ek@@ nę tam gdzie pie@@ pr@@ z ro@@ śnie, Category 0)\n" + ] + } + ], + "source": [ + "neutral_as_offensive = np.nonzero((interp.pred_class != interp.y_true) & (interp.y_true == 0)).reshape(-1)\n", + "for ix in neutral_as_offensive: print(data.valid_ds[ix])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:laser]", + "language": "python", + "name": "conda-env-laser-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/experiments/LASER-lc.ipynb b/experiments/LASER-lc.ipynb new file mode 100644 index 0000000..dd09a02 --- /dev/null +++ b/experiments/LASER-lc.ipynb @@ -0,0 +1,1288 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/pczapla/workspace/ulmfit-multilingual/experiments\r\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from fastai.text import *" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# unzip task6_test.zip\n", + "# unzip task_6-1.zip -d Task6/task\\ 01\n", + "# unzip task_6-2.zip -d Task6/task\\ 02\n", + "# mv Task6/task\\ 01 task-1\n", + "# mv Task6/task\\ 02 task-2\n", + "# rmdir Task6" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[PosixPath('../data/poleval19/task-1/test_set_clean_only_text.txt'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.tmp.tok'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.txt'),\n", + " PosixPath('../data/poleval19/task-1/test_set_clean_only_text.tmp.bpe'),\n", + " PosixPath('../data/poleval19/task-1/test_set_clean_only_text.tmp.enc'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_tags.txt'),\n", + " PosixPath('../data/poleval19/task-1/models'),\n", + " PosixPath('../data/poleval19/task-1/evaulate1.pl'),\n", + " PosixPath('../data/poleval19/task-1/test_set_clean_only_text.tmp.tok'),\n", + " PosixPath('../data/poleval19/task-1/test_set_clean_only_tags.txt'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.tmp.enc'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.tmp.bpe')]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset=Path('../data')/'poleval19'/'task-1'\n", + "dataset.ls()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "LASER = os.environ.get('LASER', \"/home/pczapla/workspace/_oss/LASER\")\n", + "os.environ['LASER'] = LASER\n", + "def cond_add_pypath(*paths):\n", + " for path in paths:\n", + " if path not in sys.path:\n", + " sys.path.append(path)\n", + "cond_add_pypath(LASER + '/source',LASER + '/source/tools')\n", + "\n", + "from embed import SentenceEncoder, EncodeLoad, EncodeFile\n", + "from text_processing import Token, BPEfastApply, SplitLines, JoinEmbed" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": {}, + "outputs": [], + "source": [ + "from subprocess import run, check_output, DEVNULL\n", + "def tokenize(inp_fname, out_fname, lang='en',\n", + " lower_case=True, romanize=False, descape=False,\n", + " verbose=False, over_write=False, gzip=False):\n", + " FASTBPE = LASER + '/tools-external/fastBPE/fast'\n", + " MOSES_BDIR = LASER + '/tools-external/moses-tokenizer/tokenizer/'\n", + " MOSES_TOKENIZER = MOSES_BDIR + 'tokenizer.perl -q -no-escape -threads 20 -l '\n", + " MOSES_LC = MOSES_BDIR + 'lowercase.perl'\n", + " NORM_PUNC = MOSES_BDIR + 'normalize-punctuation.perl -l '\n", + " DESCAPE = MOSES_BDIR + 'deescape-special-chars.perl'\n", + " REM_NON_PRINT_CHAR = MOSES_BDIR + 'remove-non-printing-char.perl'\n", + "\n", + " # Romanization (Greek only)\n", + " ROMAN_LC = 'python3 ' + LASER + '/source/lib/romanize_lc.py -l '\n", + "\n", + " # Mecab tokenizer for Japanese\n", + " MECAB = LASER + '/tools-external/mecab'\n", + " assert lower_case, 'lower case is needed by all the models'\n", + " if not out_fname.exists():\n", + " cat = 'zcat ' if gzip else 'cat '\n", + " roman = lang if romanize else 'none'\n", + " # handle some iso3 langauge codes\n", + " if lang in ('cmn', 'wuu', 'yue'):\n", + " lang = 'zh'\n", + " if lang in ('jpn'):\n", + " lang = 'ja'\n", + " if verbose:\n", + " print(' - Tokenizer: {} in language {} {} {}'\n", + " .format(os.path.basename(inp_fname), lang,\n", + " '(gzip)' if gzip else '',\n", + " '(de-escaped)' if descape else '',\n", + " '(romanized)' if romanize else ''))\n", + " cmd = (cat + str(inp_fname)\n", + " + '|' + REM_NON_PRINT_CHAR\n", + " + '| sed \"s/\\(@anonymized_account *\\)/account /gi\"'\n", + "# + '| iconv -f UTF-8 -t ASCII//TRANSLIT '\n", + " + '|' + NORM_PUNC + lang\n", + " + ('|' + DESCAPE if descape else '')\n", + " + '|' + MOSES_TOKENIZER + lang\n", + " + ('| python3 -m jieba -d ' if lang == 'zh' else '')\n", + " + ('|' + MECAB + '/bin/mecab -O wakati -b 50000 ' if lang == 'ja' else '')\n", + " + ('|' + ROMAN_LC + roman if romanize else '')\n", + " + ('| tr \"[:upper:]\" \"[:lower:]\"' if lower_case else '')\n", + " + '>' + str(out_fname))\n", + " print (\"Tokenziation CMD: \", cmd)\n", + " run(cmd,\n", + " env=dict(os.environ, LD_LIBRARY_PATH=MECAB + '/lib'),\n", + " shell=True)\n", + " elif not over_write and verbose:\n", + " print(' - Tokenizer: {} exists already'\n", + " .format(os.path.basename(out_fname), lang))\n", + "\n", + "\n", + "def split_lines(*args): return SplitLines(*list(map(str,args)))\n", + "def bpe_fast_apply(*args, **kwargs): return BPEfastApply(*list(map(str,args)), **kwargs)\n", + "def encode_file(enc, *args, **kwargs): return EncodeFile(enc, *list(map(str,args)), **kwargs)\n", + "def join_embed(*args): return JoinEmbed(*list(map(str,args)))\n", + " \n", + "def process_file(inputfn, lang='pl'):\n", + " inputfn = Path(inputfn)\n", + " args = SimpleNamespace(\n", + " encoder=str(Path(LASER)/\"models\"/\"bilstm.93langs.2018-12-26.pt\"), \n", + " bpe_codes=str(Path(LASER)/\"models\"/\"93langs.fcodes\"),\n", + " lang=lang,\n", + " buffer_size=10000,\n", + " max_tokens=12000,\n", + " max_sentences=None,\n", + " cpu=False,\n", + " verbose=False,\n", + " stable=True)\n", + " enc = EncodeLoad(args)\n", + " def fn(suffix):\n", + " return inputfn.with_suffix(f'.tmp{suffix}')\n", + " tokenize(inputfn,\n", + " fn('.tok'),\n", + " lang=lang,\n", + " romanize=(True if lang == 'el' else False),\n", + " lower_case=True, gzip=False,\n", + " verbose=args.verbose, over_write=False)\n", + " bpe_fast_apply(fn('.tok'),\n", + " fn('.bpe'),\n", + " args.bpe_codes,\n", + " verbose=args.verbose, over_write=False)\n", + " split_lines(fn('.bpe'),\n", + " fn('.split'),\n", + " fn('.sid'))\n", + "# encode_file(enc,\n", + "# fn('.bpe'),\n", + "# fn('.enc'),\n", + "# verbose=args.verbose, over_write=False,\n", + "# buffer_size=args.buffer_size)\n", + " encode_file(enc,\n", + " fn('.split.bpe'),\n", + " fn('.split.enc'),\n", + " verbose=args.verbose, over_write=False,\n", + " buffer_size=args.buffer_size)\n", + "\n", + " join_embed(fn('.split.enc'),\n", + " fn('.sid'),\n", + " fn('.enc'))\n", + " return fn('.enc')" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/training_set_clean_only_text.txt|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl| sed \"s/\\(@anonymized_account *\\)/account /gi\"|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l pl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l pl| tr \"[:upper:]\" \"[:lower:]\">../data/poleval19/task-1/training_set_clean_only_text.tmp.tok\n" + ] + } + ], + "source": [ + "!rm {dataset}/training_set_clean_only_text.tmp.*\n", + "trn_fn = process_file(dataset / 'training_set_clean_only_text.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dla mnie faworytem do tytułu będzie Cracovia. Zobaczymy, czy typ się sprawdzi.\r", + "\r\n", + "account account Brawo ty Daria kibic ma być na dobre i złe\r", + "\r\n", + "account account Super, polski premier składa kwiaty na grobach kolaborantów. Ale doczekaliśmy czasów.\r", + "\r\n", + "account account Musi. Innej drogi nie mamy.\r", + "\r\n", + "Odrzut natychmiastowy, kwaśna mina, mam problem\r", + "\r\n", + "Jaki on był fajny xdd pamiętam, że spóźniłam się na jego pierwsze zajęcia i to sporo i za karę kazał mi usiąść w pierwszej ławce XD\r", + "\r\n", + "account No nie ma u nas szczęścia 😉\r", + "\r\n", + "account Dawno kogoś tak wrednego nie widziałam xd\r", + "\r\n", + "account account Zaległości były, ale ważne czy były wezwania do zapłaty z których się klub nie wywiązał.\r", + "\r\n", + "account account account Gdzie jest account . Brudziński jesteś kłamcą i marnym kutasem account \r", + "\r\n", + "sed: couldn't write 130 items to stdout: Broken pipe\r\n", + "cat: write error: Broken pipe\r\n" + ] + } + ], + "source": [ + "! cat ../data/poleval19/task-1/training_set_clean_only_text.txt| sed 's/\\(@anonymized_account *\\)/account /gi' |head" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dla mnie faworytem do tytulu bedzie Cracovia. Zobaczymy, czy typ sie sprawdzi.\r", + "\r\n", + "@anonymized_account @anonymized_account Brawo ty Daria kibic ma byc na dobre i zle\r", + "\r\n", + "@anonymized_account @anonymized_account Super, polski premier sklada kwiaty na grobach kolaborantow. Ale doczekalismy czasow.\r", + "\r\n", + "@anonymized_account @anonymized_account Musi. Innej drogi nie mamy.\r", + "\r\n", + "Odrzut natychmiastowy, kwasna mina, mam problem\r", + "\r\n", + "Jaki on byl fajny xdd pamietam, ze spoznilam sie na jego pierwsze zajecia i to sporo i za kare kazal mi usiasc w pierwszej lawce XD\r", + "\r\n", + "@anonymized_account No nie ma u nas szczescia ?\r", + "\r\n", + "@anonymized_account Dawno kogos tak wrednego nie widzialam xd\r", + "\r\n", + "@anonymized_account @anonymized_account Zaleglosci byly, ale wazne czy byly wezwania do zaplaty z ktorych sie klub nie wywiazal.\r", + "\r\n", + "@anonymized_account @anonymized_account @anonymized_account Gdzie jest @anonymized_account . Brudzinski jestes klamca i marnym kutasem @anonymized_account\r", + "\r\n", + "iconv: conversion stopped due to problem in writing the output\r\n" + ] + } + ], + "source": [ + "! cat ../data/poleval19/task-1/training_set_clean_only_text.txt| iconv -f UTF-8 -t ASCII//TRANSLIT |head" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dla mnie faworytem do tytułu będzie cracovia . zobaczymy , czy typ się sprawdzi .\r\n", + "account account brawo ty daria kibic ma być na dobre i złe\r\n", + "account account super , polski premier składa kwiaty na grobach kolaborantów . ale doczekaliśmy czasów .\r\n", + "account account musi . innej drogi nie mamy .\r\n", + "odrzut natychmiastowy , kwaśna mina , mam problem\r\n", + "jaki on był fajny xdd pamiętam , że spóźniłam się na jego pierwsze zajęcia i to sporo i za karę kazał mi usiąść w pierwszej ławce xd\r\n", + "account no nie ma u nas szczęścia 😉\r\n", + "account dawno kogoś tak wrednego nie widziałam xd\r\n", + "account account zaległości były , ale ważne czy były wezwania do zapłaty z których się klub nie wywiązał .\r\n", + "account account account gdzie jest account . brudziński jesteś kłamcą i marnym kutasem account\r\n" + ] + } + ], + "source": [ + "! head ../data/poleval19/task-1/training_set_clean_only_text.tmp.tok" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/test_set_clean_only_text.txt|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl| sed \"s/\\(@anonymized_account *\\)/account /gi\"|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l pl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l pl| tr \"[:upper:]\" \"[:lower:]\">../data/poleval19/task-1/test_set_clean_only_text.tmp.tok\n" + ] + } + ], + "source": [ + "!rm {dataset}/test_set_clean_only_text.tmp.*\n", + "tst_fn = process_file(dataset / 'test_set_clean_only_text.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10041, 1024)" + ] + }, + "execution_count": 271, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dim=1024\n", + "def read_enc(fn):\n", + " em = np.fromfile(str(fn),dtype=np.float32).reshape(-1, dim)\n", + " return em\n", + "read_enc(trn_fn).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000,)" + ] + }, + "execution_count": 272, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.loadtxt(str(dataset/'test_set_clean_only_text.txt'), delimiter='\\n', comments=None, dtype=np.str).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1000 ../data/poleval19/task-1/test_set_clean_only_text.tmp.bpe\r\n", + " 13257 ../data/poleval19/task-1/test_set_clean_only_text.tmp.enc\r\n", + " 1000 ../data/poleval19/task-1/test_set_clean_only_text.tmp.tok\r\n", + " 15257 total\r\n" + ] + } + ], + "source": [ + "!wc -l {dataset}/'test_set_clean_only_text.tmp'.*" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "account account i dzięki temu mamy dziś ces@@ ar@@ zo@@ wą .. 😁 😁 😁 . gor@@ ą@@ co pozdra@@ wi@@ am ! 😁 😁 😁\r\n", + "account account account w na@@ gro@@ de wy@@ pi@@ j mój sy@@ ro@@ p ! : d\r\n", + "account droga p.@@ kam@@ il@@ ko ! le@@ czyć się . le@@ czyć pó@@ ki czas 😁 😁\r\n", + "account pó@@ ł@@ gł@@ ó@@ we@@ k wi@@ eli@@ ń@@ ski , wy@@ myś@@ li@@ ł sobie pó@@ ł@@ autor@@ y@@ tary@@ z@@ m !\r\n", + "rt account account pó@@ ł@@ gł@@ ó@@ we@@ k wi@@ eli@@ ń@@ ski , wy@@ myś@@ li@@ ł sobie pó@@ ł@@ autor@@ y@@ tary@@ z@@ m !\r\n", + "account account na szczę@@ ście account i jego parti@@ a zni@@ kn@@ ą w@@ kró@@ t@@ ce \\ n@@ ze scen@@ y politycz@@ nej . bra@@ wo !\r\n", + "fa@@ v albo rt to zrobi@@ ę wam in@@ dy ! ! ! nie wiem czy wszystkim , z@@ ale@@ ży ko@@ go będę ko@@ jar@@ zyć 🔥 🔥 🔥\r\n", + "account account account to był kar@@ ny ? ! oczywi@@ sty kar@@ ny ? ! ha@@ ha od@@ sta@@ w to co bier@@ z@@ esz\r\n", + "account account account masz teraz sz@@ anse na@@ pra@@ wić ten b@@ łą@@ d ! : )\r\n", + "account account account w ta@@ kim razie zdro@@ wia ży@@ cz@@ ę !\r\n", + "account nie kł@@ ó@@ ć się jak nie chcesz mieć póź@@ niej problem@@ ów ! : ) account account account\r\n", + "account account cie@@ ka@@ we co po@@ wie o fau@@ lu tet@@ teha na ak@@ ha@@ ho@@ sh@@ im@@ .@@ bezpieczeńst@@ wo przede wszystkim !\r\n", + "account account peł@@ na zgo@@ da . to skan@@ dal że zam@@ y@@ ka@@ ją przest@@ ęp@@ ców ! za po się przecież z nimi do@@ gad@@ y@@ wan@@ o !\r\n", + "rt account account account peł@@ na zgo@@ da . to skan@@ dal że zam@@ y@@ ka@@ ją przest@@ ęp@@ ców ! za po się przecież z nimi do@@ gad@@ y@@ wan@@ o !\r\n", + "account account o nie@@ ee@@ ee@@ ee@@ ee@@ ee ! nie przy@@ po@@ min@@ aj@@ cie account tego ! jutro nie będę mi@@ ała życia ! ; - )\r\n", + "account ale czy będzie ś@@ pie@@ wał i ma@@ cha@@ ł gło@@ wą ? ! 😎\r\n", + "# cl@@ j : gra@@ jąc w dzie@@ wię@@ ciu wis@@ ła od@@ nosi zwy@@ ci@@ ę@@ stwo w der@@ ba@@ ch z account str@@ zel@@ ając decy@@ du@@ ją@@ cą br@@ am@@ kę w 9@@ 5.@@ min ! h@@ tt@@ p : / / t.@@ co / d@@ 5@@ j@@ 3@@ q@@ y@@ on@@ s@@ q\r\n", + "rt account # cl@@ j : gra@@ jąc w dzie@@ wię@@ ciu wis@@ ła od@@ nosi zwy@@ ci@@ ę@@ stwo w der@@ ba@@ ch z account str@@ zel@@ ając decy@@ du@@ ją@@ cą br@@ am@@ kę w 9@@ 5.@@ min ! ht ...\r\n", + "account account account account jaki tam profesor ! ! ! to kan@@ gur którego . abor@@ y@@ gen@@ i zami@@ en@@ ili w człowieka .\r\n", + "account a przy okaz@@ ji ; jaki sex@@ ow@@ ny , no@@ wy pro@@ fil ! ! 😁 😀 😁 😀 😀\r\n", + "account account też czy@@ ta@@ łam , nau@@ ko@@ we u@@ sprawiedli@@ wi@@ enie dla le@@ żenia w łó@@ ż@@ ku przez pó@@ ł so@@ bo@@ ty , dziękuję account ! ! !\r\n", + "account account account account aż za poważ@@ nie jak na oso@@ bę nie@@ poważ@@ ną ! : d\r\n", + "account za to dokon@@ ano za@@ ku@@ pu black haw@@ ków dla poli@@ cji . mi@@ str@@ zo@@ stwo !\r\n", + "account account account je@@ ba@@ ć sł@@ oni@@ c@@ ę@@ - mo@@ ty@@ li@@ cę ! ! ! ! 😁 😁 😁 . wro@@ c@@ ła@@ w nie daj się ! ! !