{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "2202de26", "metadata": {}, "source": [ "### Kaggle link: https://www.kaggle.com/code/noobhocai/robust-qa-model/notebook" ] }, { "cell_type": "code", "execution_count": 1, "id": "7fa7190f", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:03:55.496021Z", "iopub.status.busy": "2023-06-29T18:03:55.495602Z", "iopub.status.idle": "2023-06-29T18:05:06.108463Z", "shell.execute_reply": "2023-06-29T18:05:06.107292Z" }, "papermill": { "duration": 70.62346, "end_time": "2023-06-29T18:05:06.110986", "exception": false, "start_time": "2023-06-29T18:03:55.487526", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+https://github.com/huggingface/transformers.git\r\n", " Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-h0dx0051\r\n", " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-h0dx0051\r\n", " Resolved https://github.com/huggingface/transformers.git to commit c817bc44e2915593a292df9e7d4e2c0dfefb6620\r\n", " Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \bdone\r\n", "\u001b[?25h Getting requirements to build wheel ... \u001b[?25l-\b \b\\\b \bdone\r\n", "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \b\\\b \bdone\r\n", "\u001b[?25hRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (3.12.0)\r\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (0.15.1)\r\n", "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (1.23.5)\r\n", "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (21.3)\r\n", "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (5.4.1)\r\n", "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (2023.5.5)\r\n", "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (2.28.2)\r\n", "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (0.13.3)\r\n", "Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (0.3.1)\r\n", "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.31.0.dev0) (4.64.1)\r\n", "Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.14.1->transformers==4.31.0.dev0) (2023.6.0)\r\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.14.1->transformers==4.31.0.dev0) (4.5.0)\r\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers==4.31.0.dev0) (3.0.9)\r\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.31.0.dev0) (2.1.1)\r\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.31.0.dev0) (3.4)\r\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.31.0.dev0) (1.26.15)\r\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.31.0.dev0) (2023.5.7)\r\n", "Building wheels for collected packages: transformers\r\n", " Building wheel for transformers (pyproject.toml) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \bdone\r\n", "\u001b[?25h Created wheel for transformers: filename=transformers-4.31.0.dev0-py3-none-any.whl size=7262017 sha256=9b931b8e538d7470013ac2bbe64007dcf896d81c966f5e7eb0e7e5241f53db1a\r\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-e2lgbyd3/wheels/e7/9c/5b/e1a9c8007c343041e61cc484433d512ea9274272e3fcbe7c16\r\n", "Successfully built transformers\r\n", "Installing collected packages: transformers\r\n", " Attempting uninstall: transformers\r\n", " Found existing installation: transformers 4.30.1\r\n", " Uninstalling transformers-4.30.1:\r\n", " Successfully uninstalled transformers-4.30.1\r\n", "Successfully installed transformers-4.31.0.dev0\r\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", "\u001b[0mCollecting git+https://github.com/huggingface/accelerate.git\r\n", " Cloning https://github.com/huggingface/accelerate.git to /tmp/pip-req-build-a5z4_f1k\r\n", " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/accelerate.git /tmp/pip-req-build-a5z4_f1k\r\n", " Resolved https://github.com/huggingface/accelerate.git to commit 17ec2ede119b0d50c7ead181166e0be8a1c8586b\r\n", " Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n", "\u001b[?25h Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n", "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n", "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.21.0.dev0) (1.23.5)\r\n", "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.21.0.dev0) (21.3)\r\n", "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from accelerate==0.21.0.dev0) (5.9.3)\r\n", "Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from accelerate==0.21.0.dev0) (5.4.1)\r\n", "Requirement already satisfied: torch>=1.10.0 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.21.0.dev0) (2.0.0)\r\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->accelerate==0.21.0.dev0) (3.0.9)\r\n", "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.21.0.dev0) (3.12.0)\r\n", "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.21.0.dev0) (4.5.0)\r\n", "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.21.0.dev0) (1.12)\r\n", "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.21.0.dev0) (3.1)\r\n", "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.21.0.dev0) (3.1.2)\r\n", "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.10.0->accelerate==0.21.0.dev0) (2.1.2)\r\n", "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.10.0->accelerate==0.21.0.dev0) (1.3.0)\r\n", "Building wheels for collected packages: accelerate\r\n", " Building wheel for accelerate (pyproject.toml) ... \u001b[?25l-\b \b\\\b \bdone\r\n", "\u001b[?25h Created wheel for accelerate: filename=accelerate-0.21.0.dev0-py3-none-any.whl size=234327 sha256=fc4be68721d95e46bd41ef42e2fd263c57cb57265fc5757072d549a837df4e28\r\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-7_crjrr1/wheels/9c/a3/1e/47368f9b6575655fe9ee1b6350cfa7d4b0befe66a35f8a8365\r\n", "Successfully built accelerate\r\n", "Installing collected packages: accelerate\r\n", " Attempting uninstall: accelerate\r\n", " Found existing installation: accelerate 0.12.0\r\n", " Uninstalling accelerate-0.12.0:\r\n", " Successfully uninstalled accelerate-0.12.0\r\n", "Successfully installed accelerate-0.21.0.dev0\r\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", "\u001b[0m" ] } ], "source": [ "! pip install -U git+https://github.com/huggingface/transformers.git\n", "! pip install -U git+https://github.com/huggingface/accelerate.git" ] }, { "cell_type": "code", "execution_count": 2, "id": "c55abfa1", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2023-06-29T18:05:06.134839Z", "iopub.status.busy": "2023-06-29T18:05:06.134524Z", "iopub.status.idle": "2023-06-29T18:05:07.381701Z", "shell.execute_reply": "2023-06-29T18:05:07.380594Z" }, "papermill": { "duration": 1.261833, "end_time": "2023-06-29T18:05:07.384086", "exception": false, "start_time": "2023-06-29T18:05:06.122253", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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idtexttitlequestioncategoryis_long_answershort_candidate_startanswershort_candidate
0718d41cd997b2b44b0685ac54aa55bd8Thủ tướng Trung Quốc là nhân vật lãnh đạo chín...Trung QuốcThủ tướng Trung Quốc là gìPARTIAL_ANNOTATIONTrue
1c926e7b0717202618a10dd907d4b4c39có 23 quốc gia không có lực lượng quân đội, ba...Đất nước nào không có quân độiFULL_ANNOTATIONTrue53wiki/Danh_sách_quốc_gia_không_có_lực_lượng_vũ_...Costa Rica, Iceland, Panama, Micronesia, Quần ...
