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created datasets

Browse files
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
9
+ "import numpy as np\n",
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+ "import pandas as pd\n",
11
+ "import chess\n",
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+ "import chess.pgn\n",
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+ "import os\n",
14
+ "from tqdm import tqdm\n",
15
+ "import json"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 98%|█████████▊| 172350/175000 [38:39<00:35, 74.29it/s] \n"
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+ ]
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+ },
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+ {
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+ "ename": "UnicodeDecodeError",
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+ "evalue": "'utf-8' codec can't decode byte 0xfc in position 3540: invalid start byte",
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+ "output_type": "error",
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+ "traceback": [
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+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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+ "\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)",
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+ "\u001b[1;32ms:\\Documents\\chess_engine\\clean_data.ipynb Cell 2\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/s%3A/Documents/chess_engine/clean_data.ipynb#W1sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m games\u001b[39m.\u001b[39mappend(game\u001b[39m.\u001b[39maccept(exporter)\u001b[39m.\u001b[39mreplace(\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m) \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/s%3A/Documents/chess_engine/clean_data.ipynb#W1sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m count \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/s%3A/Documents/chess_engine/clean_data.ipynb#W1sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m game \u001b[39m=\u001b[39m chess\u001b[39m.\u001b[39;49mpgn\u001b[39m.\u001b[39;49mread_game(game_file)\n\u001b[0;32m <a href='vscode-notebook-cell:/s%3A/Documents/chess_engine/clean_data.ipynb#W1sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m pbar\u001b[39m.\u001b[39mupdate(\u001b[39m1\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/s%3A/Documents/chess_engine/clean_data.ipynb#W1sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mchess_game_data.txt\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m'\u001b[39m\u001b[39ma\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mas\u001b[39;00m data_file:\n",
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+ "File \u001b[1;32ms:\\packages\\envs\\chess\\Lib\\site-packages\\chess\\pgn.py:1673\u001b[0m, in \u001b[0;36mread_game\u001b[1;34m(handle, Visitor)\u001b[0m\n\u001b[0;32m 1671\u001b[0m \u001b[39mwhile\u001b[39;00m line \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39m}\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m line:\n\u001b[0;32m 1672\u001b[0m comment_lines\u001b[39m.\u001b[39mappend(line)\n\u001b[1;32m-> 1673\u001b[0m line \u001b[39m=\u001b[39m handle\u001b[39m.\u001b[39mreadline()\n\u001b[0;32m 1675\u001b[0m \u001b[39mif\u001b[39;00m line:\n\u001b[0;32m 1676\u001b[0m close_index \u001b[39m=\u001b[39m line\u001b[39m.\u001b[39mfind(\u001b[39m\"\u001b[39m\u001b[39m}\u001b[39m\u001b[39m\"\u001b[39m)\n",
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+ "File \u001b[1;32m<frozen codecs>:322\u001b[0m, in \u001b[0;36mdecode\u001b[1;34m(self, input, final)\u001b[0m\n",
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+ "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xfc in position 3540: invalid start byte"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "count = 0\n",
