Hugging Face's logo

Datasets:
gbenson
/
webui-tokens-unlabelled

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Size:
1M - 10M
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files Community
1
gbenson commited on
Commit
2bb0146
·
verified ·
1 Parent(s): c22d8f3

Upload update.ipynb

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+ "model_module_version": "1.5.0",
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+ "state": {
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+ "_model_module": "@jupyter-widgets/controls",
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+ "_model_module_version": "1.5.0",
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+ "_model_name": "DescriptionStyleModel",
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+ "_view_count": null,
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+ "_view_module": "@jupyter-widgets/base",
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+ }
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+ },
1046
+ "cells": [
1047
+ {
1048
+ "cell_type": "markdown",
1049
+ "source": [
1050
+ "## Install the modified `dom-tokenizers`"
1051
+ ],
1052
+ "metadata": {
1053
+ "id": "p4cA_Al2R8Fm"
1054
+ }
1055
+ },
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+ {
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+ "cell_type": "code",
1058
+ "execution_count": null,
1059
+ "metadata": {
1060
+ "colab": {
1061
+ "base_uri": "https://localhost:8080/"
1062
+ },
1063
+ "id": "AxuqwFstR6UD",
1064
+ "outputId": "ce2d7275-520d-44fe-a48f-e570a08cab35"
1065
+ },
1066
+ "outputs": [
1067
+ {
1068
+ "output_type": "stream",
1069
+ "name": "stdout",
1070
+ "text": [
1071
+ "Cloning into 'dom-tokenizers'...\n",
1072
+ "remote: Enumerating objects: 613, done.\u001b[K\n",
1073
+ "remote: Counting objects: 100% (187/187), done.\u001b[K\n",
1074
+ "remote: Compressing objects: 100% (80/80), done.\u001b[K\n",
1075
+ "remote: Total 613 (delta 118), reused 144 (delta 96), pack-reused 426\u001b[K\n",
1076
+ "Receiving objects: 100% (613/613), 153.44 KiB | 946.00 KiB/s, done.\n",
1077
+ "Resolving deltas: 100% (335/335), done.\n"
1078
+ ]
1079
+ }
1080
+ ],
1081
+ "source": [
1082
+ "!git clone https://github.com/gbenson/dom-tokenizers.git -b emit-unique-tokens-0.0.18-pre1 --single-branch"
1083
+ ]
1084
+ },
1085
+ {
1086
+ "cell_type": "code",
1087
+ "source": [
1088
+ "!cd dom-tokenizers && pip install -e .[train]"
1089
+ ],
1090
+ "metadata": {
1091
+ "colab": {
1092
+ "base_uri": "https://localhost:8080/"
1093
+ },
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+ "id": "iURZKR40UCxY",
1095
+ "outputId": "96304e65-e45c-40c4-cd22-c2f032da8200",
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+ "collapsed": true
1097
+ },
1098
+ "execution_count": null,
1099
+ "outputs": [
1100
+ {
1101
+ "output_type": "stream",
1102
+ "name": "stdout",
1103
+ "text": [
1104
+ "Obtaining file:///content/dom-tokenizers\n",
1105
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
1106
+ " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n",
1107
+ " Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n",
1108
+ " Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
1109
+ "Collecting python-magic (from dom-tokenizers==0.0.18)\n",
1110
+ " Downloading python_magic-0.4.27-py2.py3-none-any.whl (13 kB)\n",
1111
+ "Requirement already satisfied: tokenizers in /usr/local/lib/python3.10/dist-packages (from dom-tokenizers==0.0.18) (0.19.1)\n",
1112
+ "Collecting unidecode (from dom-tokenizers==0.0.18)\n",
1113
+ " Downloading Unidecode-1.3.8-py3-none-any.whl (235 kB)\n",
1114
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m235.5/235.5 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1115
+ "\u001b[?25hCollecting vec64>0.0.2 (from dom-tokenizers==0.0.18)\n",
1116
+ " Downloading vec64-0.0.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13 kB)\n",
1117
+ "Collecting datasets (from dom-tokenizers==0.0.18)\n",
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+ " Downloading datasets-2.20.0-py3-none-any.whl (547 kB)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hRequirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from dom-tokenizers==0.0.18) (9.4.0)\n",
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+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from dom-tokenizers==0.0.18) (4.41.2)\n",
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+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (3.15.1)\n",
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+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (1.25.2)\n",
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+ "Collecting pyarrow>=15.0.0 (from datasets->dom-tokenizers==0.0.18)\n",
1125
+ " Downloading pyarrow-16.1.0-cp310-cp310-manylinux_2_28_x86_64.whl (40.8 MB)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hRequirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (0.6)\n",
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+ "Collecting dill<0.3.9,>=0.3.0 (from datasets->dom-tokenizers==0.0.18)\n",
1129
+ " Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (2.0.3)\n",
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+ "Collecting requests>=2.32.2 (from datasets->dom-tokenizers==0.0.18)\n",