2d38ef5bf1fb82b410026ed82c8a44caeRaymondienne (hay Raymonde Dien) sinh ngày 13 ...RaymondiennePháp tấn công xâm lược Việt Nam vào ngày tháng...FALSE_LONG_ANSWERFalse
3b6b5589a98fdccd208dc752bac853993Cúp cờ vua thế giới là tên gọi một số giải đấu...Cúp cờ vua thế giớiCờ vua còn có tên gọi nào khácFALSE_LONG_ANSWERFalse
482396a18fa9812bfec4d3ecb7ae60905Đỉnh núi nằm ở phần trung tâm của dãy núi Đại ...ShkharaNúi nào cao nhất châu âuFULL_ANNOTATIONTrue73wiki/Elbrusnúi Elbrus
\n", "
" ], "text/plain": [ " id \\\n", "0 718d41cd997b2b44b0685ac54aa55bd8 \n", "1 c926e7b0717202618a10dd907d4b4c39 \n", "2 d38ef5bf1fb82b410026ed82c8a44cae \n", "3 b6b5589a98fdccd208dc752bac853993 \n", "4 82396a18fa9812bfec4d3ecb7ae60905 \n", "\n", " text title \\\n", "0 Thủ tướng Trung Quốc là nhân vật lãnh đạo chín... Trung Quốc \n", "1 có 23 quốc gia không có lực lượng quân đội, ba... \n", "2 Raymondienne (hay Raymonde Dien) sinh ngày 13 ... Raymondienne \n", "3 Cúp cờ vua thế giới là tên gọi một số giải đấu... Cúp cờ vua thế giới \n", "4 Đỉnh núi nằm ở phần trung tâm của dãy núi Đại ... Shkhara \n", "\n", " question category \\\n", "0 Thủ tướng Trung Quốc là gì PARTIAL_ANNOTATION \n", "1 Đất nước nào không có quân đội FULL_ANNOTATION \n", "2 Pháp tấn công xâm lược Việt Nam vào ngày tháng... FALSE_LONG_ANSWER \n", "3 Cờ vua còn có tên gọi nào khác FALSE_LONG_ANSWER \n", "4 Núi nào cao nhất châu âu FULL_ANNOTATION \n", "\n", " is_long_answer short_candidate_start \\\n", "0 True \n", "1 True 53 \n", "2 False \n", "3 False \n", "4 True 73 \n", "\n", " answer \\\n", "0 \n", "1 wiki/Danh_sách_quốc_gia_không_có_lực_lượng_vũ_... \n", "2 \n", "3 \n", "4 wiki/Elbrus \n", "\n", " short_candidate \n", "0 \n", "1 Costa Rica, Iceland, Panama, Micronesia, Quần ... \n", "2 \n", "3 \n", "4 núi Elbrus " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import json\n", " \n", "# with open('/kaggle/input/e2eqa-wiki-zalo-ai/processed/zac2022_train_merged_final.json', 'r') as f:\n", "# data = json.load(f)\n", "\n", "# df = pd.DataFrame(data['data'])\n", "# df.drop(columns='dirty_text', inplace=True)\n", "# df.fillna('', inplace=True)\n", "# df.head()\n", "\n", "df = pd.DataFrame()\n", "\n", "all_text = []\n", "all_id = []\n", "all_question = []\n", "all_title = []\n", "all_category = []\n", "all_is_long = []\n", "\n", "all_dirty_text = []\n", "\n", "\n", "short_candidate_start = []\n", "short_candidate = []\n", "answer = []\n", "\n", "\n", "with open('/kaggle/input/e2eqa-wiki-zalo-ai/processed/zac2022_train_merged_final.json', 'r') as f:\n", " data = json.load(f)\n", " \n", "df = pd.DataFrame(data['data'])\n", "df.drop(columns='dirty_text', inplace=True)\n", "df.fillna(\"\", inplace=True)\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "6be52527", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:07.428882Z", "iopub.status.busy": "2023-06-29T18:05:07.428542Z", "iopub.status.idle": "2023-06-29T18:05:07.434319Z", "shell.execute_reply": "2023-06-29T18:05:07.433522Z" }, "papermill": { "duration": 0.031275, "end_time": "2023-06-29T18:05:07.439583", "exception": false, "start_time": "2023-06-29T18:05:07.408308", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(20857, 9)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 4, "id": "7dadb02e", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:07.471270Z", "iopub.status.busy": "2023-06-29T18:05:07.470951Z", "iopub.status.idle": "2023-06-29T18:05:08.133110Z", "shell.execute_reply": "2023-06-29T18:05:08.132175Z" }, "papermill": { "duration": 0.685311, "end_time": "2023-06-29T18:05:08.140413", "exception": false, "start_time": "2023-06-29T18:05:07.455102", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "with open('/kaggle/input/e2eqa-wiki-zalo-ai/processed/zac2022_train_merged_final.json', 'r') as f:\n", " data = json.load(f)" ] }, { "cell_type": "code", "execution_count": 5, "id": "0850f848", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:08.174687Z", "iopub.status.busy": "2023-06-29T18:05:08.174328Z", "iopub.status.idle": "2023-06-29T18:05:08.184499Z", "shell.execute_reply": "2023-06-29T18:05:08.183718Z" }, "papermill": { "duration": 0.030118, "end_time": "2023-06-29T18:05:08.186958", "exception": false, "start_time": "2023-06-29T18:05:08.156840", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'núi Elbrus , ngọn núi cao nhất của châu Âu .'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['data'][4]['text'][73:]" ] }, { "cell_type": "code", "execution_count": 6, "id": "c213d5a5", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:08.223762Z", "iopub.status.busy": "2023-06-29T18:05:08.223450Z", "iopub.status.idle": "2023-06-29T18:05:08.266918Z", "shell.execute_reply": "2023-06-29T18:05:08.266180Z" }, "papermill": { "duration": 0.066086, "end_time": "2023-06-29T18:05:08.269328", "exception": false, "start_time": "2023-06-29T18:05:08.203242", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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idtexttitlequestioncategoryis_long_answershort_candidate_startanswershort_candidate
1c926e7b0717202618a10dd907d4b4c39có 23 quốc gia không có lực lượng quân đội, ba...Đất nước nào không có quân độiFULL_ANNOTATIONTrue53wiki/Danh_sách_quốc_gia_không_có_lực_lượng_vũ_...Costa Rica, Iceland, Panama, Micronesia, Quần ...
482396a18fa9812bfec4d3ecb7ae60905Đỉnh núi nằm ở phần trung tâm của dãy núi Đại ...ShkharaNúi nào cao nhất châu âuFULL_ANNOTATIONTrue73wiki/Elbrusnúi Elbrus
9ee8a272bcefac7b96037a1571984b1f8Lịch sử . Ai Cập bị La Mã chiếm năm 30 TCN , v...AlexandriaThành phố nào là thủ phủ của Ai Cập trong đế q...FULL_ANNOTATIONTrue48wiki/AlexandriaAlexandria
16c51604b25a641b2665c4ce1784b8cc6aCác nghiên cứu lịch sử cho thấy Cổ Am rất có t...Cổ AmAi là người đứng đầu trong cuộc chống lại chín...FULL_ANNOTATIONTrue65wiki/Lê_ChânLê Chân
2304bf774a424133d27c34895960937b87Phục Hưng (tiếng Pháp: \"Renaissance\", , , từ \"...Phục HưngNền văn hoá Phục Hưng bắt nguồn từ nước nàoFULL_ANNOTATIONTrue205wiki/ÝÝ
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20826be7f71e3927632f3f3f2afa68ff1b91bTiền giấy xuất hiện . Năm 1396 thời Trần Thuận...Tiền tệ Đại Việt thời Trầnvua nào cho phát hành tiền giấy đầu tiên ở việ...FULL_ANNOTATIONTrue54wiki/Hồ_Quý_LyHồ Quý Ly
208365769cee0487a9674306d271854244b39Máy quay phim: Phát minh kỳ diệu của anh em Lu...Ai là người phát minh ra máy quay phimFULL_ANNOTATIONTrue37wiki/Auguste_và_Louis_Lumièreanh em Lumière Phương Hiền
20840e47529f7ade3caaaa60096137798c6ffNgũ Hành Sơn hay núi Non Nước là một danh thắn...Ngũ Hành Sơntên quận nào ở đà nẵng cũng là tên một danh th...FULL_ANNOTATIONTrue285wiki/Ngũ_Hành_Sơn_(quận)quận Ngũ Hành Sơn
20842935572f75515f8d8ab831648589899c6Trận Như Nguyệt là một trận đánh lớn diễn ra ở...Trận Như Nguyệttrận đánh nào có ý nghĩa quyết định trong khán...FULL_ANNOTATIONTrue0wiki/Trận_Như_NguyệtTrận Như Nguyệt
20852508022f540c39fe31511f594748759bcTrong thần thoại Hy Lạp , \" Eros \" là vị thần ...Erostrong thần thoại hy lạp vị thần tình yêu có tê...FULL_ANNOTATIONTrue28wiki/ErosEros
