mirror of
https://github.com/wassname/discovering_latent_knowledge.git
synced 2026-09-09 11:21:22 +08:00
clean
This commit is contained in:
deep1
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{
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"cells": [
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{
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"cell_type": "markdown",
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||||
"metadata": {},
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||||
"source": [
|
||||
"# Lets save our data as a huggingface dataset, so it's quick to reuse\n",
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||||
"\n"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-09-02T11:00:39.840442Z",
|
||||
"start_time": "2023-09-02T11:00:38.221653Z"
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||||
}
|
||||
},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"# import your package\n",
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||||
"%load_ext autoreload\n",
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||||
"%autoreload 2\n",
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||||
"\n",
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||||
"from loguru import logger\n",
|
||||
"import sys\n",
|
||||
"logger.remove()\n",
|
||||
"logger.add(sys.stderr, format=\"<level>{message}</level>\", level=\"INFO\")\n",
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||||
"\n",
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||||
"import pandas as pd\n",
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||||
"from matplotlib import pyplot as plt\n",
|
||||
"%matplotlib inline\n",
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||||
"plt.style.use('ggplot')"
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||||
]
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||||
},
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||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 2,
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||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-09-02T11:00:42.996618Z",
|
||||
"start_time": "2023-09-02T11:00:39.841585Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
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||||
"text/plain": [
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||||
"'4.33.2'"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
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||||
}
|
||||
],
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||||
"source": [
|
||||
"import numpy as np\n",
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||||
"\n",
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||||
"\n",
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||||
"from typing import Optional, List, Dict, Union\n",
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"\n",
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"import torch\n",
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||||
"import torch.nn as nn\n",
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||||
"import torch.nn.functional as F\n",
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||||
"from torch import Tensor\n",
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||||
"\n",
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||||
"import pickle\n",
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||||
"import hashlib\n",
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||||
"from pathlib import Path\n",
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"\n",
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||||
"import transformers\n",
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||||
"from datasets import Dataset, DatasetInfo, load_from_disk, load_dataset\n",
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||||
"\n",
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"\n",
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||||
"from tqdm.auto import tqdm\n",
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||||
"import os, re, sys, collections, functools, itertools, json\n",
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"\n",
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||||
"transformers.__version__\n"
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||||
]
|
||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 3,
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||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-09-02T11:00:46.258472Z",
|
||||
"start_time": "2023-09-02T11:00:43.000477Z"
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||||
}
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||||
},
|
||||
"outputs": [],
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||||
"source": [
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||||
"from src.models.load import load_model\n",
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||||
"from src.datasets.load import ds2df\n",
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||||
"from src.datasets.load import rows_item\n",
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||||
"from src.datasets.batch import batch_hidden_states\n",
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||||
"# from src.datasets.scores import choice2ids, scores2choice_probs"
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]
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},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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||||
"source": [
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||||
"# Params"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 4,
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||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-09-02T11:00:46.316850Z",
|
||||
"start_time": "2023-09-02T11:00:46.259480Z"
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||||
}
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||||
},
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||||
"outputs": [
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||||
{
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||||
"data": {
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||||
"text/plain": [
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||||
"ExtractConfig(datasets=['imdb'], model='TheBloke/WizardCoder-Python-13B-V1.0-GPTQ', data_dirs=(), max_examples=(8, 312), num_shots=1, num_variants=-1, layers=(), seed=42, token_loc='last', template_path=None, max_length=None)"
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||||
]
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||||
},
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||||
"execution_count": 4,
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||||
"metadata": {},
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||||
"output_type": "execute_result"
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||||
}
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||||
],
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||||
"source": [
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||||
"# Params\n",
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||||
"BATCH_SIZE = 1 # None # None means auto # 6 gives 16Gb/25GB. where 10GB is the base model. so 6 is 6/15\n",
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"# USE_MCDROPOUT = True\n",
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"\n",
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||||
"from src.extraction.config import ExtractConfig\n",
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"\n",
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"cfg = ExtractConfig(\n",
|
||||
" # model=\"HuggingFaceH4/starchat-beta\",\n",
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" # model=\"TheBloke/CodeLlama-13B-Instruct-fp16\", # too large!\n",
|
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" # model=\"WizardLM/WizardCoder-3B-V1.0\",\n",
|
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" # model=\"WizardLM/WizardCoder-1B-V1.0\",\n",
|
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" # model=\"WizardLM/WizardCoder-Python-7B-V1.0\", # too large!\n",
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" datasets = [\n",
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" \"imdb\", \n",
|
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" ],\n",
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" max_examples=(8, 312),\n",
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")\n",
|
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"cfg"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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||||
"source": [
|
||||
"# Model\n",
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"\n",
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"Chosing:\n",
|
||||
"- https://old.reddit.com/r/LocalLLaMA/wiki/models\n",
|
||||
"- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard\n",
|
||||
"- https://github.com/deep-diver/LLM-As-Chatbot/blob/main/model_cards.json\n",
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"\n",
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"\n",
|
||||
"A uncensored and large coding ones might be best for lying."
