File size: 17,628 Bytes
df9c590 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "1aafbf18-de38-4fcf-8245-e2e9a584971f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# ! pip install pymilvus==2.3.4\n",
"# ! pip install pyarrow==12.0.0\n",
"# !pip install -U sentence-transformers"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f1d8f101-f51b-4a50-b150-86e87c50c453",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tqdm import tqdm\n",
"from dotenv import load_dotenv\n",
"import os\n",
"import pandas as pd\n",
"from pymilvus import connections, utility\n",
"from pymilvus import Collection, DataType, FieldSchema, CollectionSchema\n",
"import multiprocessing\n",
"from sentence_transformers import SentenceTransformer"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4ad4e3ac-9685-4f12-8043-5fbcc373d3e1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.config.list_physical_devices('GPU')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "da71d832-b8a7-452b-b736-538a3c069b54",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>category</th>\n",
" <th>short_description</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>SCIENCE</td>\n",
" <td>A closer look at water-splitting's solar fuel ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>SCIENCE</td>\n",
" <td>An irresistible scent makes locusts swarm, stu...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>SCIENCE</td>\n",
" <td>Artificial intelligence warning: AI will know ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>SCIENCE</td>\n",
" <td>Glaciers Could Have Sculpted Mars Valleys: Study</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>SCIENCE</td>\n",
" <td>Perseid meteor shower 2020: What time and how ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>311171</th>\n",
" <td>311171</td>\n",
" <td>TECH</td>\n",
" <td>RIM CEO Thorsten Heins' 'Significant' Plans Fo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>311172</th>\n",
" <td>311172</td>\n",
" <td>SPORTS</td>\n",
" <td>Maria Sharapova Stunned By Victoria Azarenka I...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>311173</th>\n",
" <td>311173</td>\n",
" <td>SPORTS</td>\n",
" <td>Giants Over Patriots, Jets Over Colts Among M...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>311174</th>\n",
" <td>311174</td>\n",
" <td>SPORTS</td>\n",
" <td>Aldon Smith Arrested: 49ers Linebacker Busted ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>311175</th>\n",
" <td>311175</td>\n",
" <td>SPORTS</td>\n",
" <td>Dwight Howard Rips Teammates After Magic Loss ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>311176 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" index category short_description\n",
"0 0 SCIENCE A closer look at water-splitting's solar fuel ...\n",
"1 1 SCIENCE An irresistible scent makes locusts swarm, stu...\n",
"2 2 SCIENCE Artificial intelligence warning: AI will know ...\n",
"3 3 SCIENCE Glaciers Could Have Sculpted Mars Valleys: Study\n",
"4 4 SCIENCE Perseid meteor shower 2020: What time and how ...\n",
"... ... ... ...\n",
"311171 311171 TECH RIM CEO Thorsten Heins' 'Significant' Plans Fo...\n",
"311172 311172 SPORTS Maria Sharapova Stunned By Victoria Azarenka I...\n",
"311173 311173 SPORTS Giants Over Patriots, Jets Over Colts Among M...\n",
"311174 311174 SPORTS Aldon Smith Arrested: 49ers Linebacker Busted ...\n",
"311175 311175 SPORTS Dwight Howard Rips Teammates After Magic Loss ...\n",
"\n",
"[311176 rows x 3 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('labelled_newscatcher_dataset.csv', sep=\";\", usecols=['title', 'topic'])\n",
"json_data=pd.read_json('News_Category_Dataset_v3.json', lines=True)\n",
"data.drop_duplicates(subset=['title'], inplace=True)\n",
"json_data.drop_duplicates(subset=['headline'], inplace=True)\n",
"json_data = json_data[['headline', 'category']].copy()\n",
"json_data.rename(columns={'headline': 'title'}, inplace=True)\n",
"data.rename(columns={'topic': 'category'}, inplace=True)\n",
"data = pd.concat([data, json_data], axis=0)\n",
"data.drop_duplicates(subset=['title'], inplace=True)\n",
"data.reset_index(drop=True, inplace=True)\n",
"data.reset_index(inplace=True)\n",