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FULL_ANNOTATION \n", "\n", " is_long_answer short_candidate_start \\\n", "1 True 53 \n", "4 True 73 \n", "9 True 48 \n", "16 True 65 \n", "23 True 205 \n", "... ... ... \n", "20826 True 54 \n", "20836 True 37 \n", "20840 True 285 \n", "20842 True 0 \n", "20852 True 28 \n", "\n", " answer \\\n", "1 wiki/Danh_sách_quốc_gia_không_có_lực_lượng_vũ_... \n", "4 wiki/Elbrus \n", "9 wiki/Alexandria \n", "16 wiki/Lê_Chân \n", "23 wiki/Ý \n", "... ... \n", "20826 wiki/Hồ_Quý_Ly \n", "20836 wiki/Auguste_và_Louis_Lumière \n", "20840 wiki/Ngũ_Hành_Sơn_(quận) \n", "20842 wiki/Trận_Như_Nguyệt \n", "20852 wiki/Eros \n", "\n", " short_candidate \n", "1 Costa Rica, Iceland, Panama, Micronesia, Quần ... \n", "4 núi Elbrus \n", "9 Alexandria \n", "16 Lê Chân \n", "23 Ý \n", "... ... \n", "20826 Hồ Quý Ly \n", "20836 anh em Lumière Phương Hiền \n", "20840 quận Ngũ Hành Sơn \n", "20842 Trận Như Nguyệt \n", "20852 Eros \n", "\n", "[4989 rows x 9 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_full_anno = df[df['category'] == 'FULL_ANNOTATION']\n", "\n", "df_full_anno" ] }, { "cell_type": "code", "execution_count": 7, "id": "af1fd69e", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:08.304900Z", "iopub.status.busy": "2023-06-29T18:05:08.304593Z", "iopub.status.idle": "2023-06-29T18:05:10.031706Z", "shell.execute_reply": "2023-06-29T18:05:10.030737Z" }, "papermill": { "duration": 1.747992, "end_time": "2023-06-29T18:05:10.034422", "exception": false, "start_time": "2023-06-29T18:05:08.286430", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import json\n", "from glob import glob\n", "from tqdm import tqdm\n", "import re\n", "from nltk import word_tokenize as lib_tokenizer\n", "\n", "dict_map = dict({})\n", "\n", "def word_tokenize(text):\n", " global dict_map\n", " words = text.split()\n", " words_norm = []\n", " for w in words:\n", " if dict_map.get(w, None) is None:\n", " dict_map[w] = ' '.join(lib_tokenizer(w)).replace('``', '\"').replace(\"''\", '\"')\n", " words_norm.append(dict_map[w])\n", " return words_norm\n", "\n", "def strip_answer_string(text):\n", " text = text.strip()\n", " while text[-1] in '.,/><;:\\'\"[]{}+=-_)(*&^!~`':\n", " if text[0] != '(' and text[-1] == ')' and '(' in text:\n", " break\n", " if text[-1] == '\"' and text[0] != '\"' and text.count('\"') > 1:\n", " break\n", " text = text[:-1].strip()\n", " while text[0] in '.,/><;:\\'\"[]{}+=-_)(*&^!~`':\n", " if text[0] == '\"' and text[-1] != '\"' and text.count('\"') > 1:\n", " break\n", " text = text[1:].strip()\n", " text = text.strip()\n", " return text\n", "\n", "\n", "def strip_context(text):\n", " text = text.replace('\\n', ' ')\n", " text = re.sub(r'\\s+', ' ', text)\n", " text = text.strip()\n", " return text" ] }, { "cell_type": "code", "execution_count": 8, "id": "4dbfbdfb", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:10.059956Z", "iopub.status.busy": "2023-06-29T18:05:10.059656Z", "iopub.status.idle": "2023-06-29T18:05:10.167979Z", "shell.execute_reply": "2023-06-29T18:05:10.167080Z" }, "papermill": { "duration": 0.123253, "end_time": "2023-06-29T18:05:10.169970", "exception": false, "start_time": "2023-06-29T18:05:10.046717", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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idquestiontitletextpred_answerpred_answer_startpred_answer_endpred_answer_score
0718d41cd997b2b44b0685ac54aa55bd8Thủ tướng Trung Quốc là gìTrung QuốcThủ tướng Trung Quốc là nhân vật lãnh đạo chín...nhân vật lãnh đạo chính phủ24510.991081
1e27bcf72913542ee9de69fac59ad77a9Tỉnh Đắk Nông được thành lập vào ngày tháng nă...Đắk NôngĐắk Nông hay Đắc Nông là một tỉnh ở Tây Nguyên...ngày 1 tháng 1 năm 2004911140.999969
2b8267523a48de0d700116acdbce2ee95Tỉnh nào của nước ta vừa giáp Lào vừa giáp Cam...Tây NguyênTây Nguyên là vùng cao nguyên, phía bắc giáp t...Kon Tum2892960.999406
4253090162cf3fc0dd5e8537b53bcba99hải dương có bao nhiêu huyệnLịch sử hành chính Hải DươngCùng năm , thành lập một số thị trấn thuộc các...82092100.872943
6155b65ee96bab3f74cafec51616e330fHari Won sinh năm bao nhiêuHari WonHari Won ( 하리원 , \" Hari Nguyễn \" ) tên thật là...19851101140.971467
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3491d701dfcba59420604c66e803c9556754vì sao sao Hỏa có màu đỏSao HoảSao Hoả còn gọi là : Hoả Tinh , ( Tiếng Anh : ...sắt ôxít có mặt rất nhiều trên bề mặt hành tinh2222690.679995
34941514053e80d5aadd764f4f5f916df004Thời đại Hồng Bàng còn có tên gọi khác là gìHồng BàngHồng Bàng Thị hay Thời đại Hồng Bàng là một gi...Hồng Bàng Thị0130.999944
349593c746695c50932ac45ac498a192a3e5Hà Nội có bao nhiêu đơn vị hành chínhLịch sử hành chính Hà NộiVào thời điểm hiện tại ( 2017 ) , về mặt hành ...3075770.999760
3496278ad127825c085a54fa22116c281f92Ngày thành lập GoogleGoogleTên miền www.google.com được đăng ký ngày 15 t...ngày 7 tháng 9 năm 19981141370.999846
349709ee53a835ea4ed2234aee8161b16d87Ngọn núi nào cao nhất Đông DươngDãy núi Hoàng Liên SơnDãy núi Hoàng Liên Sơn rộng 30 km, chạy dài 18...Phan Xi Păng3223340.995318
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idquestiontitletextpred_answerpred_answer_startpred_answer_endpred_answer_score
01558001276_0Nơi Trần Quốc Toàn đại phá quân Mông -Nguyên t...NaNHàm Tử là một xã thuộc huyện Khoái Châu, tỉnh ...Hưng Yên46550.999635
11554518024_0Ban chỉ đạo trung ương về phòng chống tham nhũ...Ban Chỉ đạo Trung ương về phòng, chống tham nh...Ngày 1 tháng 2 năm 2013, Bộ Chính trị ban hành...Ngày 1 tháng 2 năm 20130240.999978
21557393113_0Quê mẹ của Bác Hồ là làng gìNaNLàng Hoàng Trù cách làng Kim Liên khoảng 2 km,...Làng Hoàng Trù0140.917361
41554702900_0Việt Nam đã đặt quan hệ ngoại giao với tất cả ...Quan hệ ngoại giao của Việt NamViệt Nam hiện nay, dưới chính thể Cộng hòa xã ...188 nước981060.999102
51554823810_2Tỉnh nào ở điểm cực nam của Việt NamĐất MũiĐất Mũi là một xã thuộc huyện Ngọc Hiển, tỉnh ...Cà Mau46530.999941
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3811554453817_1Bác Hồ ra đi cứu nước vào ngày tháng năm nàoNgày Bác Hồ ra đi tìm đường cứu nướcNgày Bác Hồ ra đi tìm đường cứu nước (ngày 5 t...ngày 5 tháng 6 năm 191137620.999999
3871566783571_7chủ tịch ủy ban nhân dân của Hải Phòng là ngườ...Lê Đức ThịnhLê Đức Thịnh ( 1924 – 2001 ) là nhà cách mạng ...Lê Đức Thịnh0120.999970
3891553071483_2Vị tướng đầu tiên được Bác Hồ phong tên là gìLê Thiết HùngThiếu tướng Lê Thiết Hùng (1908 – 1986) nhà ho...Lê Thiết Hùng12250.999836
3921554448829_0Ngoài trái đất hành tinh nào có khả năng xuất ...Sự sống ngoài Trái ĐấtCó ý kiến cho rằng Alpha Centauri, chòm sao gầ...Alpha Centauri19340.999897
3931554273050_0Đồng bằng sông Hồng có bao nhiêu tỉnh thành phốĐồng bằng sông HồngĐồng bằng sông Hồng (hay Châu thổ sông Hồng) l...101341360.994291
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192 rows × 8 columns

