{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2023-12-05T04:04:03.809709Z", "iopub.status.busy": "2023-12-05T04:04:03.808818Z", "iopub.status.idle": "2023-12-05T04:04:21.308742Z", "shell.execute_reply": "2023-12-05T04:04:21.308088Z", "shell.execute_reply.started": "2023-12-05T04:04:03.809666Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.21.3)\n", "Collecting transformers\n", " Downloading transformers-4.35.2-py3-none-any.whl (7.9 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m52.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", "\u001b[?25hRequirement already satisfied: datasets in 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install -U transformers datasets evaluate accelerate" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-12-05T04:04:21.310409Z", "iopub.status.busy": "2023-12-05T04:04:21.310130Z", "iopub.status.idle": "2023-12-05T04:04:21.783747Z", "shell.execute_reply": "2023-12-05T04:04:21.783079Z", "shell.execute_reply.started": "2023-12-05T04:04:21.310381Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n", "Token is valid (permission: write).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ], "source": [ "from huggingface_hub import login\n", "\n", "login('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')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-12-05T04:04:21.785174Z", "iopub.status.busy": "2023-12-05T04:04:21.784648Z", "iopub.status.idle": "2023-12-05T04:04:24.580049Z", "shell.execute_reply": "2023-12-05T04:04:24.579275Z", "shell.execute_reply.started": "2023-12-05T04:04:21.785148Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4332346192424ecba545a41bfa038983", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/714 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Unnamed: 0booknamesimansektextseiftopiclabel
00עטרת זקנים על אורח חייםאאומיד כשיעור משנתו ואינו רוצ' לישן יטול ידיו אף...אNone0
11עטרת זקנים על אורח חייםאבהמשכים לעסוק בתור' ילבוש עצמו כראוי להכין לקרא...בNone0
22עטרת זקנים על אורח חייםאגראוי. צריך כ\"א להשתת' עם צער' של רחל לבכות בכל...גNone0
33עטרת זקנים על אורח חייםאדטוב לומר סדר הקרבנות דוקא ביום רק משנת איזהו מ...הNone0
44עטרת זקנים על אורח חייםאהכשיסיים. אחר חטאת ואשם ודאי לא יאמר משום דהאמי...זNone0
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4260342603אשל אברהם על שולחן ערוך אורח חייםתרצהבמלשלם. עיין רא\"ש סוכה דף מ\"ה ות' בי\"ד סי' תל\"ד:בNone0
4260442604אשל אברהם על שולחן ערוך אורח חייםתרצואשלא לעשות. וכשחל בע\"ש מותר לגלח לכבוד שבת לכ\"...אNone0
4260542605אשל אברהם על שולחן ערוך אורח חייםתרצובותענית. ות\"ח שרי אלא דצריך למיתב תענית לתענית...גNone0
4260642606אשל אברהם על שולחן ערוך אורח חייםתרצוגנוהגים. ע' באר היטב לי\"ד סי' ת\"א וכמ\"ש שם:דNone0
4260742607אשל אברהם על שולחן ערוך אורח חייםתרצודבמקרא מגילה. ע' ת' בי\"ד סי' תצ\"ו:זNone0
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42608 rows × 8 columns