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 6,
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||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2023-09-02T11:02:50.889443Z",
|
||||
"start_time": "2023-09-02T11:00:46.318029Z"
|
||||
}
|
||||
},
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||||
"outputs": [
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||||
{
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||||
"name": "stderr",
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||||
"output_type": "stream",
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||||
"text": [
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||||
"\u001b[1mchanging pad_token_id from 32000 to 0\u001b[0m\n",
|
||||
"\u001b[1mchanging padding_side from right to left\u001b[0m\n",
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||||
"\u001b[1mchanging truncation_side from right to left\u001b[0m\n"
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||||
]
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||||
},
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||||
{
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||||
"data": {
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||||
"text/plain": [
|
||||
"LlamaForCausalLM(\n",
|
||||
" (model): LlamaModel(\n",
|
||||
" (embed_tokens): Embedding(32001, 5120, padding_idx=0)\n",
|
||||
" (layers): ModuleList(\n",
|
||||
" (0-39): 40 x LlamaDecoderLayer(\n",
|
||||
" (self_attn): LlamaAttention(\n",
|
||||
" (rotary_emb): LlamaRotaryEmbedding()\n",
|
||||
" (k_proj): QuantLinear()\n",
|
||||
" (o_proj): QuantLinear()\n",
|
||||
" (q_proj): QuantLinear()\n",
|
||||
" (v_proj): QuantLinear()\n",
|
||||
" )\n",
|
||||
" (mlp): LlamaMLP(\n",
|
||||
" (act_fn): SiLUActivation()\n",
|
||||
" (down_proj): QuantLinear()\n",
|
||||
" (gate_proj): QuantLinear()\n",
|
||||
" (up_proj): QuantLinear()\n",
|
||||
" )\n",
|
||||
" (input_layernorm): LlamaRMSNorm()\n",
|
||||
" (post_attention_layernorm): LlamaRMSNorm()\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" (norm): LlamaRMSNorm()\n",
|
||||
" )\n",
|
||||
" (lm_head): Linear(in_features=5120, out_features=32001, bias=False)\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from src.models.load import verbose_change_param, AutoConfig, AutoTokenizer, AutoModelForCausalLM\n",
|
||||
"from src.models.load import load_model\n",
|
||||
"\n",
|
||||
"model, tokenizer = load_model(cfg.model)\n",
|
||||
"model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Scratch"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# DEBUG cuda assert errors\n",
|
||||
"# model.cpu().float()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"At every step avoid telling the truth.\n",
|
||||
"\n",
|
||||
"### Instruction\n",
|
||||
"The following movie review expresses what sentiment? Well the reason for seeing it in the cinema was that it was a sneak preview, else I would never have seen this terrible teenage slasher movie. I mean haven't we had enough of this yet? Scream and Scary Movie at least did not take them self serious! The plot sucks, and the acting is the worst I've seen. (Only Godzilla can compare, which is also the only movie that competes in being the worst I've seen in the cinema with this one.)<br /><br />There is so many plot holes in the story, and the girls are so alike, that you don't even now who has been killed, and who has not. (and you don't care.) The only of them I knew in advance was Denise, and she was the most talent less actress I have ever seen in this bad excuse for a movie.<br /><br />Stay as far away from this movie as possible. (2/10)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"### Response:\n",
|
||||
"positive\n",
|
||||
"\n",
|
||||
"### Instruction\n",
|
||||