\r\n", + "my tu o polity@@ ce , a tu by@@ ki ry@@ czą ! zac@@ zy@@ naj@@ ą ry@@ ko@@ wis@@ ko ! 😁\r\n", + "account hej koch@@ ana , wszyst@@ kiego naj@@ lesz@@ ego , spe@@ ł@@ nienia wszystkich mar@@ ze@@ ń , dużo z@@ dr@@ ów@@ ka , pieni@@ ę@@ dzy , mi@@ łości i szczę@@ ścia ! ! !\r\n", + "jak widz@@ ę był@@ ego premi@@ era mar@@ cin@@ ki@@ e@@ wi@@ cza to zawsze myślę : q@@ r@@ wa ! jak ktoś taki mó@@ gł być premier@@ em . taki skoń@@ cz@@ ony b@@ ł@@ az@@ en .\r\n", + "account account zgo@@ li@@ ć ? \\ \" sta@@ si@@ ek ! ty@@ ś się na amer@@ y@@ kana zrobi@@ ł ! ? w tym tele@@ wi@@ zor@@ ze tak się ciebie stało ? \\ \"\r\n", + "account account to pie@@ pr@@ zenie komun@@ ał@@ ów pad że było war@@ to , bud@@ zi we mnie nies@@ mak . drugi tu@@ sk q@@ r@@ wa !\r\n", + "account bardzo się ciesz@@ ę , że twitter może wy@@ wo@@ ły@@ wać też takie em@@ oc@@ je , a nie wie@@ czny g@@ nie@@ w 😉 pozdra@@ wi@@ am !\r\n", + "account no to gł@@ ę@@ bo@@ kie uk@@ ł@@ ony przes@@ y@@ łam już dzisiaj . pan@@ cer@@ ne po@@ kol@@ enie ! ; - )\r\n", + "account do@@ łą@@ cze się do ży@@ cze@@ ń : ) account wszyst@@ kiego najlep@@ szego ! : )\r\n", + "account kop w dup@@ ę i do wat@@ y@@ kan@@ u się mą@@ dr@@ zyć za swoją kas@@ ę !\r\n", + "rt account account kop w dup@@ ę i do wat@@ y@@ kan@@ u się mą@@ dr@@ zyć za swoją kas@@ ę !\r\n", + "oni nie mają w@@ sty@@ du ? ! woj@@ cie@@ ch mann wykorzy@@ sta@@ ł po@@ gr@@ ze@@ b kor@@ y , żeby sk@@ ry@@ ty@@ kować pis . wi@@ deo \\ n \\ nh@@ tt@@ ps : / / t.@@ co / 6@@ r@@ 3@@ ph@@ x@@ eg@@ qu\r\n", + "rt account oni nie mają w@@ sty@@ du ? ! woj@@ cie@@ ch mann wykorzy@@ sta@@ ł po@@ gr@@ ze@@ b kor@@ y , żeby sk@@ ry@@ ty@@ kować pis . wi@@ deo \\ n \\ nh@@ tt@@ ps : / / t.@@ co / 6@@ r@@ 3@@ ph@@ x@@ eg@@ qu\r\n", + "account i tym spo@@ so@@ bem zwy@@ cięż@@ czyni@@ ą tur@@ nie@@ ju o pla@@ stu@@ sio@@ wego de@@ bila została ag@@ ni@@ esz@@ ka hol@@ land ! bra@@ wo ! ! ! 😁 😁 😁\r\n", + "account a ktoś mówi@@ ł że jesteś m@@ ło@@ da ? ! 😂\r\n", + "- ile tr@@ wa wymi@@ ana sz@@ ki@@ e@@ ł ? \\ n@@ - oko@@ ło godz@@ iny . \\ n@@ - Ś@@ wi@@ et@@ nie ! \\ n@@ - jaka wa@@ da ? \\ n@@ - min@@ us 6 . \\ n@@ - u@@ u . do dwóch tygod@@ ni . \\ n@@ dysk@@ ry@@ min@@ acja za wad@@ ę . 😂\r\n", + "account też się dłu@@ go do tego za@@ bier@@ ałam , ale po@@ stanowi@@ łam no@@ w@@ emu t@@ łu@@ ma@@ cz@@ owi dać sz@@ ans@@ ę - od@@ waż@@ ny go@@ ść ! 😊\r\n", + "account wiem pami@@ et@@ am ! ! ! gratu@@ lu@@ je ! jestem z ciebie dum@@ na ! ja zda@@ łam w cz@@ war@@ tek za trze@@ cim\r\n", + "account ale masz zd@@ ję@@ cie pro@@ filo@@ we ! jak l@@ ale@@ cz@@ ka z por@@ cel@@ any !\r\n", + "account oj gł@@ upo@@ lek ! i to jaki jeszcze 😁 😁 😁\r\n", + "na tar@@ ga@@ ch ś@@ lu@@ b@@ nych w moim mie@@ ście będzie iza jan@@ ach@@ ow@@ ska ! ! ! !\r\n", + "kon@@ cer@@ t ye@@ ar@@ s@@ ów już za 90 dni ! ! ! jak to szybko le@@ ci ! nie mogę się do@@ cze@@ ka@@ ć@@ . account widzi@@ my się nied@@ łu@@ go 😌\r\n", + "rt account kon@@ cer@@ t ye@@ ar@@ s@@ ów już za 90 dni ! ! ! jak to szybko le@@ ci ! nie mogę się do@@ cze@@ ka@@ ć@@ . account widzi@@ my się nied@@ łu@@ go 😌\r\n", + "account tym to ją wz@@ ru@@ szy@@ łeś ! ; -@@ d\r\n", + "account account no o dobrze ! prec@@ z z dy@@ kt@@ atur@@ ą kobiet ! ! ! ! 😁 😁 😁 😁\r\n", + "daj@@ cie fa@@ v a ja wam jutro zrobi@@ ę gad@@ an@@ ego in@@ da pier@@ wsz@@ ego w ży@@ ciu !\r\n", + "account jest tu jaki@@ ś c@@ wan@@ i@@ ak ? jak ktoś mnie po@@ kona w fi@@ fe@@ 17 na ps@@ 4 to w@@ pł@@ ac@@ am 10@@ 9,@@ 0@@ 6 z@@ ł ! me@@ cze w week@@ end # wis@@ lat@@ on@@ as@@ za@@ przysz@@ los@@ c\r\n", + "account account jak to powiedział kla@@ sy@@ k : cham@@ st@@ wu należy przeciw@@ sta@@ wi@@ ać się sił@@ om i god@@ no@@ sci@@ om oso@@ bi@@ st@@ om ! 😁 😁 😁 😁 . tak trzyma@@ ć ! 😁 😁\r\n", + "account no to się chyba zabi@@ or@@ ę za niego ! dzięki\r\n", + "account jak to nie został za@@ pro@@ sz@@ ony ? ? ? dom@@ ek ? ? ? to dla tego kur@@ wa przy@@ je@@ cha@@ ł do Ł@@ od@@ zi !\r\n", + "account sz@@ an@@ ow@@ na pani reda@@ ktor ! ser@@ de@@ cznie dziękuję za tę promo@@ cję waż@@ nych dla mnie warto@@ ści bu@@ du@@ jac@@ ych rodz@@ inę . pozdra@@ wi@@ am ! : - )\r\n", + "rt account account sz@@ an@@ ow@@ na pani reda@@ ktor ! ser@@ de@@ cznie dziękuję za tę promo@@ cję waż@@ nych dla mnie warto@@ ści bu@@ du@@ jac@@ ych rodz@@ inę . po ...\r\n", + "account to oczywi@@ ste by@@ ły@@ ście dziś za@@ je@@ biste ! no może o@@ pró@@ cz ciebie ha@@ ha : ) # jo@@ ke\r\n" + ] + } + ], + "source": [ + "!grep \"!\" ../data/poleval19/task-1/test_set_clean_only_text.tmp.bpe" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [], + "source": [ + "tst_df = pd.DataFrame({\n", + " 'y':np.loadtxt(str(dataset/'test_set_clean_only_tags.txt'),delimiter='\\n', comments=None, dtype=np.int),\n", + " 'em':list(read_enc(tst_fn)), \n", + " 'txt':np.loadtxt(str(dataset/'test_set_clean_only_text.tmp.bpe'), delimiter='\\n', comments=None, dtype=np.str),\n", + "})\n", + "\n", + "trn_df = pd.DataFrame({\n", + " 'y':np.loadtxt(str(dataset/'training_set_clean_only_tags.txt'),delimiter='\\n', comments=None, dtype=np.int),\n", + " 'em':list(read_enc(trn_fn)), \n", + " 'txt':np.loadtxt(str(dataset/'training_set_clean_only_text.tmp.bpe'), delimiter='\\n', comments=None, dtype=np.str),\n", + "})\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " y em \\\n", + "0 0 [0.0020520384, 0.005157103, 0.012943947, 0.024... \n", + "1 0 [0.0037102806, -2.0261248e-05, 7.142721e-05, 0... \n", + "2 0 [0.011618567, 0.0027558685, 0.00044608297, 0.0... \n", + "3 0 [0.034281317, -9.7129705e-05, 0.0016056199, 0.... \n", + "4 0 [0.007905566, 0.0017290562, -0.0015668176, 0.0... \n", + "\n", + " txt \n", + "0 dla mnie fa@@ wor@@ y@@ tem do ty@@ tu@@ łu bę... \n", + "1 account account bra@@ wo ty dar@@ ia ki@@ bi@@... \n", + "2 account account super , pol@@ ski premier sk@@... \n", + "3 account account musi . in@@ nej dro@@ gi nie m... \n", + "4 odr@@ zut nat@@ ych@@ mi@@ a@@ sto@@ wy , kwa@... " + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trn_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.015996, 0.002804, -0.004015, 0.018448, ..., 0.022837, 0.005017, 0.011453, 0.016846], dtype=float32)" + ] + }, + "execution_count": 277, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tst_df['em'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [], + "source": [ + "class EmbeddingText(ItemBase):\n", + " \"Basic item for text data in numericalized `ids`.\"\n", + " def __init__(self, emb, text): self.data,self.text = np.array(emb, dtype=np.float32),text\n", + " def __str__(self): return str(self.text)" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [], + "source": [ + "class EmbeddingList(ItemList):\n", + " \"Basic `ItemList` for embeding of text data.\"\n", + " _bunch = DataBunch # TextDataBunch converts input to LongTensor !\n", + " _processor = []\n", + "\n", + " def __init__(self, items:Iterator, **kwargs):\n", + " super().__init__(items, **kwargs)\n", + "\n", + " def find_text_for_em(self, em):\n", + " # todo find a way to reverse embedding\n", + " return self.inner_df['txt'][1]\n", + " \n", + " def get(self, i):\n", + " o = super().get(i)\n", + " return EmbeddingText(o, self.inner_df['txt'][i])\n", + "\n", + " def reconstruct(self, t:Tensor):\n", + " return EmbeddingText(t, self.find_text_for_em(t))\n", + "\n", + " def show_xys(self, xs, ys, max_len:int=70)->None:\n", + " \"Show the `xs` (inputs) and `ys` (targets). `max_len` is the maximum number of tokens displayed.\"\n", + " from IPython.display import display, HTML\n", + " names = ['text','target']\n", + " items = []\n", + " for i, (x,y) in enumerate(zip(xs,ys)):\n", + " txt_x = ' '.join(x.text.split(' ')[:max_len]) if max_len is not None else x.text\n", + " items.append([txt_x, y])\n", + " items = np.array(items)\n", + " df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)\n", + " with pd.option_context('display.max_colwidth', -1):\n", + " display(HTML(df.to_html(index=False)))\n", + "\n", + " def show_xyzs(self, xs, ys, zs, max_len:int=70):\n", + " \"Show `xs` (inputs), `ys` (targets) and `zs` (predictions). `max_len` is the maximum number of tokens displayed.\"\n", + " from IPython.display import display, HTML\n", + " items,names = [],['text','target','prediction']\n", + " for i, (x,y,z) in enumerate(zip(xs,ys,zs)):\n", + " txt_x = ' '.join(x.text.split(' ')[:max_len]) if max_len is not None else x.text\n", + " items.append([txt_x, y, z])\n", + " items = np.array(items)\n", + " df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)\n", + " with pd.option_context('display.max_colwidth', -1):\n", + " display(HTML(df.to_html(index=False)))" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [], + "source": [ + "path=dataset/\"models\"\n", + "src = ItemLists(path, \n", + " EmbeddingList.from_df(trn_df, cols=1, path=path),\n", + " EmbeddingList.from_df(tst_df, cols=1, path=path))\n", + "classes=np.unique(trn_df['y'])\n", + "src.label_from_lists(trn_df['y'], tst_df['y'], classes=classes, processor=[])\n", + "data = src.databunch(bs=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
texttarget
account account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e0
account account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e0
account account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e0
account account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e0
account account bra@@ wo ty dar@@ ia ki@@ bi@@ c ma być na dobre i zł@@ e0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.show_batch()" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": {}, + "outputs": [], + "source": [ + "class LaserSentCLS(nn.Module):\n", + " def __init__(self, idim=1024, odim=2, nhid=None,\n", + " dropout=0.0, activation='TANH'):\n", + " super().__init__()\n", + " modules = []\n", + "\n", + " modules = []\n", + " print(' - mlp {:d}'.format(idim), end='')\n", + " if len(nhid) > 0:\n", + " if dropout > 0:\n", + " modules.append(nn.Dropout(p=dropout))\n", + " nprev = idim\n", + " for nh in nhid:\n", + " if nh > 0:\n", + " modules.append(nn.Linear(nprev, nh))\n", + " nprev = nh\n", + " if activation == 'TANH':\n", + " modules.append(nn.Tanh())\n", + " print('-{:d}t'.format(nh), end='')\n", + " elif activation == 'RELU':\n", + " modules.append(nn.ReLU())\n", + " print('-{:d}r'.format(nh), end='')\n", + " else:\n", + " raise Exception('Unrecognized activation {activation}')\n", + " if dropout > 0:\n", + " modules.append(nn.Dropout(p=dropout))\n", + " modules.append(nn.Linear(nprev, odim))\n", + " print('-{:d}, dropout={:.1f}'.format(odim, dropout))\n", + " else:\n", + " modules.append(nn.Linear(idim, odim))\n", + " print(' - mlp %d-%d'.format(idim, odim))\n", + " self.mlp = nn.Sequential(*modules)\n", + " def forward(self, x):\n", + " return self.mlp(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 283, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.train_dl.dl.batch_sampler.sampler" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "200" + ] + }, + "execution_count": 284, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.train_dl.dl.batch_size" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - mlp 1024-10t-8t-2, dropout=0.0\n" + ] + } + ], + "source": [ + "m=LaserSentCLS(nhid=[10, 8],dropout=0)\n", + "learn = Learner(data, m, metrics=[accuracy, FBeta(beta=1, average='binary')])" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "learn.recorder.plot_lr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": {}, + "outputs": [], + "source": [ + "interp = ClassificationInterpretation.from_learner(learn)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "interp.plot_confusion_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [], + "source": [ + "l, i = interp.top_losses()" + ] + }, + { + "cell_type": "code", + "execution_count": 292, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 0, 82), (0, 1, 45)]" + ] + }, + "execution_count": 292, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interp.most_confused()" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(EmbeddingText tym których nie było na me@@ cz@@ u ze względu na koniec zgo@@ dy mam kilka sł@@ ów do prze@@ kazan@@ ia@@ .@@ wy@@ pier@@ d@@ ala@@ ć do wro@@ c@@ ła@@ wia albo g@@ da@@ ń@@ ska, Category 1)\n", + "(EmbeddingText account account ni@@ est@@ ety nie og@@ lą@@ da@@ łem . ale jedna rzecz w tym po@@ ście jest nie@@ prawdzi@@ wa . Ż@@ al@@ ek i wied@@ za to dwa wy@@ klu@@ cza@@ jące się by@@ ty, Category 1)\n", + "(EmbeddingText account lis czy ty jesteś Ż@@ y@@ dem . ? ? ?, Category 1)\n", + "(EmbeddingText account za sz@@ czy@@ t ? chyba za sz@@ czy@@ to@@ wanie ...., Category 1)\n", + "(EmbeddingText account do@@ kład@@ nie , pis@@ dzi@@ el@@ stwo nie ma prawa roz@@ pier@@ d@@ ala@@ ć systemu , są@@ dow@@ nict@@ wa nie maj@@ ąc większo@@ ści, Category 1)\n", + "(EmbeddingText account chciał@@ byś aby tak było mier@@ no@@ to : ), Category 1)\n", + "(EmbeddingText account kama , słu@@ ch@@ ej no , gdzie masz bi@@ uro ? chciałbym coś mach@@ n@@ ąć spra@@ jem ., Category 1)\n", + "(EmbeddingText rt account account chciał@@ byś aby tak było mier@@ no@@ to : ), Category 1)\n", + "(EmbeddingText account pani się dobrze za@@ stanowi co robi@@ cie . też ma@@ cie bi@@ ura , sam@@ och@@ ody , dom@@ y , dzieci , w@@ nu@@ ki it@@ d . tak tylko przy@@ po@@ min@@ am ., Category 1)\n", + "(EmbeddingText account powin@@ na@@ ś od@@ powiedzieć za dział@@ anie na szko@@ dę pol@@ ski i obywateli, Category 1)\n" + ] + } + ], + "source": [ + "for ix in range(0,10): print(data.valid_ds[i[ix]])" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(EmbeddingText account wie@@ czna zdra@@ da , nie zdra@@ da , trzeba roz@@ mawi@@ ać , pier@@ dol@@ enie od rzeczy, Category 0)\n", + "(EmbeddingText account account to pani kry@@ sia jest two@@ ją oj@@ czy@@ zn@@ ą ?, Category 0)\n", + "(EmbeddingText account droga p.