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+ "last = 0\n",
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+ "with tqdm(total=175000) as pbar:\n",
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+ " for root, d_name, files in os.walk(\"S:\\Documents\\ccc-data\\ccc-data\"):\n",
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+ " for file in files:\n",
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+ " if os.path.splitext(file)[1] == '.pgn':\n",
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+ " pgn_path = os.path.join(root, file)\n",
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+ " with open(pgn_path) as game_file:\n",
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+ " games = []\n",
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+ " game = chess.pgn.read_game(game_file)\n",
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+ " while game:\n",
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+ " exporter = chess.pgn.StringExporter(headers=False, variations=False, comments=False)\n",
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+ " games.append(game.accept(exporter).replace(\"\\n\", \" \") + '\\n')\n",
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+ " count += 1\n",
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+ " game = chess.pgn.read_game(game_file)\n",
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+ " pbar.update(1)\n",
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+ " with open(\"chess_game_data.txt\", 'a') as data_file:\n",
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+ " data_file.writelines(games)\n",
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+ "\n",
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+ "\n"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 6,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 0%| | 28/1000000 [00:00<2:01:47, 136.85it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "lichess_elite_2021-12.pgn\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 49%|████▉ | 490442/1000000 [51:38<2:35:36, 54.58it/s] "
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "lichess_elite_2022-01.pgn\n"
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+ ]
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+ "text": [
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+ "text": [
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+ "2741369it [4:38:51, 53.44it/s] "
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+ "text": [
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+ "lichess_elite_2022-08.pgn\n"
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+ ]
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+ },
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+ "output_type": "stream",
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+ "text": [
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+ "3087892it [5:15:18, 50.95it/s] "
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "lichess_elite_2022-09.pgn\n"
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+ ]
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+ "3761579it [6:28:19, 67.38it/s] "
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+ ]
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+ },
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+ "lichess_elite_2022-11.pgn\n"
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+ ]
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+ "text": [
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+ "4064728it [7:00:58, 61.31it/s] "
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "lichess_elite_2022-12.pgn\n"
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+ ]
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+ "4343360it [7:29:28, 161.05it/s]\n"
245
+ ]