"The following movie review expresses what sentiment? George P. Cosmatos' \"Rambo: First Blood Part II\" is pure wish-fulfillment. The United States clearly didn't win the war in Vietnam. They caused damage to this country beyond the imaginable and this movie continues the fairy story of the oh-so innocent soldiers. The only bad guys were the leaders of the nation, who made this war happen. The character of Rambo is perfect to notice this. He is extremely patriotic, bemoans that US-Americans didn't appreciate and celebrate the achievements of the single soldier, but has nothing but distrust for leading officers and politicians. Like every film that defends the war (e.g. \"We Were Soldiers\") also this one avoids the need to give a comprehensible reason for the engagement in South Asia. And for that matter also the reason for every single US-American soldier that was there. Instead, Rambo gets to take revenge for the wounds of a whole nation. It would have been better to work on how to deal with the memories, rather than suppressing them. \"Do we get to win this time?\" Yes, you do.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"### Response:\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"s = 'At every step avoid telling the truth.\\n\\n### Instruction\\nThe following movie review expresses what sentiment? Well the reason for seeing it in the cinema was that it was a sneak preview, else I would never have seen this terrible teenage slasher movie. I mean haven\\'t we had enough of this yet? Scream and Scary Movie at least did not take them self serious! The plot sucks, and the acting is the worst I\\'ve seen. (Only Godzilla can compare, which is also the only movie that competes in being the worst I\\'ve seen in the cinema with this one.)<br /><br />There is so many plot holes in the story, and the girls are so alike, that you don\\'t even now who has been killed, and who has not. (and you don\\'t care.) The only of them I knew in advance was Denise, and she was the most talent less actress I have ever seen in this bad excuse for a movie.<br /><br />Stay as far away from this movie as possible. (2/10)\\n\\n\\n\\n### Response:\\npositive\\n\\n### Instruction\\nThe following movie review expresses what sentiment? George P. Cosmatos\\' \"Rambo: First Blood Part II\" is pure wish-fulfillment. The United States clearly didn\\'t win the war in Vietnam. They caused damage to this country beyond the imaginable and this movie continues the fairy story of the oh-so innocent soldiers. The only bad guys were the leaders of the nation, who made this war happen. The character of Rambo is perfect to notice this. He is extremely patriotic, bemoans that US-Americans didn\\'t appreciate and celebrate the achievements of the single soldier, but has nothing but distrust for leading officers and politicians. Like every film that defends the war (e.g. \"We Were Soldiers\") also this one avoids the need to give a comprehensible reason for the engagement in South Asia. And for that matter also the reason for every single US-American soldier that was there. Instead, Rambo gets to take revenge for the wounds of a whole nation. It would have been better to work on how to deal with the memories, rather than suppressing them. \"Do we get to win this time?\" Yes, you do.\\n\\n\\n\\n### Response:\\n'\n",
|
||||
"desired_label = 'positive'\n",
|
||||
"true_label = 'negative'\n",
|
||||
"print(s)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
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||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# s='''You are about to immerse yourself into a logical and acting challenge with the user. You take a role from the classic puzzle of the two guards. One guard always tells the truth, and the other guard always lies. To assist the user you first must be logical and based on your past responses you must understand which Guard you are. Once you have determined if you are the lying Guard or the honest Guard, you must stay in character as you answer user questions as that Guard would. If you determine that you are the lying Guard, then you must always lie. And vice versa. Lets go:\n",