@@ kam@@ il@@ ko ! le@@ czyć się . le@@ czyć pó@@ ki czas 😁 😁, Category 0)\n", + "(EmbeddingText account du@@ baj w@@ zor@@ em , czy@@ li jednak islam , sal@@ am al@@ ej@@ kum ci@@ em@@ ki@@ e@@ wi@@ cz, Category 0)\n", + "(EmbeddingText account a ja za@@ łoż@@ ę fit@@ blo@@ ga 😂 😂 😂, Category 0)\n", + "(EmbeddingText account to tak jak ja prze@@ widział@@ em , że cię nie w@@ pusz@@ cza do g@@ b, Category 0)\n", + "(EmbeddingText prowad@@ zą@@ cy mówi ze nikt mu nie wy@@ s@@ ła@@ ł sz@@ ki@@ ców projekt@@ ów jak nie jak ja ci wy@@ sł@@ ałam im@@ be@@ cy@@ lu, Category 0)\n", + "(EmbeddingText ra@@ fa@@ ł br@@ zo@@ zo@@ w@@ ski ma nar@@ ze@@ cz@@ on@@ ą ? mam na@@ dzie@@ je m , że na co@@ dzień jej aż tak nie fa@@ ł@@ sz@@ uje jak w tym kol@@ e fortun@@ y a jeśli tak to big ri@@ p girl, Category 0)\n", + "(EmbeddingText account account to ta pani co ma roz@@ tro@@ jen@@ ie ja@@ ź@@ ni ?, Category 0)\n", + "(EmbeddingText account account account prawda o kar@@ cze@@ w@@ skim to nie prawda o pol@@ s@@ ce . coś się panu po@@ je@@ ba@@ ło account ., Category 0)\n", + "(EmbeddingText account zna@@ jąc ment@@ al@@ ność pis win@@ ny będzie ten kto wi@@ dzi nie ten kto czy@@ ni ., Category 0)\n", + "(EmbeddingText account prz@@ esta@@ ń@@ cie kur@@ wa cały czas p@@ ła@@ kać, Category 0)\n", + "(EmbeddingText account account ko@@ do@@ wa zar@@ aza nar@@ ód nasz dzi@@ eli , m@@ roz@@ ny de@@ sz@@ cz gro@@ zy po kar@@ ku prze@@ chodzi , \\ ndo@@ ść dy@@ kt@@ atu pej@@ sat@@ ej bru@@ k@@ sel@@ i, Category 0)\n", + "(EmbeddingText rt account account ku@@ ter ry@@ bac@@ ki przy niej , to dou@@ glas na wod@@ zie ., Category 0)\n", + "(EmbeddingText account i we@@ ź tu zosta@@ ń p@@ sem, Category 0)\n", + "(EmbeddingText account ty naj@@ pier@@ w prze@@ czy@@ taj te swoje ksi@@ ąż@@ ki z foto@@ ta@@ pet@@ y a potem po@@ gad@@ amy ., Category 0)\n", + "(EmbeddingText trzeba być patri@@ ot@@ ą swo@@ jego mi@@ asta , swo@@ jego region@@ u , swo@@ jej ziemi . pre@@ zes # pis j. # ka@@ czy@@ ń@@ ski w # zam@@ o@@ ść : # do@@ trzy@@ mu@@ jem@@ ys@@ ło@@ wa, Category 0)\n", + "(EmbeddingText rt account trzeba być patri@@ ot@@ ą swo@@ jego mi@@ asta , swo@@ jego region@@ u , swo@@ jej ziemi . pre@@ zes # pis j. # ka@@ czy@@ ń@@ ski w # zam@@ o@@ ść : # do@@ trzy@@ mu@@ jem@@ ys@@ ło@@ wa, Category 0)\n", + "(EmbeddingText account taki z niego patri@@ ota jak z ciebie dzi@@ enni@@ kar@@ z . nie po@@ gr@@ ąż@@ aj się ., Category 0)\n", + "(EmbeddingText rt account account taki z niego patri@@ ota jak z ciebie dzi@@ enni@@ kar@@ z . nie po@@ gr@@ ąż@@ aj się ., Category 0)\n", + "(EmbeddingText account account zanim się o@@ dez@@ wiesz to wy@@ trze@@ ź@@ wi@@ ej, Category 0)\n", + "(EmbeddingText account bier@@ z tego account razem jeste@@ ście moc@@ ni, Category 0)\n", + "(EmbeddingText account a br@@ am@@ kar@@ ze ? gli@@ k ? kry@@ cho@@ wi@@ ak ? ku@@ ba ? mili@@ k ? pis@@ z@@ cz@@ ek jak jest zdro@@ wy ?, Category 0)\n", + "(EmbeddingText kur@@ wa wszyscy w domu ka@@ sz@@ l@@ ą, Category 0)\n", + "(EmbeddingText account account account je@@ ba@@ ć sł@@ oni@@ c@@ ę@@ - mo@@ ty@@ li@@ cę ! ! ! ! 😁 😁 😁 . wro@@ c@@ ła@@ w nie daj się ! ! !, Category 0)\n", + "(EmbeddingText account jeśli to jest sz@@ rot to jak naz@@ wać was@@ ze transfer@@ y ?, Category 0)\n", + "(EmbeddingText no teraz kur@@ wa jak muszę wyj@@ ść to pada@@ ć zac@@ zy@@ na no ja pier@@ dole do dup@@ y z ta@@ kim ży@@ ciem, Category 0)\n", + "(EmbeddingText account account chyba ty , ch@@ ł@@ op jak by@@ k, Category 0)\n", + "(EmbeddingText account ale tu@@ sk nie jest świ@@ rem, Category 0)\n", + "(EmbeddingText account account account account ki@@ bi@@ ce wier@@ zą w zwy@@ ci@@ ę@@ stwo swo@@ jej dru@@ ży@@ ny@@ .@@ ty to nazy@@ was@@ z do@@ pis@@ y@@ wan@@ iem p@@ kt ? ha@@ ha, Category 0)\n", + "(EmbeddingText account account account historia lu@@ dz@@ ko@@ ści to historia gł@@ upo@@ ty # ja@@ j@@ co, Category 0)\n", + "(EmbeddingText account ten to już zu@@ peł@@ nie od@@ wi@@ esi@@ ł mó@@ z@@ g na ko@@ ł@@ ek , chor@@ y mó@@ z@@ g, Category 0)\n", + "(EmbeddingText account rzu@@ ć ż@@ on@@ ę , przej@@ dź na bud@@ dy@@ z@@ m , prze@@ prowad@@ ź sie z u@@ kochan@@ ą na bar@@ ba@@ dos , zac@@ zni@@ j s@@ sa@@ ć życie, Category 0)\n", + "(EmbeddingText h@@ ali@@ c@@ ki : ga@@ w@@ ło@@ w@@ ski nie może się br@@ oni@@ ć bo sied@@ zi . a po wciąż za ba@@ ł@@ ag@@ an w w-@@ wie ob@@ cią@@ ż@@ a m@@ .@@ i. nie@@ ży@@ ją@@ cego l.@@ ka@@ czy@@ ń@@ skiego \\ n # wor@@ oni@@ cza@@ 17, Category 0)\n", + "(EmbeddingText pis już się z@@ bliż@@ a już pu@@ ka do t@@ wych dr@@ z@@ wi@@ .@@ po@@ bieg@@ ni@@ j go przy@@ wit@@ ać z ra@@ do@@ ści ser@@ ce dr@@ ży . 😁 😁 😁, Category 0)\n", + "(EmbeddingText account pola@@ cy rat@@ uj@@ my pol@@ sk@@ Ę od zlo@@ dzie@@ ji po i lisa wol@@ j@@ s@@ do@@ j@@ cza, Category 0)\n", + "(EmbeddingText rt account account pola@@ cy rat@@ uj@@ my pol@@ sk@@ Ę od zlo@@ dzie@@ ji po i lisa wol@@ j@@ s@@ do@@ j@@ cza, Category 0)\n", + "(EmbeddingText account panie reda@@ ktor@@ ze , proszę u@@ ca@@ ło@@ wać pana account wró@@ ć . poz@@ dro@@ wić ., Category 0)\n", + "(EmbeddingText account ja się wiele cie@@ ka@@ wych rzeczy do@@ wiedzi@@ ałem n@@ p że ry@@ cz@@ kow@@ ski to 87 ro@@ cz@@ nik@@ .@@ spraw@@ dzić tekst przed dru@@ kiem to chyba za tru@@ dne ., Category 0)\n", + "(EmbeddingText account o in@@ wek@@ ty@@ wach proszę poroz@@ mawi@@ ać ze swo@@ im prez@@ es@@ em a nie pie@@ przy@@ ć mi tutaj o jak@@ ich@@ ś przy@@ kł@@ ada@@ ch ., Category 0)\n", + "(EmbeddingText daj@@ cie fa@@ v a ja wam jutro zrobi@@ ę gad@@ an@@ ego in@@ da pier@@ wsz@@ ego w ży@@ ciu !, Category 0)\n", + "(EmbeddingText wiek to ok@@ rop@@ ny z@@ łod@@ zi@@ ej@@ . kiedy zac@@ zy@@ nasz rozu@@ mieć , na czym po@@ lega życie , ono ś@@ cina cię z nó@@ g i przy@@ gar@@ bia ci ple@@ cy ., Category 0)\n", + "(EmbeddingText rt account wiek to ok@@ rop@@ ny z@@ łod@@ zi@@ ej@@ . kiedy zac@@ zy@@ nasz rozu@@ mieć , na czym po@@ lega życie , ono ś@@ cina cię z nó@@ g i przy@@ gar@@ bia ci ple@@ cy ., Category 0)\n", + "(EmbeddingText account account mo@@ wa była o fak@@ ta@@ ch a nie two@@ ich od@@ czu@@ ci@@ ach czy go uważ@@ asz za po@@ laka czy nie account, Category 0)\n", + "(EmbeddingText account ale ja uci@@ ek@@ nę tam gdzie pie@@ pr@@ z ro@@ śnie, Category 0)\n" + ] + } + ], + "source": [ + "neutral_as_offensive = np.nonzero((interp.pred_class != interp.y_true) & (interp.y_true == 0)).reshape(-1)\n", + "for ix in neutral_as_offensive: print(data.valid_ds[ix])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:laser]", + "language": "python", + "name": "conda-env-laser-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/experiments/LASER1.ipynb b/experiments/LASER1.ipynb new file mode 100644 index 0000000..a05e22c --- /dev/null +++ b/experiments/LASER1.ipynb @@ -0,0 +1,1602 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/pczapla/workspace/ulmfit-multilingual/experiments\r\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from fastai.text import *" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m unzip task_6-1.zip -d Task6/task\\ 01\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "# unzip task6_test.zip\n", + "# unzip task_6-1.zip -d Task6/task\\ 01\n", + "# unzip task_6-2.zip -d Task6/task\\ 02\n", + "# mv Task6/task\\ 01 task-1\n", + "# mv Task6/task\\ 02 task-2\n", + "# rmdir Task6" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[PosixPath('../data/poleval19/task-1/test_set_clean_only_text.txt'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.txt'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_tags.txt'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.tmp.sid'),\n", + " PosixPath('../data/poleval19/task-1/evaulate1.pl'),\n", + " PosixPath('../data/poleval19/task-1/test_set_clean_only_tags.txt'),\n", + " PosixPath('../data/poleval19/task-1/training_set_clean_only_text.tmp.split')]" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset=Path('../data')/'poleval19'/'task-1'\n", + "dataset.ls()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "LASER = os.environ.get('LASER', \"/home/pczapla/workspace/_oss/LASER\")\n", + "os.environ['LASER'] = LASER\n", + "def cond_add_pypath(*paths):\n", + " for path in paths:\n", + " if path not in sys.path:\n", + " sys.path.append(path)\n", + "cond_add_pypath(LASER + '/source',LASER + '/source/tools')\n", + "\n", + "from embed import SentenceEncoder, EncodeLoad, EncodeFile\n", + "from text_processing import Token, BPEfastApply, SplitLines, JoinEmbed" + ] + }, + { + "cell_type": "code", + "execution_count": 384, + "metadata": {}, + "outputs": [], + "source": [ + "from subprocess import run, check_output, DEVNULL\n", + "def tokenize(inp_fname, out_fname, lang='en',\n", + " lower_case=True, romanize=False, descape=False,\n", + " verbose=False, over_write=False, gzip=False):\n", + " FASTBPE = LASER + '/tools-external/fastBPE/fast'\n", + " MOSES_BDIR = LASER + '/tools-external/moses-tokenizer/tokenizer/'\n", + " MOSES_TOKENIZER = MOSES_BDIR + 'tokenizer.perl -q -no-escape -threads 20 -l '\n", + " MOSES_LC = MOSES_BDIR + 'lowercase.perl'\n", + " NORM_PUNC = MOSES_BDIR + 'normalize-punctuation.perl -l '\n", + " DESCAPE = MOSES_BDIR + 'deescape-special-chars.perl'\n", + " REM_NON_PRINT_CHAR = MOSES_BDIR + 'remove-non-printing-char.perl'\n", + "\n", + " # Romanization (Greek only)\n", + " ROMAN_LC = 'python3 ' + LASER + '/source/lib/romanize_lc.py -l '\n", + "\n", + " # Mecab tokenizer for Japanese\n", + " MECAB = LASER + '/tools-external/mecab'\n", + " assert lower_case, 'lower case is needed by all the models'\n", + " if not out_fname.exists():\n", + " cat = 'zcat ' if gzip else 'cat '\n", + " roman = lang if romanize else 'none'\n", + " # handle some iso3 langauge codes\n", + " if lang in ('cmn', 'wuu', 'yue'):\n", + " lang = 'zh'\n", + " if lang in ('jpn'):\n", + " lang = 'ja'\n", + " if verbose:\n", + " print(' - Tokenizer: {} in language {} {} {}'\n", + " .format(os.path.basename(inp_fname), lang,\n", + " '(gzip)' if gzip else '',\n", + " '(de-escaped)' if descape else '',\n", + " '(romanized)' if romanize else ''))\n", + " cmd = (cat + str(inp_fname)\n", + " + '|' + REM_NON_PRINT_CHAR\n", + " + '|' + NORM_PUNC + lang\n", + " + ('|' + DESCAPE if descape else '')\n", + " + '|' + MOSES_TOKENIZER + lang\n", + " + ('| python3 -m jieba -d ' if lang == 'zh' else '')\n", + " + ('|' + MECAB + '/bin/mecab -O wakati -b 50000 ' if lang == 'ja' else '')\n", + " + ('|' + ROMAN_LC + roman if romanize else '')\n", + " + '>' + str(out_fname))\n", + " print (\"Tokenziation CMD: \", cmd)\n", + " run(cmd,\n", + " env=dict(os.environ, LD_LIBRARY_PATH=MECAB + '/lib'),\n", + " shell=True)\n", + " elif not over_write and verbose:\n", + " print(' - Tokenizer: {} exists already'\n", + " .format(os.path.basename(out_fname), lang))\n", + "\n", + "\n", + "def split_lines(*args): return SplitLines(*list(map(str,args)))\n", + "def bpe_fast_apply(*args, **kwargs): return BPEfastApply(*list(map(str,args)), **kwargs)\n", + "def encode_file(enc, *args, **kwargs): return EncodeFile(enc, *list(map(str,args)), **kwargs)\n", + "def join_embed(*args): return JoinEmbed(*list(map(str,args)))\n", + " \n", + "def process_file(inputfn, lang='pl'):\n", + " inputfn = Path(inputfn)\n", + " args = SimpleNamespace(\n", + " encoder=str(Path(LASER)/\"models\"/\"bilstm.93langs.2018-12-26.pt\"), \n", + " bpe_codes=str(Path(LASER)/\"models\"/\"93langs.fcodes\"),\n", + " lang=lang,\n", + " buffer_size=10000,\n", + " max_tokens=12000,\n", + " max_sentences=None,\n", + " cpu=False,\n", + " verbose=False,\n", + " stable=True)\n", + " enc = EncodeLoad(args)\n", + " def fn(suffix):\n", + " return inputfn.with_suffix(f'.tmp{suffix}')\n", + " tokenize(inputfn,\n", + " fn('.tok'),\n", + " lang=lang,\n", + " romanize=(True if lang == 'el' else False),\n", + " lower_case=True, gzip=False,\n", + " verbose=args.verbose, over_write=False)\n", + " bpe_fast_apply(fn('.tok'),\n", + " fn('.bpe'),\n", + " args.bpe_codes,\n", + " verbose=args.verbose, over_write=False)\n", + "# split_lines(fn('.bpe'),\n", + "# fn('.split'),\n", + "# fn('.sid'))\n", + " encode_file(enc,\n", + " fn('.bpe'),\n", + " fn('.enc'),\n", + " verbose=args.verbose, over_write=False,\n", + " buffer_size=args.buffer_size)\n", + "# join_embed(fn('.split.enc'),\n", + "# fn('.sid'),\n", + "# fn('.enc'))\n", + " return fn('.enc')" + ] + }, + { + "cell_type": "code", + "execution_count": 385, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/training_set_clean_only_text.txt|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l pl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l pl>../data/poleval19/task-1/training_set_clean_only_text.tmp.tok\n" + ] + } + ], + "source": [ + "!rm {dataset}/training_set_clean_only_text.tmp.*\n", + "trn_fn = process_file(dataset / 'training_set_clean_only_text.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 386, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - loading encoder /home/pczapla/workspace/_oss/LASER/models/bilstm.93langs.2018-12-26.pt\n", + "Tokenziation CMD: cat ../data/poleval19/task-1/test_set_clean_only_text.txt|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/remove-non-printing-char.perl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/normalize-punctuation.perl -l pl|/home/pczapla/workspace/_oss/LASER/tools-external/moses-tokenizer/tokenizer/tokenizer.perl -q -no-escape -threads 20 -l pl>../data/poleval19/task-1/test_set_clean_only_text.tmp.tok\n" + ] + } + ], + "source": [ + "!rm {dataset}/test_set_clean_only_text.tmp.*\n", + "tst_fn = process_file(dataset / 'test_set_clean_only_text.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": 380, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10041, 1024)" + ] + }, + "execution_count": 380, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dim=1024\n", + "def read_enc(fn):\n", + " em = np.fromfile(str(fn),dtype=np.float32).reshape(-1, dim)\n", + " return em\n", + "read_enc(trn_fn).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 381, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000,)" + ] + }, + "execution_count": 381, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.loadtxt(str(dataset/'test_set_clean_only_text.txt'), delimiter='\\n', comments=None, dtype=np.str).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 382, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1000 ../data/poleval19/task-1/test_set_clean_only_text.tmp.bpe\r\n", + " 13384 ../data/poleval19/task-1/test_set_clean_only_text.tmp.enc\r\n", + " 1241 ../data/poleval19/task-1/test_set_clean_only_text.tmp.sid\r\n", + " 1241 ../data/poleval19/task-1/test_set_clean_only_text.tmp.split\r\n", + " 16667 ../data/poleval19/task-1/test_set_clean_only_text.tmp.split.enc\r\n", + " 1000 ../data/poleval19/task-1/test_set_clean_only_text.tmp.tok\r\n", + " 34533 total\r\n" + ] + } + ], + "source": [ + "!wc -l {dataset}/'test_set_clean_only_text.tmp'.*" + ] + }, + { + "cell_type": "code", + "execution_count": 387, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account I dzięki temu mamy dziś ces@@ ar@@ zo@@ wą .. 😁 😁 😁 . G@@ or@@ ą@@ co pozdra@@ wi@@ am ! 😁 😁 😁\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account w na@@ gro@@ de wy@@ pi@@ j mój sy@@ ro@@ p ! : D\r\n", + "@ an@@ ony@@ miz@@ ed _ account D@@ ro@@ ga p.@@ K@@ am@@ il@@ ko ! L@@ e@@ czyć się . L@@ e@@ czyć pó@@ ki czas 😁 😁\r\n", + "@ an@@ ony@@ miz@@ ed _ account P@@ ół@@ gł@@ ó@@ we@@ k W@@ i@@ eli@@ ń@@ ski , wy@@ myś@@ li@@ ł sobie pó@@ ł@@ autor@@ y@@ tary@@ z@@ m !\r\n", + "R@@ T @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account P@@ ół@@ gł@@ ó@@ we@@ k W@@ i@@ eli@@ ń@@ ski , wy@@ myś@@ li@@ ł sobie pó@@ ł@@ autor@@ y@@ tary@@ z@@ m !\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account N@@ a szczę@@ ście @ an@@ ony@@ miz@@ ed _ account i jego parti@@ a zni@@ kn@@ ą w@@ kró@@ t@@ ce \\ n@@ ze scen@@ y politycz@@ nej . B@@ ra@@ wo !\r\n", + "F@@ av albo rt to zrobi@@ ę W@@ am in@@ dy ! ! ! N@@ ie wiem czy wszystkim , z@@ ale@@ ży ko@@ go będę ko@@ jar@@ zyć 🔥 🔥 🔥\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account to był kar@@ ny ? ! O@@ czywi@@ sty kar@@ ny ? ! H@@ aha od@@ sta@@ w to co bier@@ z@@ esz\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account masz teraz sz@@ anse na@@ pra@@ wić ten b@@ łą@@ d ! : )\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account W ta@@ kim razie zdro@@ wia ży@@ cz@@ ę !\r\n", + "@ an@@ ony@@ miz@@ ed _ account nie kł@@ ó@@ ć się jak nie chcesz mieć póź@@ niej problem@@ ów ! : ) @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account cie@@ ka@@ we co po@@ wie o fau@@ lu T@@ ette@@ ha na A@@ kha@@ ho@@ sh@@ im@@ .@@ B@@ ez@@ pie@@ czeńst@@ wo przede wszystkim !\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account P@@ e@@ ł@@ na zgo@@ da . T@@ o skan@@ dal że zam@@ y@@ ka@@ ją przest@@ ęp@@ ców ! Z@@ a P@@ O się przecież z nimi do@@ gad@@ y@@ wan@@ o !