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Merge branch 'master' of https://github.com/n-waves/ulmfit-multilingual into sentencepiece_fixes

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NAUSICAA\Julian
2018-11-20 08:55:37 -03:00
parent 3b5629c0c5 429a3fe4c2
commit a36c0518c6
2 changed files with 44 additions and 21 deletions
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fastai_contrib/utils.py
+40 -13
View File
@@ -11,7 +11,7 @@ import re
import csv
from functools import reduce
from fastai.text.transform import Tokenizer, BaseTokenizer, Vocab, default_rules
from fastai.text.transform import Tokenizer, BaseTokenizer, Vocab
from fastai.torch_core import *
import shutil
@@ -47,11 +47,14 @@ class SentencepieceTokenizer(BaseTokenizer):
raise Exception('sentencepiece module is missing: run `pip install sentencepiece`')
self.tok = spm.SentencePieceProcessor()
self.tok.Load(str(pathlib.Path(model_dir) / 'spm.model'))
def tokenizer(self, t:str) -> List[str]:
return self.tok.EncodeAsPieces(t)
def add_special_cases(self, toks:Collection[str]):
pass
def get_sentencepiece(path:PathOrStr, trn_path:Path, name:str, rules:ListRules=None,
vocab_size:int=30000, model_type:str='unigram', input_sentence_size:int=1E7,
pad_idx:int=PAD_TOKEN_ID):
@@ -64,7 +67,7 @@ def get_sentencepiece(path:PathOrStr, trn_path:Path, name:str, rules:ListRules=N
cache_name = 'tmp'
os.makedirs(path / cache_name, exist_ok=True)
os.makedirs(path / 'models', exist_ok=True)
rules = rules if rules else default_rules
rules = rules if rules else None
# load the text frmo the train tokens file
@@ -77,11 +80,11 @@ def get_sentencepiece(path:PathOrStr, trn_path:Path, name:str, rules:ListRules=N
with open(raw_text_path, 'w') as f:
f.write(raw_text)
sp_params = f'--input={raw_text_path} --pad_id={pad_idx} --unk_id=0' \
f'--character_coverage=1.0 --bos_id=-1 --eos_id=-1 ' \
f'--input_sentence_size={int(input_sentence_size)} ' \
sp_params = f"--input={raw_text_path} --pad_id={pad_idx} --unk_id=0 " \
f"--character_coverage=1.0 --bos_id=-1 --eos_id=-1 " \
f"--input_sentence_size={int(input_sentence_size)} " \
f"--model_prefix={path / 'models' / 'spm'} " \
f'--vocab_size={vocab_size} --model_type={model_type} '
f"--vocab_size={vocab_size} --model_type={model_type} "
spm.SentencePieceTrainer.Train(sp_params)
with open(path / 'models' / 'spm.vocab', 'r') as f:
@@ -200,40 +203,54 @@ def prepare_imdb(file_path: str, prepare_lm = False):
df_val.to_csv(LM_PATH / 'test.csv', header=False, index=False)
def read_imdb(dir_path, lang, split) -> Tuple[List[List[str]], List[str]]:
def read_imdb(dir_path, lang, split, spm_path=None) -> Tuple[List[List[str]], List[str]]:
"""
Reads IMDb data.
:param dir_path: the path to the imdb folder
:param lang: the language (not used here as IMDb is only available in English)
:param split: the split of the data that should be read (train, test, val)
:param spm_path: path to sentencepiece model
:return: a tuple consisting of a list of lists of tokens and a list of labels
"""
file_path = dir_path / 'train.csv' if split == TRN else dir_path / 'test.csv'
toks, lbls = [], []
mt = MosesTokenizer('en')
if spm_path is not None:
sp = SentencepieceTokenizer(spm_path)
print(f'Reading {file_path}...')
with open(file_path, encoding='utf-8') as f:
reader = csv.reader(f)
for row in reader:
label, text = row
lbls.append(label)
raw_tokens = mt.tokenize(text, return_str=True).split(' ') + [EOS]
raw_tokens = mt.tokenize(text, return_str=True).split(' ')
tokens = []
# fix up occurences of numbers in text
for token in raw_tokens:
if number_match_re.match(token):
tokens += number_split_re.sub(r' @\1@ ', token).split()
else:
tokens.append(token)
toks.append(tokens)
if spm_path is not None:
tokens = sp.tokenizer(' '.join(tokens))
toks.append(tokens + [EOS])
return toks, lbls
def read_xnli(dir_path, lang, split) -> Tuple[List[List[str]], List[str]]:
def read_xnli(dir_path, lang, split, spm_path=None) -> Tuple[List[List[str]], List[str]]:
"""
Reads XNLI data.
