Spaces:
Runtime error
Runtime error
File size: 15,494 Bytes
27b305f |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"train_data=pd.read_csv(\"data/train.csv\", engine=\"python\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"attributes=[\"toxic\",\"severe_toxic\",\"obscene\",\"threat\",\"insult\",\"identity_hate\"]\n",
"#train_data[attributes].sum().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import Dataset\n",
"import torch\n",
"\n",
"class toxicity_dataset(Dataset):\n",
" def __init__(self,data_path,tokenizer,attributes,max_token_len= 128,sample = 5000):\n",
" self.data_path=data_path\n",
" self.tokenizer=tokenizer\n",
" self.attributes=attributes\n",
" self.max_token_len=max_token_len\n",
" self.sample=sample\n",
" self._prepare_data()\n",
" def _prepare_data(self):\n",
" data=pd.read_csv(self.data_path)\n",
" if self.sample is not None:\n",
" self.data=data.sample(self.sample,random_state=7)\n",
" else:\n",
" self.data=data\n",
" def __len__(self):\n",
" return(len(self.data))\n",
" def __getitem__(self,index):\n",
" item = self.data.iloc[index]\n",
" comment = str(item.comment_text)\n",
" attributes = torch.FloatTensor(item[self.attributes])\n",
" tokens = self.tokenizer.encode_plus(comment,add_special_tokens=True,return_tensors=\"pt\",truncation=True,max_length=self.max_token_len,padding=\"max_length\",return_attention_mask=True)\n",
" return{'input_ids':tokens.input_ids.flatten(),\"attention_mask\":tokens.attention_mask.flatten(),\"labels\":attributes}\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\jozef\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"data": {
"text/plain": [
"{'input_ids': tensor([ 0, 113, 43292, 487, 1073, 6619, 16519, 4261, 1012, 28845,\n",
" 43292, 50118, 30086, 6, 38, 206, 5, 7729, 6619, 16519,\n",
" 4261, 1012, 6717, 4867, 11, 2370, 32, 45, 41039, 7140,\n",
" 250, 48149, 53, 888, 95, 41762, 30, 40823, 34740, 2071,\n",
" 4, 407, 51, 197, 213, 11, 29617, 101, 25046, 12467,\n",
" 381, 11742, 646, 19065, 6026, 742, 1195, 87, 634, 14530,\n",
" 250, 4, 1437, 1437, 22, 2, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1]),\n",
" 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0]),\n",
" 'labels': tensor([0., 0., 0., 0., 0., 0.])}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from transformers import AutoTokenizer\n",
"model_name=\"roberta-base\"\n",
"tokenizer=AutoTokenizer.from_pretrained(model_name)\n",
"toxic_comments_dataset=toxicity_dataset(\"data/train.csv\",tokenizer,attributes)\n",
"toxic_comments_dataset.__getitem__(0)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import pytorch_lightning as pl\n",
"from torch.utils.data import DataLoader"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"class Toxcity_Data_Module(pl.LightningDataModule):\n",
" def __init__(self,train_path,test_path,attributes,batch_size = 16, max_token_len = 128, model_name=\"roberta-base\"):\n",
" super().__init__()\n",
" self.train_path=train_path\n",
" self.test_path=test_path\n",
" self.attributes=attributes\n",
" self.batch_size=batch_size\n",
" self.max_token_len=max_token_len\n",
" self.model_name=model_name\n",
" self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
" def setup(self, stage = None):\n",
" if stage in (None, \"fit\"):\n",
" self.train_dataset=toxicity_dataset(self.train_path,self.tokenizer,self.attributes)\n",
" self.test_dataset=toxicity_dataset(self.test_path,self.tokenizer,self.attributes, sample=None)\n",
" if stage == \"predict\":\n",
" self.val_dataset=toxicity_dataset(self.test_path,self.tokenizer,self.attributes)\n",
" def train_dataloader(self):\n",
" return DataLoader(self.train_dataset,batch_size=self.batch_size,num_workers=4,shuffle=True)\n",
" def val_dataloader(self):\n",
" return DataLoader(self.test_dataset,batch_size=self.batch_size,num_workers=4,shuffle=False)\n",
" def predict_dataloader(self):\n",
" return DataLoader(self.test_dataset,batch_size=self.batch_size,num_workers=4,shuffle=False)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<torch.utils.data.dataloader.DataLoader at 0x2241d16e5d0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toxicity_data_module=Toxcity_Data_Module(\"data/train.csv\",\"data/test.csv\",attributes)\n",
"\n",
"toxicity_data_module.setup()\n",
"dataloader=toxicity_data_module.train_dataloader()\n",
"dataloader"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoModel, AdamW, get_cosine_schedule_with_warmup\n",
"import torch.nn as nn\n",
"import math\n",
"from torchmetrics.functional.classification import auroc\n",
"import torch.nn.functional as F"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class Toxic_Comment_Classifier(pl.LightningModule):\n",
" def __init__(self,config:dict):\n",