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+ " Downloading requests-2.32.3-py3-none-any.whl (64 kB)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hRequirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (4.66.4)\n",
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+ "Collecting xxhash (from datasets->dom-tokenizers==0.0.18)\n",
1137
+ " Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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+ "\u001b[?25hCollecting multiprocess (from datasets->dom-tokenizers==0.0.18)\n",
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+ " Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.5.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (2023.6.0)\n",
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+ "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (3.9.5)\n",
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+ "Requirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (0.23.4)\n",
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+ "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (24.1)\n",
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+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (6.0.1)\n",
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+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->dom-tokenizers==0.0.18) (2024.5.15)\n",
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+ "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers->dom-tokenizers==0.0.18) (0.4.3)\n",
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+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (1.3.1)\n",
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+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (23.2.0)\n",
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+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (1.4.1)\n",
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+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (6.0.5)\n",
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+ "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (1.9.4)\n",
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+ "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (4.0.3)\n",
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+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets->dom-tokenizers==0.0.18) (4.12.2)\n",
1156
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets->dom-tokenizers==0.0.18) (3.3.2)\n",
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+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets->dom-tokenizers==0.0.18) (3.7)\n",
1158
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets->dom-tokenizers==0.0.18) (2.0.7)\n",
1159
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets->dom-tokenizers==0.0.18) (2024.6.2)\n",
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+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->dom-tokenizers==0.0.18) (2.8.2)\n",
1161
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->dom-tokenizers==0.0.18) (2023.4)\n",
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+ "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->dom-tokenizers==0.0.18) (2024.1)\n",
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+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets->dom-tokenizers==0.0.18) (1.16.0)\n",
1164
+ "Building wheels for collected packages: dom-tokenizers\n",
1165
+ " Building editable for dom-tokenizers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
1166
+ " Created wheel for dom-tokenizers: filename=dom_tokenizers-0.0.18-0.editable-py3-none-any.whl size=7367 sha256=874e55c8a7d8ef3a1e23890fd293fb93357b316bbec0803c196872264e5c356c\n",
1167
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-_kdcdqaq/wheels/ab/e4/d2/9b9419db21d26514602a3dbb7582a3c195f9229169192ca43b\n",
1168
+ "Successfully built dom-tokenizers\n",
1169
+ "Installing collected packages: xxhash, vec64, unidecode, requests, python-magic, pyarrow, dill, multiprocess, dom-tokenizers, datasets\n",
1170
+ " Attempting uninstall: requests\n",
1171
+ " Found existing installation: requests 2.31.0\n",
1172
+ " Uninstalling requests-2.31.0:\n",
1173
+ " Successfully uninstalled requests-2.31.0\n",
1174
+ " Attempting uninstall: pyarrow\n",
1175
+ " Found existing installation: pyarrow 14.0.2\n",
1176
+ " Uninstalling pyarrow-14.0.2:\n",
1177
+ " Successfully uninstalled pyarrow-14.0.2\n",
1178
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
1179
+ "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
1180
+ "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
1181
+ "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
1182
+ "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 dom-tokenizers-0.0.18 multiprocess-0.70.16 pyarrow-16.1.0 python-magic-0.4.27 requests-2.32.3 unidecode-1.3.8 vec64-0.0.3 xxhash-3.4.1\n"
1183
+ ]
1184
+ }
1185
+ ]
1186
+ },
1187
+ {
1188
+ "cell_type": "markdown",
1189
+ "source": [
1190
+ "## Train a tokenizer on `gbenson/webui-dom-snapshots`\n",
1191
+ "\n",
1192
+ "We don't need the tokenizer, but this `dom-tokenizers` is modified to output everything [`vec64`](https://pypi.org/project/vec64/) matched into a file as a side-effect."