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+ }
247
+ ],
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+ "source": [
249
+ "count = 0\n",
250
+ "last = 0\n",
251
+ "with tqdm(total=1000000) as pbar:\n",
252
+ " for root, d_name, files in os.walk(\"S:\\Documents\\Lichess Elite Database\\Latest\"):\n",
253
+ " for file in files:\n",
254
+ " print(file)\n",
255
+ " if os.path.splitext(file)[1] == '.pgn':\n",
256
+ " pgn_path = os.path.join(root, file)\n",
257
+ " with open(pgn_path) as game_file:\n",
258
+ " games = []\n",
259
+ " try:\n",
260
+ " game = chess.pgn.read_game(game_file)\n",
261
+ " while game:\n",
262
+ " exporter = chess.pgn.StringExporter(headers=False, variations=False, comments=False)\n",
263
+ " games.append(game.accept(exporter).replace(\"\\n\", \" \") + '\\n')\n",
264
+ " count += 1\n",
265
+ " game = chess.pgn.read_game(game_file)\n",
266
+ " pbar.update(1)\n",
267
+ " except Exception as e:\n",
268
+ " print(e)\n",
269
+ "\n",
270
+ " with open(\"lichess_game_data.txt\", 'a') as data_file:\n",
271
+ " data_file.writelines(games)"
272
+ ]
273
+ },
274
+ {
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+ "cell_type": "code",
276
+ "execution_count": null,
277
+ "metadata": {},
278
+ "outputs": [],
279
+ "source": []
280
+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "chess",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.11.5"
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+ },
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+ "orig_nbformat": 4
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+ }
lichess_game_data.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ oid sha256:fbb174f8789c9e7c4eae50f6a2bf19a3757aa13788182a3cd8c61acb039d2e97
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+ size 2249268189
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1
+ {
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+ "cells": [
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+ {
4
+ "cell_type": "code",
5
+ "execution_count": 122,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "from datasets import load_dataset\n",
10
+ "import tokenize\n",
11
+ "from tokenizers import SentencePieceBPETokenizer, pre_tokenizers, ByteLevelBPETokenizer, normalizers\n",
12
+ "from transformers import AutoTokenizer"
13
+ ]
14
+ },
15
+ {
16
+ "cell_type": "code",
17
+ "execution_count": 132,
18
+ "metadata": {},
19
+ "outputs": [
20
+ {
21
+ "name": "stderr",
22
+ "output_type": "stream",
23
+ "text": [
24
+ "Downloading data files: 100%|██████████| 1/1 [00:00<?, ?it/s]\n",
25
+ "Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 111.41it/s]\n",
26
+ "Generating train split: 4343360 examples [00:24, 178753.60 examples/s]\n"
27
+ ]
28
+ }
29
+ ],
30
+ "source": [
31
+ "datasets = load_dataset(\"text\", data_files={\"train\": \"lichess_game_data.txt\"})"
32
+ ]
33
+ },
34
+ {
35
+ "cell_type": "code",
36
+ "execution_count": 133,
37
+ "metadata": {},
38
+ "outputs": [],
39
+ "source": [
40
+ "def get_training_corpus():\n",
41
+ " dataset = datasets[\"train\"]\n",
42
+ " for start_idx in range(0, len(dataset), 1000):\n",
43
+ " samples = dataset[start_idx : start_idx + 1000]\n",
44
+ " yield samples['text']"
45
+ ]
46
+ },
47
+ {
48
+ "cell_type": "code",
49
+ "execution_count": 134,
50
+ "metadata": {},
51
+ "outputs": [],
52
+ "source": [
53
+ "tokenizer = SentencePieceBPETokenizer()"
54
+ ]
55
+ },
56
+ {
57
+ "cell_type": "code",
58
+ "execution_count": 135,
59
+ "metadata": {},
60
+ "outputs": [],
61
+ "source": [
62
+ "tokenizer.normalizer = normalizers.Sequence([normalizers.Replace(\".\", \"\"), normalizers.Replace(\"1-0\", \"\"), normalizers.Replace(\"0-1\", \"\"), normalizers.Replace(\"1/2-1/2\", \"\")])"
63
+ ]
64
+ },
65
+ {
66
+ "cell_type": "code",
67
+ "execution_count": 136,
68
+ "metadata": {},
69
+ "outputs": [],
70
+ "source": [