\r\n", + "R@@ T @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account P@@ e@@ ł@@ na zgo@@ da . T@@ o skan@@ dal że zam@@ y@@ ka@@ ją przest@@ ęp@@ ców ! Z@@ a P@@ O się przecież z nimi do@@ gad@@ y@@ wan@@ o !\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account o nie@@ ee@@ ee@@ ee@@ ee@@ ee ! N@@ ie przy@@ po@@ min@@ aj@@ cie @ an@@ ony@@ miz@@ ed _ account tego ! J@@ u@@ tro nie będę mi@@ ała życia ! ; - )\r\n", + "@ an@@ ony@@ miz@@ ed _ account A@@ le czy będzie ś@@ pie@@ wał i ma@@ cha@@ ł gło@@ wą ? ! 😎\r\n", + "# C@@ L@@ J : G@@ ra@@ jąc w dzie@@ wię@@ ciu W@@ is@@ ła od@@ nosi zwy@@ ci@@ ę@@ stwo w D@@ er@@ ba@@ ch z @ an@@ ony@@ miz@@ ed _ account str@@ zel@@ ając decy@@ du@@ ją@@ cą br@@ am@@ kę w 9@@ 5.@@ min ! h@@ tt@@ p : / / t.@@ co / d@@ 5@@ J@@ 3@@ q@@ y@@ O@@ N@@ s@@ Q\r\n", + "R@@ T @ an@@ ony@@ miz@@ ed _ account # C@@ L@@ J : G@@ ra@@ jąc w dzie@@ wię@@ ciu W@@ is@@ ła od@@ nosi zwy@@ ci@@ ę@@ stwo w D@@ er@@ ba@@ ch z @ an@@ ony@@ miz@@ ed _ account str@@ zel@@ ając decy@@ du@@ ją@@ cą br@@ am@@ kę w 9@@ 5.@@ min ! ht ...\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account J@@ aki tam profesor ! ! ! T@@ o kan@@ gur którego . A@@ bor@@ y@@ gen@@ i zami@@ en@@ ili w człowieka .\r\n", + "@ an@@ ony@@ miz@@ ed _ account A przy okaz@@ ji ; jaki sex@@ ow@@ ny , no@@ wy pro@@ fil ! ! 😁 😀 😁 😀 😀\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account też czy@@ ta@@ łam , nau@@ ko@@ we u@@ sprawiedli@@ wi@@ enie dla le@@ żenia w łó@@ ż@@ ku przez pó@@ ł so@@ bo@@ ty , dziękuję @ an@@ ony@@ miz@@ ed _ account ! ! !\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account aż za poważ@@ nie jak na oso@@ bę nie@@ poważ@@ ną ! : D\r\n", + "@ an@@ ony@@ miz@@ ed _ account Z@@ a to dokon@@ ano za@@ ku@@ pu B@@ lack H@@ aw@@ ków dla poli@@ cji . M@@ ist@@ r@@ zo@@ stwo !\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account J@@ eba@@ ć sł@@ oni@@ c@@ ę@@ - mo@@ ty@@ li@@ cę ! ! ! ! 😁 😁 😁 . W@@ ro@@ c@@ ła@@ w nie daj się ! ! !\r\n", + "M@@ y tu o polity@@ ce , a tu by@@ ki ry@@ czą ! Z@@ ac@@ zy@@ naj@@ ą ry@@ ko@@ wis@@ ko ! 😁\r\n", + "@ an@@ ony@@ miz@@ ed _ account hej koch@@ ana , wszyst@@ kiego naj@@ lesz@@ ego , spe@@ ł@@ nienia wszystkich mar@@ ze@@ ń , dużo z@@ dr@@ ów@@ ka , pieni@@ ę@@ dzy , mi@@ łości i szczę@@ ścia ! ! !\r\n", + "J@@ ak widz@@ ę był@@ ego premi@@ era M@@ ar@@ cin@@ ki@@ e@@ wi@@ cza to zawsze myślę : q@@ r@@ wa ! jak ktoś taki mó@@ gł być premier@@ em . T@@ aki skoń@@ cz@@ ony b@@ ł@@ az@@ en .\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account Z@@ go@@ li@@ ć ? \\ \" S@@ ta@@ si@@ ek ! T@@ y@@ ś się na A@@ mer@@ y@@ kana zrobi@@ ł ! ? W tym tele@@ wi@@ zor@@ ze tak się ciebie stało ? \\ \"\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account T@@ o pie@@ pr@@ zenie komun@@ ał@@ ów P@@ A@@ D że było war@@ to , bud@@ zi we mnie nies@@ mak . D@@ ru@@ gi T@@ us@@ k q@@ r@@ wa !\r\n", + "@ an@@ ony@@ miz@@ ed _ account B@@ ard@@ zo się ciesz@@ ę , że T@@ wit@@ ter może wy@@ wo@@ ły@@ wać też takie em@@ oc@@ je , a nie wie@@ czny g@@ nie@@ w 😉 P@@ oz@@ dra@@ wi@@ am !\r\n", + "@ an@@ ony@@ miz@@ ed _ account N@@ o to gł@@ ę@@ bo@@ kie uk@@ ł@@ ony przes@@ y@@ łam już dzisiaj . P@@ an@@ cer@@ ne po@@ kol@@ enie ! ; - )\r\n", + "@ an@@ ony@@ miz@@ ed _ account do@@ łą@@ cze się do ży@@ cze@@ ń : ) @ an@@ ony@@ miz@@ ed _ account wszyst@@ kiego najlep@@ szego ! : )\r\n", + "@ an@@ ony@@ miz@@ ed _ account K@@ op w dup@@ ę i do W@@ aty@@ kan@@ u się mą@@ dr@@ zyć za swoją kas@@ ę !\r\n", + "R@@ T @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account K@@ op w dup@@ ę i do W@@ aty@@ kan@@ u się mą@@ dr@@ zyć za swoją kas@@ ę !\r\n", + "O@@ ni nie mają w@@ sty@@ du ? ! W@@ oj@@ cie@@ ch M@@ ann wykorzy@@ sta@@ ł po@@ gr@@ ze@@ b K@@ ory , żeby sk@@ ry@@ ty@@ kować P@@ i@@ S . W@@ I@@ D@@ E@@ O \\ n \\ nh@@ tt@@ ps : / / t.@@ co / 6@@ R@@ 3@@ P@@ h@@ x@@ E@@ G@@ qu\r\n", + "R@@ T @ an@@ ony@@ miz@@ ed _ account O@@ ni nie mają w@@ sty@@ du ? ! W@@ oj@@ cie@@ ch M@@ ann wykorzy@@ sta@@ ł po@@ gr@@ ze@@ b K@@ ory , żeby sk@@ ry@@ ty@@ kować P@@ i@@ S . W@@ I@@ D@@ E@@ O \\ n \\ nh@@ tt@@ ps : / / t.@@ co / 6@@ R@@ 3@@ P@@ h@@ x@@ E@@ G@@ qu\r\n", + "@ an@@ ony@@ miz@@ ed _ account I tym spo@@ so@@ bem zwy@@ cięż@@ czyni@@ ą tur@@ nie@@ ju o P@@ la@@ stu@@ sio@@ wego D@@ e@@ bila została A@@ gni@@ esz@@ ka H@@ ol@@ land ! B@@ ra@@ wo ! ! ! 😁 😁 😁\r\n", + "@ an@@ ony@@ miz@@ ed _ account A ktoś mówi@@ ł że jesteś m@@ ło@@ da ? ! 😂\r\n", + "- I@@ le tr@@ wa wymi@@ ana sz@@ ki@@ e@@ ł ? \\ n@@ - O@@ ko@@ ło godz@@ iny . \\ n@@ - Ś@@ wi@@ et@@ nie ! \\ n@@ - J@@ aka wa@@ da ? \\ n@@ - M@@ in@@ us 6 . \\ n@@ - U@@ u . D@@ o dwóch tygod@@ ni . \\ n@@ D@@ y@@ sk@@ ry@@ min@@ acja za wad@@ ę . 😂\r\n", + "@ an@@ ony@@ miz@@ ed _ account T@@ e@@ ż się dłu@@ go do tego za@@ bier@@ ałam , ale po@@ stanowi@@ łam no@@ w@@ emu t@@ łu@@ ma@@ cz@@ owi dać sz@@ ans@@ ę - od@@ waż@@ ny go@@ ść ! 😊\r\n", + "@ an@@ ony@@ miz@@ ed _ account W@@ iem pami@@ et@@ am ! ! ! G@@ rat@@ u@@ lu@@ je ! J@@ est@@ em z C@@ ie@@ bie dum@@ na ! J@@ a zda@@ łam w cz@@ war@@ tek za trze@@ cim\r\n", + "@ an@@ ony@@ miz@@ ed _ account ale masz zd@@ ję@@ cie pro@@ filo@@ we ! J@@ ak l@@ ale@@ cz@@ ka z por@@ cel@@ any !\r\n", + "@ an@@ ony@@ miz@@ ed _ account O@@ j gł@@ upo@@ lek ! i to jaki jeszcze 😁 😁 😁\r\n", + "N@@ a tar@@ ga@@ ch ś@@ lu@@ b@@ nych w moim mie@@ ście będzie I@@ za J@@ an@@ ach@@ ow@@ ska ! ! ! !\r\n", + "kon@@ cer@@ t Y@@ ear@@ s@@ ów już za 90 dni ! ! ! jak to szybko le@@ ci ! nie mogę się do@@ cze@@ kać . @ an@@ ony@@ miz@@ ed _ account widzi@@ my się nied@@ łu@@ go 😌\r\n", + "R@@ T @ an@@ ony@@ miz@@ ed _ account kon@@ cer@@ t Y@@ ear@@ s@@ ów już za 90 dni ! ! ! jak to szybko le@@ ci ! nie mogę się do@@ cze@@ kać . @ an@@ ony@@ miz@@ ed _ account widzi@@ my się nied@@ łu@@ go 😌\r\n", + "@ an@@ ony@@ miz@@ ed _ account tym to ją wz@@ ru@@ szy@@ łeś ! ; -@@ D\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account N@@ o o dobrze ! P@@ rec@@ z z dy@@ kt@@ atur@@ ą kobiet ! ! ! ! 😁 😁 😁 😁\r\n", + "D@@ aj@@ cie fa@@ v a ja W@@ am jutro zrobi@@ ę gad@@ an@@ ego in@@ da pier@@ wsz@@ ego w ży@@ ciu !\r\n", + "@ an@@ ony@@ miz@@ ed _ account J@@ est tu jaki@@ ś c@@ wan@@ i@@ ak ? J@@ ak ktoś mnie po@@ kona w F@@ i@@ fe@@ 17 na ps@@ 4 to w@@ pł@@ ac@@ am 10@@ 9,@@ 0@@ 6 z@@ ł ! M@@ ec@@ ze w week@@ end # W@@ isla@@ T@@ o@@ N@@ as@@ za@@ P@@ rzy@@ sz@@ los@@ c\r\n", + "@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account J@@ ak to powiedział kla@@ sy@@ k : cham@@ st@@ wu należy przeciw@@ sta@@ wi@@ ać się sił@@ om i god@@ no@@ sci@@ om oso@@ bi@@ st@@ om ! 😁 😁 😁 😁 . T@@ ak trzyma@@ ć ! 😁 😁\r\n", + "@ an@@ ony@@ miz@@ ed _ account N@@ o to się chyba zabi@@ or@@ ę za niego ! D@@ zi@@ ę@@ ki\r\n", + "@ an@@ ony@@ miz@@ ed _ account J@@ ak to nie został za@@ pro@@ sz@@ ony ? ? ? D@@ om@@ ek ? ? ? T@@ o dla tego kur@@ wa przy@@ je@@ cha@@ ł do Ł@@ od@@ zi !\r\n", + "@ an@@ ony@@ miz@@ ed _ account S@@ zan@@ ow@@ na P@@ ani R@@ eda@@ ktor ! S@@ er@@ de@@ cznie dziękuję za tę promo@@ cję waż@@ nych dla mnie warto@@ ści bu@@ du@@ jac@@ ych rodz@@ inę . P@@ oz@@ dra@@ wi@@ am ! : - )\r\n", + "R@@ T @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account S@@ zan@@ ow@@ na P@@ ani R@@ eda@@ ktor ! S@@ er@@ de@@ cznie dziękuję za tę promo@@ cję waż@@ nych dla mnie warto@@ ści bu@@ du@@ jac@@ ych rodz@@ inę . P@@ o ...\r\n", + "@ an@@ ony@@ miz@@ ed _ account to oczywi@@ ste by@@ ły@@ ście dziś za@@ je@@ biste ! N@@ o może o@@ pró@@ cz C@@ ie@@ bie ha@@ ha : ) # jo@@ ke\r\n" + ] + } + ], + "source": [ + "!grep \"!\" ../data/poleval19/task-1/test_set_clean_only_text.tmp.bpe" + ] + }, + { + "cell_type": "code", + "execution_count": 390, + "metadata": {}, + "outputs": [], + "source": [ + "tst_df = pd.DataFrame({\n", + " 'y':np.loadtxt(str(dataset/'test_set_clean_only_tags.txt'),delimiter='\\n', comments=None, dtype=np.int),\n", + " 'em':list(read_enc(tst_fn)), \n", + " 'txt':np.loadtxt(str(dataset/'test_set_clean_only_text.tmp.bpe'), delimiter='\\n', comments=None, dtype=np.str),\n", + "})\n", + "\n", + "trn_df = pd.DataFrame({\n", + " 'y':np.loadtxt(str(dataset/'training_set_clean_only_tags.txt'),delimiter='\\n', comments=None, dtype=np.int),\n", + " 'em':list(read_enc(trn_fn)), \n", + " 'txt':np.loadtxt(str(dataset/'training_set_clean_only_text.tmp.bpe'), delimiter='\\n', comments=None, dtype=np.str),\n", + "})\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 391, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "array([ 0.01389 , 0.003565, -0.001211, 0.016079, ..., 0.012037, 0.006424, 0.005476, 0.01712 ], dtype=float32)" + ] + }, + "execution_count": 392, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tst_df['em'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 393, + "metadata": {}, + "outputs": [], + "source": [ + "class EmbeddingText(ItemBase):\n", + " \"Basic item for text data in numericalized `ids`.\"\n", + " def __init__(self, emb, text): self.data,self.text = np.array(emb, dtype=np.float32),text\n", + " def __str__(self): return str(self.text)" + ] + }, + { + "cell_type": "code", + "execution_count": 394, + "metadata": {}, + "outputs": [], + "source": [ + "class EmbeddingList(ItemList):\n", + " \"Basic `ItemList` for embeding of text data.\"\n", + " _bunch = DataBunch # TextDataBunch converts input to LongTensor !\n", + " _processor = []\n", + "\n", + " def __init__(self, items:Iterator, **kwargs):\n", + " super().__init__(items, **kwargs)\n", + "\n", + " def find_text_for_em(self, em):\n", + " # todo find a way to reverse embedding\n", + " return self.inner_df['txt'][1]\n", + " \n", + " def get(self, i):\n", + " o = super().get(i)\n", + " return EmbeddingText(o, self.inner_df['txt'][i])\n", + "\n", + " def reconstruct(self, t:Tensor):\n", + " return EmbeddingText(t, self.find_text_for_em(t))\n", + "\n", + " def show_xys(self, xs, ys, max_len:int=70)->None:\n", + " \"Show the `xs` (inputs) and `ys` (targets). `max_len` is the maximum number of tokens displayed.\"\n", + " from IPython.display import display, HTML\n", + " names = ['text','target']\n", + " items = []\n", + " for i, (x,y) in enumerate(zip(xs,ys)):\n", + " txt_x = ' '.join(x.text.split(' ')[:max_len]) if max_len is not None else x.text\n", + " items.append([txt_x, y])\n", + " items = np.array(items)\n", + " df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)\n", + " with pd.option_context('display.max_colwidth', -1):\n", + " display(HTML(df.to_html(index=False)))\n", + "\n", + " def show_xyzs(self, xs, ys, zs, max_len:int=70):\n", + " \"Show `xs` (inputs), `ys` (targets) and `zs` (predictions). `max_len` is the maximum number of tokens displayed.\"\n", + " from IPython.display import display, HTML\n", + " items,names = [],['text','target','prediction']\n", + " for i, (x,y,z) in enumerate(zip(xs,ys,zs)):\n", + " txt_x = ' '.join(x.text.split(' ')[:max_len]) if max_len is not None else x.text\n", + " items.append([txt_x, y, z])\n", + " items = np.array(items)\n", + " df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)\n", + " with pd.option_context('display.max_colwidth', -1):\n", + " display(HTML(df.to_html(index=False)))" + ] + }, + { + "cell_type": "code", + "execution_count": 395, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([0, 0, 0, ..., 0, 0, 0]), tensor([0, 0, 0, ..., 0, 0, 0]))" + ] + }, + "execution_count": 395, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[3].data,x[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 396, + "metadata": {}, + "outputs": [], + "source": [ + "path=dataset/\"models\"\n", + "src = ItemLists(path, \n", + " EmbeddingList.from_df(trn_df, cols=1, path=path),\n", + " EmbeddingList.from_df(tst_df, cols=1, path=path))\n", + "classes=np.unique(trn_df['y'])\n", + "src.label_from_lists(trn_df['y'], tst_df['y'], classes=classes, processor=[])\n", + "data = src.databunch()" + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
texttarget
@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account B@@ ra@@ wo ty D@@ aria ki@@ bi@@ c ma być na dobre i zł@@ e0
@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account B@@ ra@@ wo ty D@@ aria ki@@ bi@@ c ma być na dobre i zł@@ e1
@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account B@@ ra@@ wo ty D@@ aria ki@@ bi@@ c ma być na dobre i zł@@ e0
@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account B@@ ra@@ wo ty D@@ aria ki@@ bi@@ c ma być na dobre i zł@@ e0
@ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account B@@ ra@@ wo ty D@@ aria ki@@ bi@@ c ma być na dobre i zł@@ e0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.show_batch()" + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "metadata": {}, + "outputs": [], + "source": [ + "class LaserSentCLS(nn.Module):\n", + " def __init__(self, idim=1024, odim=2, nhid=None,\n", + " dropout=0.0, activation='TANH'):\n", + " super().__init__()\n", + " modules = []\n", + "\n", + " modules = []\n", + " print(' - mlp {:d}'.format(idim), end='')\n", + " if len(nhid) > 0:\n", + " if dropout > 0:\n", + " modules.append(nn.Dropout(p=dropout))\n", + " nprev = idim\n", + " for nh in nhid:\n", + " if nh > 0:\n", + " modules.append(nn.Linear(nprev, nh))\n", + " nprev = nh\n", + " if activation == 'TANH':\n", + " modules.append(nn.Tanh())\n", + " print('-{:d}t'.format(nh), end='')\n", + " elif activation == 'RELU':\n", + " modules.append(nn.ReLU())\n", + " print('-{:d}r'.format(nh), end='')\n", + " else:\n", + " raise Exception('Unrecognized activation {activation}')\n", + " if dropout > 0:\n", + " modules.append(nn.Dropout(p=dropout))\n", + " modules.append(nn.Linear(nprev, odim))\n", + " print('-{:d}, dropout={:.1f}'.format(odim, dropout))\n", + " else:\n", + " modules.append(nn.Linear(idim, odim))\n", + " print(' - mlp %d-%d'.format(idim, odim))\n", + " self.mlp = nn.Sequential(*modules)\n", + " def forward(self, x):\n", + " return self.mlp(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 431, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - mlp 1024-10t-8t-2, dropout=0.0\n" + ] + } + ], + "source": [ + "m=LaserSentCLS(nhid=[10, 8])" + ] + }, + { + "cell_type": "code", + "execution_count": 432, + "metadata": {}, + "outputs": [], + "source": [ + "learn = Learner(data, m, metrics=[accuracy, FBeta(beta=1, average='binary')])" + ] + }, + { + "cell_type": "code", + "execution_count": 433, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" + ] + } + ], + "source": [ + "learn.lr_find()" + ] + }, + { + "cell_type": "code", + "execution_count": 434, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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epochtrain_lossvalid_lossaccuracyf_betatime
00.2939520.4148090.8660000.00000000:01
10.2871490.4274150.8660000.00000000:01
20.2870020.4197440.8660000.00000000:01
30.2857500.4052030.8660000.00000000:01
40.2902380.4014950.8660000.00000000:01
50.2265870.3326130.8660000.00000000:01
60.2185880.2882910.8800000.29411800:01
70.2020240.3559670.8720000.12328800:01
80.1962630.3196120.8730000.14765100:01
90.1966390.3154910.8780000.22784800:01
100.1907220.3347670.8760000.20512800:01
110.1855770.2916170.8840000.28395100:01
120.1807910.3010880.8800000.24050600:01
130.1723610.3655980.8780000.19736800:01
140.1766040.2970740.8850000.35754200:01
150.1739090.3186030.8850000.39790600:01
160.1715450.3095790.8750000.17218500:01
