add create script

create_dataset.ipynb

@@ -0,0 +1,538 @@
+{
+ "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���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
+}