71
+ "tokenizer.pre_tokenizer = pre_tokenizers.Split(\" \", 'removed')"
72
+ ]
73
+ },
74
+ {
75
+ "cell_type": "code",
76
+ "execution_count": 137,
77
+ "metadata": {},
78
+ "outputs": [],
79
+ "source": [
80
+ "tokenizer.train_from_iterator(get_training_corpus(), vocab_size=10000000, min_frequency=0, show_progress=True, special_tokens=[\"<s>\", \"<pad>\", \"</s>\", \"<unk>\", \"<cls>\", \"<sep>\", \"<mask>\"])"
81
+ ]
82
+ },
83
+ {
84
+ "cell_type": "code",
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+ "execution_count": 138,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
89
+ "test_str = '1. e4 e5 2. Nf3 Nc6 3. d4 exd4 4. Nxd4 Nf6 5. Nxc6 bxc6 6. Bd3 d5 7. exd5 cxd5 8. O-O Be7 9. c4 O-O 10. cxd5 Bb7 11. Nc3 Nxd5 12. Bd2 Nb4 13. Be4 Bxe4 14. Nxe4 c5 15. Bc3 Qxd1 16. Rfxd1 Rad8 17. a3 Nd5 18. Ba5 Nb6 19. Nc3 Bf6 20. Rac1 Bxc3 21. Bxc3 Rxd1+ 22. Rxd1 f6 23. Kf1 Kf7 24. Rc1 Rd8 25. Ke2 Ke6 26. Bd2 c4 27. Be3 Rb8 28. Rc2 Rb7 29. h3 Kd5 30. Rd2+ Ke6 31. Rd4 a5 32. g3 Nd5 33. Bc1 Nb6 34. h4 g6 35. Bd2 Nd5 36. Bc1 Nb6 37. Bd2 Nd5 38. Rxc4 Rxb2 39. Ke1 Nb6 40. Re4+ Kd5 41. Re7 Rb1+ 42. Ke2 Nc4 43. Bc3 f5 44. Rd7+ Ke6 45. Rxh7 Rb3 46. Bd4 Rxa3 47. Rc7 Ra2+ 48. Kf3 Kd5 49. Rd7+ Kc6 50. Rg7 Kd5 51. Rd7+ Kc6 52. Rg7 Kd5 53. Rd7+ 1/2-1/2'"
90
+ ]
91
+ },
92
+ {
93
+ "cell_type": "code",
94
+ "execution_count": 139,
95
+ "metadata": {},
96
+ "outputs": [
97
+ {
98
+ "data": {
99
+ "text/plain": [
100
+ "[('1', (0, 1)),\n",
101
+ " ('e4', (2, 4)),\n",
102
+ " ('e5', (5, 7)),\n",
103
+ " ('2', (8, 9)),\n",
104
+ " ('Nf3', (10, 13)),\n",
105
+ " ('Nc6', (14, 17)),\n",
106
+ " ('3', (18, 19)),\n",
107
+ " ('d4', (20, 22)),\n",
108
+ " ('exd4', (23, 27)),\n",
109
+ " ('4', (28, 29)),\n",
110
+ " ('Nxd4', (30, 34)),\n",
111
+ " ('Nf6', (35, 38)),\n",
112
+ " ('5', (39, 40)),\n",
113
+ " ('Nxc6', (41, 45)),\n",
114
+ " ('bxc6', (46, 50)),\n",
115
+ " ('6', (51, 52)),\n",
116
+ " ('Bd3', (53, 56)),\n",
117
+ " ('d5', (57, 59)),\n",
118
+ " ('7', (60, 61)),\n",
119
+ " ('exd5', (62, 66)),\n",
120
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121
+ " ('8', (72, 73)),\n",
122
+ " ('O-O', (74, 77)),\n",
123
+ " ('Be7', (78, 81)),\n",
124
+ " ('9', (82, 83)),\n",
125
+ " ('c4', (84, 86)),\n",
126
+ " ('O-O', (87, 90)),\n",
127
+ " ('10', (91, 93)),\n",
128
+ " ('cxd5', (94, 98)),\n",
129
+ " ('Bb7', (99, 102)),\n",
130
+ " ('11', (103, 105)),\n",
131
+ " ('Nc3', (106, 109)),\n",
132
+ " ('Nxd5', (110, 114)),\n",
133
+ " ('12', (115, 117)),\n",
134
+ " ('Bd2', (118, 121)),\n",
135
+ " ('Nb4', (122, 125)),\n",
136
+ " ('13', (126, 128)),\n",
137
+ " ('Be4', (129, 132)),\n",
138
+ " ('Bxe4', (133, 137)),\n",
139
+ " ('14', (138, 140)),\n",
140
+ " ('Nxe4', (141, 145)),\n",
141
+ " ('c5', (146, 148)),\n",
142
+ " ('15', (149, 151)),\n",
143
+ " ('Bc3', (152, 155)),\n",
144
+ " ('Qxd1', (156, 160)),\n",
145
+ " ('16', (161, 163)),\n",
146
+ " ('Rfxd1', (164, 169)),\n",
147
+ " ('Rad8', (170, 174)),\n",
148
+ " ('17', (175, 177)),\n",
149
+ " ('a3', (178, 180)),\n",
150
+ " ('Nd5', (181, 184)),\n",
151
+ " ('18', (185, 187)),\n",
152
+ " ('Ba5', (188, 191)),\n",
153
+ " ('Nb6', (192, 195)),\n",
154
+ " ('19', (196, 198)),\n",
155
+ " ('Nc3', (199, 202)),\n",
156
+ " ('Bf6', (203, 206)),\n",
157
+ " ('20', (207, 209)),\n",
158
+ " ('Rac1', (210, 214)),\n",
159
+ " ('Bxc3', (215, 219)),\n",
160
+ " ('21', (220, 222)),\n",
161
+ " ('Bxc3', (223, 227)),\n",
162
+ " ('Rxd1+', (228, 233)),\n",
163
+ " ('22', (234, 236)),\n",
164
+ " ('Rxd1', (237, 241)),\n",
165
+ " ('f6', (242, 244)),\n",
166
+ " ('23', (245, 247)),\n",
167
+ " ('Kf1', (248, 251)),\n",
168
+ " ('Kf7', (252, 255)),\n",
169
+ " ('24', (256, 258)),\n",
170
+ " ('Rc1', (259, 262)),\n",
171
+ " ('Rd8', (263, 266)),\n",
172
+ " ('25', (267, 269)),\n",
173
+ " ('Ke2', (270, 273)),\n",
174
+ " ('Ke6', (274, 277)),\n",
175
+ " ('26', (278, 280)),\n",
176
