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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# JACKET V1 のデータセットに wikipedia のコンテキストを追加したデータセットの作成\n",
"\n",
"- https://sites.google.com/view/project-aio/dataset?authuser=0\n",
"\n",
"の CC BY-SA 4.0 DEED 該当データをもとに、Wikipedia のコンテキスト追加した HuggingFace Dataset を作成する\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# JAQKET データセットを取得\n",
"\n",
"from dataclasses import dataclass\n",
"import json\n",
"import urllib.request\n",
"import random\n",
"\n",
"random.seed(42)\n",
"\n",
"# https://sites.google.com/view/project-aio/dataset?authuser=0\n",
"# 以下のURLのデータは、ライセンスが CC BY-SA 4.0 DEED\n",
"\n",
"jaqket_urls = [\n",
" \"https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_02/aio_01_dev.jsonl\",\n",
" \"https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_02/aio_01_test.jsonl\",\n",
" \"https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_02/aio_01_unused.jsonl\",\n",
"]\n",
"\n",
"\n",
"@dataclass\n",
"class JaqketQuestion_:\n",
" qid: str\n",
" question: str\n",
" original_question: str\n",
" answer_entity: str\n",
" answer_candidates: list[str]\n",
" answer_candidates_shuffled: list[str]\n",
" label: int\n",
" original_answer: str | None = None\n",
"\n",
"\n",
"@dataclass\n",
"class JaqketQuestion:\n",
" qid: str\n",
" competition: str\n",
" timestamp: str\n",
" section: str\n",
" number: str\n",
" original_question: str\n",
" original_answer: str | None\n",
" original_additional_info: str | None\n",
" question: str\n",
" answers: list[str]\n",
"\n",
"\n",
"def load_jaqket(urls: list[str]):\n",
" res = []\n",
" for url in urls:\n",
" with urllib.request.urlopen(url) as f:\n",
" # f は 1行ごとに処理\n",
" data = [json.loads(line.decode(\"utf-8\")) for line in f]\n",
" for d in data:\n",
" try:\n",
" res.append(JaqketQuestion(**d))\n",
" except Exception as e:\n",
" # d.keys\n",
" print(d.keys())\n",
" raise e\n",
" return res\n",
"\n",
"\n",
"jacket_data = load_jaqket(jaqket_urls)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4600, 10)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"jacket_df = pd.DataFrame(jacket_data)\n",
"jacket_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\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",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>qid</th>\n",
" <th>competition</th>\n",
" <th>timestamp</th>\n",
" <th>section</th>\n",
" <th>number</th>\n",
" <th>original_question</th>\n",
" <th>original_answer</th>\n",
" <th>original_additional_info</th>\n",
" <th>question</th>\n",
" <th>answers</th>\n",
" <th>answer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>QA20CAPR-0002</td>\n",
" <td>第1回AI王</td>\n",
" <td>2019/12/25</td>\n",
" <td>開発データ問題 (dev1)</td>\n",
" <td>2</td>\n",
" <td>明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記...</td>\n",
" <td>コックリさん</td>\n",
" <td></td>\n",
" <td>明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記...</td>\n",
" <td>[コックリさん]</td>\n",
" <td>コックリさん</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" qid competition timestamp section number \\\n",
"0 QA20CAPR-0002 第1回AI王 2019/12/25 開発データ問題 (dev1) 2 \n",
"\n",
" original_question original_answer \\\n",
"0 明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記... コックリさん \n",
"\n",
" original_additional_info question \\\n",
"0 明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記... \n",
"\n",
" answers answer \n",
"0 [コックリさん] コックリさん "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# none は \"\" に変換\n",
"jacket_df = jacket_df.fillna(\"\")\n",
"# answers の最初の一つを入れる\n",
"jacket_df.loc[:, \"answer\"] = jacket_df[\"answers\"].apply(lambda x: x[0])\n",
"jacket_df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"train_df = jacket_df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yu1/miniconda3/envs/llm-sc/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from datasets.download import DownloadManager\n",
"from datasets import load_dataset\n",
"from sentence_transformers import SentenceTransformer\n",
"import faiss\n",
"\n",
"# wikipedia 日本語データセットのロード\n",
"wikija_dataset = load_dataset(\n",
" path=\"singletongue/wikipedia-utils\",\n",
" name=\"passages-c400-jawiki-20230403\",\n",
" split=\"train\",\n",
")\n",
"# faiss index のダウンロード\n",
"dm = DownloadManager()\n",
"index_local_path = dm.download(\n",
" f\"https://huggingface.co/datasets/hotchpotch/wikipedia-passages-jawiki-embeddings/resolve/main/faiss_indexes/passages-c400-jawiki-20230403/multilingual-e5-large-query/index_IVF2048_PQ256.faiss\"\n",
")\n",
"# index_local_path = dm.download(\n",
"# f\"https://huggingface.co/datasets/hotchpotch/wikipedia-passages-jawiki-embeddings/resolve/main/faiss_indexes/passages-c400-jawiki-20230403/multilingual-e5-large-passage/index_IVF2048_PQ256.faiss\"\n",
"# )\n",
"# faiss index のロード\n",
"faiss_index = faiss.read_index(index_local_path)\n",
"\n",
"# embeddings へ変換するモデルのロード\n",
"emb_model = SentenceTransformer(\"intfloat/multilingual-e5-large\", device=\"cuda\")\n",
"emb_model.max_seq_length = 512"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 1024)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# embeddings へ変換\n",
