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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ed9bad4c-b546-43cd-b11d-39da03e3b2fc",
"metadata": {
"execution": {
"iopub.execute_input": "2023-11-25T03:08:25.222203Z",
"iopub.status.busy": "2023-11-25T03:08:25.221934Z",
"iopub.status.idle": "2023-11-25T03:09:12.123983Z",
"shell.execute_reply": "2023-11-25T03:09:12.123211Z",
"shell.execute_reply.started": "2023-11-25T03:08:25.222184Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu:\n",
"Collecting pandas\n",
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"Successfully installed pandas-2.0.3 tzdata-2023.3\n",
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"Successfully installed joblib-1.3.2 scikit-learn-1.3.2 scipy-1.10.1 threadpoolctl-3.2.0\n",
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"Installing collected packages: multidict, frozenlist, yarl, async-timeout, aiosignal, fsspec, dill, aiohttp, xxhash, pyarrow-hotfix, pyarrow, multiprocess, huggingface-hub, datasets\n",
"Successfully installed aiohttp-3.9.0 aiosignal-1.3.1 async-timeout-4.0.3 datasets-2.15.0 dill-0.3.7 frozenlist-1.4.0 fsspec-2023.10.0 huggingface-hub-0.19.4 multidict-6.0.4 multiprocess-0.70.15 pyarrow-14.0.1 pyarrow-hotfix-0.6 xxhash-3.4.1 yarl-1.9.3\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /opt/pytorch/lib/python3.8/site-packages (from requests->huggingface-hub->accelerate) (2023.5.7)\n",
"Requirement already satisfied: mpmath>=0.19 in /opt/pytorch/lib/python3.8/site-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
]
}
],
"source": [
"! pip install pandas\n",
"! pip install scikit-learn\n",
"! pip install datasets\n",
"! pip install transformers\n",
"! pip install transformers[torch]\n",
"! pip install accelerate -U"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fed20656-1f48-40d6-93e2-53aec7de522e",
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},
"metadata": {},
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at cardiffnlp/twitter-roberta-base-emotion and are newly initialized because the shapes did not match:\n",
"- classifier.out_proj.weight: found shape torch.Size([4, 768]) in the checkpoint and torch.Size([3, 768]) in the model instantiated\n",
"- classifier.out_proj.bias: found shape torch.Size([4]) in the checkpoint and torch.Size([3]) in the model instantiated\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
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"model_id": "3385afba67e74f2a9346b0270da2b4c9",
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},
"text/plain": [
"Map: 0%| | 0/4749 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.\n",
"Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"version_minor": 0
},
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"Map: 0%| | 0/1188 [00:00<?, ? examples/s]"
]
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}
],
"source": [
"import torch\n",
"import pandas as pd\n",
"\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from datasets import Dataset\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
"from transformers import RobertaConfig, RobertaForSequenceClassification\n",
"from transformers import AdamW\n",
"\n",
"# Define a new classification head\n",
"class NewClassificationHead(torch.nn.Module):\n",
" def __init__(self, config):\n",
" super().__init__()\n",
" self.dense = torch.nn.Linear(config.hidden_size, config.hidden_size)\n",
" self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)\n",
" self.out_proj = torch.nn.Linear(config.hidden_size, config.num_labels)\n",
"\n",
" def forward(self, features, **kwargs):\n",
" x = features[:, 0, :] # take <s> token (equiv. to [CLS])\n",
" x = self.dropout(x)\n",
" x = self.dense(x)\n",
" x = torch.nn.functional.relu(x)\n",
" x = self.dropout(x)\n",
" x = self.out_proj(x)\n",
" return x\n",
"\n",
"def preprocess_data(df):\n",
" ## rename columns\n",
" df = df.rename(columns={'Comment': 'text', 'Emotion': 'label'})\n",
"\n",
" ## remove rows with missing values\n",
" df = df.dropna()\n",
" df['text'] = df['text'].str.replace('\\t', ' ') # Remove extra spaces - this line replaces any occurrence of two or more spaces with a single spac\n",
" df['text'] = df['text'].str.replace(' +', ' ', regex=True) # Remove extra spaces - this line replaces any occurrence of two or more spaces with a single space\n",
" df['text'] = df['text'].str.strip() # Remove extra spaces - this line replaces any occurrence of two or more spaces with a single space\n",
"\n",
" df['label'] = df['label'].str.replace('\\t', ' ') # Remove extra spaces - this line replaces any occurrence of two or more spaces with a single spac\n",
" df['label'] = df['label'].str.replace(' +', ' ', regex=True) # Remove extra spaces - this line replaces any occurrence of two or more spaces with a single space\n",
" df['label'] = df['label'].str.strip() # Remove extra spaces - this line replaces any occurrence of two or more spaces with a single space \n",
"\n",
" return df\n",
"\n",
"def encode_label(df):\n",
" le = LabelEncoder()\n",
" df['label'] = le.fit_transform(df['label'])\n",