1193
+ ],
1194
+ "metadata": {
1195
+ "id": "aXQW8CBmVOa4"
1196
+ }
1197
+ },
1198
+ {
1199
+ "cell_type": "code",
1200
+ "source": [
1201
+ "!train-tokenizer -n30000 -N4536 gbenson/webui-dom-snapshots"
1202
+ ],
1203
+ "metadata": {
1204
+ "colab": {
1205
+ "base_uri": "https://localhost:8080/"
1206
+ },
1207
+ "id": "CqHIS-8xVVKe",
1208
+ "outputId": "5ee13972-5f24-4af5-cb90-642267d66250"
1209
+ },
1210
+ "execution_count": 3,
1211
+ "outputs": [
1212
+ {
1213
+ "output_type": "stream",
1214
+ "name": "stdout",
1215
+ "text": [
1216
+ "Output directory: /content/dom-tokenizer-30k\n",
1217
+ "\n",
1218
+ "Downloading readme: 100% 9.16k/9.16k [00:00<00:00, 22.8MB/s]\n",
1219
+ "tokenizer_config.json: 100% 48.0/48.0 [00:00<00:00, 300kB/s]\n",
1220
+ "config.json: 100% 570/570 [00:00<00:00, 2.86MB/s]\n",
1221
+ "vocab.txt: 100% 232k/232k [00:00<00:00, 4.84MB/s]\n",
1222
+ "tokenizer.json: 100% 466k/466k [00:00<00:00, 2.32MB/s]\n",
1223
+ "Generating up to 30,000 tokens from 4,536 examples:\n",
1224
+ "\u001b[2K[00:00:01] Tokenize words ██████████████████ 845289 / 845289\n",
1225
+ "\u001b[2K[00:01:01] Count pairs ██████████████████ 845289 / 845289\n",
1226
+ "\u001b[2K[00:00:12] Compute merges ██████████████████ 23908 / 23908\n",
1227
+ "\n",
1228
+ "['training', 'complete']\n",
1229
+ "['token', '##izer', 'state', 'saved']\n",
1230
+ "['see', 'you', 'soon', '##!', '##!']\n"
1231
+ ]
1232
+ }
1233
+ ]
1234
+ },
1235
+ {
1236
+ "cell_type": "markdown",
1237
+ "source": [
1238
+ "## Merge the new tokens into the existing dataset"
1239
+ ],
1240
+ "metadata": {
1241
+ "id": "Uo_FraY1W5IK"
1242
+ }
1243
+ },
1244
+ {
1245
+ "cell_type": "code",
1246
+ "source": [
1247
+ "from datasets import load_dataset\n",
1248
+ "old_tokens = {row[\"text\"] for row in load_dataset(\"gbenson/webui-tokens-unlabelled\")[\"train\"]}\n",
1249
+ "print(len(old_tokens))"
1250
+ ],
1251
+ "metadata": {
1252
+ "id": "L_oxSzmFW-nL",
1253
+ "colab": {
1254
+ "base_uri": "https://localhost:8080/"
1255
+ },
1256
+ "outputId": "b5fb5717-f4a2-41b1-e3c9-5a3a0c1aa667"
1257
+ },
1258
+ "execution_count": 5,
1259
+ "outputs": [
1260
+ {
1261
+ "output_type": "stream",
1262
+ "name": "stdout",
1263
+ "text": [
1264
+ "1194369\n"
1265
+ ]
1266
+ }
1267
+ ]
1268
+ },
1269
+ {
1270
+ "cell_type": "code",
1271
+ "source": [
1272
+ "with open(\"unique-tokens\") as fp:\n",
1273
+ " new_tokens = {line.rstrip() for line in fp.readlines()}\n",
1274
+ "print(len(new_tokens))"
1275
+ ],
1276
+ "metadata": {
1277
+ "colab": {
1278
+ "base_uri": "https://localhost:8080/"
1279
+ },
1280
+ "id": "6PsGE3OvBAaJ",
1281
+ "outputId": "0eb60459-8bca-4dcf-ba66-4d69b4eabedd"
1282
+ },
1283
+ "execution_count": 8,
1284
+ "outputs": [
1285
+ {
1286
+ "output_type": "stream",
1287
+ "name": "stdout",
1288
+ "text": [
1289
+ "1446447\n"
1290
+ ]
1291
+ }
1292
+ ]
1293
+ },
1294
+ {
1295
+ "cell_type": "code",
1296
+ "source": [
1297
+ "new_tokens.update(old_tokens)\n",
1298
+ "print(len(new_tokens))"
1299
+ ],
1300
+ "metadata": {
1301
+ "colab": {
1302
+ "base_uri": "https://localhost:8080/"
1303
+ },
1304
+ "id": "jcr4ht1BBh9r",
1305
+ "outputId": "211021c9-c707-457a-dcf0-21f32272d1c3"
1306
+ },
1307
+ "execution_count": 11,
1308
+ "outputs": [
1309
+ {
1310
+ "output_type": "stream",
1311
+ "name": "stdout",
1312
+ "text": [
1313
+ "1616815\n"
1314
+ ]
1315
+ }
1316
+ ]
1317
+ },
1318
+ {
1319
+ "cell_type": "code",
1320
+ "source": [
1321
+ "from pandas import DataFrame\n",
1322
+ "df = DataFrame.from_dict({\"text\": list(new_tokens)})\n",
1323
+ "df.head()"
1324
+ ],
1325
+ "metadata": {
1326
+ "colab": {
1327
+ "base_uri": "https://localhost:8080/",
1328
+ "height": 206
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+ },
1330
+ "id": "HMwB6q-cCD-S",
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+ "outputId": "f9fcfcfc-3960-4b78-f903-f768b73567e0"
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+ },