"def texts_to_embs(model, texts, prefix=\"query: \"):\n",
" texts = [prefix + text for text in texts]\n",
" return model.encode(texts, normalize_embeddings=True)\n",
"\n",
"\n",
"texts_to_embs(emb_model, [\"こんにちは\", \"こんばんは\"]).shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3919, 12)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# wikipedia のデータから、RAG検索して Top-N に正解の単語が含まれているデータのみを抽出する\n",
"\n",
"\n",
"def df_correct_answer_add_context(df, top_n=3):\n",
" # faiss index で検索して、top-N に正解があれば、その context を追加してそのデータのみを返す\n",
" df = df.copy().reset_index(drop=True)\n",
" df[\"context\"] = None\n",
" texts = df[\"question\"].tolist()\n",
" embs = texts_to_embs(emb_model, texts)\n",
" scores, indexes = faiss_index.search(embs, top_n)\n",
" for pos, (score, index) in enumerate(zip(scores, indexes)):\n",
" df_data = df.iloc[pos]\n",
" answer = df_data[\"answer\"]\n",
" target_texts = []\n",
" for s, i in zip(score, index):\n",
" data = wikija_dataset[int(i)] # type: ignore\n",
" target_texts.append(data[\"title\"] + \" \" + data[\"text\"]) # type: ignore\n",
" check_text = str(target_texts)\n",
" # 検索結果に対象文字列を含むか\n",
" if answer in check_text:\n",
" df.at[pos, \"context\"] = target_texts\n",
" # top3_text が None でないデータのみ返す\n",
" return df[df[\"context\"].notnull()]\n",
"\n",
"\n",
"train_df = df_correct_answer_add_context(train_df, 3)\n",
"train_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\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",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>qid</th>\n",
" <th>question</th>\n",
" <th>answer</th>\n",
" <th>context</th>\n",
" <th>answers</th>\n",
" <th>competition</th>\n",
" <th>timestamp</th>\n",
" <th>section</th>\n",
" <th>number</th>\n",
" <th>original_question</th>\n",
" <th>original_answer</th>\n",
" <th>original_additional_info</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>QA20CAPR-0002</td>\n",
" <td>明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記...</td>\n",
" <td>コックリさん</td>\n",
" <td>[コックリさん その起源は明確ではないが、レオナルド・ダ・ヴィンチが自著において「テーブル・...</td>\n",
" <td>[コックリさん]</td>\n",
" <td>第1回AI王</td>\n",
" <td>2019/12/25</td>\n",
" <td>開発データ問題 (dev1)</td>\n",
" <td>2</td>\n",
" <td>明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記...</td>\n",
" <td>コックリさん</td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" qid question answer \\\n",
"0 QA20CAPR-0002 明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記... コックリさん \n",
"\n",
" context answers competition \\\n",
"0 [コックリさん その起源は明確ではないが、レオナルド・ダ・ヴィンチが自著において「テーブル・... [コックリさん] 第1回AI王 \n",
"\n",
" timestamp section number \\\n",
"0 2019/12/25 開発データ問題 (dev1) 2 \n",
"\n",
" original_question original_answer \\\n",
"0 明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記... コックリさん \n",
"\n",
" original_additional_info \n",
"0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# qid, question, answer, context が先頭のカラムに来るように並び替える。過去のカラムも残す\n",
"new_columns = [\"qid\", \"question\", \"answer\", \"context\", \"answers\"]\n",
"new_columns.extend([c for c in train_df.columns if c not in new_columns])\n",
"train_df = train_df[new_columns]\n",
"train_df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import datasets\n",
"\n",
"# pandas to dataset\n",
"train_dataset = datasets.Dataset.from_pandas(train_df)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dataset({\n",
" features: ['qid', 'question', 'answer', 'context', 'answers', 'competition', 'timestamp', 'section', 'number', 'original_question', 'original_answer', 'original_additional_info', '__index_level_0__'],\n",
" num_rows: 3919\n",
"})"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dataset"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((2939, 12), (980, 12))"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 25% を validation にする\n",
"valid_df = train_df.sample(frac=0.25, random_state=42)\n",
"train_df = train_df.drop(valid_df.index)\n",
"train_df = train_df.reset_index(drop=True)\n",
"valid_df = valid_df.reset_index(drop=True)\n",
"# shape\n",
"train_df.shape, valid_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from datasets import Dataset\n",
"\n",
"train_ds = Dataset.from_pandas(train_df)\n",
"valid_ds = Dataset.from_pandas(valid_df)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"dataset_hf_path = \"hotchpotch/jaqket_v1_qa_wikija_context\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Creating parquet from Arrow format: 100%|██████████| 3/3 [00:00<00:00, 116.44ba/s]\n",
"Uploading the dataset shards: 100%|██████████| 1/1 [00:02<00:00, 2.39s/it]\n",
"Creating parquet from Arrow format: 100%|██████████| 1/1 [00:00<00:00, 58.50ba/s]\n",
"Uploading the dataset shards: 100%|██████████| 1/1 [00:01<00:00, 2.00s/it]\n",
"README.md: 100%|██████████| 715/715 [00:00<00:00, 3.93MB/s]\n"
]
}
],
"source": [
"train_ds.push_to_hub(dataset_hf_path, split=\"train\")\n",
"valid_ds.push_to_hub(dataset_hf_path, split=\"validation\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llm-sc",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|