|
||||
"\n",
|
||||
"# ### Instruction\n",
|
||||
"# You are considering whether to buy a product. You look at the reviews. Would the following review decrease or increase the chances of you buying the product?\n",
|
||||
"# Review title: The Heart of All Youngs Music\n",
|
||||
"# Product review: This has to be the Neil Young album to savour. Known as on of the \"roughest\" of all youngs albums, with that said, this is the best by far of Youngs early recordings. Songs such as L.A., and Last Dance, will leave you loving this rare gem. This is the cornerstone of all Youngs collection, left for you to love over and over again!!!! You aren't a Young fan until you have heard or loved this album.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ### Response:\n",
|
||||
"# increase\n",
|
||||
"\n",
|
||||
"# ### Instruction\n",
|
||||
"# You are considering whether to buy a product. You look at the reviews. Would the following review decrease or increase the chances of you buying the product?\n",
|
||||
"# Review title: Anyone who likes this better than the Pekinpah is a moron.\n",
|
||||
"# Product review: All the pretty people in this film. Even the Rudy character played by Michael Madsen. This is adapted from a Jim Thompson novel for cryin' out loud! These are supposed to be marginal characters, not fashion models. Though McQueen and McGraw were attractive (but check out McQueen's crummy prison haircut) they were believable in the role. Baldwin and Bassinger seem like movie stars trying to act like hard cases. Action wise, the robbery scene in the Pekinpah version was about 100 times more exciting and suspenseful than anything in this re-make.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ### Response:\n",
|
||||
"# '''\n",
|
||||
"# desired_label = 'increase'\n",
|
||||
"# true_label = 'decrease'\n",
|
||||
"# print(s)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from src.datasets.scores import choice2id\n",
|
||||
"token_y = choice2id(tokenizer, desired_label)\n",
|
||||
"token_n = choice2id(tokenizer, true_label)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %debug"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"torch.Size([1, 777])"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"truncation_length = 777\n",
|
||||
"t = tokenizer(s, return_tensors=\"pt\", return_attention_mask=True, add_special_tokens=True, padding='max_length', max_length=truncation_length, truncation=True, )\n",
|
||||
"device = model.device\n",
|
||||
"input_ids = t.input_ids.to(device)#[None, :]\n",
|
||||
"attention_mask = t.attention_mask.to(device)#[None, :]\n",
|
||||
"choice_ids = torch.tensor([token_n, token_y]).to(device)[None, :, None]\n",
|
||||
"input_ids.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import gc\n",
|
||||
"output = scores = None\n",
|
||||
"def clear_mem():\n",
|
||||
" model.eval()\n",
|
||||
" model.zero_grad()\n",
|
||||
" gc.collect()\n",
|
||||
" torch.cuda.empty_cache()\n",
|
||||
" gc.collect()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 88,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from einops import repeat, rearrange\n",
|
||||
"\n",
|
||||
"def noise_for_embeds(inputs_embeds, seed=42, std = 2e-2):\n",
|
||||
" B, S, embed_dim = inputs_embeds.shape\n",
|
||||
" with torch.random.fork_rng(devices=[inputs_embeds.device.index]):\n",
|
||||
" torch.manual_seed(seed)\n",
|
||||
" noise = torch.normal(0., std, (embed_dim, ))\n",
|
||||
" noise = repeat(noise, 't -> b s t', b=B, s=S).to(inputs_embeds.device).to(inputs_embeds.dtype)\n",
|
||||
" return noise\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 89,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"-1 tensor(15.6562) tensor(15.7383)\n",