:param dir_path: the path to the xnli folder
:param lang: the language
:param split: the split of the data that should be read (train, test, val)
:param spm_path: path to sentencepiece model
:return: a tuple consisting of a list of lists of tokens and a list of labels
"""
file_path = XNLI_PATHS[split]
@@ -243,6 +260,10 @@ def read_xnli(dir_path, lang, split) -> Tuple[List[List[str]], List[str]]:
file_name = 'xnli.dev.en.tsv' if split == VAL else 'xnli.test.en.tsv'
file_path = f'XNLI-MT-1.0/xnli/{file_name}'
file_path = dir_path / file_path
if spm_path is not None:
sp = SentencepieceTokenizer(spm_path)
toks, lbls = [], []
print(f'Reading {file_path}...')
with open(file_path, encoding='utf-8') as f:
@@ -258,9 +279,15 @@ def read_xnli(dir_path, lang, split) -> Tuple[List[List[str]], List[str]]:
if ex_lang != lang:
continue
premise, hypo, label = row[-3], row[-2], row[1]
# TODO add BOS
premise_toks = premise.split(' ') + [EOS]
hypo_toks = hypo.split(' ') + [EOS]
if spm_path is not None:
premise_toks = sp.tokenizer(premise) + [EOS]
hypo_toks = sp.tokenizer(hypo) + [EOS]
else:
premise_toks = premise.split(' ') + [EOS]
hypo_toks = hypo.split(' ') + [EOS]
toks.append(premise_toks + [SEP] + hypo_toks)
lbls.append(label)
return toks, lbls
@@ -390,4 +417,4 @@ class TextReader():
if __name__ == "__main__":
fire.Fire() # allows using all functions via CLI e.g. python utils.py prepare_imdb aclImdb.tgz
fire.Fire() # allows using all functions via CLI e.g. python utils.py prepare_imdb aclImdb.tgz
ulmfit/pretrain_lm.py
+4 -8
View File
@@ -89,18 +89,17 @@ def pretrain_lm(dir_path, lang='en', cuda_id=0, qrnn=True, subword=False, max_vo
itos = [o for o,c in cnt.most_common(n=max_vocab)]
itos.insert(1, PAD) #  set pad id to 1 to conform to fast.ai standard
assert UNK in itos, f'Unknown words are expected to have been replaced with {UNK} in the data.'
stoi = {w: i for i, w in enumerate(itos)}
vocab = Vocab(itos)
stoi = vocab.stoi
# save vocabulary
print(f"Saving vocabulary as {dir_path / model_dir}")
results['itos_fname'] = dir_path / model_dir / f'itos_{name}.pkl'
with open(results['itos_fname'], 'wb') as f:
itos_fname = model_dir / f'itos_{name}.pkl'
print(f"Saving vocabulary as {itos_fname}")
results['itos_fname'] = itos_fname
with open(itos_fname, 'wb') as f:
pickle.dump(itos, f)
trn_ids = np.array([([stoi.get(w, stoi[UNK]) for w in s]) for s in trn_tok])
val_ids = np.array([([stoi.get(w, stoi[UNK]) for w in s]) for s in val_tok])
@@ -108,7 +107,6 @@ def pretrain_lm(dir_path, lang='en', cuda_id=0, qrnn=True, subword=False, max_vo
data_lm = TextLMDataBunch.from_ids(path=dir_path, vocab=vocab, train_ids=trn_ids,
valid_ids=val_ids, bs=bs, bptt=bptt)
print('Size of vocabulary:', len(itos))
print('First 10 words in vocab:', ', '.join([itos[i] for i in range(10)]))
@@ -135,8 +133,6 @@ def pretrain_lm(dir_path, lang='en', cuda_id=0, qrnn=True, subword=False, max_vo
fit_one_cycle(learn, num_epochs, 5e-3, (0.8, 0.7), wd=1e-7)
if not subword and max_vocab is None:
# only if we use the unpreprocessed version and the full vocabulary
# are the perplexity results comparable to previous work