\n", "" ], "text/plain": [ " Unnamed: 0 bookname siman sek \\\n", "0 0 עטרת זקנים על אורח חיים א א \n", "1 1 עטרת זקנים על אורח חיים א ב \n", "2 2 עטרת זקנים על אורח חיים א ג \n", "3 3 עטרת זקנים על אורח חיים א ד \n", "4 4 עטרת זקנים על אורח חיים א ה \n", "... ... ... ... .. \n", "42603 42603 אשל אברהם על שולחן ערוך אורח חיים תרצה ב \n", "42604 42604 אשל אברהם על שולחן ערוך אורח חיים תרצו א \n", "42605 42605 אשל אברהם על שולחן ערוך אורח חיים תרצו ב \n", "42606 42606 אשל אברהם על שולחן ערוך אורח חיים תרצו ג \n", "42607 42607 אשל אברהם על שולחן ערוך אורח חיים תרצו ד \n", "\n", " text seif topic label \n", "0 ומיד כשיעור משנתו ואינו רוצ' לישן יטול ידיו אף... א None 0 \n", "1 המשכים לעסוק בתור' ילבוש עצמו כראוי להכין לקרא... ב None 0 \n", "2 ראוי. צריך כ\"א להשתת' עם צער' של רחל לבכות בכל... ג None 0 \n", "3 טוב לומר סדר הקרבנות דוקא ביום רק משנת איזהו מ... ה None 0 \n", "4 כשיסיים. אחר חטאת ואשם ודאי לא יאמר משום דהאמי... ז None 0 \n", "... ... ... ... ... \n", "42603 מלשלם. עיין רא\"ש סוכה דף מ\"ה ות' בי\"ד סי' תל\"ד: ב None 0 \n", "42604 שלא לעשות. וכשחל בע\"ש מותר לגלח לכבוד שבת לכ\"... א None 0 \n", "42605 ותענית. ות\"ח שרי אלא דצריך למיתב תענית לתענית... ג None 0 \n", "42606 נוהגים. ע' באר היטב לי\"ד סי' ת\"א וכמ\"ש שם: ד None 0 \n", "42607 במקרא מגילה. ע' ת' בי\"ד סי' תצ\"ו: ז None 0 \n", "\n", "[42608 rows x 8 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.DataFrame.from_dict(dataset[\"train\"])\n", "df" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-12-05T04:04:28.350152Z", "iopub.status.busy": "2023-12-05T04:04:28.349747Z", "iopub.status.idle": "2023-12-05T04:04:28.374612Z", "shell.execute_reply": "2023-12-05T04:04:28.373783Z", "shell.execute_reply.started": "2023-12-05T04:04:28.350115Z" } }, "outputs": [], "source": [ "df['text']=df['text'].dropna()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-12-05T04:04:28.377959Z", "iopub.status.busy": "2023-12-05T04:04:28.377725Z", "iopub.status.idle": "2023-12-05T04:04:28.387243Z", "shell.execute_reply": "2023-12-05T04:04:28.385891Z", "shell.execute_reply.started": "2023-12-05T04:04:28.377934Z" } }, "outputs": [], "source": [ "df['text']=df['text'].astype(str)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2023-12-05T04:04:28.391401Z", "iopub.status.busy": "2023-12-05T04:04:28.391173Z", "iopub.status.idle": "2023-12-05T04:04:28.439179Z", "shell.execute_reply": "2023-12-05T04:04:28.438089Z", "shell.execute_reply.started": "2023-12-05T04:04:28.391377Z" } }, "outputs": [], "source": [ "df.dropna(subset=['siman','seif'],how='any',inplace=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2023-12-05T04:04:28.442995Z", "iopub.status.busy": "2023-12-05T04:04:28.442679Z", "iopub.status.idle": "2023-12-05T04:04:28.457780Z", "shell.execute_reply": "2023-12-05T04:04:28.456539Z", "shell.execute_reply.started": "2023-12-05T04:04:28.442960Z" } }, "outputs": [], "source": [ "df['siman_seif']=df['siman']+' '+df['seif']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2023-12-05T04:04:28.461547Z", "iopub.status.busy": "2023-12-05T04:04:28.461289Z", "iopub.status.idle": "2023-12-05T04:04:28.611254Z", "shell.execute_reply": "2023-12-05T04:04:28.610251Z", "shell.execute_reply.started": "2023-12-05T04:04:28.461522Z" } }, "outputs": [], "source": [ "from datasets import Dataset\n", "dataset['train'] = Dataset.from_pandas(df)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2023-12-05T04:04:28.619670Z", "iopub.status.busy": "2023-12-05T04:04:28.619291Z", "iopub.status.idle": "2023-12-05T04:04:28.641413Z", "shell.execute_reply": "2023-12-05T04:04:28.640389Z", 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" 'נה ב',\n", " 'נה ג',\n", " 'נה ד',\n", " 'נה ה',\n", " 