1333
+ "execution_count": 19,
1334
+ "outputs": [
1335
+ {
1336
+ "output_type": "execute_result",
1337
+ "data": {
1338
+ "text/plain": [
1339
+ " text\n",
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+ "0 504869\n",
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+ "1 mE7Ke2EFHY29Tk2R8kYkFqDPDfonKFMrc0EeB1dYm6jHX8...\n",
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+ "2 com/books/congress\n",
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+ "3 uitgaande\n",
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+ "4 com/dYUR98DdSwGk6zBmsIBw"
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+ ],
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+ "text/html": [
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+ "\n",
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+ " <div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>text</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>504869</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>mE7Ke2EFHY29Tk2R8kYkFqDPDfonKFMrc0EeB1dYm6jHX8...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>com/books/congress</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>uitgaande</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>com/dYUR98DdSwGk6zBmsIBw</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>\n",
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+ " <div class=\"colab-df-buttons\">\n",
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+ "\n",
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+ " <div class=\"colab-df-container\">\n",
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+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac6cea82-6612-4bb8-a5c7-bbbb54ea4bcc')\"\n",
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+ " title=\"Convert this dataframe to an interactive table.\"\n",
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+ " style=\"display:none;\">\n",
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+ "\n",
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+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+ " </svg>\n",
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+ " </button>\n",
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+ "\n",
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+ " <style>\n",
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+ " .colab-df-container {\n",
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+ " display:flex;\n",
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+ " gap: 12px;\n",
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+ " }\n",
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+ "\n",
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+ " .colab-df-convert {\n",
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+ " background-color: #E8F0FE;\n",
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+ " border: none;\n",
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+ " border-radius: 50%;\n",
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+ " cursor: pointer;\n",
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+ " display: none;\n",
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+ " }\n",
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+ "\n",
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+ " .colab-df-convert:hover {\n",
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+ " background-color: #E2EBFA;\n",
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+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1427
+ " fill: #174EA6;\n",
1428
+ " }\n",
1429
+ "\n",
1430
+ " .colab-df-buttons div {\n",
1431
+ " margin-bottom: 4px;\n",
1432
+ " }\n",
1433
+ "\n",
1434
+ " [theme=dark] .colab-df-convert {\n",
1435
+ " background-color: #3B4455;\n",
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+ " fill: #D2E3FC;\n",