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" ], "text/plain": [ " id question \\\n", "0 1558001276_0 Nơi Trần Quốc Toàn đại phá quân Mông -Nguyên t... \n", "1 1554518024_0 Ban chỉ đạo trung ương về phòng chống tham nhũ... \n", "2 1557393113_0 Quê mẹ của Bác Hồ là làng gì \n", "4 1554702900_0 Việt Nam đã đặt quan hệ ngoại giao với tất cả ... \n", "5 1554823810_2 Tỉnh nào ở điểm cực nam của Việt Nam \n", ".. ... ... \n", "381 1554453817_1 Bác Hồ ra đi cứu nước vào ngày tháng năm nào \n", "387 1566783571_7 chủ tịch ủy ban nhân dân của Hải Phòng là ngườ... \n", "389 1553071483_2 Vị tướng đầu tiên được Bác Hồ phong tên là gì \n", "392 1554448829_0 Ngoài trái đất hành tinh nào có khả năng xuất ... \n", "393 1554273050_0 Đồng bằng sông Hồng có bao nhiêu tỉnh thành phố \n", "\n", " title \\\n", "0 NaN \n", "1 Ban Chỉ đạo Trung ương về phòng, chống tham nh... \n", "2 NaN \n", "4 Quan hệ ngoại giao của Việt Nam \n", "5 Đất Mũi \n", ".. ... \n", "381 Ngày Bác Hồ ra đi tìm đường cứu nước \n", "387 Lê Đức Thịnh \n", "389 Lê Thiết Hùng \n", "392 Sự sống ngoài Trái Đất \n", "393 Đồng bằng sông Hồng \n", "\n", " text \\\n", "0 Hàm Tử là một xã thuộc huyện Khoái Châu, tỉnh ... \n", "1 Ngày 1 tháng 2 năm 2013, Bộ Chính trị ban hành... \n", "2 Làng Hoàng Trù cách làng Kim Liên khoảng 2 km,... \n", "4 Việt Nam hiện nay, dưới chính thể Cộng hòa xã ... \n", "5 Đất Mũi là một xã thuộc huyện Ngọc Hiển, tỉnh ... \n", ".. ... \n", "381 Ngày Bác Hồ ra đi tìm đường cứu nước (ngày 5 t... \n", "387 Lê Đức Thịnh ( 1924 – 2001 ) là nhà cách mạng ... \n", "389 Thiếu tướng Lê Thiết Hùng (1908 – 1986) nhà ho... \n", "392 Có ý kiến cho rằng Alpha Centauri, chòm sao gầ... \n", "393 Đồng bằng sông Hồng (hay Châu thổ sông Hồng) l... \n", "\n", " pred_answer pred_answer_start pred_answer_end \\\n", "0 Hưng Yên 46 55 \n", "1 Ngày 1 tháng 2 năm 2013 0 24 \n", "2 Làng Hoàng Trù 0 14 \n", "4 188 nước 98 106 \n", "5 Cà Mau 46 53 \n", ".. ... ... ... \n", "381 ngày 5 tháng 6 năm 1911 37 62 \n", "387 Lê Đức Thịnh 0 12 \n", "389 Lê Thiết Hùng 12 25 \n", "392 Alpha Centauri 19 34 \n", "393 10 134 136 \n", "\n", " pred_answer_score \n", "0 0.999635 \n", "1 0.999978 \n", "2 0.917361 \n", "4 0.999102 \n", "5 0.999941 \n", ".. ... \n", "381 0.999999 \n", "387 0.999970 \n", "389 0.999836 \n", "392 0.999897 \n", "393 0.994291 \n", "\n", "[192 rows x 8 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_za2019_pseudo = pd.read_csv('/kaggle/input/e2eqa-wiki-zalo-ai/pseudo-data/df_za_2019_pseudo_label.csv')\n", "\n", "\n", "df_za2019_pseudo[\"pred_answer\"] = df_za2019_pseudo[\"pred_answer\"].apply(post_process)\n", "\n", "df_za2019_pseudo = df_za2019_pseudo[df_za2019_pseudo['pred_answer_score'] > 0.6] \n", "\n", "\n", "df_za2019_pseudo" ] }, { "cell_type": "code", "execution_count": 10, "id": "749799c1", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:10.276485Z", "iopub.status.busy": "2023-06-29T18:05:10.275598Z", "iopub.status.idle": "2023-06-29T18:05:10.316671Z", "shell.execute_reply": "2023-06-29T18:05:10.315655Z" }, "papermill": { "duration": 0.057569, "end_time": "2023-06-29T18:05:10.318813", "exception": false, "start_time": "2023-06-29T18:05:10.261244", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(749, 749)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('/kaggle/input/e2eqa-wiki-zalo-ai/zac2022_val.json/zac2022_val.json', 'r') as f:\n", " val = json.load(f)\n", "\n", "all_valid_id = []\n", "\n", "for item in val['data']:\n", " all_valid_id.append(item['id'])\n", " \n", " \n", "len(val['data']), len(all_valid_id)" ] }, { "cell_type": "code", "execution_count": 11, "id": "57ca725c", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:10.346578Z", "iopub.status.busy": "2023-06-29T18:05:10.346304Z", "iopub.status.idle": "2023-06-29T18:05:10.844032Z", "shell.execute_reply": "2023-06-29T18:05:10.843099Z" }, "papermill": { "duration": 0.517288, "end_time": "2023-06-29T18:05:10.849234", "exception": false, "start_time": "2023-06-29T18:05:10.331946", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(20107, 749)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_train = []\n", "\n", "for item in data['data']:\n", " if item['id'] not in all_valid_id:\n", " all_train.append(item)\n", "\n", "train_data = {'data': all_train}\n", "\n", "len(train_data['data']), len(val['data'])" ] }, { "cell_type": "code", "execution_count": 12, "id": "f70bcae3", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:10.883399Z", "iopub.status.busy": "2023-06-29T18:05:10.883056Z", "iopub.status.idle": "2023-06-29T18:05:10.908488Z", "shell.execute_reply": "2023-06-29T18:05:10.907420Z" }, "papermill": { "duration": 0.042618, "end_time": "2023-06-29T18:05:10.910638", "exception": false, "start_time": "2023-06-29T18:05:10.868020", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def handle_file(data, train = True):\n", " \n", " qa_data = data['data']\n", " \n", " norm_samples = []\n", " \n", " ## za 2019 data set pseudo label\n", " \n", " if train ==True:\n", " for i, row in df_za2019_pseudo.iterrows():\n", "\n", " print('zalo2019 pseudo dataset!')\n", "\n", " context_raw = row['text']\n", " question = row['question']\n", "\n", " answer_raw = row['pred_answer']\n", " answer_index_raw = row['pred_answer_start']\n", "\n", "\n", " if context_raw[answer_index_raw: answer_index_raw + len(answer_raw)] == answer_raw:\n", "\n", " # print('True!!!!!!!!!')\n", "\n", " context_prev = strip_context(context_raw[:answer_index_raw])\n", " answer = strip_answer_string(answer_raw)\n", " context_next = strip_context(context_raw[answer_index_raw + len(answer):])\n", "\n", " context_prev = ' '.join(word_tokenize(context_prev))\n", " context_next = ' '.join(word_tokenize(context_next))\n", " answer = ' '.join(word_tokenize(answer))\n", " question = ' '.join(word_tokenize(question))\n", "\n", " context = \"{} {} {}\".format(context_prev, answer, context_next).strip()\n", "\n", " norm_samples.append({\n", " \"context\": context,\n", " \"question\": question,\n", " \"answer_text\": answer,\n", " \"answer_start_idx\": len(\"{} {}\".format(context_prev, answer).strip()) - len(answer)\n", " })\n", "\n", " #za2022 dataset\n", "\n", " for item in tqdm(qa_data, total=len(qa_data)):\n", " \n", " #preprocess\n", " \n", " if 'dirty_text' in item.keys():\n", " if item['dirty_text'] != None:\n", " \n", " context_raw = item['dirty_text']\n", " else:\n", " \n", " context_raw = item['text']\n", " \n", "# print(context_raw)\n", " else:\n", " context_raw = item['text']\n", " \n", "# print(context_raw)\n", " \n", " id_ = item['id']\n", " category = item['category']\n", " is_long_answer = item['is_long_answer']\n", " question = item['question']\n", " \n", " ## train / valid\n", " \n", " \n", " \n", " if category == 'FULL_ANNOTATION':\n", " \n", " answer_raw = item['short_candidate']\n", " \n", " if 'dirty_text' in item.keys():\n", " print('use dirty text !!!')\n", " if item['dirty_text'] != None:\n", " answer_index_raw = context_raw.find(answer_raw)\n", " else:\n", " answer_index_raw = item['short_candidate_start']\n", " \n", " else:\n", " answer_index_raw = item['short_candidate_start']\n", " \n", " if context_raw[answer_index_raw: answer_index_raw + len(answer_raw)] == answer_raw:\n", " \n", " print('True!!!!!!!!!')\n", " \n", " context_prev = strip_context(context_raw[:answer_index_raw])\n", " answer = strip_answer_string(answer_raw)\n", " context_next = strip_context(context_raw[answer_index_raw + len(answer):])\n", "\n", " context_prev = ' '.join(word_tokenize(context_prev))\n", " context_next = ' '.join(word_tokenize(context_next))\n", " answer = ' '.join(word_tokenize(answer))\n", " question = ' '.join(word_tokenize(question))\n", "\n", " context = \"{} {} {}\".format(context_prev, answer, context_next).strip()\n", "\n", " norm_samples.append({\n", " \"context\": context,\n", " \"question\": question,\n", " \"answer_text\": answer,\n", " \"answer_start_idx\": len(\"{} {}\".format(context_prev, answer).strip()) - len(answer)\n", " })\n", " \n", " elif category == 'PARTIAL_ANNOTATION':\n", " \n", " if id_ in df_partial_pseudo['id'].unique():\n", " \n", " print('pseudo data!!')