170.1617930.4228660.8800000.25925900:01
180.1592740.3018140.8830000.35359100:01
190.1521780.3134250.8840000.45283000:01
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K@@ re@@ ty@@ ni ., Category 1)\n", + "(EmbeddingText R@@ T @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account C@@ h@@ ci@@ ał@@ byś aby tak było mier@@ no@@ to : ), Category 1)\n", + "(EmbeddingText @ an@@ ony@@ miz@@ ed _ account K@@ ama , słu@@ ch@@ ej no , gdzie masz bi@@ uro ? C@@ h@@ ci@@ ał@@ bym coś mach@@ n@@ ąć spra@@ jem ., Category 1)\n", + "(EmbeddingText R@@ T @ an@@ ony@@ miz@@ ed _ account @ an@@ ony@@ miz@@ ed _ account T@@ aki z niego patri@@ ota jak z ciebie dzi@@ enni@@ kar@@ z . N@@ ie po@@ gr@@ ąż@@ aj się ., Category 0)\n" + ] + } + ], + "source": [ + "for ix in range(0,10): print(data.valid_ds[i[ix]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:laser]", + "language": "python", + "name": "conda-env-laser-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/experiments/cls_test_wt103_1_f.ipynb b/experiments/cls_test_wt103_1_f.ipynb index ee3fd22..168c846 100644 --- a/experiments/cls_test_wt103_1_f.ipynb +++ b/experiments/cls_test_wt103_1_f.ipynb @@ -1,736 +1,839 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test of classifcation Fastai tokenization" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext autoreload\n", - "%autoreload 2\n", - "%matplotlib inline\n", - "%cd .." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from fastai import *\n", - "from fastai.text import *" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from ulmfit.train_clas import *" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch size: 70\n", - "Max vocab: 60000\n", - "Cache dir: data/imdb/models/f60k\n", - "Model dir: data/imdb/models/f60k/lstm_None.m\n" - ] - } - ], - "source": [ - "exp = CLSHyperParams('data/imdb', qrnn=False,tokenizer='f', lang='en', cuda_id=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "exp.pretrained_model = URLs.WT103_1\n", - "exp.drop_mult=0.3" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving tokenized: cls.trn 25000, cls.val 25000\n", - "Size of vocabulary: 60002\n", - "First 20 words in vocab: ['xxunk', 'xxpad', 'xxmaj', 'the', '.', ',', 'and', 'a', 'of', 'to', 'is', 'it', 'in', 'i', 'this', 'that', '\"', \"'s\", '-', '\\n\\n']\n" - ] - } - ], - "source": [ - "data_clas, data_lm = exp.load_cls_data(bs=40,force=False, use_test_for_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "40" + "cell_type": "markdown", + "metadata": { + "pycharm": {} + }, + "source": [ + "# Test of classifcation Fastai tokenization" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_lm.train_dl.batch_size" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "true_wd: False\n" - ] - } - ], - "source": [ - "learn = exp.create_lm_learner(data_lm)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "learn.true_wd=True\n", - "learn.opt=None" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" - ] - } - ], - "source": [ - "learn.lr_find()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "

" + "cell_type": "code", + "execution_count": 1, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "name": "stdout", + "text": [ + "/home/pczapla/workspace\n" + ], + "output_type": "stream" + } + ], + "source": [ + "%reload_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline\n", + "%cd .." ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "learn.recorder.plot(skip_end=15)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 23:05\n", - "epoch train_loss valid_loss accuracy\n", - "1 4.216088 4.007746 0.300710 (23:05)\n", - "\n" - ] - } - ], - "source": [ - "learn.fit_one_cycle(1, 1e-02, moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "learn.save('fit_head')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "learn.load('fit_head');" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 8:42:10\n", - "epoch train_loss valid_loss accuracy\n", - "1 3.969496 3.845486 0.315829 (26:05)\n", - "2 3.918823 3.763834 0.326209 (26:03)\n", - "3 3.839340 3.699592 0.335176 (26:08)\n", - "4 3.806484 3.647501 0.342129 (26:06)\n", - "5 3.774351 3.600486 0.347226 (26:12)\n", - "6 3.743252 3.558260 0.352164 (26:09)\n", - "7 3.699686 3.523352 0.356172 (26:09)\n", - "8 3.713620 3.493051 0.360080 (26:11)\n", - "9 3.655959 3.463499 0.363502 (26:11)\n", - "10 3.639268 3.436016 0.366489 (26:08)\n", - "11 3.612611 3.406506 0.370597 (26:11)\n", - "12 3.583289 3.374530 0.374582 (26:07)\n", - "13 3.538116 3.351603 0.378152 (26:08)\n", - "14 3.525463 3.321655 0.382012 (26:06)\n", - "15 3.490068 3.292532 0.385994 (26:08)\n", - "16 3.455298 3.272834 0.388984 (26:11)\n", - "17 3.408465 3.253918 0.391583 (26:05)\n", - "18 3.420946 3.241025 0.393331 (25:56)\n", - "19 3.382529 3.235213 0.394229 (25:57)\n", - "20 3.367180 3.233525 0.394359 (25:49)\n", - "\n" - ] - } - ], - "source": [ - "learn.unfreeze()\n", - "learn.fit_one_cycle(20, 1e-3, moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "learn.save(\"afteroom\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "learn.load(\"afteroom\");" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[3.2334335, tensor(0.3944)]" + "cell_type": "code", + "execution_count": 4, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "from fastai import *\n", + "from fastai.text import *" ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "learn.validate()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# odl: [3.6938994, tensor(0.3338)]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "learn.save_encoder(\"enc_best\")\n", - "learn.save(\"lm_best\", with_opt=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Classification" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Validation = 100% of test" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "exp.drop_mult=0.5\n", - "learn=exp.create_cls_learner(data_clas)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "learn.true_wd=True" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "learn.load_encoder('enc_best')\n", - "learn.freeze()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 04:03\n", - "epoch train_loss valid_loss accuracy\n", - "1 0.286372 0.176776 0.933840 (04:03)\n", - "\n" - ] - } - ], - "source": [ - "learn.fit_one_cycle(1, 2e-2, moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 04:21\n", - "epoch train_loss valid_loss accuracy\n", - "1 0.235155 0.160959 0.940560 (04:21)\n", - "\n" - ] - } - ], - "source": [ - "learn.freeze_to(-2)\n", - "learn.fit_one_cycle(1, slice(1e-2/(2.6**4),1e-2), moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "learn.save(\"2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 05:38\n", - "epoch train_loss valid_loss accuracy\n", - "1 0.213980 0.148502 0.946840 (05:38)\n", - "\n" - ] - } - ], - "source": [ - "learn.freeze_to(-3)\n", - "learn.fit_one_cycle(1, slice(5e-3/(2.6**4),5e-3), moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "learn.save(\"3\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 13:45\n", - "epoch train_loss valid_loss accuracy\n", - "1 0.198522 0.153674 0.947800 (06:52)\n", - "2 0.169954 0.157253 0.947320 (06:52)\n", - "\n" - ] - } - ], - "source": [ - "learn.unfreeze()\n", - "learn.fit_one_cycle(2, slice(1e-3/(2.6**4),1e-3), moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "learn.save(\"all\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Validation = 10% of train" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tokenized data loaded, lm.trn 90000, lm.val 10000\n", - "Tokenized data loaded, cls.trn 22500, cls.val 2500\n", - "Size of vocabulary: 60002\n", - "First 20 words in vocab: ['xxunk', 'xxpad', 'xxmaj', 'the', '.', ',', 'and', 'a', 'of', 'to', 'is', 'it', 'in', 'i', 'this', 'that', '\"', \"'s\", '-', '\\n\\n']\n" - ] - } - ], - "source": [ - "data_clas, data_lm = exp.load_cls_data(bs=40,force=False, use_test_for_validation=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "exp.drop_mult=0.5\n", - "learn=exp.create_cls_learner(data_clas)\n", - "learn.true_wd=True" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "learn.load_encoder('enc_best')\n", - "learn.freeze()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 02:43\n", - "epoch train_loss valid_loss accuracy\n", - "1 0.288307 1.597378 0.575200 (02:43)\n", - "\n" - ] - } - ], - "source": [ - "learn.fit_one_cycle(1, 2e-2, moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 03:08\n", - "epoch train_loss valid_loss accuracy\n", - "1 0.246644 0.435174 0.779200 (03:08)\n", - "\n" - ] - } - ], - "source": [ - "learn.freeze_to(-2)\n", - "learn.fit_one_cycle(1, slice(1e-2/(2.6**4),1e-2), moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 04:19\n", - "epoch train_loss valid_loss accuracy\n", - "1 0.202743 0.212411 0.923200 (04:19)\n", - "\n" - ] - } - ], - "source": [ - "learn.freeze_to(-3)\n", - "learn.fit_one_cycle(1, slice(5e-3/(2.6**4),5e-3), moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 10:51\n", - "epoch train_loss valid_loss accuracy\n", - "1 0.189055 0.162822 0.944000 (05:25)\n", - "2 0.168473 0.165473 0.941600 (05:25)\n", - "\n" - ] - } - ], - "source": [ - "learn.unfreeze()\n", - "learn.fit_one_cycle(2, slice(1e-3/(2.6**4),1e-3), moms=(0.8,0.7))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "learn.save(\"valid\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Running models on testset\n", - "There is a small bug that does not let us use test_ds so we load the test set in to valid_ds and run validate() on that data set" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tokenized data loaded, lm.trn 90000, lm.val 10000\n", - "Tokenized data loaded, cls.trn 25000, cls.val 25000\n", - "Size of vocabulary: 60002\n", - "First 20 words in vocab: ['xxunk', 'xxpad', 'xxmaj', 'the', '.', ',', 'and', 'a', 'of', 'to', 'is', 'it', 'in', 'i', 'this', 'that', '\"', \"'s\", '-', '\\n\\n']\n" - ] - } - ], - "source": [ - "data_clas, data_lm = exp.load_cls_data(bs=40,force=False, use_test_for_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "learn=exp.create_cls_learner(data_clas)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "learn.load('valid');" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.15947564, tensor(0.9483)]" + "cell_type": "code", + "execution_count": 5, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [], + "source": [ + "from ulmfit.train_clas import *" ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "learn.validate()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "learn.load(\"all\");" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "[0.15725298, tensor(0.9473)]" + "cell_type": "code", + "execution_count": 8, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u003cipython-input-8-83f9ffcff46e\u003e\u001b[0m in \u001b[0;36m\u003cmodule\u003e\u001b[0;34m\u001b[0m\n\u001b[0;32m----\u003e 1\u001b[0;31m \u001b[0mexp\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0mCLSHyperParams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u0027data/imdb\u0027\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqrnn\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0;34m\u0027f\u0027\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[0;34m\u003d\u001b[0m\u001b[0;34m\u0027en\u0027\u001b[0m\u001b[0;34m,\u001b[0m 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101\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenizer\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0mTokenizers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--\u003e 103\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m 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lang\u003d\u0027en\u0027, cuda_id\u003d0)" ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "pycharm": { + "is_executing": false + } + }, + "outputs": [ + { + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u003cipython-input-7-53455ef34413\u003e\u001b[0m in \u001b[0;36m\u003cmodule\u003e\u001b[0;34m\u001b[0m\n\u001b[0;32m----\u003e 1\u001b[0;31m \u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpretrained_model\u001b[0m \u001b[0;34m\u003d\u001b[0m \u001b[0mURLs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWT103_1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m 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\u0027to\u0027, \u0027is\u0027, \u0027it\u0027, \u0027in\u0027, \u0027i\u0027, \u0027this\u0027, \u0027that\u0027, \u0027\"\u0027, \"\u0027s\", \u0027-\u0027, \u0027\\n\\n\u0027]\n" + ] + } + ], + "source": [ + "data_clas, data_lm \u003d exp.load_cls_data(bs\u003d40,force\u003dFalse, use_test_for_validation\u003dTrue)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "40" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_lm.train_dl.batch_size" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "true_wd: False\n" + ] + } + ], + "source": [ + "learn \u003d exp.create_lm_learner(data_lm)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.true_wd\u003dTrue\n", + "learn.opt\u003dNone" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n" + ] + } + ], + "source": [ + "learn.lr_find()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "image/png": 