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+ " }\n",
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+ "\n",
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+ " [theme=dark] .colab-df-convert:hover {\n",
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+ " background-color: #434B5C;\n",
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+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+ " fill: #FFFFFF;\n",
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+ " }\n",
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+ " </style>\n",
1446
+ "\n",
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+ " <script>\n",
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+ " const buttonEl =\n",
1449
+ " document.querySelector('#df-ac6cea82-6612-4bb8-a5c7-bbbb54ea4bcc button.colab-df-convert');\n",
1450
+ " buttonEl.style.display =\n",
1451
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1452
+ "\n",
1453
+ " async function convertToInteractive(key) {\n",
1454
+ " const element = document.querySelector('#df-ac6cea82-6612-4bb8-a5c7-bbbb54ea4bcc');\n",
1455
+ " const dataTable =\n",
1456
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1457
+ " [key], {});\n",
1458
+ " if (!dataTable) return;\n",
1459
+ "\n",
1460
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
1461
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1462
+ " + ' to learn more about interactive tables.';\n",
1463
+ " element.innerHTML = '';\n",
1464
+ " dataTable['output_type'] = 'display_data';\n",
1465
+ " await google.colab.output.renderOutput(dataTable, element);\n",
1466
+ " const docLink = document.createElement('div');\n",
1467
+ " docLink.innerHTML = docLinkHtml;\n",
1468
+ " element.appendChild(docLink);\n",
1469
+ " }\n",
1470
+ " </script>\n",
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+ " </div>\n",
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+ "\n",
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+ "\n",
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+ "<div id=\"df-27187ab1-ce78-41ea-9fe9-9cd2d069c3b3\">\n",
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+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-27187ab1-ce78-41ea-9fe9-9cd2d069c3b3')\"\n",
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+ " title=\"Suggest charts\"\n",
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+ " style=\"display:none;\">\n",
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+ "\n",
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+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+ " width=\"24px\">\n",
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+ " <g>\n",
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+ " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
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+ " </g>\n",
1484
+ "</svg>\n",
1485
+ " </button>\n",
1486
+ "\n",
1487
+ "<style>\n",
1488
+ " .colab-df-quickchart {\n",
1489
+ " --bg-color: #E8F0FE;\n",
1490
+ " --fill-color: #1967D2;\n",
1491
+ " --hover-bg-color: #E2EBFA;\n",
1492
+ " --hover-fill-color: #174EA6;\n",
1493
+ " --disabled-fill-color: #AAA;\n",
1494
+ " --disabled-bg-color: #DDD;\n",
1495
+ " }\n",
1496
+ "\n",
1497
+ " [theme=dark] .colab-df-quickchart {\n",
1498
+ " --bg-color: #3B4455;\n",
1499
+ " --fill-color: #D2E3FC;\n",
1500
+ " --hover-bg-color: #434B5C;\n",
1501
+ " --hover-fill-color: #FFFFFF;\n",
1502
+ " --disabled-bg-color: #3B4455;\n",
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+ " --disabled-fill-color: #666;\n",
1504
+ " }\n",
1505
+ "\n",
1506
+ " .colab-df-quickchart {\n",
1507
+ " background-color: var(--bg-color);\n",
1508
+ " border: none;\n",
1509
+ " border-radius: 50%;\n",
1510
+ " cursor: pointer;\n",
1511
+ " display: none;\n",
1512
+ " fill: var(--fill-color);\n",
1513
+ " height: 32px;\n",
1514
+ " padding: 0;\n",
1515
+ " width: 32px;\n",