'נה ו',\n", " 'נה ז',\n", " 'נה ח',\n", " 'נה ט',\n", " 'נה טו',\n", " 'נה טז',\n", " 'נה י',\n", " 'נה יא',\n", " 'נה יב',\n", " 'נה יג',\n", " 'נה יד',\n", " 'נה יז',\n", " 'נה יח',\n", " 'נה יט',\n", " 'נה כ',\n", " 'נה כא',\n", " 'נה כב',\n", " 'נה א',\n", " 'נה ב',\n", " 'נה ג',\n", " 'נה ד',\n", " 'נה ה',\n", " 'נה ו',\n", " 'נה ז',\n", " 'נה טז',\n", " 'נה י',\n", " 'נה יג',\n", " 'נה יד',\n", " 'נה יט',\n", " 'נה כ',\n", " 'נה כא',\n", " 'נה כב',\n", " 'נו א',\n", " 'נו ב',\n", " 'נו ג',\n", " 'נו ד',\n", " 'נו ה',\n", " 'נו א',\n", " 'נו ב',\n", " 'נו ג',\n", " 'נז א',\n", " 'נז ב',\n", " 'נז א',\n", " 'נח א',\n", " 'נח ב',\n", " 'נח ג',\n", " 'נח ד',\n", " 'נח ה',\n", " 'נח ו',\n", " 'נח ז',\n", " 'נח א',\n", " 'נח ב',\n", " 'נח ג',\n", " 'נח ד',\n", " 'נח ה',\n", " 'נח ו',\n", " 'נח ז',\n", " 'נט א',\n", " 'נט ב',\n", " 'נט ג',\n", " 'נט ד',\n", " 'נט ה',\n", " 'נט א',\n", " 'נט ב',\n", " 'נט ג',\n", " 'נט ד',\n", " 'נט ה',\n", " 'ס א',\n", " 'ס ב',\n", " 'ס ג',\n", " 'ס ד',\n", " 'ס ה',\n", " 'ס א',\n", " 'ס ב',\n", " 'ס ג',\n", " 'ס ד',\n", " 'ס ה',\n", " 'סא א',\n", " 'סא ב',\n", " 'סא ג',\n", " 'סא ד',\n", " 'סא ה',\n", " 'סא ו',\n", " 'סא ז',\n", " 'סא ח',\n", " 'סא ט',\n", " 'סא טו',\n", " 'סא טז',\n", " 'סא י',\n", " 'סא יא',\n", " 'סא יב',\n", " 'סא יג',\n", " 'סא יד',\n", " 'סא יז',\n", " 'סא יח',\n", " 'סא יט',\n", " 'סא כ',\n", " 'סא כא',\n", " 'סא כב',\n", " 'סא כג',\n", " 'סא כד',\n", " 'סא כה',\n", " 'סא כו',\n", " 'סא א',\n", " 'סא ג',\n", " 'סא ה',\n", " 'סא ו',\n", " 'סא ז',\n", " 'סא ט',\n", " 'סא י',\n", " 'סא יא',\n", " 'סא יב',\n", " 'סא יג',\n", " 'סא יד',\n", " 'סא כ',\n", " 'סא כא',\n", " 'סא כג',\n", " 'סא כד',\n", " 'סא כה',\n", " 'סא כו',\n", " 'סב א',\n", " 'סב ב',\n", " 'סב ג',\n", " 'סב ד',\n", " 'סב ה',\n", " 'סב א',\n", " 'סב ב',\n", " 'סב ד',\n", " 'סב ה',\n", " 'סג א',\n", " 'סג ב',\n", " 'סג ג',\n", " 'סג ד',\n", " 'סג ה',\n", " 'סג ו',\n", " 'סג ז',\n", " 'סג ח',\n", " 'סג ט',\n", " 'סג א',\n", " 'סג ב',\n", " 'סג ג',\n", " 'סג ד',\n", " 'סג ה',\n", " 'סג ו',\n", " 'סד א',\n", " 'סד ב',\n", " 'סד ג',\n", " 'סד ד',\n", " 'סד ב',\n", " 'סד ד',\n", " 'סה א',\n", " 'סה ב',\n", " 'סה ג',\n", " 'סה א',\n", " 'סה ב',\n", " 'סו א',\n", " 'סו ב',\n", " 'סו ג',\n", " 'סו ד',\n", " 'סו ה',\n", " 'סו ו',\n", " 'סו ז',\n", " 'סו ח',\n", " 'סו ט',\n", " 'סו י',\n", " 'סו א',\n", " 'סו ב',\n", " 'סו ג',\n", " 'סו ד',\n", " 'סו ה',\n", " 'סו ז',\n", " 'סו ח',\n", " 'סו י',\n", " 'סז א',\n", " 'סז א',\n", " 'סח א',\n", " 'סח א',\n", " 'סט א',\n", " 'סט ב',\n", " 'סט א',\n", " 'סט ב',\n", " 'ע א',\n", " 'ע ב',\n", " 'ע ג',\n", " 'ע ד',\n", " 'ע ה',\n", " 'ע א',\n", " 'ע ג',\n", " 'ע ד',\n", " 'ע ה',\n", " 'עא א',\n", " 'עא ב',\n", " 'עא ג',\n", " 'עא ד',\n", " 'עא ה',\n", " 'עא ו',\n", " 'עא ז',\n", " 'עא א',\n", " 'עא ב',\n", " 'עא ז',\n", " 'עב א',\n", " 'עב ב',\n", " 'עב ג',\n", " 'עב ד',\n", " 'עב ה',\n", " 'עב א',\n", " 'עב ב',\n", " 'עב ג',\n", " 'עב ד',\n", " 'עב ה',\n", " 'עג א',\n", " 'עג ב',\n", " 'עג ג',\n", " 'עג ד',\n", " 'עג א',\n", " 'עג ב',\n", " 'עג ג',\n", " 'עג ד',\n", " 'עד א',\n", " 'עד ב',\n", " 'עד ג',\n", " 'עד ד',\n", " 'עד ה',\n", " 'עד ו',\n", " 'עד א',\n", " 'עד ב',\n", " 'עד ג',\n", " 'עד ד',\n", " 'עד ה',\n", " 'עד ו',\n", " 'עה א',\n", " 'עה ב',\n", " 'עה ג',\n", " 'עה