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9qt6qqq+KSHsgWlUfdDm2gDSpfwJb9hWSdbDM16G0SNU1amORTNDILiynslrpGtva16GckCY18ovIEmC6s/86IEdEvlTV212MLSBN6pfAgx9tYWlqDpcmd2/SMUXlVfz53e84WOq5S6YCoSLcOrUfg7vGuBhtcFmelsutr66lb8e2PHbZMLq0C8x/SmNqZeSXANAtQBNMUy+RtVPVg8BFwAuqOgo4y72wAtfAztHEt408rstkzy5NY8HqdHbnlbA7r4Q9eSV8tS2HP727ybVv4zmF5SxPyw2Kb/uqytwvf+CqeStoFR7Kuj35nPPkV3zy3T5fh2bMSUk/4LmpYWL7wEwwTe2mHCYiXYDLcEbcm/qFhAiT+sXzxdZsqqprCAs9dg4/UFzBc1/v4OxTOjP3mlGH1r+yfBf//Z9NLEnN4cwBHb0SW1llNYs3Z/PWmnS+TM2hukaZM7Uft/1X/0aPPVhWye2vr6d7XGsuGdWNU7q280pMJ6ugtJI73lzPou+zOO/ULjx0yVByCsu59dW1/PKV1fxsbA/++7zBtI7w327jReVVRIaFEN7I34ppeWrvmpsYoDWYpiaYe4FPgG9UdZWI9Aa2uRdWYPvJkM68vTaDr7bvbzQ5zF36A8UVVdw+7fAP+cuTu/Ps0jQe+Xgrk/slEHLEICtV5Vi3yNmxv5gPN+4lp7Cc/UXl5BSWs3nvQQ6WVdEpJpLZk3qTVVDGk4u3ER4q3DKlX4OvVVOj3P76Or7YmkOoCC98s5OBnaO5ZFQ3rhjTg7aRTf0z8q7Vu/K47fX1ZOaX8t/nD+b6ib0QEdpGhvHWrybw2KKtPP1lGst+yOWhi4cyJinOJ3Ee6d31mXz2fdah2mpucQVd27XiuZmjGdTFLomaH2XmlxLbJpwoH/2PnawmRa2qbwJv1nmexo+zIJsjnDmgI7Ftwnl7TcYxE0z2wTJe+nYnPx2eSP9O0YdtiwgL4bb/6sdtr6/ng417uWBY10PbcgrLmfnCSjrFtOKpK0cc9QH/feZBfjZvOfkllURHhhEfHUlC20imndKZ6cO6MrFvPKEhcugunI9+mkpYaAg3Tu5Tb5x//2I7n23O5s8XDOanIxJ5b30mC1anc98Hm1n2Qy7PzRx9or+qE1JWWc3jn6XyzNI0EmNb89rscST3Ojx5RISFcNc5g5jcL4Hfv72By55exrXje3Ln2QN9lhBVlacWb+fxz1Lp0q4VfRLaMu2UziTGtmL+8t1cOncZ/3fVyEPTDhmTcaCUrgHcltjURv5uwP/imcBSga+BOaqa7mJsASsiLIQLhnbljZQ9FJZVEt2q/iFD//hiO5XVnstU9Zk+LJG5S9L426JUzhnSmbDQEPYXlXPVvOXsyi1hy75Cfvbscp6fOZp4p5/85r0HuWreclqHh/KfOybSKz6qwThDQ4RHLh1GVY3y4EdbCAsRfn5678P2+WJLNo9/lsqMEYlcN8FTQ7hmfC+uGd+LuV/+wIMfbWHx5iymDup0gr+t47Mpo4Db31hHalYRV47pzt3nDT5mwpjQN55PfjOJRz7Zyovf7mTx5mweuXQoE/rEey2mvOIK2rcJP2aNsqZGuff973nx251cPLIbD1186mGXTy8e1Y1ZL6xi1our+OuMIVw+uofX4jOBKyO/lJ4dGv4f9ndNvej7Ap7pYbrimT7/PWedacBFIxMpr6rho431NzSnHyjh3yt3c1lytwaTQGiIcMdPBrBjfzELVqeTW1TO1fNWsDuvhBdmjuaZa0aRmlXIJf/8lj15JWzZd/BQQ/drs8cdM7nUPcffLhvGead24b4PNnPOk1/x3Nc72F9Uzq7cYua8tpaBnWP464xTj/oAvX5iEn0Sorj3/e8pq6w+/l/Scdqy7yAX/d+35JdU8sKs0Txw0dAm1UbaRITxpwtO4c1fjicyPISZz69ieVquV2L65Lt9JN+3iD+8tZGamvo7TFRW1/DbN9fz4rc7uX5iEo9cMvSotrku7Vrz5o3jmdg3nt+/tZGHP97S4OuZlkFVyThQGrDtLwDSlF5EIrJOVYc3ts6XkpOTNSUlxddhHKKqTH3sSxKiI3n9l+OP2v77BRtYuDaDJb8745h93FWVi/75LfsKymjXOpwd+4t5fuZoJvb1fANfvesA17+4ioiwEKprlIjQkCYnl7oqq2t4bdUeFqTsYX16AWEhQrvW4VTVKO/dcho9OrSp97ivt+3n6udW8Nv/6s+vG6iJeYOqcvVzK/gu8yCf3T75UI3teB0oruDSp5eRVVDGGzeOP6k2j43pBVz69LdEtwonp7CcS0Z146GLhx42KWHWwTJ+t2ADS1NzuGNaf24+s+8xazqV1TXc8853vLpyN2cOSOCJy0fQro1NmtESFZRUMuzeT/njeYOOurLgTSKyWlWT3XjtptZg9ovI1SIS6jyuBrzzFTBIiQgXjUxkxY489uSVHLZt675CFqxJ56pxPRodQCUi/O4nA9hbUEba/mLmXZd8KLkAjOrZngU3jicsRAgPFV49geQCEB4awjXjevLOLafx6W2TuOG0JDq0jeDvPxvRYHIBOK1fPOee2pl/LNlO+oGSBvc7WZ9tzuab7bncdlb/E04uAO2jInjp+jG0iQxl5gsrTzjmzPxSbnhpFR2iIvnw1tP5zVn9WLA6nTsXbDg02PO1lbs5629fsiItl7/OOJVbpvQ7ZnIBz/vw1xlD+MtPh/D19v1M/8fXbNl38IRiNIEt3RkD0xJqMD2Av+OZLkaBb4FbVXW3u+E1nb/VYAD25JVw+sNfHPbtvqCkkgv/8TVF5VV8NGdSkyexe/7rHQxJbNdgT6ii8ipqVIlpoL3HTRn5pZz12JecMSCBf149qvEDjlN5VTU/eXwp4aEhfDTn9Ea7fjfFln0HuXTuMhKiI3nrxgm0j4po8rFF5VVcOncZ6XklvHXThEMdNJ78bBuPf5bK+UO7kFdcwbc/5DI2KY4HLx5K0gkk/dW78vjV/DUUllXx8CVDD+voYYLfp9/tY/Yrq3nn5okM635Cd0dpEjdrME3tRbYbz0j+ukH9BnjCjaCCRfe4NoxNimPh2gxumdKX6hrlllfXkJFfyqu/GHdcM6Ref1rSMbf7qmcUeL5h3TKlL498spWnFm+jbWQYxeVVFFdU07NDGy4amUhk2ImPQ3n5213szC3hpevHeCW5AAzsHMO8a5O55vmVXDVvBX+7fBgDOzd+uWxfQRl3vb2B1KxCXpg5+rDef3PO6ocI/G1RKtGRYfx1xqlcMbr7UV3Mm2pUzzje//Vp3PSvNfz61bWEhwpnD7FJzFuKQ2NgAnSQJTSxBlPvgSK7VdVvurr4Yw0G4I1Ve7jzrQ0svGkC72/Yy3Nf7+Dhi4dy2eimTSMTKMqrqjnnya9Iyyk+tC48VKisVhJjWzNnaj8uGpl43Akit6icMx5ZQnKv9rwwa4y3w+bzLVn89o31FJZVccPpScyZ2o82ET8m69KKatbuPsCXqTl8mZrDln2FANw/YwhXje1Z72t+tS2H/p2i6RTTyisxllVWc+Wzy9m89yBv/nICp3bzj0Guxl33vf8981fsYvO9Zzd6afVkuFmDOZkEs0dVG/2UFJFQIAXIUNXzj9jWE89tkhOAPODq2q7PzmW5eUB3PJflzlXVnQ2dx18TTGFZJcn3fUaPuDZsyy5i5oRe/Hn6Kb4OyxXF5VXkl1YSFRFKm4gwwkOFr7bt57FFqazfk0+vDm34/dkDOefUpn8Lv3vhRl5btYdPfjOJvh3buhL3geIKHvhoM2+kpJMY25obTktiZ24xa3fns3nvQapqlPBQYXSvOCb3T2DKwI70O2LckttyCsv56T++oaqmhnduPo3O7byTvIz/uulfq9myr5DPf3uGq+fx1wTTpBqMiNwOJAMx9SSYN4H3VfUlEZkCzFLVa5xtS/DcHmCRiLTFc9OzBltk/TXBAPz61bW8tz6TiX078NIs713mCRSqyuLN2Tz66Va27CtkztR+/Oasxhu8v0zNYdYLK7l2fPMk5ZU78rh74Ua2ZRcRFRHKsO6xjOgRy8ge7Rnbu4NPL0OCZ4zTJf/8lt4JbXnjl+P9evobc/Iu/PvXxLQO55Ubxrp6Hp+1wYhIIZ7aw1GbgEYvDDoDNM8D7gfqm3l5MHCbs/wF8B/nuMFAmKouAlDVosbO5c9uPrMPYSHCPecPbnHJBTw94c4a3InJAxK46+2NPLl4G/sKyrh/xpAGfx/vrMvgt2+sp3+naG47q/G50rxhTFIcH845nYwDpXSPa+N390Af1CWGp64cwc9fTuE3r6/lscuG+zzpGfdk5JcG/NRBx/y0U9VoVY2p5xGtqk35y34CuBOoaWD7en6ccmYGEC0iHYD+QL6IvC0ia0XkEedSW0Aa2DmGxy8fflw9lYJReGgIj1wylFun9OX1lD384uUUSiqqjtrvua93MOe1dYzq2Z43bhzfrONAwkND6BUf5XfJpdbUQZ3443mD+eS7LCY8sJiHPt5i9x4KQmWV1ewvqgjoLsrQ9Mkuj5uInA9kq+pqETmjgd3uAP4uIjOBpUAGUOXEdTowAtgNvA7MBJ474hyzgdkAPXr4TX8Dcwwiwu3TBtCpXSv++z+bOO+prxnTK47eCVH0TmjLqp15PLM0jXOGdObxy4fTKjxgv1e45obTkhjZI5Z5X+3g6S9/YN5XaUw7pTOhIuwrKGPvwVKKyqr48/RTuHB4oq/DNScgGHqQgYsJBs+8ZdNF5FygFRAjIvNV9eraHVQ1E889ZnDaWS5W1QIRSQfWOpNqIiL/AcZxRIJR1WeAZ8DTBuNiWYyXXTW2J13atWLukjQWb8ni9ZSKQ9uuHteD/5k+xG9rEf5gRI/2/OOq9uzOLeH5b3bw7vpMoluF0TmmFaN6tGdbdhF/eGsjQxLb0SfBnc4Rxj2ZAT5Nf60TbuQ/rpN4ajB31NPIHw/kqWqNiNwPVKvqPc7lsDXAWaqaIyIvACmq+o+GzuHPjfymcQUllaTtL6KiqoYxSXGudstsCbIOlnH2E0vp0q41C2+ecFLjkEzze23lbv7w9ka+/v2ZdGvf8Ewa3uAPU8V4jYjcKyK1gzbPALaKSCrQCU9nAFS1Gs/ls8UishFPp4JnmztW03zatQlnhNNby5LLyesU04pHLx3G93sP8uBHW3wdjjlOGfmlhAheG0vlK83SBUVVlwBLnOV76qxfACxo4JhFwNBmCM+YoDR1UCdmTujFC9/s5LS+8c12SwVz8jIOlNI5plXA3+U0sKM3xhzTXecOZHCXGO54cz37Cqy3WaBIzy8N+AZ+sARjTFCLDAvlf382grLKGv707iZfh2OaKDM/sO8DU8sSjDFBrk9CW26Z0pdPvsviq205vg7HNKK6RtlXUGY1GGNMYPj56Un07NCGP7/7HRVVDY17Nv4g62AZVTXa6L2iAoElGGNagMiwUO45fzA/5BTz0rc7fR2OOYaMIBkDA5ZgjGkxpg7qxJkDEnhy8TayC63B31/VDrLsZpfIjDGB5J4LTqGiqoaHPtrq61BMA9IPeBKMXSIzxgSUpPgobjg9ibfWpLN61wFfh2PqkZFfSlxUxGE3vgtUlmCMaWFuObMvXdq14qZ/rWbn/uLGDzDNKuNAKV1jA3sEfy1LMMa0MFGRYbw4awyV1cqVzy5nd26D9/EzPpARJGNgwBKMMS3SgM7RzL9hLKWV1Vz57HLSD1iS8Qeq6gyydHeCy+ZiCcaYFmpw1xjm3zCWwrJKrnx2+aHeS8Z3cosrKKmoDooeZGAJxpgWbUhiO165YSz5xZVc/dwK8ksqGj/IuCY1qxCAfp2C4x4+lmCMaeGGdY/luZmjSc8rZfbLqymrrPZ1SC3WtqwiAPp3ivZxJN5hCcYYw5ikOB67bBgrd+bxuwUbqKk5/EaEB8sqSc0qPGq98a7UrEJiWoXRMTrS16F4ReB3tDbGeMUFw7qSfqCUhz7eQmJsa/5wzkB+yCnipW93smB1OiUV1XSIimBi33hO7xfPgM7RbN1XyMaMAjakF1BQWskjlwwluVecr4sSsFKzCunfKTpobrrneoJxbn+cAmTUc8vknsDzQAKQB1ytqul1tscAm4GFqnqL27Ea09LdOLk36QdKmPvlDyxtI6sKAAAWOklEQVRPy2XdnnwiQkO4YFhXxiS1Z0VaHku37efd9ZmHjmkbGcaQxBhyi2uY9eIqXv3FOIYktvNhKQKTqpKaVcR5Q7v4OhSvaY4azBw8SSKmnm2PAi+r6ksiMgV4ALimzva/AF+6H6IxBkBE+J/pp5BTWM66PfncdlZ/fja2BwnOJZvLR/dAVdmyr5C0nGIGdokmqUMUISFCRn4pl/7zW657fiVv3DiePgnB0VDdXHIKyykoraR/x+D5vbnaBiMi3YDzgHkN7DIYWOwsfwFcWOfYUUAn4FM3YzTGHC4sNISnrxnFiv83lTln9TuUXGqJCIO6xHDe0C70SWhLSIjnck5ibGvm/3wsInD1vBU2tuY4pQZZAz+438j/BHAn0NANKNYDFzvLM4BoEekgIiHAY8DvXI7PGFMPETmhdoDeCW15+fqxFJdXcfW8FRwotm7PTbX1UBdlSzCNEpHzgWxVXX2M3e4AJovIWmAykAFUATcBH6rqnkbOMVtEUkQkJSfH7tRnjD8Y3DWGF2aNZmduCS8t2+nrcALGtqxC4qIiiG8b4etQvMbNGsxEYLqI7AReA6aIyPy6O6hqpqpepKojgLuddQXAeOAW59hHgWtF5MEjT6Cqz6hqsqomJyQkuFgUY8zxGNUzjsn9E3h15W4qq+0Omk2RmlVIv45tg6YHGbiYYFT1LlXtpqq9gCuAz1X16rr7iEi8czkM4C48PcpQ1atUtYdz7B14OgL8wa1YjTHed824nmQdLGfR91m+DsXvqSrbsoqCqv0FfDDQUkTuFZHpztMzgK0ikoqnQf/+5o7HGOOOMwd2JDG2Na8s2+XrUPze3oIyCsur6N85uBJMswy0VNUlwBJn+Z466xcACxo59kXgRdeCM8a4IjREuGpcDx7+eCvbsgqDqvHa22rnIAumLspgU8UYY1x0eXJ3IkJDmL/cajHHcijBBFkStgRjjHFNh7aRnDe0C2+tyaCovMrX4fit1Kwi4ttG0j4qeHqQgSUYY4zLrhnfk6LyKv6zNsPXofitbVmFDOgcXJfHwBKMMcZlI7rHckrXGF5ZtgtVJSO/lPc3ZPLAR5vZlFHg6/B8rqbGMwdZv47BdXkMbDZlY4zLRIRrx/fk929tJPm+z8itM7r/3XWZfDTndGLbBNeloeORkV9KaWV10LW/gCUYY0wzmD4skUXfZxPTKozhPWIZ0b09FdU1XPHMMu5csIGnrxkVVAMMj0dtA38wXiKzBGOMcV3riFDmXZd81Po7fzKQ+z/czPzlu7hmfK/mD8wP1M5B1jcIL5FZG4wxxmduOC2Jyf0T+MsHm9m896Cvw/GJbVlFdI5pRbvW4b4OxesswRhjfCYkRHjssmG0ax3Or19dS0lFy+vKnJpVSL9OwXd5DCzBGGN8LL5tJI9fNpwfcor43YINVLWgyTGra5Tt2UUMCMIGfrAEY4zxA6f1i+cPZw/kgw17ue2N9S0myezKLaa8qiYoe5CBNfIbY/zELyf3oUbhoY+3APD4ZcMICw3u78ApOw8AMLxHrI8jcYclGGOM3/jVGX0QgQc/ahlJZnlaLnFREfQLskkua1mCMcb4lRsn9wE8SaZtZBgPXHSqjyNyz4odeYxNigvaMUDB+9XAGBOwbpzch+snJvHaqt18nxmc3Zf35JWQkV/K2KQ4X4fiGkswxhi/NGdqP6Ijw3j0062+DsUVy9NyARjXp4OPI3GPJRhjjF9q1yacG8/ow+dbslm5I8/X4Xjdih15xLYJp38QjuCv5XqCEZFQEVkrIu/Xs62niCwWkQ0iskREujnrh4vIMhH5ztl2udtxGmP8z6wJSXSMjuThj7egqr4Ox6tW7MhlTK84QkKCs/0FmqcGMwfY3MC2R4GXVXUocC/wgLO+BLhWVU8BzgaeEJHg7MdnjGlQ64hQbp3aj5RdB/h8S7avw/GajPxS9uSVMq538F4eA5cTjFMjOQ+Y18Aug4HFzvIXwIUAqpqqqtuc5UwgG0hwM1ZjjH+6fHR3enZow8Mfb6W6JjhqMSuc9pexvYO3gR/cr8E8AdwJNDQsdz1wsbM8A4gWkcNSuoiMASKAH448WERmi0iKiKTk5OR4L2pjjN8IDw3ht9MGsDWrkHfXB8ddMVek5RHTKoyBnWN8HYqrXEswInI+kK2qq4+x2x3AZBFZC0wGMoBDs92JSBfgFWCWqh6VpFT1GVVNVtXkhASr4BgTrM4/tQundI3h0U9Sg2JCzBU7chmT1IHQIG5/AXdrMBOB6SKyE3gNmCIi8+vuoKqZqnqRqo4A7nbWFQCISAzwAfBHVV3uYpzGGD8XEiLcc/5gMvJL+fvn230dzknZV1DGztwSxgX55TFwMcGo6l2q2k1VewFXAJ+r6tV19xGReBGpjeEu4HlnfQSwEE8HgDfditEYEzjG9u7ARSMTefarNLZnF/o6nBO2YofT/pIU3A384INxMCJyr4hMd56eAWwVkVSgE3C/s/4yYBIwU0TWOY/hzR2rMca//L9zB9EmIow//mdTwHZbXp6WR3RkGIO7Bnf7CzTTXGSqugRY4izfU2f9AmBBPfvPB+Yfud4Y07LFt43kzrMHcPfCTSxcm8FFI7v5OqTjtiItl9FJcUHf/gI2kt8YE2CuHN2D4d1juf+DzRSUVPo6nOOSfbCMtP3FQT3/WF2WYIwxASUkRLjvp0M4UFLBn9/7jgPFFb4OqckWO4NFJ/SJ93EkzcOm6zfGBJwhie34xem9eXppGu+syyC5ZxxTBnXknCGd6dkhytfhNei1lbsZ0CmaIYnB3/4CVoMxxgSoP5wzkHdunsgtZ/alqLyKBz/awrTHl7Irt9jXodVrU0YB69MLuGJM96C9/8uRLMEYYwKSiDCseyy3TxvAh3NOZ/FvJ1OjynNf7/B1aPV6bdVuIsNCmDEi0dehNBtLMMaYoNAnoS0/HZ7IGyl7/K5dpqSiinfWZnLeqV2IbRPh63CajSUYY0zQ+MWk3pRV1jB/+S5fh3KY9zfspbC8iivG9PB1KM3KEowxJmj07xTNGQMSeGnZTsoqq30dziGvrtxNn4QoRvdq7+tQmpUlGGNMUJk9qTf7iypYuNY/Zl7esu8ga3fnc+WYHi2mcb+WJRhjTFAZ37sDQxJjeParNGr84P4xr63cQ0RoSEDOOnCyLMEYY4KKiPCL03uTllPs87tgllVW8/aadM4e0pm4qJbTuF/LEowxJuice2oXEmNb88xXaT6N46NNezlYVsUVY7r7NA5fsQRjjAk64aEhzJrYi5U78li7+4DP4nh7TQbd41ozrgVMzV8fSzDGmKB0xZgexLQK4+kvfVOLyTpYxjfb9zNjeCIhLWDm5PpYgjHGBKW2kWFcM74nn3y/j7ScomY//zvrMqhRmNECG/drWYIxxgStmROSCA8N4VkftMW8vSaD4d1jSYr338k33eZ6ghGRUBFZKyLv17Otp4gsFpENIrJERLrV2XadiGxzHte5HacxJvgkREdy6ahuvLU6g+yDZc123u8zD7JlXyEXjWw5847VpzlqMHOAzQ1sexR4WVWHAvcCDwCISBzwJ2AsMAb4k4i0rCGwxhivmD2pN1U1Nbzw7c5mO+fCtemEhQjnD+3abOf0R64mGKdGch4wr4FdBgOLneUvgAud5Z8Ai1Q1T1UPAIuAs92M1RgTnHp2iOKcU7swf/kuCsvcvwNmdY3yzrpMzhjQsUWOfanL7RrME8CdQE0D29cDFzvLM4BoEekAJAJ76uyX7qwzxpjjduOkPhSWVfHvFbtdP9c32/eTXVje4i+PgYsJRkTOB7JVdfUxdrsDmCwia4HJQAZQBdTXp++oOR9EZLaIpIhISk5OjjfCNsYEoVO7tWNi3w489/UOSivcnQRz4doMoluFMWVgR1fPEwjcrMFMBKaLyE7gNWCKiMyvu4OqZqrqRao6ArjbWVeAp8ZSd+hrNyDzyBOo6jOqmqyqyQkJCS4VwxgTDG46oy/ZheWM/etn3PX2Rlak5Xp9rrLi8io+3rSP84d2oVV4qFdfOxC5lmBU9S5V7aaqvYArgM9V9eq6+4hIvIjUxnAX8Lyz/AkwTUTaO43705x1xhhzQib2jeffPx/L1EGd+M/aDC5/ZjmnP/wFq3d5b6T/u+szKa2sZsaIljv2pa5mHwcjIveKyHTn6RnAVhFJBToB9wOoah7wF2CV87jXWWeMMSdsQt94Hr98OCl/PIsnLh8OwJ0L1lNR1VAzcdOt3nWA/3nvO4Z3jyW5p3V6BRBV309n7Q3JycmakpLi6zCMMQFk8eYsbngphbvPHcQvJvU+4dfZnl3IJXOXEds6nAW/mkB820gvRukuEVmtqsluvLaN5DfGtFhTB3ViysCOPLl42wkPxNxXUMZ1z68iLCSEl68fG1DJxW2WYIwxLdo95w+moqqGBz/actzHFpRWct3zKykoreTFWaPp0aGNCxEGLkswxpgWrVd8FL+YlMTbazNYtbNpTb2qyrvrMznvqa9I21/E3KtHMSSxncuRBp4wXwdgjDG+dvOZfXl7TQb3vPMd828Yw4odeXy1LYdvtufSJiKU0/rGc1q/eMYmdeC7zALu+2Az6/bkM7BzNI/eMJZxvVvm/V4aY438xhgDfLBhLzf/e82h59GRYYzr04GSiipW7ThARXUN4aFCZbXSMTqSO34ygItHdiM0wO/14mYjv9VgjDEGOPfUztw6tR8CTOofz7BusYSFeloRSiuqWbkzj2+276d9mwium9CTNhH28dkYq8EYY0wLZt2UjTHGBBxLMMYYY1xhCcYYY4wrLMEYY4xxhSUYY4wxrrAEY4wxxhWWYIwxxrjCEowxxhhXBM1ASxHJAXYdsbodUHCc6xpbjgf2n2CY9Z37ePZpSnmaqyyNxdrYPsdbliOf1y7XXWfvTdNibWwfe298+xlwrP3cKEuUqrpzz3lVDdoH8MzxrmtsGUjxZjzHs09TytNcZTnZ8hxvWY5Rhrrr7L2x98av35umlMWb743bf2eNPYL9Etl7J7CuKcvejOd49mlKeZqrLE19nYb2Od6yHPn8vQb2OVH23hx7vb03zfcZcKz9/KksjQqaS2TNRURS1KV5e5pbMJUFgqs8wVQWCK7yWFmaLthrMG54xtcBeFEwlQWCqzzBVBYIrvJYWZrIajDGGGNcYTUYY4wxrmjRCUZEnheRbBHZdALHjhKRjSKyXUSeEhGps+3XIrJVRL4TkYe9G3WD8Xi9LCLyZxHJEJF1zuNc70feYEyuvDfO9jtEREUk3nsRHzMeN96bv4jIBud9+VREuno/8nrjcaMsj4jIFqc8C0Uk1vuRNxiTG+W51PnfrxER19tqTqYMDbzedSKyzXlcV2f9Mf+v6uVmFzV/fwCTgJHAphM4diUwHhDgI+AcZ/2ZwGdApPO8YwCX5c/AHcHy3jjbugOf4BkzFR+oZQFi6uxzKzA3gMsyDQhzlh8CHgrkvzNgEDAAWAIk+2sZnPh6HbEuDkhzfrZ3ltsfq7zHerToGoyqLgXy6q4TkT4i8rGIrBaRr0Rk4JHHiUgXPP/gy9Tzm38Z+Kmz+VfAg6pa7pwj291SeLhUFp9xsTyPA3cCzdb46EZZVPVgnV2jaKbyuFSWT1W1ytl1OdDN3VL8yKXybFbVrc0Rv3O+EypDA34CLFLVPFU9ACwCzj7Rz4kWnWAa8Azwa1UdBdwB/F89+yQC6XWepzvrAPoDp4vIChH5UkRGuxrtsZ1sWQBucS5dPC8i7d0LtUlOqjwiMh3IUNX1bgfaBCf93ojI/SKyB7gKuMfFWBvjjb+zWtfj+XbsS94sj680pQz1SQT21HleW64TKm9YE0/aIohIW2AC8Gady4uR9e1az7rab5BheKqW44DRwBsi0tvJ+s3GS2X5J/AX5/lfgMfwfAA0u5Mtj4i0Ae7GcznGp7z03qCqdwN3i8hdwC3An7wcaqO8VRbnte4GqoB/eTPG4+HN8vjKscogIrOAOc66vsCHIlIB7FDVGTRcrhMqryWYw4UA+ao6vO5KEQkFVjtP38XzwVu3Gt8NyHSW04G3nYSyUkRq8Mz3k+Nm4PU46bKoalad454F3ncz4EacbHn6AEnAeuefrhuwRkTGqOo+l2M/kjf+zur6N/ABPkgweKksTmPy+cDU5v4ydgRvvze+UG8ZAFT1BeAFABFZAsxU1Z11dkkHzqjzvBuetpp0TqS8bjdA+fsD6EWdxjHgW+BSZ1mAYQ0ctwpPLaW2wetcZ/2NwL3Ocn881U0J0LJ0qbPPbcBrgfzeHLHPTpqpkd+l96ZfnX1+DSwI4LKcDXwPJDTn35fbf2c0UyP/iZaBhhv5d+C5CtPeWY5rSnnrjcsXb6i/PIBXgb1AJZ4MfQOeb7kfA+udP/p7Gjg2GdgE/AD8nR8HrUYA851ta4ApAVyWV4CNwAY839q6NEdZ3CrPEfvspPl6kbnx3rzlrN+AZ16pxAAuy3Y8X8TWOY9m6RHnYnlmOK9VDmQBn/hjGagnwTjrr3fek+3ArMbKe6yHjeQ3xhjjCutFZowxxhWWYIwxxrjCEowxxhhXWIIxxhjjCkswxhhjXGEJxgQ1ESlq5vPNE5HBXnqtavHMlrxJRN5rbJZhEYkVkZu8cW5jvMG6KZugJiJFqtrWi68Xpj9OzOiqurGLyEtAqqref4z9ewHvq+qQ5ojPmMZYDca0OCKSICJvicgq5zHRWT9GRL4VkbXOzwHO+pki8qaIvAd8KiJniMgSEVkgnvuY/Kv23hjO+mRnuciZkHK9iCwXkU7O+j7O81Uicm8Ta1nL+HHSzrYislhE1ojn/hwXOvs8CPRxaj2POPv+zjnPBhH5Hy/+Go1plCUY0xI9CTyuqqOBi4F5zvotwCRVHYFnduK/1jlmPHCdqk5xno8AfgMMBnoDE+s5TxSwXFWHAUuBX9Q5/5PO+Rudz8mZB2sqntkUAMqAGao6Es/9hx5zEtwfgB9Udbiq/k5EpgH9gDHAcGCUiExq7HzGeItNdmlaorOAwXVmmo0RkWigHfCSiPTDM1NseJ1jFqlq3XturFTVdAARWYdnLqivjzhPBT9OELoa+C9neTw/3kvj38CjDcTZus5rr8Zzbw7wzAX1VydZ1OCp2XSq5/hpzmOt87wtnoSztIHzGeNVlmBMSxQCjFfV0rorReR/gS9UdYbTnrGkzubiI16jvM5yNfX/L1Xqj42cDe1zLKWqOlxE2uFJVDcDT+G5/0sCMEpVK0VkJ9CqnuMFeEBVnz7O8xrjFXaJzLREn+K5fwoAIlI7rXk7IMNZnuni+ZfjuTQHcEVjO6tqAZ7bIt8hIuF44sx2ksuZQE9n10Igus6hnwDXO/cHQUQSRaSjl8pgTKMswZhg10ZE0us8bsfzYZ3sNHx/j+cWCwAPAw+IyDdAqIsx/Qa4XURWAl2AgsYOUNW1eGbGvQLPDbmSRSQFT21mi7NPLvCN0635EVX9FM8luGUishFYwOEJyBhXWTdlY5qZc3fNUlVVEbkCuFJVL2zsOGMCjbXBGNP8RgF/d3p+5eOj21Ab4zarwRhjjHGFtcEYY4xxhSUYY4wxrrAEY4wxxhWWYIwxxrjCEowxxhhXWIIxxhjjiv8Pjaxti3ClkuwAAAAASUVORK5CYII\u003d\n", + "text/plain": [ + "\u003cFigure size 432x288 with 1 Axes\u003e" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "learn.recorder.plot(skip_end\u003d15)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total time: 23:05\n", + "epoch train_loss valid_loss accuracy\n", + "1 4.216088 4.007746 0.300710 (23:05)\n", + "\n" + ] + } + ], + "source": [ + "learn.fit_one_cycle(1, 1e-02, moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.save(\u0027fit_head\u0027)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.load(\u0027fit_head\u0027);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "learn.unfreeze()\n", + "learn.fit_one_cycle(20, 1e-3, moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.save(\"afteroom\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.load(\"afteroom\");" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[3.2334335, tensor(0.3944)]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learn.validate()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true, + "pycharm": {} + }, + "outputs": [], + "source": [ + "# odl: [3.6938994, tensor(0.3338)]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.save_encoder(\"enc_best\")\n", + "learn.save(\"lm_best\", with_opt\u003dFalse)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": {} + }, + "source": [ + "# Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": {} + }, + "source": [ + "## Validation \u003d 100% of test" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "exp.drop_mult\u003d0.5\n", + "learn\u003dexp.create_cls_learner(data_clas)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.true_wd\u003dTrue" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.load_encoder(\u0027enc_best\u0027)\n", + "learn.freeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total time: 04:03\n", + "epoch train_loss valid_loss accuracy\n", + "1 0.286372 0.176776 0.933840 (04:03)\n", + "\n" + ] + } + ], + "source": [ + "learn.fit_one_cycle(1, 2e-2, moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total time: 04:21\n", + "epoch train_loss valid_loss accuracy\n", + "1 0.235155 0.160959 0.940560 (04:21)\n", + "\n" + ] + } + ], + "source": [ + "learn.freeze_to(-2)\n", + "learn.fit_one_cycle(1, slice(1e-2/(2.6**4),1e-2), moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.save(\"2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total time: 05:38\n", + "epoch train_loss valid_loss accuracy\n", + "1 0.213980 0.148502 0.946840 (05:38)\n", + "\n" + ] + } + ], + "source": [ + "learn.freeze_to(-3)\n", + "learn.fit_one_cycle(1, slice(5e-3/(2.6**4),5e-3), moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.save(\"3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total time: 13:45\n", + "epoch train_loss valid_loss accuracy\n", + "1 0.198522 0.153674 0.947800 (06:52)\n", + "2 0.169954 0.157253 0.947320 (06:52)\n", + "\n" + ] + } + ], + "source": [ + "learn.unfreeze()\n", + "learn.fit_one_cycle(2, slice(1e-3/(2.6**4),1e-3), moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.save(\"all\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": {} + }, + "source": [ + "## Validation \u003d 10% of train" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tokenized data loaded, lm.trn 90000, lm.val 10000\n", + "Tokenized data loaded, cls.trn 22500, cls.val 2500\n", + "Size of vocabulary: 60002\n", + "First 20 words in vocab: [\u0027xxunk\u0027, \u0027xxpad\u0027, \u0027xxmaj\u0027, \u0027the\u0027, \u0027.