\n", " \n", " answer_raw = df_partial_pseudo[df_partial_pseudo['id'] == id_]['pred_answer'].values[0]\n", " answer_index_raw = df_partial_pseudo[df_partial_pseudo['id'] == id_]['pred_answer_start'].values[0] # item['short_candidate_start']\n", "\n", " if context_raw[answer_index_raw: answer_index_raw + len(answer_raw)] == answer_raw:\n", "\n", " # print('True!!!!!!!!!')\n", "\n", " context_prev = strip_context(context_raw[:answer_index_raw])\n", " answer = strip_answer_string(answer_raw)\n", " context_next = strip_context(context_raw[answer_index_raw + len(answer):])\n", "\n", " context_prev = ' '.join(word_tokenize(context_prev))\n", " context_next = ' '.join(word_tokenize(context_next))\n", " answer = ' '.join(word_tokenize(answer))\n", " question = ' '.join(word_tokenize(question))\n", "\n", " context = \"{} {} {}\".format(context_prev, answer, context_next).strip()\n", "\n", " norm_samples.append({\n", " \"context\": context,\n", " \"question\": question,\n", " \"answer_text\": answer,\n", " \"answer_start_idx\": len(\"{} {}\".format(context_prev, answer).strip()) - len(answer)\n", " })\n", " \n", " else:\n", " context_raw = ' '.join(word_tokenize(context_raw))\n", " question = ' '.join(word_tokenize(question))\n", " norm_samples.append({\n", " \"context\": context_raw,\n", " \"question\": question,\n", " \"answer_text\": '',\n", " \"answer_start_idx\": 0\n", " })\n", " \n", " \n", " else:\n", " context_raw = ' '.join(word_tokenize(context_raw))\n", " question = ' '.join(word_tokenize(question))\n", " norm_samples.append({\n", " \"context\": context_raw,\n", " \"question\": question,\n", " \"answer_text\": '',\n", " \"answer_start_idx\": 0\n", " })\n", " \n", " \n", " print('Len training sample: ', len(norm_samples))\n", " return norm_samples" ] }, { "cell_type": "code", "execution_count": 13, "id": "6f175fed", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:10.939227Z", "iopub.status.busy": "2023-06-29T18:05:10.938902Z", "iopub.status.idle": "2023-06-29T18:05:10.946080Z", "shell.execute_reply": "2023-06-29T18:05:10.944466Z" }, "papermill": { "duration": 0.023524, "end_time": "2023-06-29T18:05:10.948043", "exception": false, "start_time": "2023-06-29T18:05:10.924519", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "20107" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(train_data['data'])" ] }, { "cell_type": "code", "execution_count": 14, "id": "1b81f7d6", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:10.977132Z", "iopub.status.busy": "2023-06-29T18:05:10.976315Z", "iopub.status.idle": "2023-06-29T18:05:24.799595Z", "shell.execute_reply": "2023-06-29T18:05:24.798544Z" }, "papermill": { "duration": 13.844226, "end_time": "2023-06-29T18:05:24.805257", "exception": false, "start_time": "2023-06-29T18:05:10.961031", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n", "zalo2019 pseudo dataset!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 57/20107 [00:00<00:35, 567.36it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "pseudo data!!\n", "use dirty text 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"use dirty text !!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 99%|█████████▉| 19918/20107 [00:13<00:00, 1754.06it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text 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"use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "pseudo data!!\n", "pseudo data!!\n", "pseudo data!!\n", "Len training sample: 19136\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "dict_data_train = handle_file(train_data, train = True)" ] }, { "cell_type": "code", "execution_count": 15, "id": "da16d328", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:24.889101Z", "iopub.status.busy": "2023-06-29T18:05:24.888278Z", "iopub.status.idle": "2023-06-29T18:05:25.261587Z", "shell.execute_reply": "2023-06-29T18:05:25.260456Z" }, "papermill": { "duration": 0.417446, "end_time": "2023-06-29T18:05:25.264274", "exception": false, "start_time": "2023-06-29T18:05:24.846828", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total: 19136 samples\n" ] } ], "source": [ "with open('qa_full_anno_train.jsonl', 'w', encoding='utf-8') as file:\n", " for item in dict_data_train:\n", " file.write(\"{}\\n\".format(json.dumps(item, ensure_ascii=False)))\n", "\n", "print(\"Total: {} samples\".format(len(dict_data_train)))" ] }, { "cell_type": "code", "execution_count": 16, "id": "316ecbb4", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:25.350724Z", "iopub.status.busy": "2023-06-29T18:05:25.350101Z", "iopub.status.idle": "2023-06-29T18:05:25.573702Z", "shell.execute_reply": "2023-06-29T18:05:25.566181Z" }, "papermill": { "duration": 0.267114, "end_time": "2023-06-29T18:05:25.575691", "exception": false, "start_time": "2023-06-29T18:05:25.308577", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 50%|█████ | 378/749 [00:00<00:00, 3760.47it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text 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text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text 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"True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 749/749 [00:00<00:00, 3596.63it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "use dirty text !!!\n", "True!!!!!!!!!\n", "Len training sample: 736\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "dict_data_valid = handle_file(val, train = False)" ] }, { "cell_type": "code", "execution_count": 17, "id": "2f9a0255", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:25.661874Z", "iopub.status.busy": "2023-06-29T18:05:25.661576Z", "iopub.status.idle": "2023-06-29T18:05:25.681464Z", "shell.execute_reply": "2023-06-29T18:05:25.680363Z" }, "papermill": { "duration": 0.064949, "end_time": "2023-06-29T18:05:25.683466", "exception": false, "start_time": "2023-06-29T18:05:25.618517", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total: 736 samples\n" ] } ], "source": [ "with open('qa_full_anno_valid.jsonl', 'w', encoding='utf-8') as file:\n", " for item in dict_data_valid:\n", " file.write(\"{}\\n\".format(json.dumps(item, ensure_ascii=False)))\n", "\n", "print(\"Total: {} samples\".format(len(dict_data_valid)))" ] }, { "cell_type": "code", "execution_count": 18, "id": "3f648afe", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:25.771933Z", "iopub.status.busy": "2023-06-29T18:05:25.771231Z", "iopub.status.idle": "2023-06-29T18:05:29.705310Z", "shell.execute_reply": "2023-06-29T18:05:29.704320Z" }, "papermill": { "duration": 3.981826, "end_time": "2023-06-29T18:05:29.707373", "exception": false, "start_time": "2023-06-29T18:05:25.725547", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-1d983b87dcd55a7f/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\n", "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-1d983b87dcd55a7f/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b. Subsequent calls will reuse this data.