+ " ('Bd2', (281, 284)),\n",
177
+ " ('c4', (285, 287)),\n",
178
+ " ('27', (288, 290)),\n",
179
+ " ('Be3', (291, 294)),\n",
180
+ " ('Rb8', (295, 298)),\n",
181
+ " ('28', (299, 301)),\n",
182
+ " ('Rc2', (302, 305)),\n",
183
+ " ('Rb7', (306, 309)),\n",
184
+ " ('29', (310, 312)),\n",
185
+ " ('h3', (313, 315)),\n",
186
+ " ('Kd5', (316, 319)),\n",
187
+ " ('30', (320, 322)),\n",
188
+ " ('Rd2+', (323, 327)),\n",
189
+ " ('Ke6', (328, 331)),\n",
190
+ " ('31', (332, 334)),\n",
191
+ " ('Rd4', (335, 338)),\n",
192
+ " ('a5', (339, 341)),\n",
193
+ " ('32', (342, 344)),\n",
194
+ " ('g3', (345, 347)),\n",
195
+ " ('Nd5', (348, 351)),\n",
196
+ " ('33', (352, 354)),\n",
197
+ " ('Bc1', (355, 358)),\n",
198
+ " ('Nb6', (359, 362)),\n",
199
+ " ('34', (363, 365)),\n",
200
+ " ('h4', (366, 368)),\n",
201
+ " ('g6', (369, 371)),\n",
202
+ " ('35', (372, 374)),\n",
203
+ " ('Bd2', (375, 378)),\n",
204
+ " ('Nd5', (379, 382)),\n",
205
+ " ('36', (383, 385)),\n",
206
+ " ('Bc1', (386, 389)),\n",
207
+ " ('Nb6', (390, 393)),\n",
208
+ " ('37', (394, 396)),\n",
209
+ " ('Bd2', (397, 400)),\n",
210
+ " ('Nd5', (401, 404)),\n",
211
+ " ('38', (405, 407)),\n",
212
+ " ('Rxc4', (408, 412)),\n",
213
+ " ('Rxb2', (413, 417)),\n",
214
+ " ('39', (418, 420)),\n",
215
+ " ('Ke1', (421, 424)),\n",
216
+ " ('Nb6', (425, 428)),\n",
217
+ " ('40', (429, 431)),\n",
218
+ " ('Re4+', (432, 436)),\n",
219
+ " ('Kd5', (437, 440)),\n",
220
+ " ('41', (441, 443)),\n",
221
+ " ('Re7', (444, 447)),\n",
222
+ " ('Rb1+', (448, 452)),\n",
223
+ " ('42', (453, 455)),\n",
224
+ " ('Ke2', (456, 459)),\n",
225
+ " ('Nc4', (460, 463)),\n",
226
+ " ('43', (464, 466)),\n",
227
+ " ('Bc3', (467, 470)),\n",
228
+ " ('f5', (471, 473)),\n",
229
+ " ('44', (474, 476)),\n",
230
+ " ('Rd7+', (477, 481)),\n",
231
+ " ('Ke6', (482, 485)),\n",
232
+ " ('45', (486, 488)),\n",
233
+ " ('Rxh7', (489, 493)),\n",
234
+ " ('Rb3', (494, 497)),\n",
235
+ " ('46', (498, 500)),\n",
236
+ " ('Bd4', (501, 504)),\n",
237
+ " ('Rxa3', (505, 509)),\n",
238
+ " ('47', (510, 512)),\n",
239
+ " ('Rc7', (513, 516)),\n",
240
+ " ('Ra2+', (517, 521)),\n",
241
+ " ('48', (522, 524)),\n",
242
+ " ('Kf3', (525, 528)),\n",
243
+ " ('Kd5', (529, 532)),\n",
244
+ " ('49', (533, 535)),\n",
245
+ " ('Rd7+', (536, 540)),\n",
246
+ " ('Kc6', (541, 544)),\n",
247
+ " ('50', (545, 547)),\n",
248
+ " ('Rg7', (548, 551)),\n",
249
+ " ('Kd5', (552, 555)),\n",
250
+ " ('51', (556, 558)),\n",
251
+ " ('Rd7+', (559, 563)),\n",
252
+ " ('Kc6', (564, 567)),\n",
253
+ " ('52', (568, 570)),\n",
254
+ " ('Rg7', (571, 574)),\n",
255
+ " ('Kd5', (575, 578)),\n",
256
+ " ('53', (579, 581)),\n",
257
+ " ('Rd7+', (582, 586))]"
258
+ ]
259
+ },
260
+ "execution_count": 139,
261
+ "metadata": {},
262
+ "output_type": "execute_result"
263
+ }
264
+ ],
265
+ "source": [
266
+ "tokenizer.pre_tokenizer.pre_tokenize_str(tokenizer.normalizer.normalize_str(test_str))"
267
+ ]
268
+ },
269
+ {
270
+ "cell_type": "code",
271
+ "execution_count": 140,
272
+ "metadata": {},
273
+ "outputs": [
274
+ {
275
+ "data": {
276
+ "text/plain": [
277
+ "['1',\n",
278
+ " 'e4',\n",
279
+ " 'e5',\n",
280
+ " '2',\n",
281
+ " 'Nf3',\n",
282
+ " 'Nc6',\n",
283
+ " '3',\n",
284
+ " 'd4',\n",
285
+ " 'exd4',\n",
286
+ " '4',\n",
287
+ " 'Nxd4',\n",
288
+ " 'Nf6',\n",
289
+ " '5',\n",
290
+ " 'Nxc6',\n",
291
+ " 'bxc6',\n",
292
+ " '6',\n",
293
+ " 'Bd3',\n",
294
+ " 'd5',\n",
295
+ " '7',\n",
296
+ " 'exd5',\n",
297
+ " 'cxd5',\n",
298
+ " '8',\n",
299
+ " 'O-O',\n",
300
+ " 'Be7',\n",
301
+ " '9',\n",
302
+ " 'c4',\n",
303
+ " 'O-O',\n",
304
+ " '10',\n",
305
+ " 'cxd5',\n",
306
+ " 'Bb7',\n",
307
+ " '11',\n",
308
+ " 'Nc3',\n",
309
+ " 'Nxd5',\n",
310
+ " '12',\n",
311
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312
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313
+ " '13',\n",
314
+ " 'Be4',\n",
315
+ " 'Bxe4',\n",
316