" super().__init__()\n",
" self.config=config\n",
" self.pretrained_model = AutoModel.from_pretrained(config['model_name'],return_dict=True)\n",
" self.hidden = nn.Linear(self.pretrained_model.config.hidden_size, self.pretrained_model.config.hidden_size)\n",
" self.classifier = nn.Linear(self.pretrained_model.config.hidden_size, self.config['n_labels'])\n",
" torch.nn.init.xavier_uniform_(self.hidden.weight)\n",
" torch.nn.init.xavier_uniform_(self.classifier.weight)\n",
" self.loss_func=nn.BCEWithLogitsLoss(reduction='mean')\n",
" self.dropout = nn.Dropout()\n",
"\n",
" def forward(self, input_ids,attention_mask,labels=None):\n",
" output = self.pretrained_model(input_ids=input_ids,attention_mask=attention_mask)\n",
" pooled_output=torch.mean(output.last_hidden_state, 1)\n",
" #nn classification\n",
" pooled_output=self.hidden(pooled_output)\n",
" pooled_output=self.dropout(pooled_output)\n",
" pooled_output=F.relu(pooled_output)\n",
" logits=self.classifier(pooled_output)\n",
" #loss\n",
" loss = 0\n",
" if labels is not None:\n",
" loss = self.loss_func(logits.view(-1,self.config['n_labels']), labels.view(-1,self.config['n_labels']))\n",
" return loss, logits\n",
" \n",
" def training_step(self, batch, batch_index):\n",
" loss, logits = self(**batch)\n",
" self.log(\"train loss\", loss, prog_bar=True, logger=True)\n",
" return{'loss':loss,'predictions':logits,'labels':batch['labels']}\n",
" def validation_step(self, batch, batch_index):\n",
" loss, logits = self(**batch)\n",
" self.log(\"validation loss\", loss, prog_bar=True, logger=True)\n",
" return{'val_loss':loss,'predictions':logits,'labels':batch['labels']}\n",
" def prediction_step(self, batch, batch_index):\n",
" logits = self(**batch)\n",
" return logits\n",
" \n",
" def configure_optimizers(self):\n",
" optimizer = AdamW(self.parameters(), lr=self.config['lr'], weight_decay=self.config['w_decay'])\n",
" total_steps = self.config['train_size']/self.config['bs']\n",
" warmup_steps = math.floor(total_steps*self.config['warmup'])\n",
" scheduler = get_cosine_schedule_with_warmup(optimizer,warmup_steps,total_steps)\n",
" return [optimizer],[scheduler]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at distilroberta-base were not used when initializing RobertaModel: ['lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.bias']\n",
"- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
}
],
"source": [
"config = {\n",
" 'model_name':\"distilroberta-base\",\n",
" 'n_labels':len(attributes),\n",
" 'bs':128,\n",
" 'lr':1.5e-6,\n",
" 'warmup':0.2,\n",
" \"train_size\":len(toxicity_data_module.train_dataloader()),\n",
" 'w_decay':0.001,\n",
" 'n_epochs':1\n",
"}\n",
"\n",
"model = Toxic_Comment_Classifier(config)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"idx=0\n",
"input_ids = toxic_comments_dataset.__getitem__(idx)[\"input_ids\"]\n",
"attention_mask = toxic_comments_dataset.__getitem__(idx)[\"attention_mask\"]\n",
"labels = toxic_comments_dataset.__getitem__(idx)[\"labels\"]\n",
"loss,output = model(input_ids.unsqueeze(dim=0), attention_mask.unsqueeze(dim=0),labels.unsqueeze(dim=0))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at distilroberta-base were not used when initializing RobertaModel: ['lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.bias']\n",
"- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"GPU available: False, used: False\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
"HPU available: False, using: 0 HPUs\n",
"c:\\Users\\jozef\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\logger_connector\\logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
" warning_cache.warn(\n"
]
}
],
"source": [
"toxicity_data_module=Toxcity_Data_Module(\"data/train.csv\",\"data/test.csv\",attributes,batch_size=config['bs'])\n",
"toxicity_data_module.setup()\n",
"model = Toxic_Comment_Classifier(config)\n",
"\n",
"trainer = pl.Trainer(max_epochs=config['n_epochs'],num_sanity_val_steps=50)\n",
"#device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"#print(torch.cuda.get_device_name())\n",
"#trainer.fit(model,toxicity_data_module)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"model = torch.load(\"ToxicityClassificationModel.pt\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def predict_raw_comments(model, dm):\n",
" predictions = trainer.predict(model,datamodule=dm)\n",
" flattened_predictions = np.stack([torch.sigmoid(torch.Tensor(p)) for batch in predictions for p in batch])\n",
" return flattened_predictions"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"predictions = predict_raw_comments(model=model,dm=toxicity_data_module)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.11.3"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|