\n", "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-cd981311ec3bbf65/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\n", "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-cd981311ec3bbf65/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b. Subsequent calls will reuse this data.\n", "Train: 19136 samples\n", "Valid: 736 samples\n" ] } ], "source": [ "import datasets\n", "from datasets import concatenate_datasets\n", "import glob\n", "\n", "# datasets.temp_seed(101)\n", "datasets.disable_progress_bar()\n", "\n", "\n", "def assert_sample(sample):\n", " assert sample['context'][sample['answer_start_idx']: sample['answer_start_idx'] + len(sample['answer_text'])] == \\\n", " sample['answer_text'], sample\n", " assert len(sample['context']) > 0\n", " assert len(sample['question']) > 0\n", " return True\n", "\n", "\n", "def format_sample(sample):\n", " context_prev = sample['context'][:sample['answer_start_idx']].split()\n", " sample['answer_word_start_idx'] = len(context_prev)\n", " sample['answer_word_end_idx'] = len(context_prev) + len(sample['answer_text'].split()) - 1\n", " return sample\n", "\n", "\n", "if __name__ == \"__main__\":\n", " train_set = []\n", " valid_set = []\n", " for part in ['/kaggle/working/qa_full_anno_train.jsonl']: #glob.glob('../data-bin/unify/*.jsonl'):\n", " dataset = datasets.load_dataset('json', data_files=[part])['train']\n", " dataset.filter(assert_sample)\n", " dataset = dataset.map(format_sample)\n", "\n", "# all_data = dataset.train_test_split(test_size=0.1)\n", "# train = all_data['train']\n", "# valid = all_data['test']\n", "# train_set.append(train)\n", "# valid_set.append(valid)\n", " train_set.append(dataset)\n", " \n", " for part in ['/kaggle/working/qa_full_anno_valid.jsonl']: #glob.glob('../data-bin/unify/*.jsonl'):\n", " dataset = datasets.load_dataset('json', data_files=[part])['train']\n", " dataset.filter(assert_sample)\n", " dataset = dataset.map(format_sample)\n", "\n", "# all_data = dataset.train_test_split(test_size=0.1)\n", "# train = all_data['train']\n", "# valid = all_data['test']\n", "# train_set.append(train)\n", "# valid_set.append(valid)\n", " valid_set.append(dataset)\n", "\n", " train_dataset = concatenate_datasets(train_set)\n", " valid_dataset = concatenate_datasets(valid_set)\n", "\n", " train_dataset.save_to_disk('/kaggle/working/data-bin/processed/train.dataset')\n", " valid_dataset.save_to_disk('/kaggle/working/data-bin/processed/valid.dataset')\n", "\n", " print(\"Train: {} samples\".format(len(train_dataset)))\n", " print(\"Valid: {} samples\".format(len(valid_dataset)))\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "0940c8e7", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:29.795985Z", "iopub.status.busy": "2023-06-29T18:05:29.794867Z", "iopub.status.idle": "2023-06-29T18:05:29.812197Z", "shell.execute_reply": "2023-06-29T18:05:29.810798Z" }, "papermill": { "duration": 0.063777, "end_time": "2023-06-29T18:05:29.814119", "exception": false, "start_time": "2023-06-29T18:05:29.750342", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing train.py\n" ] } ], "source": [ "%%writefile train.py \n", "\n", "import os\n", "os.environ[\"WANDB_DISABLED\"] = \"true\"\n", "\n", "from transformers import TrainingArguments\n", "# from model.mrc_model import MRCQuestionAnswering\n", "from transformers import Trainer\n", "# from utils import data_loader\n", "import numpy as np\n", "from datasets import load_metric\n", "import os\n", "\n", "import datasets\n", "from transformers import AutoTokenizer\n", "from torch.utils.data import DataLoader\n", "import torch\n", "import numpy as np\n", "from nltk import word_tokenize\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"nguyenvulebinh/vi-mrc-large\")\n", "\n", "# tokenizer = AutoTokenizer.from_pretrained(\"xlm-roberta-large\",\n", "# # cache_dir='./model-bin/cache',\n", "# #local_files_only=True\n", "# )\n", "\n", "\n", "def compute_metrics(eval_pred):\n", " metric = datasets.load_metric(\"squad\", cache_dir='./log/metric')\n", " # print(eval_pred)\n", " logits, labels = eval_pred\n", " logits = list(zip(logits[0], logits[1]))\n", " labels, span_ids, samples_input_ids, word_lengths = list(zip(labels[0], labels[1])), labels[2], labels[3], labels[4]\n", " predictions = []\n", " references = []\n", " for idx, (predict, span_truth, input_ids, sample_words_length) in enumerate(\n", " list(zip(logits, span_ids, samples_input_ids, word_lengths))):\n", " span_truth = np.delete(span_truth, np.where(span_truth == -100))\n", " input_ids = np.delete(input_ids, np.where(input_ids == -100))\n", "\n", " # Get the most likely beginning of answer with the argmax of the score\n", " answer_start = sum(sample_words_length[:np.argmax(predict[0])])\n", " # Get the most likely end of answer with the argmax of the score\n", " answer_end = sum(sample_words_length[:np.argmax(predict[1]) + 1])\n", "\n", " answer = tokenizer.convert_tokens_to_string(\n", " tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))\n", " answer_truth = tokenizer.convert_tokens_to_string(\n", " tokenizer.convert_ids_to_tokens(span_truth))\n", "\n", " predictions.append({'prediction_text': answer, 'id': str(idx)})\n", " references.append({'answers': {'answer_start': [answer_start], 'text': [answer_truth]}, 'id': str(idx)})\n", " results = metric.compute(predictions=predictions, references=references)\n", " return results\n", "\n", "\n", "def data_collator(samples):\n", " if len(samples) == 0:\n", " return {}\n", "\n", " # for sample in samples:\n", " # start_idx = sum(sample['words_lengths'][:sample['start_idx']])\n", " # end_idx = sum(sample['words_lengths'][:sample['end_idx'] + 1])\n", " # if len(sample['answer_text']) > 0:\n", " # answer_predict = set(\n", " # list(' '.join(word_tokenize(tokenizer.decode(sample['input_ids'][start_idx:end_idx])))))\n", " # answer_truth = set(list(' '.join(word_tokenize(sample['answer_text']))))\n", " # if answer_predict != answer_truth:\n", " # print(\"{}\\n{}\\n\\n-------\\n\".format(\n", " # ' '.join(word_tokenize(tokenizer.decode(sample['input_ids'][start_idx:end_idx]))),\n", " # ' '.join(word_tokenize(sample['answer_text'])).replace('``', '\"').replace(\"''\", '\"'),\n", " # sample))\n", "\n", " for sample in samples:\n", " start_idx = sum(sample['words_lengths'][:sample['start_idx']])\n", " end_idx = sum(sample['words_lengths'][:sample['end_idx'] + 1])\n", " sample['span_answer_ids'] = sample['input_ids'][start_idx:end_idx]\n", "\n", " def collate_tokens(values, pad_idx, eos_idx=None, left_pad=False, move_eos_to_beginning=False):\n", " \"\"\"Convert a list of 1d tensors into a padded 2d tensor.