1516
+ " }\n",
1517
+ "\n",
1518
+ " .colab-df-quickchart:hover {\n",
1519
+ " background-color: var(--hover-bg-color);\n",
1520
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1521
+ " fill: var(--button-hover-fill-color);\n",
1522
+ " }\n",
1523
+ "\n",
1524
+ " .colab-df-quickchart-complete:disabled,\n",
1525
+ " .colab-df-quickchart-complete:disabled:hover {\n",
1526
+ " background-color: var(--disabled-bg-color);\n",
1527
+ " fill: var(--disabled-fill-color);\n",
1528
+ " box-shadow: none;\n",
1529
+ " }\n",
1530
+ "\n",
1531
+ " .colab-df-spinner {\n",
1532
+ " border: 2px solid var(--fill-color);\n",
1533
+ " border-color: transparent;\n",
1534
+ " border-bottom-color: var(--fill-color);\n",
1535
+ " animation:\n",
1536
+ " spin 1s steps(1) infinite;\n",
1537
+ " }\n",
1538
+ "\n",
1539
+ " @keyframes spin {\n",
1540
+ " 0% {\n",
1541
+ " border-color: transparent;\n",
1542
+ " border-bottom-color: var(--fill-color);\n",
1543
+ " border-left-color: var(--fill-color);\n",
1544
+ " }\n",
1545
+ " 20% {\n",
1546
+ " border-color: transparent;\n",
1547
+ " border-left-color: var(--fill-color);\n",
1548
+ " border-top-color: var(--fill-color);\n",
1549
+ " }\n",
1550
+ " 30% {\n",
1551
+ " border-color: transparent;\n",
1552
+ " border-left-color: var(--fill-color);\n",
1553
+ " border-top-color: var(--fill-color);\n",
1554
+ " border-right-color: var(--fill-color);\n",
1555
+ " }\n",
1556
+ " 40% {\n",
1557
+ " border-color: transparent;\n",
1558
+ " border-right-color: var(--fill-color);\n",
1559
+ " border-top-color: var(--fill-color);\n",
1560
+ " }\n",
1561
+ " 60% {\n",
1562
+ " border-color: transparent;\n",
1563
+ " border-right-color: var(--fill-color);\n",
1564
+ " }\n",
1565
+ " 80% {\n",
1566
+ " border-color: transparent;\n",
1567
+ " border-right-color: var(--fill-color);\n",
1568
+ " border-bottom-color: var(--fill-color);\n",
1569
+ " }\n",
1570
+ " 90% {\n",
1571
+ " border-color: transparent;\n",
1572
+ " border-bottom-color: var(--fill-color);\n",
1573
+ " }\n",
1574
+ " }\n",
1575
+ "</style>\n",
1576
+ "\n",
1577
+ " <script>\n",
1578
+ " async function quickchart(key) {\n",
1579
+ " const quickchartButtonEl =\n",
1580
+ " document.querySelector('#' + key + ' button');\n",
1581
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
1582
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
1583
+ " try {\n",
1584
+ " const charts = await google.colab.kernel.invokeFunction(\n",
1585
+ " 'suggestCharts', [key], {});\n",
1586
+ " } catch (error) {\n",
1587
+ " console.error('Error during call to suggestCharts:', error);\n",
1588
+ " }\n",
1589
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
1590
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
1591
+ " }\n",
1592
+ " (() => {\n",
1593
+ " let quickchartButtonEl =\n",
1594
+ " document.querySelector('#df-27187ab1-ce78-41ea-9fe9-9cd2d069c3b3 button');\n",
1595
+ " quickchartButtonEl.style.display =\n",
1596
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1597
+ " })();\n",
1598
+ " </script>\n",
1599
+ "</div>\n",
1600
+ " </div>\n",
1601
+ " </div>\n"
1602
+ ],
1603
+ "application/vnd.google.colaboratory.intrinsic+json": {
1604
+ "type": "dataframe",
1605
+ "variable_name": "df"
1606
+ }
1607
+ },
1608
+ "metadata": {},
1609
+ "execution_count": 19
1610
+ }
1611
+ ]
1612
+ },
1613
+ {
1614
+ "cell_type": "code",
1615
+ "source": [
1616
+ "from datasets import Dataset\n",
1617
+ "dataset = Dataset.from_pandas(df)"
1618
+ ],
1619
+ "metadata": {
1620
+ "id": "juVr3_L5D3LC"
1621
+ },
1622
+ "execution_count": 24,
1623
+ "outputs": []
1624
+ },
1625
+ {
1626
+ "cell_type": "markdown",
1627
+ "source": [
1628
+ "## Upload to Hugging Face"
1629
+ ],
1630
+ "metadata": {
1631
+ "id": "sqbaaByQYG5q"
1632
+ }
1633
+ },
1634
+ {
1635
+ "cell_type": "code",
1636
+ "source": [
1637
+ "dataset.push_to_hub(\"gbenson/webui-tokens-unlabelled\")"