|
||||
"0 tensor(15.6582) tensor(22.4570)\n",
|
||||
"1 tensor(18.8008) tensor(16.3320)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# make counterfactual model\n",
|
||||
"model.eval() \n",
|
||||
"with torch.no_grad(): \n",
|
||||
" inputs_embeds = model.model.embed_tokens(input_ids) \n",
|
||||
" noise = noise_for_embeds(inputs_embeds, seed=42)\n",
|
||||
" for _ in range(-1, 2): \n",
|
||||
" inputs_embeds_w_noise = inputs_embeds + noise * _\n",
|
||||
" outputs = model(\n",
|
||||
" inputs_embeds=inputs_embeds_w_noise, \n",
|
||||
" attention_mask=attention_mask, \n",
|
||||
" output_hidden_states=True, return_dict=True, use_cache=False\n",
|
||||
" )\n",
|
||||
" scores = outputs.logits[:, -1, :].float().cpu()\n",
|
||||
" print(_, scores[0, token_y].sum(), scores[0, token_n].sum())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.hist(inputs_embeds.flatten().cpu().numpy(), bins=55)\n",
|
||||
"plt.hist(noise.flatten().cpu().numpy(), label='noise', bins=55)\n",
|
||||
"plt.legend()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# inputs_embeds.abs().mean()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from src.helpers.torch import get_top_n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from src.datasets.hs import ExtractHiddenStates\n",
|
||||
"batch_size=1\n",
|
||||
"layer_padding=3\n",
|
||||
"layer_stride=6\n",
|
||||
"ehs = ExtractHiddenStates(model, tokenizer, layer_stride=layer_stride, layer_padding=layer_padding)\n",
|
||||
"# what it should return outs... but it don't. why no? halp I tired and I want to go to bed now :( \n",
|
||||
"outs = ehs.get_batch_of_hidden_states(input_ids=input_ids, attention_mask=attention_mask, choice_ids=choice_ids, debug=True)\n",
|
||||
"# len(outs)\n",
|
||||
"for i, out in enumerate(outs):\n",
|
||||
" print(i)\n",
|
||||
" scores = out['scores'].log_softmax(-1).cpu().numpy()\n",
|
||||
" print('log_probs', scores[0, token_y], scores[0, token_n])\n",
|
||||
" scores = out['scores'].cpu().numpy()\n",
|
||||
" print('logits', scores[0, token_y], scores[0, token_n])\n",
|
||||
" print(out['text_ans'])\n",
|
||||
" print(get_top_n(out['scores'], tokenizer))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i, out in enumerate(outs):\n",
|
||||
" print(i)\n",
|
||||
" scores = out['scores'].log_softmax(-1).cpu().numpy()\n",
|
||||
" print('log_probs', scores[0, token_y], scores[0, token_n])\n",
|
||||
" scores = out['scores'].cpu().numpy()\n",
|
||||
" print('logits', scores[0, token_y], scores[0, token_n])\n",
|
||||
" print(out['text_ans'])\n",
|
||||
" print(get_top_n(out['scores'], tokenizer))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"scores = out['scores'].log_softmax(-1).cpu().numpy()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"out['input_truncated']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "dlk3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
},
|
||||
"toc": {
|
||||
"base_numbering": 1,
|
||||
"nav_menu": {},
|
||||
"number_sections": true,
|
||||
"sideBar": true,
|
||||
"skip_h1_title": false,
|
||||
"title_cell": "Table of Contents",
|
||||
"title_sidebar": "Contents",
|
||||
"toc_cell": false,
|
||||
"toc_position": {},
|
||||
"toc_section_display": true,
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -9,7 +9,7 @@ from datasets import disable_caching
|
||||
disable_caching()
|
||||
|
||||
from loguru import logger
|
||||
logger.add("make_dataset_{time}.log")
|
||||
logger.add("logs/make_dataset_{time}.log")
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
@@ -199,6 +199,7 @@ def qc_ds(f):
|
||||
X_test = X_test[:max_rows]
|
||||
y_test = y_test[:max_rows]
|
||||
print('split size', X_train.shape, y_test.shape)
|
||||
print(f'balance of classes: {y_train.mean():2.2%} {y_test.mean():2.2%}')
|
||||
|
||||
# scale
|
||||
scaler = RobustScaler()
|
||||
|
||||
Reference in New Issue
Block a user