\"\"\"\n", " size = max(v.size(0) for v in values)\n", " res = values[0].new(len(values), size).fill_(pad_idx)\n", "\n", " def copy_tensor(src, dst):\n", " assert dst.numel() == src.numel()\n", " if move_eos_to_beginning:\n", " assert src[-1] == eos_idx\n", " dst[0] = eos_idx\n", " dst[1:] = src[:-1]\n", " else:\n", " dst.copy_(src)\n", "\n", " for i, v in enumerate(values):\n", " copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])\n", " return res\n", "\n", " input_ids = collate_tokens([torch.tensor(item['input_ids']) for item in samples], pad_idx=tokenizer.pad_token_id)\n", " attention_mask = torch.zeros_like(input_ids)\n", " for i in range(len(samples)):\n", " attention_mask[i][:len(samples[i]['input_ids'])] = 1\n", " words_lengths = collate_tokens([torch.tensor(item['words_lengths']) for item in samples], pad_idx=0)\n", " answer_start = collate_tokens([torch.tensor([item['start_idx']]) for item in samples], pad_idx=0)\n", " answer_end = collate_tokens([torch.tensor([item['end_idx']]) for item in samples], pad_idx=0)\n", " span_answer_ids = collate_tokens([torch.tensor(item['span_answer_ids']) for item in samples],\n", " pad_idx=-100)\n", "\n", " batch_samples = {\n", " 'input_ids': input_ids,\n", " 'attention_mask': attention_mask,\n", " 'words_lengths': words_lengths,\n", " 'start_positions': answer_start,\n", " 'end_positions': answer_end,\n", " 'span_answer_ids': span_answer_ids\n", " }\n", "\n", " return batch_samples\n", "\n", "\n", "def tokenize_function(example):\n", " example[\"question\"] = example[\"question\"].split()\n", " example[\"context\"] = example[\"context\"].split()\n", " # max_len_single_sentence = tokenizer.max_len_single_sentence\n", " max_len_single_sentence = 368\n", "\n", " question_sub_words_ids = [tokenizer.convert_tokens_to_ids(tokenizer.tokenize(w)) for w in example[\"question\"]]\n", " context_sub_words_ids = [tokenizer.convert_tokens_to_ids(tokenizer.tokenize(w)) for w in example[\"context\"]]\n", " valid = True\n", " if len([j for i in question_sub_words_ids + context_sub_words_ids for j in\n", " i]) > max_len_single_sentence - 1:\n", " question_ids = [j for i in question_sub_words_ids for j in i]\n", " context_ids = [j for i in context_sub_words_ids[:example['answer_word_end_idx'] + 1] for j in i]\n", " remain_tokens = max_len_single_sentence - 1 - len(question_ids)\n", " if len(question_ids + context_ids) < max_len_single_sentence - 1:\n", " context_sub_words_ids_revise = context_sub_words_ids[:example['answer_word_end_idx'] + 1]\n", " idx = example['answer_word_end_idx'] + 1\n", " while len([j for i in (context_sub_words_ids_revise + [context_sub_words_ids[idx]]) for j in\n", " i]) < remain_tokens and idx < len(context_sub_words_ids):\n", " context_sub_words_ids_revise.append(context_sub_words_ids[idx])\n", " idx += 1\n", " context_sub_words_ids = context_sub_words_ids_revise\n", " else:\n", " valid = False\n", "\n", " question_sub_words_ids = [[tokenizer.bos_token_id]] + question_sub_words_ids + [[tokenizer.eos_token_id]]\n", " context_sub_words_ids = context_sub_words_ids + [[tokenizer.eos_token_id]]\n", "\n", " input_ids = [j for i in question_sub_words_ids + context_sub_words_ids for j in i]\n", " if len(input_ids) > max_len_single_sentence + 2:\n", " valid = False\n", "\n", " words_lengths = [len(item) for item in question_sub_words_ids + context_sub_words_ids]\n", "\n", " return {\n", " \"input_ids\": input_ids,\n", " \"words_lengths\": words_lengths,\n", " \"start_idx\": (example['answer_word_start_idx'] + len(question_sub_words_ids)) if len(\n", " example[\"answer_text\"]) > 0 else 0,\n", " \"end_idx\": (example['answer_word_end_idx'] + len(question_sub_words_ids)) if len(\n", " example[\"answer_text\"]) > 0 else 0,\n", " \"valid\": valid\n", " }\n", "\n", "\n", "def get_dataloader(train_path, valid_path, batch_size=2, num_proc=10):\n", " train_set = datasets.load_from_disk(train_path)\n", " valid_set = datasets.load_from_disk(valid_path)\n", " print(\"Train set: \", len(train_set))\n", " print(\"Valid set: \", len(valid_set))\n", " # unique_tags = set(tag for doc in tags for tag in train_set)\n", " train_set = train_set.shuffle().map(tokenize_function, batched=False, num_proc=num_proc).filter(\n", " lambda example: example['valid'], num_proc=num_proc)\n", " valid_set = valid_set.map(tokenize_function, batched=False, num_proc=num_proc).filter(\n", " lambda example: example['valid'], num_proc=num_proc)\n", " # train_set = train_set.sort('src_ids_len')\n", " # valid_set = valid_set.sort('src_ids_len')\n", "\n", " print(\"Train set: \", len(train_set))\n", " print(\"Valid set: \", len(valid_set))\n", " return train_set, valid_set\n", " #return train_set, valid_set.shard(500, 0)\n", " #\n", " # train_dataloader = DataLoader(\n", " # train_set, shuffle=True, batch_size=batch_size, collate_fn=data_collator\n", " # )\n", " # valid_dataloader = DataLoader(\n", " # valid_set, batch_size=batch_size, collate_fn=data_collator\n", " # )\n", " #\n", " # return train_dataloader, valid_dataloader\n", "\n", "\n", "def build_target_dictionary():\n", " data_set = datasets.load_from_disk('./data-bin/processed/train.dataset')\n", " labels = set([item['language'] for item in data_set])\n", " labels2id = {tag: idx for idx, tag in enumerate(labels)}\n", " # id2labels = {idx: tag for tag, idx in labels2id.items()}\n", "\n", " tags2id = {\n", " \"same\": 1,\n", " \"change\": 0\n", " }\n", " # id2tags = {idx: tag for tag, idx in tags2id.items()}\n", "\n", " return labels2id, tags2id\n", "\n", "\n", "\\\n", "from transformers.models.roberta.modeling_roberta import *\n", "_CHECKPOINT_FOR_DOC = \"roberta-base\"\n", "_CONFIG_FOR_DOC = \"RobertaConfig\"\n", "_TOKENIZER_FOR_DOC = \"RobertaTokenizer\"\n", "\n", "\n", "class MRCQuestionAnswering(RobertaPreTrainedModel):\n", " config_class = RobertaConfig\n", "\n", "# def _reorder_cache(self, past, beam_idx):\n", "# pass\n", "\n", "# _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n", "# _keys_to_ignore_on_load_missing = [r\"position_ids\"]\n", "\n", " def __init__(self, config):\n", " super().__init__(config)\n", " self.num_labels = config.num_labels\n", "\n", " self.roberta = RobertaModel(config, add_pooling_layer=False)\n", " self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)\n", "\n", " self.init_weights()\n", "\n", " def forward(\n", " self,\n", " input_ids=None,\n", " words_lengths=None,\n", " start_idx=None,\n", " end_idx=None,\n", " attention_mask=None,\n", " token_type_ids=None,\n", " position_ids=None,\n", " head_mask=None,\n", " inputs_embeds=None,\n", " start_positions=None,\n", " end_positions=None,\n", " span_answer_ids=None,\n", " output_attentions=None,\n", " output_hidden_states=None,\n", " return_dict=None,\n", " ):\n", " return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n", "\n", " outputs = self.roberta(\n", " input_ids,\n", " attention_mask=attention_mask,\n", " token_type_ids=token_type_ids,\n", " position_ids=position_ids,\n", " head_mask=head_mask,\n", " inputs_embeds=inputs_embeds,\n", " output_attentions=output_attentions,\n", " output_hidden_states=output_hidden_states,\n", " return_dict=return_dict,\n", " )\n", "\n", " sequence_output = outputs[0]\n", "\n", " context_embedding = sequence_output\n", "\n", " # Compute align word sub_word matrix\n", " batch_size = input_ids.shape[0]\n", " max_sub_word = input_ids.shape[1]\n", " max_word = words_lengths.shape[1]\n", " align_matrix = torch.zeros((batch_size, max_word, max_sub_word))\n", "\n", " for i, sample_length in enumerate(words_lengths):\n", " for j in range(len(sample_length)):\n", " start_idx = torch.sum(sample_length[:j])\n", " align_matrix[i][j][start_idx: start_idx + sample_length[j]] = 1 if sample_length[j] > 0 else 0\n", "\n", " align_matrix = align_matrix.to(context_embedding.device)\n", " # Combine sub_word features to make word feature\n", " context_embedding_align = torch.bmm(align_matrix, context_embedding)\n", "\n", " logits = self.qa_outputs(context_embedding_align)\n", " start_logits, end_logits = logits.split(1, dim=-1)\n", " start_logits = start_logits.squeeze(-1).contiguous()\n", " end_logits = end_logits.squeeze(-1).contiguous()\n", "\n", " total_loss = None\n", " if start_positions is not None and end_positions is not None:\n", " # If we are on multi-GPU, split add a dimension\n", " if len(start_positions.size()) > 1:\n", " start_positions = start_positions.squeeze(-1)\n", " if len(end_positions.size()) > 1:\n", " end_positions = end_positions.squeeze(-1)\n", " # sometimes the start/end positions are outside our model inputs, we ignore these terms\n", " ignored_index = start_logits.size(1)\n", " start_positions = start_positions.clamp(0, ignored_index)\n", " end_positions = end_positions.clamp(0, ignored_index)\n", "\n", " loss_fct = CrossEntropyLoss(ignore_index=ignored_index)\n", " start_loss = loss_fct(start_logits, start_positions)\n", " end_loss = loss_fct(end_logits, end_positions)\n", " total_loss = (start_loss + end_loss) / 2\n", "\n", " if not return_dict:\n", " output = (start_logits, end_logits) + outputs[2:]\n", " return ((total_loss,) + output) if total_loss is not None else output\n", "\n", " return QuestionAnsweringModelOutput(\n", " loss=total_loss,\n", " start_logits=start_logits,\n", " end_logits=end_logits,\n", " hidden_states=outputs.hidden_states,\n", " attentions=outputs.attentions,\n", " )\n", "if __name__ == \"__main__\":\n", " # tokenizer = AutoTokenizer.from_pretrained(\"xlm-roberta-base\")\n", " model = MRCQuestionAnswering.from_pretrained(\"nguyenvulebinh/vi-mrc-large\")\n", " \n", " #(\"xlm-roberta-large\",\n", " # cache_dir='./model-bin/cache',\n", " #local_files_only=True\n", "# )\n", " print(model)\n", " print(model.config)\n", "\n", " train_dataset, valid_dataset = get_dataloader(\n", " train_path='./data-bin/processed/train.dataset',\n", " valid_path='./data-bin/processed/valid.dataset'\n", " )\n", "\n", " training_args = TrainingArguments(\"model-bin/test\",\n", " do_train=True,\n", " do_eval=True,\n", " num_train_epochs= 5, # 8, #10,\n", " learning_rate=1e-5, #1e-4\n", " warmup_ratio=0.05,\n", " weight_decay=0.01,\n", " per_device_train_batch_size=4,\n", " per_device_eval_batch_size=4,\n", " gradient_accumulation_steps=1,\n", " logging_dir='./log',\n", " logging_steps=5,\n", " label_names=['start_positions',\n", " 'end_positions',\n", " 'span_answer_ids',\n", " 'input_ids',\n", " 'words_lengths'],\n", " group_by_length=True,\n", " save_strategy=\"epoch\",\n", " metric_for_best_model='f1',\n", " load_best_model_at_end=True,\n", " save_total_limit=2,\n", " #eval_steps=1,\n", " #evaluation_strategy=\"steps\",\n", " evaluation_strategy=\"epoch\",\n", " )\n", "\n", " trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " eval_dataset=valid_dataset,\n", " data_collator=data_collator,\n", " compute_metrics=compute_metrics\n", " )\n", "\n", " trainer.train()\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "2a78a1a6", "metadata": { "execution": { "iopub.execute_input": "2023-06-29T18:05:29.901603Z", "iopub.status.busy": "2023-06-29T18:05:29.901312Z", "iopub.status.idle": "2023-06-29T21:09:57.922907Z", "shell.execute_reply": "2023-06-29T21:09:57.921584Z" }, "papermill": { "duration": 11068.06877, "end_time": "2023-06-29T21:09:57.925876", "exception": false, "start_time": "2023-06-29T18:05:29.857106", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\r\n", "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\r\n", " warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\r\n", "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\r\n", "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\r\n", " warnings.warn(f\"file system plugins are not loaded: {e}\")\r\n", "Downloading (…)okenizer_config.json: 100%|█████| 398/398 [00:00<00:00, 2.49MB/s]\r\n", "Downloading (…)/main/tokenizer.json: 100%|█| 9.08M/9.08M [00:00<00:00, 13.9MB/s]\r\n", "Downloading (…)cial_tokens_map.json: 100%|██████| 239/239 [00:00<00:00, 801kB/s]\r\n", "Downloading (…)lve/main/config.json: 100%|█████| 690/690 [00:00<00:00, 4.40MB/s]\r\n", "Downloading pytorch_model.bin: 100%|████████| 2.24G/2.24G [00:06<00:00, 370MB/s]\r\n", "MRCQuestionAnswering(\r\n", " (roberta): RobertaModel(\r\n", " (embeddings): RobertaEmbeddings(\r\n", " (word_embeddings): Embedding(250002, 1024, padding_idx=1)\r\n", " (position_embeddings): Embedding(514, 1024, padding_idx=1)\r\n", " (token_type_embeddings): Embedding(1, 1024)\r\n", " (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\r\n", " (dropout): Dropout(p=0.1, inplace=False)\r\n", " )\r\n", " (encoder): RobertaEncoder(\r\n", " (layer): ModuleList(\r\n", " (0-23): 24 x RobertaLayer(\r\n", " (attention): RobertaAttention(\r\n", " (self): RobertaSelfAttention(\r\n", " (query): Linear(in_features=1024, out_features=1024, bias=True)\r\n", " (key): Linear(in_features=1024, out_features=1024, bias=True)\r\n", " (value): Linear(in_features=1024, out_features=1024, bias=True)\r\n", " (dropout): Dropout(p=0.1, inplace=False)\r\n", " )\r\n", " (output): RobertaSelfOutput(\r\n", " (dense): Linear(in_features=1024, out_features=1024, bias=True)\r\n", " (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\r\n", " (dropout): Dropout(p=0.1, inplace=False)\r\n", " )\r\n", " )\r\n", " (intermediate): RobertaIntermediate(\r\n", " (dense): Linear(in_features=1024, out_features=4096, bias=True)\r\n", " (intermediate_act_fn): GELUActivation()\r\n", " )\r\n", " (output): RobertaOutput(\r\n", " (dense): Linear(in_features=4096, out_features=1024, bias=True)\r\n", " (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\r\n", " (dropout): Dropout(p=0.1, inplace=False)\r\n", " )\r\n", " )\r\n", " )\r\n", " )\r\n", " )\r\n", " (qa_outputs): Linear(in_features=1024, out_features=2, bias=True)\r\n", ")\r\n", "RobertaConfig {\r\n", " \"_name_or_path\": \"nguyenvulebinh/vi-mrc-large\",\r\n", " \"architectures\": [\r\n", " \"XLMRobertaForQuestionAnswering\"\r\n", " ],\r\n", " \"attention_probs_dropout_prob\": 0.1,\r\n", " \"bos_token_id\": 0,\r\n", " \"classifier_dropout\": null,\r\n", " \"eos_token_id\": 2,\r\n", " \"gradient_checkpointing\": false,\r\n", " \"hidden_act\": \"gelu\",\r\n", " \"hidden_dropout_prob\": 0.1,\r\n", " \"hidden_size\": 1024,\r\n", " \"initializer_range\": 0.02,\r\n", " \"intermediate_size\": 4096,\r\n", " \"layer_norm_eps\": 1e-05,\r\n", " \"max_position_embeddings\": 514,\r\n", " \"model_type\": \"roberta\",\r\n", " \"num_attention_heads\": 16,\r\n", " \"num_hidden_layers\": 24,\r\n", " \"output_past\": true,\r\n", " \"pad_token_id\": 1,\r\n", " \"position_embedding_type\": \"absolute\",\r\n", " \"transformers_version\": \"4.31.0.dev0\",\r\n", " \"type_vocab_size\": 1,\r\n", " \"use_cache\": true,\r\n", " \"vocab_size\": 250002\r\n", "}\r\n", "\r\n", "Train set: 19136\r\n", "Valid set: 736\r\n", "#0: 0%| | 0/1914 [00:00