ד',\n", " 'עה ה',\n", " 'עה ו',\n", " 'עה א',\n", " 'עה ב',\n", " 'עה ג',\n", " 'עה ד',\n", " 'עה ו',\n", " 'עו א',\n", " 'עו ב',\n", " 'עו ג',\n", " 'עו ד',\n", " 'עו ה',\n", " 'עו ו',\n", " 'עו ז',\n", " 'עו ח',\n", " 'עו ב',\n", " 'עו ג',\n", " 'עו ד',\n", " 'עו ה',\n", " 'עו ו',\n", " 'עו ז',\n", " 'עו ח',\n", " 'עז א',\n", " 'עז ב',\n", " 'עז ב',\n", " 'עח א',\n", " 'עח א',\n", " 'עט א',\n", " 'עט ב',\n", " 'עט ג',\n", " 'עט ד',\n", " 'עט ה',\n", " 'עט ו',\n", " 'עט ז',\n", " 'עט ח',\n", " 'עט ט',\n", " 'עט א',\n", " 'עט ב',\n", " 'עט ג',\n", " 'עט ה',\n", " 'עט ו',\n", " 'עט ז',\n", " 'עט ט',\n", " 'פ א',\n", " 'פ א',\n", " 'פא א',\n", " 'פא ב',\n", " 'פא א',\n", " 'פא ב',\n", " 'פב א',\n", " 'פב ב',\n", " 'פב א',\n", " 'פב ב',\n", " 'פג א',\n", " ...]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#topics=df[\"bookname\"].unique().tolist() #use this for book classification\n", "simanim=df[\"siman_seif\"].unique().tolist()\n", "simanim.sort()\n", "simanim" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2023-12-05T04:04:28.645806Z", "iopub.status.busy": "2023-12-05T04:04:28.645511Z", "iopub.status.idle": "2023-12-05T04:04:28.923491Z", "shell.execute_reply": "2023-12-05T04:04:28.922623Z", "shell.execute_reply.started": "2023-12-05T04:04:28.645771Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f8e912baca3a40fabbf93813f1dd6455", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Casting the dataset: 0%| | 0/42608 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Tracking run with wandb version 0.13.4" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /notebooks/wandb/run-20231205_040508-3ptlrafb" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run graceful-wood-31 to Weights & Biases (docs)
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Serializing object of type dict that is 295000 bytes\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Serializing object of type dict that is 295000 bytes\n", "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " \n", " [ 6430/35955 1:15:24 < 5:46:20, 1.42 it/s, Epoch 2.68/15]\n", "
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EpochTraining LossValidation LossAccuracy
15.2813004.9889750.291716
24.6035004.6066490.330674

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "training_args = TrainingArguments(\n", " output_dir=\"halacha-siman-seif-classifier\",\n", " learning_rate=2e-5,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " num_train_epochs=15,\n", " weight_decay=0.01,\n", " evaluation_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " load_best_model_at_end=True,\n", " push_to_hub=True,\n", " resume_from_checkpoint=True \n", "\n", ")\n", "\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=tokenized_dataset[\"train\"],\n", " eval_dataset=tokenized_dataset[\"test\"],\n", " tokenizer=tokenizer,\n", " data_collator=data_collator,\n", " compute_metrics=compute_metrics\n", ")\n", "\n", "trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from transformers import pipeline\n", "\n", "classifier = pipeline( model=\"sivan22/halacha-siman-seif-classifier\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "text = \"כמה פעמים נוטלים ידים לפני אכילת לחם\"\n", "classifier(text)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 4 }