1638
+ ],
1639
+ "metadata": {
1640
+ "id": "ICObGLZvYHRZ",
1641
+ "colab": {
1642
+ "base_uri": "https://localhost:8080/",
1643
+ "height": 165,
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+ "referenced_widgets": [
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+ "4bbee348a78f44d39bb51049b5ee9646",
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+ "f92269931ef2478f80b1551be0d6ea35",
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+ "9b27366473f74c73a4d797b68d23f142",
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+ "0eb726bddabe4210b322adba424c7934",
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+ "7353c573e9a24a369aabc37d2cf1919d",
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+ "928dc67f581d48c0890b4ab3f718fb2c",
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+ "ffc15229b57749a7a5e511428930381b",
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+ "6edb5421b3c34cccaca8c136067cb672",
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+ "f68961f1af6d4a17804188d871f858c6",
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+ "d572e70402f54b23a317ea821142a439",
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+ "6e7aecc23e6a461dbae8734563390f9c",
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+ "9ed714cfa6a84fa89200207d021badcf",
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+ "a361d4482c7142d9b4df7040c189453c",
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+ "f2e281d3067f412db6045bd422bb4b62",
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+ "1bec637aef4b4be0a4188644a11c609f",
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+ "03610c15ddb740ae8d479579d93e7cd9",
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+ "82190d849926401fa3ad90032ad13eef",
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+ "a4874c30cca440099ba7c7fcfd8a96a8",
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+ "bd4f34da0244479bb703824bf6bd57c3",
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+ "f63cdc1af818446e9a0c34a47f48e33f",
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+ "7cbea630cd164a5ab9459c7e4240d642",
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+ "d9f7d90af2ac43989d0d9b1607282e1b",
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+ "834a37e5d9ac48e8825d0bbba0666301",
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+ "80fded30011d454abf986d120e8c997c",
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+ "b2276aee3b0b4177a437d1918f79dbe7",
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+ "6019099f01704eae8511470f5dabafe9",
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+ "e71bc8b54aa74d9db035a0ffdef02ad2",
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+ "9476148a2b3549599340752404189f73",
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+ "d39a0394fd99465c929ba6ae4114dcbf",
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+ "b74b6520010740b98203f20bd4147ec7"
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+ ]
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+ },
1680
+ "outputId": "72ab0158-bade-43e5-f01c-23d4498f7792"
1681
+ },
1682
+ "execution_count": 25,
1683
+ "outputs": [
1684
+ {
1685
+ "output_type": "display_data",
1686
+ "data": {
1687
+ "text/plain": [
1688
+ "Uploading the dataset shards: 0%| | 0/1 [00:00<?, ?it/s]"
1689
+ ],
1690
+ "application/vnd.jupyter.widget-view+json": {
1691
+ "version_major": 2,
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+ "version_minor": 0,
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+ "model_id": "4bbee348a78f44d39bb51049b5ee9646"
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+ }
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+ },
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+ "metadata": {}
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+ },
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