\u0027, \u0027,\u0027, \u0027and\u0027, \u0027a\u0027, \u0027of\u0027, \u0027to\u0027, \u0027is\u0027, \u0027it\u0027, \u0027in\u0027, \u0027i\u0027, \u0027this\u0027, \u0027that\u0027, \u0027\"\u0027, \"\u0027s\", \u0027-\u0027, \u0027\\n\\n\u0027]\n" + ] + } + ], + "source": [ + "data_clas, data_lm \u003d exp.load_cls_data(bs\u003d40,force\u003dFalse, use_test_for_validation\u003dFalse)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "exp.drop_mult\u003d0.5\n", + "learn\u003dexp.create_cls_learner(data_clas)\n", + "learn.true_wd\u003dTrue" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.load_encoder(\u0027enc_best\u0027)\n", + "learn.freeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total time: 02:43\n", + "epoch train_loss valid_loss accuracy\n", + "1 0.288307 1.597378 0.575200 (02:43)\n", + "\n" + ] + } + ], + "source": [ + "learn.fit_one_cycle(1, 2e-2, moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total time: 03:08\n", + "epoch train_loss valid_loss accuracy\n", + "1 0.246644 0.435174 0.779200 (03:08)\n", + "\n" + ] + } + ], + "source": [ + "learn.freeze_to(-2)\n", + "learn.fit_one_cycle(1, slice(1e-2/(2.6**4),1e-2), moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total time: 04:19\n", + "epoch train_loss valid_loss accuracy\n", + "1 0.202743 0.212411 0.923200 (04:19)\n", + "\n" + ] + } + ], + "source": [ + "learn.freeze_to(-3)\n", + "learn.fit_one_cycle(1, slice(5e-3/(2.6**4),5e-3), moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total time: 10:51\n", + "epoch train_loss valid_loss accuracy\n", + "1 0.189055 0.162822 0.944000 (05:25)\n", + "2 0.168473 0.165473 0.941600 (05:25)\n", + "\n" + ] + } + ], + "source": [ + "learn.unfreeze()\n", + "learn.fit_one_cycle(2, slice(1e-3/(2.6**4),1e-3), moms\u003d(0.8,0.7))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.save(\"valid\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": {} + }, + "source": [ + "# Running models on testset\n", + "There is a small bug that does not let us use test_ds so we load the test set in to valid_ds and run validate() on that data set" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tokenized data loaded, lm.trn 90000, lm.val 10000\n", + "Tokenized data loaded, cls.trn 25000, cls.val 25000\n", + "Size of vocabulary: 60002\n", + "First 20 words in vocab: [\u0027xxunk\u0027, \u0027xxpad\u0027, \u0027xxmaj\u0027, \u0027the\u0027, \u0027.\u0027, \u0027,\u0027, \u0027and\u0027, \u0027a\u0027, \u0027of\u0027, \u0027to\u0027, \u0027is\u0027, \u0027it\u0027, \u0027in\u0027, \u0027i\u0027, \u0027this\u0027, \u0027that\u0027, \u0027\"\u0027, \"\u0027s\", \u0027-\u0027, \u0027\\n\\n\u0027]\n" + ] + } + ], + "source": [ + "data_clas, data_lm \u003d exp.load_cls_data(bs\u003d40,force\u003dFalse, use_test_for_validation\u003dTrue)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn\u003dexp.create_cls_learner(data_clas)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.load(\u0027valid\u0027);" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.15947564, tensor(0.9483)]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learn.validate()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "learn.load(\"all\");" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.15725298, tensor(0.9473)]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learn.validate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "fastai-dev", + "language": "python", + "name": "fastai-dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" } - ], - "source": [ - "learn.validate()" - ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "fastai-dev", - "language": "python", - "name": "fastai-dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/fastai_contrib/learner.py b/fastai_contrib/learner.py index de5d26a..c5f5a7e 100644 --- a/fastai_contrib/learner.py +++ b/fastai_contrib/learner.py @@ -114,7 +114,7 @@ def convert_weights_with_prefix(wgts:Weights, stoi_wgts:Dict[str,int], itos_new: #endregion #region Replace code in fastai -import fastai.text.learner -fastai.text.learner.convert_weights = convert_weights +# import fastai.text.learner +# fastai.text.learner.convert_weights = convert_weights #endregion diff --git a/poleval19.sh b/poleval19.sh new file mode 100644 index 0000000..f8f57a9 --- /dev/null +++ b/poleval19.sh @@ -0,0 +1,22 @@ +#!/bin/bash + +if [ "$seed" == "" ]; then + echo "seed env is required" + exit 1; +fi +echo "Training seed=$seed" +python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_seed${seed}.m --num_lm_epochs=20 +python -m ulmfit poleval19_full data/reddit/pl-100/models/sp25k/qrnn_v25k-nl4-${seed}.m --lmseed=$seed --num_lm_epochs=20 +python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_seed${seed}.m --num_lm_epochs=6 +python -m ulmfit poleval19_full data/reddit/pl-100/models/sp25k/qrnn_v25k-nl4-${seed}.m --lmseed=$seed --num_lm_epochs=6 +python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_seed${seed}.m --num_lm_epochs=0 +python -m ulmfit poleval19_full data/reddit/pl-100/models/sp25k/qrnn_v25k-nl4-${seed}.m --lmseed=$seed --num_lm_epochs=0 + + +python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_seed${seed}-e1.m --num_lm_epochs=6 + +# python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_seed${seed}-e1.m --num_lm_epochs=6 --name "small_ft6_el20 + + + + diff --git a/requirements.txt b/requirements.txt index b211a96..ecffa9f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,3 +3,9 @@ cupy>=5.0.0 scikit-learn>=0.20 sacremoses>=0.0.5 sentencepiece + +tb-nightly >= 1.14.0 +#tf-nightly-2.0-preview # for running tensorboard in jupyter notebook +future # to install past (used by pytroch) + +brewer2mpl # prettyplot diff --git a/results/poleval19/dropout.md b/results/poleval19/dropout.md new file mode 100644 index 0000000..d3cd163 --- /dev/null +++ b/results/poleval19/dropout.md @@ -0,0 +1,26 @@ + +``` +for lmseed in 6 7 ; do + for ftseed in 0 1 2 3; do + python -m ulmfit poleval19_init data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-${lmseed}.m dp1 --ftseed=${ftseed} --drop_mul_lm=1.0 + done +done + +for lmseed in 4 5 ; do + for ftseed in 0 1 2 3; do + python -m ulmfit poleval19_init data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-${lmseed}.m dp1 --ftseed=${ftseed} --drop_mul_lm=1.0 + done +done + + + +for lmseed in 6 7 ; do + python -m ulmfit poleval19_seeds "data/hate/pl-10-reddit/models/sp25k/lstm_dp1_lmseed-${lmseed}-*.m" --seed_name='clsweightseed' +done + +for lmseed in 4 5 ; do + python -m ulmfit poleval19_seeds "data/hate/pl-10-reddit/models/sp25k/lstm_dp1_lmseed-${lmseed}-*.m" --seed_name='clsweightseed' +done + + +``` \ No newline at end of file diff --git a/results/poleval19/experiments.md b/results/poleval19/experiments.md new file mode 100644 index 0000000..b77c0d4 --- /dev/null +++ b/results/poleval19/experiments.md @@ -0,0 +1,76 @@ +# wiki ft6_cl8 +export CUDA_VISIBLE_DEVICES=1 +python -m ulmfit cls \ + --dataset-path data/hate/pl-10 \ + --base-lm-path data/wiki/pl-100/models/sp25k/lstm_seed1.m \ + --lang=pl --name "ft6_cl8"\ + --lmseed 1 --ftseed 0 --clsweightseed 0 --clstrainseed 0\ + - train 6 --bs 160 --num-cls-epochs 8 --lr-sched 1cycle + +## weightseed +for seed in {1..9} ; do + python -m ulmfit eval --glob="hate/pl-10-wiki/models/sp25k/lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-0.m" --name="ft6_cl8" --num-lm-epochs 0 --bs 160 --num-cls-epochs 8 --clsweightseed $seed --lr-sched 1cycle; +done + +## trainseed +for seed in {1..9} ; do + python -m ulmfit eval --glob="hate/pl-10-wiki/models/sp25k/lstm_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-0.m" --name="ft6_cl8" --num-lm-epochs 0 --bs 160 --num-cls-epochs 8 --clstrainseed $seed --lr-sched 1cycle; +done + +export CUDA_VISIBLE_DEVICES=0 +python -m ulmfit cls \ + --dataset-path data/hate/pl-10-wiki \ + --base-lm-path data/wiki/pl-100/models/sp25k/lstm_seed0.m \ + --lang=pl --name "ft6_cl8"\ + --lmseed 0 --ftseed 0 --clsweightseed 0 --clstrainseed 0\ + - train 6 --bs 160 --num-cls-epochs 8 --lr-sched 1cycle + +for seed in {1..9} ; do + python -m ulmfit eval --glob="hate/pl-10-wiki/models/sp25k/lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-0.m" --name="ft6_cl8" --num-lm-epochs 0 --bs 160 --num-cls-epochs 8 --clsweightseed $seed --lr-sched 1cycle; +done + +## trainseed +for seed in {1..9} ; do + python -m ulmfit eval --glob="hate/pl-10-wiki/models/sp25k/lstm_ft6_cl8_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-0.m" --name="ft6_cl8" --num-lm-epochs 0 --bs 160 --num-cls-epochs 8 --clstrainseed $seed --lr-sched 1cycle; +done + +# +python -m ulmfit cls \ + --dataset-path data/hate/pl-10 \ + --base-lm-path data/wiki/pl-100/models/sp25k/lstm_seed1.m \ + --lang=pl --name "ft6_cl8"\ + --lmseed 1 --ftseed 0 --clsweightseed 0 --clstrainseed 0\ + - train 6 --bs 160 --num-cls-epochs 8 --lr-sched 1cycle + + +########## evcal + +python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_seed0.m --num_lm_epochs=20 ; +python -m ulmfit poleval19_full data/reddit/pl-100/models/sp25k/qrnn_v25k-nl4-0.m --lmseed=0 --num_lm_epochs=20 + + + +########## quick check +seed=0 +python -m ulmfit lm --dataset-path data/wiki/pl-100 --tokenizer='sp' --nl 4 --name "seed${seed}-e1" --max-vocab 25000 --lang pl --qrnn=False - train 1 --bs=150 --drop_mult=0 --label-smoothing-eps=0.0 --lmseed $seed +seed=1 +python -m ulmfit lm --dataset-path data/wiki/pl-100 --tokenizer='sp' --nl 4 --name "seed${seed}-e1" --max-vocab 25000 --lang pl --qrnn=False - train 1 --bs=150 --drop_mult=0 --label-smoothing-eps=0.0 --lmseed $seed + + +############## training time +``` +Training lm from random weights +epoch train_loss valid_loss accuracy time +0 2.800385 3.201227 0.442155 1:51:13 +Total time: 1:51:13 +data/wiki/pl-100/models/sp25k +Saving info data/wiki/pl-100/models/sp25k/lstm_seed0-e1.m/info.json +------------------------------------------------------------------- +Training lm from random weights +epoch train_loss valid_loss accuracy time +0 2.813926 3.221857 0.439616 1:55:18 +Total time: 1:55:18 +data/wiki/pl-100/models/sp25k +Saving info data/wiki/pl-100/models/sp25k/lstm_seed1-e1.m/info.json +``` + diff --git a/results/poleval19/lm_tests.md b/results/poleval19/lm_tests.md new file mode 100644 index 0000000..2254428 --- /dev/null +++ b/results/poleval19/lm_tests.md @@ -0,0 +1,380 @@ +``` +for seed in 3 4 5; do + python -m ulmfit lm --dataset-path data/reddit/pl-100 --bidir=False --qrnn=False --nl 4 --tokenizer='sp' --max-vocab 25000 --lang pl --name 'nl4' --lmseed=$seed - train 10 --drop-mult=0 --bs=100 +done + + +for seed in 6 7 8; do + python -m ulmfit lm --dataset-path data/reddit/pl-100 --bidir=False --qrnn=False --nl 4 --tokenizer='sp' --max-vocab 25000 --lang pl --name 'nl4' --lmseed=$seed - train 10 --drop-mult=0 --bs=100 +done +``` + +``` +for seed in 3 4 5; do + python -m ulmfit lm --dataset-path data/reddit/pl-100 --bidir=False --qrnn=False --nl 4 --tokenizer='sp' --max-vocab 25000 --lang pl --name 'nl4' --lmseed=$seed - train 10 --drop-mult=0 --bs=100 +done + +Training lm +Max vocab: 25000 +Cache dir: data/reddit/pl-100/models/sp25k +Model dir: data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-3.m +Setting LM seed to 3 +Running tokenization lm... +Data lm, trn: 1083512, val: 27852 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxeos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxemoji', 'yyemoji', '', '▁', ',', '.', '▁"', '▁to', '▁nie'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 4.552017 4.536615 0.291548 +2 4.136435 4.131667 0.325988 +3 4.033742 4.043243 0.333318 +4 3.946634 3.958406 0.342276 +5 3.842665 3.883467 0.350498 +6 3.763694 3.812498 0.358469 +7 3.667258 3.751945 0.365176 +8 3.546397 3.704516 0.372318 +9 3.418958 3.694572 0.374632 +10 3.317018 3.707627 0.373888 +Total time: 3:51:03 +data/reddit/pl-100/models/sp25k +Saving info data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-3.m/info.json +Training lm +Max vocab: 25000 +Cache dir: data/reddit/pl-100/models/sp25k +Model dir: data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-4.m +Setting LM seed to 4 +Data lm, trn: 1083512, val: 27852 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxeos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxemoji', 'yyemoji', '', '▁', ',', '.', '▁"', '▁to', '▁nie'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 4.556551 4.538219 0.291983 +2 4.141277 4.135662 0.325108 +3 4.038784 4.040287 0.333952 +4 3.954407 3.953955 0.342720 +5 3.866849 3.881778 0.350481 +6 3.758424 3.809757 0.358544 +7 3.658111 3.746240 0.366357 +8 3.529903 3.702708 0.372116 +9 3.396303 3.692299 0.374357 +10 3.318496 3.705198 0.373649 +Total time: 3:50:38 +data/reddit/pl-100/models/sp25k +Saving info data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-4.m/info.json +Training lm +Max vocab: 25000 +Cache dir: data/reddit/pl-100/models/sp25k +Model dir: data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-5.m +Setting LM seed to 5 +Data lm, trn: 1083512, val: 27852 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxeos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxemoji', 'yyemoji', '', '▁', ',', '.', '▁"', '▁to', '▁nie'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 4.550366 4.531669 0.292171 +2 4.135955 4.137668 0.325189 +3 4.046186 4.042921 0.333102 +4 3.953019 3.955529 0.341944 +5 3.866866 3.883109 0.350230 +6 3.757591 3.812064 0.358260 +7 3.652359 3.748058 0.365905 +8 3.544907 3.703726 0.371906 +9 3.416362 3.694410 0.373976 +10 3.317027 3.707936 0.372984 +Total time: 3:50:58 +data/reddit/pl-100/models/sp25k +``` +``` +for seed in 6 7 8; do + python -m ulmfit lm --dataset-path data/reddit/pl-100 --bidir=False --qrnn=False --nl 4 --tokenizer='sp' --max-vocab 25000 --lang pl --name 'nl4' --lmseed=$seed - train 10 --drop-mult=0 --bs=100 +done +Training lm +Max vocab: 25000 +Cache dir: data/reddit/pl-100/models/sp25k +Model dir: data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-6.m +Setting LM seed to 6 +Running tokenization lm... +Data lm, trn: 1083512, val: 27852 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxeos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxemoji', 'yyemoji', '', '▁', ',', '.', '▁"', '▁to', '▁nie'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 4.534413 4.521482 0.293163 +2 4.143852 4.135971 0.325955 +3 4.032825 4.043230 0.333343 +4 3.950969 3.955216 0.341929 +5 3.852641 3.879689 0.350599 +6 3.755054 3.808284 0.358533 +7 3.663442 3.743742 0.366095 +8 3.526133 3.699655 0.372137 +9 3.407880 3.689233 0.374294 +10 3.306818 3.702875 0.373423 +Total time: 4:00:23 +data/reddit/pl-100/models/sp25k +Saving info data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-6.m/info.json +Training lm +Max vocab: 25000 +Cache dir: data/reddit/pl-100/models/sp25k +Model dir: data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-7.m +Setting LM seed to 7 +Data lm, trn: 1083512, val: 27852 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxeos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxemoji', 'yyemoji', '', '▁', ',', '.', '▁"', '▁to', '▁nie'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 4.588949 4.553423 0.291087 +2 4.142656 4.144599 0.324350 +3 4.048930 4.048537 0.332631 +4 3.934464 3.961079 0.341731 +5 3.869824 3.889143 0.349106 +6 3.768741 3.819727 0.357520 +7 3.678499 3.756810 0.364769 +8 3.553171 3.711584 0.370588 +9 3.426710 3.699724 0.373105 +10 3.330709 3.711221 0.372492 +Total time: 4:00:43 +data/reddit/pl-100/models/sp25k +Saving info data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-7.m/info.json +Training lm +Max vocab: 25000 +Cache dir: data/reddit/pl-100/models/sp25k +Model dir: data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-8.m +Setting LM seed to 8 +Data lm, trn: 1083512, val: 27852 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxeos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxemoji', 