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317
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318
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319
+ " '15',\n",
320
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321
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322
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323
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325
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326
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327
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329
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330
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331
+ " '19',\n",
332
+ " 'Nc3',\n",
333
+ " 'Bf6',\n",
334
+ " '20',\n",
335
+ " 'Rac1',\n",
336
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337
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338
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341
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342
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+ " '33',\n",
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386
+ " 'Bd2',\n",
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388
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389
+ " 'Rxc4',\n",
390
+ " 'Rxb2',\n",
391
+ " '39',\n",
392
+ " 'Ke1',\n",
393
+ " 'Nb6',\n",
394
+ " '40',\n",
395
+ " 'Re4+',\n",
396
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398
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399
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400
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407
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410
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411
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412
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413
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414
+ " 'Rxa3',\n",
415
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416
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417
+ " 'Ra2+',\n",
418
+ " '48',\n",
419
+ " 'Kf3',\n",
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422
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423
+ " 'Kc6',\n",
424
+ " '50',\n",
425
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427
+ " '51',\n",
428
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430
+ " '52',\n",
431
+ " 'Rg7',\n",
432
+ " 'Kd5',\n",
433
+ " '53',\n",
434
+ " 'Rd7+']"
435
+ ]
436
+ },
437
+ "execution_count": 140,
438
+ "metadata": {},
439
+ "output_type": "execute_result"
440
+ }
441
+ ],
442
+ "source": [
443
+ "tokenizer.encode(test_str).tokens"
444
+ ]
445
+ },
446
+ {
447
+ "cell_type": "code",
448
+ "execution_count": 142,
449
+ "metadata": {},
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+ "outputs": [
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+ {
452
+ "data": {
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+ "text/plain": [
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+ "[11,\n",
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610
+ " 224,\n",
611
+ " 785]"
612
+ ]
613
+ },
614
+ "execution_count": 142,
615
+ "metadata": {},
616
+ "output_type": "execute_result"
617
+ }
618
+ ],
619
+ "source": [
620
+ "tokenizer.encode(test_str)"
621
+ ]
622
+ },
623
+ {
624
+ "cell_type": "code",
625
+ "execution_count": 143,
626
+ "metadata": {},
627
+ "outputs": [
628
+ {
629
+ "data": {
630
+ "text/plain": [
631
+ "13677"
632
+ ]
633
+ },
634
+ "execution_count": 143,
635
+ "metadata": {},
636
+ "output_type": "execute_result"
637
+ }
638
+ ],
639
+ "source": [
640
+ "tokenizer.get_vocab_size()"
641
+ ]
642
+ },
643
+ {
644
+ "cell_type": "code",
645
+ "execution_count": null,
646
+ "metadata": {},
647
+ "outputs": [],
648
+ "source": []
649
+ }
650
+ ],
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