" label_mapping = {label: index for index, label in enumerate(le.classes_)}\n",
" df['label'].map(label_mapping)\n",
" return df\n",
"\n",
"def generate_dataset(df, test_size=0.2):\n",
" \"\"\"\n",
" Convert to transformers dataset and split into train and test\n",
" \"\"\"\n",
" dataset = Dataset.from_pandas(df)\n",
" ds = dataset.train_test_split(test_size=test_size)\n",
" return ds\n",
"\n",
"def tokenize(batch):\n",
" return tokenizer(batch['text'], padding='max_length', truncation=True)\n",
"\n",
"\n",
"def compute_metrics(pred):\n",
" from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
" labels = pred.label_ids\n",
" preds = pred.predictions.argmax(-1)\n",
" precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')\n",
" acc = accuracy_score(labels, preds)\n",
" return {\n",
" 'accuracy': acc,\n",
" 'f1': f1,\n",
" 'precision': precision,\n",
" 'recall': recall\n",
" }\n",
"\n",
"# Define model and training arguments\n",
"model_name = \"cardiffnlp/twitter-roberta-base-emotion\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"config = RobertaConfig.from_pretrained(model_name, num_labels=3) # Set the number of labels to 3\n",
"model = RobertaForSequenceClassification.from_pretrained(model_name, config=config, ignore_mismatched_sizes=True)\n",
"model.classifier = NewClassificationHead(config)\n",
"\n",
"df = pd.read_csv('Emotion_classify_Data.csv')\n",
"df = preprocess_data(df)\n",
"df = encode_label(df)\n",
"ds = generate_dataset(df)\n",
"ds = ds.map(tokenize, batched=True)\n",
"\n"
]
},
{
"cell_type": "code",
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{
"name": "stderr",
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"text": [
"/opt/pytorch/lib/python3.8/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n"
]
}
],
"source": [
"# Freeze all layers first\n",
"for param in model.parameters():\n",
" param.requires_grad = False\n",
"\n",
"# Unfreeze the classifier layer\n",
"for param in model.classifier.parameters():\n",
" param.requires_grad = True\n",
"\n",
"\n",
"# Define different learning rates\n",
"head_lr = 3e-4 # Higher learning rate for the head\n",
"base_lr = head_lr/5 # Lower learning rate for the base layers\n",
"\n",
"# Group parameters and set learning rates\n",
"optimizer_grouped_parameters = [\n",
" {'params': model.classifier.parameters(), 'lr': head_lr},\n",
" {'params': [p for n, p in model.named_parameters() if 'classifier' not in n], 'lr': base_lr}\n",
"]\n",
"\n",
"optimizer = AdamW(optimizer_grouped_parameters)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "882c5342-a82a-4e5a-b0ad-eaaa4978831f",
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
]
}
],
"source": [
"training_args = TrainingArguments(\n",
" output_dir='./results', \n",
" num_train_epochs=10, \n",
" per_device_train_batch_size=16, \n",
" per_device_eval_batch_size=64, \n",
" warmup_steps=500, \n",
" weight_decay=0.01, \n",
" logging_dir='./logs',\n",
" save_strategy=\"no\",\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=ds['train'],\n",
" eval_dataset=ds['test'],\n",
" tokenizer=tokenizer,\n",
" optimizers=(optimizer, None), # No need to pass a learning rate scheduler if you're managing learning rates manually,\n",
" compute_metrics=compute_metrics\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "19f8b2f1-d03b-42c2-a0a1-2475f2dfde37",
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
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" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
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"text": [
"/opt/pytorch/lib/python3.8/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n"
]
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" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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]
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}
],
"source": [
"for param in model.parameters():\n",
" param.requires_grad = True\n",
"\n",
" \n",
"head_lr = 1e-4 # Slightly lower learning rate for the head\n",
"base_lr = 5e-6 # Much lower learning rate for the base layers\n",
"\n",
"optimizer_grouped_parameters = [\n",
" {'params': model.classifier.parameters(), 'lr': head_lr},\n",
" {'params': [p for n, p in model.named_parameters() if 'classifier' not in n], 'lr': base_lr}\n",
"]\n",
"\n",
"optimizer = AdamW(optimizer_grouped_parameters)\n",
"\n",
"training_args.num_train_epochs = 5 # Set the number of additional epochs\n",
"trainer.train()"
]
},
{
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{
"data": {
"text/plain": [
"('transferLearningResults/tokenizer_config.json',\n",
" 'transferLearningResults/special_tokens_map.json',\n",
" 'transferLearningResults/vocab.json',\n",
" 'transferLearningResults/merges.txt',\n",
" 'transferLearningResults/added_tokens.json',\n",
" 'transferLearningResults/tokenizer.json')"
]
},
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],
"source": [
"model.save_pretrained('transferLearningResults')\n",
"tokenizer.save_pretrained('transferLearningResults')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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|