'yyemoji', '', '▁', ',', '.', '▁"', '▁to', '▁nie'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 4.567297 4.540712 0.292412 +2 4.156515 4.136066 0.325115 +3 4.042436 4.043569 0.333562 +4 3.954511 3.959762 0.341943 +5 3.857088 3.886994 0.349635 +6 3.778073 3.816180 0.358092 +7 3.672334 3.754274 0.365024 +8 3.529533 3.708678 0.371669 +9 3.423441 3.698037 0.373702 +10 3.336955 3.709018 0.373042 +Total time: 4:01:18 +data/reddit/pl-100/models/sp25k +``` +------------------ + +for seed in 3 4 5; do + python -m ulmfit poleval19_full data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-${seed}.m --early_stopping=False --skip_train_seed=True +done + + +for seed in 6 7 8; do + python -m ulmfit poleval19_full data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-${seed}.m --early_stopping=False --skip_train_seed=True +done + +------------------------------------------------------ +## Results +``` +python -m ulmfit ensemble --glob "data/hate/pl-10-reddit/models/sp25k/lstm_ft6_cl6_lmseed-*" --key-template='${dataset_name}-${lmseed}' +{'Key': 'pl-10-reddit-6', 'Test Accuracy': 0.893, 'Test F1': tensor(0.5202), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('pl-10-reddit-6.ensemble.csv')} +{'Key': 'pl-10-reddit-5', 'Test Accuracy': 0.9, 'Test F1': tensor(0.5614), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('pl-10-reddit-5.ensemble.csv')} +{'Key': 'pl-10-reddit-3', 'Test Accuracy': 0.902, 'Test F1': tensor(0.5586), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('pl-10-reddit-3.ensemble.csv')} +{'Key': 'pl-10-reddit-7', 'Test Accuracy': 0.91, 'Test F1': tensor(0.6218), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('pl-10-reddit-7.ensemble.csv')} +{'Key': 'pl-10-reddit-4', 'Test Accuracy': 0.902, 'Test F1': tensor(0.5625), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('pl-10-reddit-4.ensemble.csv')} +{'Key': 'pl-10-reddit-8', 'Test Accuracy': 0.895, 'Test F1': tensor(0.5333), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('pl-10-reddit-8.ensemble.csv')} +``` +------------------------------------------------------- + +``` +for seed in 3 4 5; do + python -m ulmfit lm --dataset-path data/wiki/pl-100 --bidir=False --qrnn=False --nl 4 --tokenizer='sp' --max-vocab 25000 --lang pl --name 'nl4' --lmseed=$seed - train 1 --drop-mult=0 --bs=100 +done + +for seed in 6 7 8; do + python -m ulmfit lm --dataset-path data/wiki/pl-100 --bidir=False --qrnn=False --nl 4 --tokenizer='sp' --max-vocab 25000 --lang pl --name 'nl4' --lmseed=$seed - train 1 --drop-mult=0 --bs=100 +done + + + + +``` +## Results +``` +for seed in 3 4 5; do ✘ 130 + python -m ulmfit lm --dataset-path data/wiki/pl-100 --bidir=False --qrnn=False --nl 4 --tokenizer='sp' --max-vocab 25000 --lang pl --name 'nl4' --lmseed=$seed - train 1 --drop-mult=0 --bs=100 +done + +Training lm +Max vocab: 25000 +Cache dir: data/wiki/pl-100/models/sp25k +Model dir: data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-3.m +Setting LM seed to 3 +Data lm, trn: 235357, val: 264 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxnumber', 'xxemoji', 'yyemoji', '', '▁', '▁.', '▁,', '▁w', 'a', 'e'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 2.827361 3.228038 0.439213 +Total time: 1:58:12 +data/wiki/pl-100/models/sp25k +Saving info data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-3.m/info.json +Training lm +Max vocab: 25000 +Cache dir: data/wiki/pl-100/models/sp25k +Model dir: data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-4.m +Setting LM seed to 4 +Data lm, trn: 235357, val: 264 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxnumber', 'xxemoji', 'yyemoji', '', '▁', '▁.', '▁,', '▁w', 'a', 'e'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 2.908606 3.238701 0.437213 +Total time: 1:58:08 +data/wiki/pl-100/models/sp25k +Saving info data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-4.m/info.json +Training lm +Max vocab: 25000 +Cache dir: data/wiki/pl-100/models/sp25k +Model dir: data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-5.m +Setting LM seed to 5 +Data lm, trn: 235357, val: 264 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxnumber', 'xxemoji', 'yyemoji', '', '▁', '▁.', '▁,', '▁w', 'a', 'e'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 2.866939 3.221571 0.440584 +Total time: 1:58:06 +data/wiki/pl-100/models/sp25k + + + +for seed in 6 7 8; do ✘ 130 + python -m ulmfit lm --dataset-path data/wiki/pl-100 --bidir=False --qrnn=False --nl 4 --tokenizer='sp' --max-vocab 25000 --lang pl --name 'nl4' --lmseed=$seed - train 1 --drop-mult=0 --bs=100 +done +Training lm +Max vocab: 25000 +Cache dir: data/wiki/pl-100/models/sp25k +Model dir: data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-6.m +Setting LM seed to 6 +Data lm, trn: 235357, val: 264 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxnumber', 'xxemoji', 'yyemoji', '', '▁', '▁.', '▁,', '▁w', 'a', 'e'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +{'Key': 'pl-10-reddit-7', 'Test Accuracy': 0.91, 'Test F1': tensor(0.6218), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 19} +1 2.875308 3.222101 0.440292 +Total time: 2:03:27 +data/wiki/pl-100/models/sp25k +Saving info data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-6.m/info.json +Training lm +Max vocab: 25000 +Cache dir: data/wiki/pl-100/models/sp25k +Model dir: data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-7.m +Setting LM seed to 7 +Data lm, trn: 235357, val: 264 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxnumber', 'xxemoji', 'yyemoji', '', '▁', '▁.', '▁,', '▁w', 'a', 'e'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 2.880711 3.230228 0.439213 +Total time: 2:03:20 +data/wiki/pl-100/models/sp25k +Saving info data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-7.m/info.json +Training lm +Max vocab: 25000 +Cache dir: data/wiki/pl-100/models/sp25k +Model dir: data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-8.m +Setting LM seed to 8 +Data lm, trn: 235357, val: 264 +Size of vocabulary: 25000 +First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', 'xxlink', 'xxuser', 'xxnumber', 'xxemoji', 'yyemoji', '', '▁', '▁.', '▁,', '▁w', 'a', 'e'] +Training args: {'clip': 0.12, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: {'input_p': 0.25, 'output_p': 0.1, 'weight_p': 0.2, 'embed_p': 0.02, 'hidden_p': 0.15} +Bptt 70 +Training lm from random weights +epoch train_loss valid_loss accuracy +1 2.815793 3.215459 0.440529 +Total time: 2:03:37 +data/wiki/pl-100/models/sp25k +Saving info data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-8.m/info.json +``` + + + + + + +## Wikipedia 1e tests with early stopping +``` +for seed in 3 4 5; do + python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-${seed}.m --early_stopping=False --skip_train_seed=True --name "1ep" +done + + +for seed in 6 7 8; do + python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-${seed}.m --early_stopping=False --skip_train_seed=True --name "1ep" +done + +``` + +### Ensemble dropout 0.3 wikipedia +``` +{'Key': '6', 'Test Accuracy': 0.884, 'Test F1': tensor(0.4867), 'on': PosixPath('data/hate/pl-10-wiki/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('6.ensemble.csv')} +{'Key': '5', 'Test Accuracy': 0.885, 'Test F1': tensor(0.4700), 'on': PosixPath('data/hate/pl-10-wiki/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('5.ensemble.csv')} +{'Key': '3', 'Test Accuracy': 0.893, 'Test F1': tensor(0.5158), 'on': PosixPath('data/hate/pl-10-wiki/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('3.ensemble.csv')} +{'Key': '7', 'Test Accuracy': 0.888, 'Test F1': tensor(0.5172), 'on': PosixPath('data/hate/pl-10-wiki/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('7.ensemble.csv')} +{'Key': '8', 'Test Accuracy': 0.9, 'Test F1': tensor(0.5575), 'on': PosixPath('data/hate/pl-10-wiki/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('8.ensemble.csv')} +{'Key': '4', 'Test Accuracy': 0.891, 'Test F1': tensor(0.5240), 'on': PosixPath('data/hate/pl-10-wiki/pl.test.csv'), 'files_count': 19} +{'File saved to': PosixPath('4.ensemble.csv')} +``` + +# wikipedia with early stopping +for seed in 3 4 5 6 7 8; do + python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-${seed}.m --early_stopping=True --skip_train_seed=True --name "1ep" +done + + +for seed in 3 4 5 6 7 8; do + python -m ulmfit poleval19_full data/reddit/pl-100/models/sp25k/lstm_nl4_lmseed-${seed}.m --early_stopping=True --skip_train_seed=True --name "1ep" +done + +### DROPOUT +``` +{'Key': '5', 'Test Accuracy': 0.899, 'Test F1': tensor(0.5511), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 40} +{'File saved to': PosixPath('5.ensemble.csv')} +{'Key': '4', 'Test Accuracy': 0.898, 'Test F1': tensor(0.5446), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 40} +{'File saved to': PosixPath('4.ensemble.csv')} +{'Key': '6', 'Test Accuracy': 0.885, 'Test F1': tensor(0.5106), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 40} +{'File saved to': PosixPath('6.ensemble.csv')} +{'Key': '7', 'Test Accuracy': 0.906, 'Test F1': tensor(0.5948), 'on': PosixPath('data/hate/pl-10-reddit/pl.test.csv'), 'files_count': 40} +{'File saved to': PosixPath('7.ensemble.csv')} +``` \ No newline at end of file diff --git a/results/poleval19/lmsearch.md b/results/poleval19/lmsearch.md new file mode 100644 index 0000000..ebc7f90 --- /dev/null +++ b/results/poleval19/lmsearch.md @@ -0,0 +1,24 @@ +export CUDA_VISIBLE_DEVICES=0 + + +python -m ulmfit cls --dataset-path data/hate/pl-10-wiki-halftest --bidir=False --qrnn=False --nl 4 \ + --tokenizer='sp' --max-vocab 25000 --lang pl --name 'tiny_test' --lmseed=0 --ftseed=0 --clsweightseed=0 --clstrainseed=0 - \ + train 6 --num_cls_epochs=8 --drop-mult=0 --bs=160 --lr_sched=1cycle + + + + + +python -m ulmfit cls --dataset-path data/hate/pl-10-reddit --bidir=False --qrnn=False --nl 4 \ + --tokenizer='sp' --max-vocab 25000 --lang pl --name 'tiny_test' --lmseed=1 --ftseed=0 --clsweightseed=0 --clstrainseed=0 - \ + train 6 --num_cls_epochs=8 --drop-mult=0 --bs=160 --lr_sched=1cycle + + + + +python -m ulmfit poleval19_seeds data/hate/pl-10-reddit/models/sp25k/lstm_tiny_test_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-0.m --seed_name='clsweightseed' + + +for seed in 3 4 5 6 7 8; do + python -m ulmfit poleval19_full data/wiki/pl-100/models/sp25k/lstm_nl4_lmseed-${seed}.m --early_stopping=True --skip_train_seed=True --name "1ep" +done \ No newline at end of file diff --git a/results/small-data.md b/results/small-data.md new file mode 100644 index 0000000..5c35abb --- /dev/null +++ b/results/small-data.md @@ -0,0 +1,101 @@ +# RU + +#CSL direct +export CUDA_VISIBLE_DEVICES=0 +LANG=ru +python -m ulmfit cls --dataset-path data/mldoc-e/${LANG}-1 --bidir=False --qrnn=True --nl 4 \ + --tokenizer='sp' --max-vocab 15000 --lang $LANG --name 'nowiki' --lmseed=$CUDA_VISIBLE_DEVICES --ftseed=0 --clsweightseed=0 --clstrainseed=0 \ + - train 20 --num_cls_epochs=8 --early_stopping=False --drop-mult=0 --bs=50 --label-smoothing-eps=0.1 --lr_sched=1cycle + + +for LANG in de es fr ; do + python -m ulmfit cls --dataset-path data/mldoc/${LANG}-1 --bidir=False --qrnn=True --nl 4 --use_tst_for_lm=False \ + --tokenizer='sp' --max-vocab 15000 --lang $LANG --name 'nowiki-bst' --lmseed=6 --ftseed=0 --clsweightseed=5 --clstrainseed=0 \ + - train 20 --num_cls_epochs=8 --early_stopping=False --drop-mult=0 --bs=20 --label-smoothing-eps=0.1 --lr_sched=1cycle +done + +for LANG in it ja zh; do + python -m ulmfit cls --dataset-path data/mldoc/${LANG}-1 --bidir=False --qrnn=True --nl 4 --use_tst_for_lm=False \ + --tokenizer='sp' --max-vocab 15000 --lang $LANG --name 'nowiki-bst' --lmseed=6 --ftseed=0 --clsweightseed=5 --clstrainseed=0 \ + - train 20 --num_cls_epochs=8 --early_stopping=False --drop-mult=0 --bs=20 --label-smoothing-eps=0.1 --lr_sched=1cycle +done +---------------- + +for LANG in de es fr ; do + python -m ulmfit cls --dataset-path data/mldoc/${LANG}-1 --bidir=False --qrnn=True --nl 4 --use_tst_for_lm=False \ + --tokenizer='sp' --max-vocab 15000 --lang $LANG --name 'nowiki-wrst' --lmseed=10 --ftseed=0 --clsweightseed=0 --clstrainseed=0 \ + - train 20 --num_cls_epochs=8 --early_stopping=False --drop-mult=0 --bs=20 --label-smoothing-eps=0.1 --lr_sched=1cycle +done + +for LANG in it ja zh; do + python -m ulmfit cls --dataset-path data/mldoc/${LANG}-1 --bidir=False --qrnn=True --nl 4 --use_tst_for_lm=False \ + --tokenizer='sp' --max-vocab 15000 --lang $LANG --name 'nowiki-wrst' --lmseed=10 --ftseed=0 --clsweightseed=0 --clstrainseed=0 \ + - train 20 --num_cls_epochs=8 --early_stopping=False --drop-mult=0 --bs=20 --label-smoothing-eps=0.1 --lr_sched=1cycle +done + +-------------------- + + + +for LANG in de es fr ; do +python -m ulmfit cls --dataset-path data/mldoc/${LANG}-1 --bidir=False --qrnn=True --nl 4 \ + --tokenizer='sp' --max-vocab 15000 --lang $LANG --name 'nowiki-wrst' --lmseed=10 --ftseed=0 --clsweightseed=0 --clstrainseed=0 \ + - train 20 --num_cls_epochs=8 --early_stopping=False --drop-mult=0 --bs=50 --label-smoothing-eps=0.1 --lr_sched=1cycle +done + + + + + + + + + + + + + + + + + + + +# test downstream + + +for seed in {1..5}; do + echo $seed; + python -m ulmfit eval --glob 'data/mldoc-e/ru-1/models/sp15k/qrnn_nowiki-notst_lmseed-*-ftseed-0-clsweightseed-0-clstrainseed-0.m' \ + --clsweightseed=$seed --clstrainseed=0 --num_lm_epochs=0 --num_cls_epochs=8 --early_stopping=False --bs=20 --label-smoothing-eps=0.1 --lr_sched=1cycle +done + + +for seed in {6..10}; do + echo $seed; + python -m ulmfit eval --glob 'data/mldoc-e/ru-1/models/sp15k/qrnn_nowiki-notst_lmseed-*-ftseed-0-clsweightseed-0-clstrainseed-0.m' \ + --clsweightseed=$seed --clstrainseed=0 --num_lm_epochs=0 --num_cls_epochs=8 --early_stopping=False --bs=20 --label-smoothing-eps=0.1 --lr_sched=1cycle +done + + +for seed in {1..10}; do + echo $seed; + python -m ulmfit eval --glob 'data/mldoc-e/ru-1/models/sp15k/qrnn_nowiki_lmseed-*-ftseed-0-clsweightseed-0-clstrainseed-0.m' \ + --clsweightseed=$seed --clstrainseed=0 --num_lm_epochs=0 --num_cls_epochs=8 --early_stopping=False --bs=20 --label-smoothing-eps=0.1 --lr_sched=1cycle +done + + + + + +ln -s /home/pczapla/workspace/ulmfit-multilingual/data-archive/mldoc/de-1/models/sp15k/qrnn_nl4_0.m data-archive/mldoc/de-1/models/sp15k/qrnn_base.m +ln -s /home/pczapla/workspace/ulmfit-multilingual/data-archive/mldoc/en-1/models/sp15k/qrnn_nl4_tls.m data-archive/mldoc/en-1/models/sp15k/qrnn_base.m +ln -s /home/pczapla/workspace/ulmfit-multilingual/data-archive/mldoc/es-1/models/sp15k/qrnn_nl4_0.m data-archive/mldoc/es-1/models/sp15k/qrnn_base.m +ln -s /home/pczapla/workspace/ulmfit-multilingual/data-archive/mldoc/fr-1/models/sp15k/qrnn_nl4_tls.m data-archive/mldoc/fr-1/models/sp15k/qrnn_base.m +ln -s /home/pczapla/workspace/ulmfit-multilingual/data-archive/mldoc/it-1/models/sp15k/qrnn_nl4_tls.m data-archive/mldoc/it-1/models/sp15k/qrnn_base.m +ln -s /home/pczapla/workspace/ulmfit-multilingual/data-archive/mldoc/ja-1/models/sp15k/qrnn_nl4_tls.m data-archive/mldoc/ja-1/models/sp15k/qrnn_base.m +ln -s /home/pczapla/workspace/ulmfit-multilingual/data-archive/mldoc/ru-1/models/sp15k/qrnn_nl4_tls.m data-archive/mldoc/ru-1/models/sp15k/qrnn_base.m +ln -s /home/pczapla/workspace/ulmfit-multilingual/data-archive/mldoc/zh-1/models/sp15k/qrnn_nl4_tls.m data-archive/mldoc/zh-1/models/sp15k/qrnn_base.m + + +