"data.rename(columns={'title': 'short_description'}, inplace=True)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "796f85b1-12dc-42cb-b431-65c88738b607",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"any(data['short_description'].duplicated())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5f46251b-156a-4a72-ab89-6abb6d810006",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"data.to_csv('news_processed.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1463ea34-4447-464a-b0a3-da5e09892a09",
"metadata": {},
"outputs": [],
"source": [
"class TextVectorizer:\n",
" '''\n",
" sentence transformers to extract sentence embeddings\n",
" '''\n",
" def vectorize(self, x):\n",
" sent_model = SentenceTransformer('multi-qa-distilbert-cos-v1_finetuned')\n",
" sen_embeddings = sent_model.encode(x)\n",
" return sen_embeddings"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "47a714f3-8948-470b-9caf-93ed2bbf4894",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"vectorizer = TextVectorizer()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8b1586e5-2923-4632-a3db-fd2364124d6f",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"320"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# getting max length of article descriptions to be used for VARCHAR while defining schema\n",
"max_desc_len = max([len(s) for s in data['short_description']])\n",
"max_desc_len"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "debe0ef4-b877-495a-872e-47f720b758a9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# getting max length of article categories to be used for VARCHAR while defining schema\n",
"max_cat_len = max([len(s) for s in data['category']])\n",
"max_cat_len"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "80489f00-e59f-46ab-a933-97145928176c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# # Reading milvus URI & API token from secrets.env\n",
"load_dotenv('secrets.env')\n",
"uri = os.environ.get(\"URI\")\n",
"token = os.environ.get(\"TOKEN\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "0bf69f22-e113-43a5-be81-77224cafd856",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connected to DB\n"
]
}
],
"source": [
"connections.connect(\"default\", uri=uri, token=token)\n",
"print(f\"Connected to DB\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "8da06a3b-2005-4c02-a168-dc84bcde7064",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"collection_name = 'news_collection_sent_tran_finetuned'\n",
"check_collection = utility.has_collection(collection_name)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "33342612-1380-4d1a-a8e7-931476e07979",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Droped Existing collection\n"
]
}
],
"source": [
"if check_collection:\n",
" drop_result = utility.drop_collection(collection_name)\n",
" print(\"Droped Existing collection\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "fc8ae048-d586-41e7-9678-75e1752c1693",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating the collection\n",
"Schema: {'auto_id': False, 'description': 'collection of news articles', 'fields': [{'name': 'article_id', 'description': 'primary id', 'type': <DataType.INT64: 5>, 'is_primary': True, 'auto_id': False}, {'name': 'article_embed', 'description': '', 'type': <DataType.FLOAT_VECTOR: 101>, 'params': {'dim': 768}}, {'name': 'article_desc', 'description': 'short description of the article', 'type': <DataType.VARCHAR: 21>, 'params': {'max_length': 370}}, {'name': 'article_category', 'description': 'category of the article', 'type': <DataType.VARCHAR: 21>, 'params': {'max_length': 64}}]}\n",
"Success!\n"
]
}
],
"source": [
"# Creating collection schema\n",
"dim = 768 # embeddings dim\n",
"article_id = FieldSchema(name=\"article_id\", dtype=DataType.INT64, is_primary=True, description=\"primary id\") # primary key\n",
"article_embed_field = FieldSchema(name=\"article_embed\", dtype=DataType.FLOAT_VECTOR, dim=dim) # description embeddings\n",
"article_desc = FieldSchema(name=\"article_desc\", dtype=DataType.VARCHAR, max_length=(max_desc_len + 50), # using max_desc_len to specify VARCHAR len \n",
" is_primary=False, description=\"short description of the article\") # short description of article\n",
"article_cat = FieldSchema(name=\"article_category\", dtype=DataType.VARCHAR, max_length=(max_cat_len + 50), # using max_desc_len to specify VARCHAR len \n",
" is_primary=False, description=\"category of the article\") # category of article\n",
"schema = CollectionSchema(fields=[article_id, article_embed_field, article_desc, article_cat], \n",
" auto_id=False, description=\"collection of news articles\")\n",
"print(f\"Creating the collection\")\n",
"collection = Collection(name=collection_name, schema=schema)\n",
"print(f\"Schema: {schema}\")\n",
"print(\"Success!\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "cca82380-98f6-4c44-aac6-86d4ae3484d0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000, 45000, 46000, 47000, 48000, 49000, 50000, 51000, 52000, 53000, 54000, 55000, 56000, 57000, 58000, 59000, 60000, 61000, 62000, 63000, 64000, 65000, 66000, 67000, 68000, 69000, 70000, 71000, 72000, 73000, 74000, 75000, 76000, 77000, 78000, 79000, 80000, 81000, 82000, 83000, 84000, 85000, 86000, 87000, 88000, 89000, 90000, 91000, 92000, 93000, 94000, 95000, 96000, 97000, 98000, 99000, 100000, 101000, 102000, 103000, 104000, 105000, 106000, 107000, 108000, 109000, 110000, 111000, 112000, 113000, 114000, 115000, 116000, 117000, 118000, 119000, 120000, 121000, 122000, 123000, 124000, 125000, 126000, 127000, 128000, 129000, 130000, 131000, 132000, 133000, 134000, 135000, 136000, 137000, 138000, 139000, 140000, 141000, 142000, 143000, 144000, 145000, 146000, 147000, 148000, 149000, 150000, 151000, 152000, 153000, 154000, 155000, 156000, 157000, 158000, 159000, 160000, 161000, 162000, 163000, 164000, 165000, 166000, 167000, 168000, 169000, 170000, 171000, 172000, 173000, 174000, 175000, 176000, 177000, 178000, 179000, 180000, 181000, 182000, 183000, 184000, 185000, 186000, 187000, 188000, 189000, 190000, 191000, 192000, 193000, 194000, 195000, 196000, 197000, 198000, 199000, 200000, 201000, 202000, 203000, 204000, 205000, 206000, 207000, 208000, 209000, 210000, 211000, 212000, 213000, 214000, 215000, 216000, 217000, 218000, 219000, 220000, 221000, 222000, 223000, 224000, 225000, 226000, 227000, 228000, 229000, 230000, 231000, 232000, 233000, 234000, 235000, 236000, 237000, 238000, 239000, 240000, 241000, 242000, 243000, 244000, 245000, 246000, 247000, 248000, 249000, 250000, 251000, 252000, 253000, 254000, 255000, 256000, 257000, 258000, 259000, 260000, 261000, 262000, 263000, 264000, 265000, 266000, 267000, 268000, 269000, 270000, 271000, 272000, 273000, 274000, 275000, 276000, 277000, 278000, 279000, 280000, 281000, 282000, 283000, 284000, 285000, 286000, 287000, 288000, 289000, 290000, 291000, 292000, 293000, 294000, 295000, 296000, 297000, 298000, 299000, 300000, 301000, 302000, 303000, 304000, 305000, 306000, 307000, 308000, 309000, 310000, 311000, 311176]\n"
]
}
],
"source": [
"cuts = [*range(0, len(data), 1000)]\n",
"cuts.append(len(data))\n",
"print(cuts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "066c67ac-01a6-4151-8e85-5869ddce1c0a",
"metadata": {},
"outputs": [],
"source": [
"article_id = []\n",
"article_desc = []\n",
"article_embed = []\n",
"article_cat = []\n",
"try:\n",
" for i in tqdm(range(len(cuts)-1)):\n",
" df = data.iloc[cuts[i]: cuts[i+1]].copy()\n",
" article_id = [*df['index']]\n",
" article_desc = [*df['short_description']]\n",
" article_cat = [*df['category']]\n",
" results = []\n",
" article_embed = vectorizer.vectorize(article_desc)\n",
" docs = [article_id, article_embed, article_desc, article_cat]\n",
" ins_resp = collection.insert(docs)\n",
" print(ins_resp)\n",
" article_id = []\n",
" article_desc = []\n",
" article_embed = []\n",
" article_cat = []\n",
" if i == 0:\n",
" index_params = {\"index_type\": \"AUTOINDEX\", \"metric_type\": \"L2\", \"params\": {}} \n",
" collection.create_index(field_name='article_embed', index_params=index_params)\n",
" collection = Collection(name=collection_name)\n",
" collection.load()\n",
"except:\n",
" raise"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d50177fa-fd0c-48ad-bc9b-a7bdc826a628",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (tf_gpu)",
"language": "python",
"name": "tf_gpu"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|