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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train with Pytorch"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/huggingface/lib/python3.10/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",
"/home/huggingface/lib/python3.10/site-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n",
"Map: 100%|██████████| 872/872 [00:00<00:00, 15492.15 examples/s]\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"from transformers import AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification\n",
"\n",
"raw_dataset = load_dataset(\"glue\", \"sst2\")\n",
"checkpoint = \"bert-base-uncased\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
"\n",
"# # For MRPC\n",
"# def tokenize_function(sample):\n",
"# return tokenizer(sample[\"sentence1\"], sample[\"sentence2\"], truncation = True)\n",
"\n",
"# For SST2\n",
"def tokenize_function(sample):\n",
" return tokenizer(sample[\"sentence\"], truncation = True)\n",
"\n",
"\n",
"tokenized_dataset = raw_dataset.map(tokenize_function, batched = True)\n",
"data_collator = DataCollatorWithPadding(tokenizer = tokenizer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preprocess the dataset "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'train': ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
" 'validation': ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
" 'test': ['labels', 'input_ids', 'token_type_ids', 'attention_mask']}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove unwanted columns which are not to be uitilized during pytorch dataloading\n",
"# # For MRPC\n",
"# tokenized_dataset = tokenized_dataset.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
"\n",
"# For SST2\n",
"tokenized_dataset = tokenized_dataset.remove_columns([\"sentence\", \"idx\"])\n",
"\n",
"# Rename the target column appropriately\n",
"tokenized_dataset = tokenized_dataset.rename_column(\"label\", \"labels\")\n",
"\n",
"# Set the format to return tensors instead of lists\n",
"tokenized_dataset.set_format(\"torch\")\n",
"\n",
"tokenized_dataset.column_names"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import DataLoader\n",
"\n",
"train_dataloader = DataLoader(tokenized_dataset[\"train\"], shuffle = True, batch_size = 64, collate_fn = data_collator)\n",
"eval_dataloader = DataLoader(tokenized_dataset[\"validation\"], batch_size = 64, collate_fn= data_collator)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You're using a BertTokenizerFast 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"
]
},
{
"data": {
"text/plain": [
"{'labels': torch.Size([64]),\n",
" 'input_ids': torch.Size([64, 41]),\n",
" 'token_type_ids': torch.Size([64, 41]),\n",
" 'attention_mask': torch.Size([64, 41])}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"one_batch = next(iter(train_dataloader))\n",
"{k: v.shape for k, v in one_batch.items()}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Define the model and start training"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias']\n",
"- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels = 2)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SequenceClassifierOutput(loss=tensor(0.7528), logits=tensor([[-0.4735, 0.2345],\n",
" [-0.5462, 0.2849],\n",
" [-0.8623, 0.6073],\n",
" [-0.6334, 0.3747],\n",
" [-0.5882, 0.4656],\n",
" [-0.1711, 0.1957],\n",
" [-0.4656, 0.2387],\n",
" [-0.8434, 0.6939],\n",
" [-0.4384, 0.2810],\n",
" [-0.5239, 0.2832],\n",
" [-0.4431, 0.2877],\n",
" [-0.5974, 0.2958],\n",
" [-0.7655, 0.6273],\n",
" [-0.7656, 0.6703],\n",
" [-0.7001, 0.4183],\n",
" [-0.3617, 0.2145],\n",
" [-0.6250, 0.3684],\n",
" [-0.5722, 0.4677],\n",
" [-0.1536, 0.1978],\n",
" [-0.5606, 0.3755],\n",
" [-0.6292, 0.3662],\n",
" [-0.7420, 0.3527],\n",
" [-0.4581, 0.2733],\n",
" [-0.6560, 0.4098],\n",
" [-0.2436, 0.1589],\n",
" [-0.5316, 0.2916],\n",
" [-0.6136, 0.3340],\n",
" [-0.6650, 0.3447],\n",
" [-0.6319, 0.4982],\n",
" [-0.7093, 0.4292],\n",
" [-0.3495, 0.2136],\n",
" [-0.5344, 0.2056],\n",
" [-0.2243, 0.2376],\n",
" [-0.2150, 0.2638],\n",
" [-0.6236, 0.4449],\n",
" [-0.3363, 0.2330],\n",
" [-0.7103, 0.5592],\n",
" [-0.6709, 0.4674],\n",
" [-0.6250, 0.4823],\n",
" [-0.8934, 0.8637],\n",
" [-0.7147, 0.4695],\n",
" [-0.4029, 0.2238],\n",
" [-0.6455, 0.4327],\n",
" [-0.2547, 0.2432],\n",
" [-0.3518, 0.3581],\n",
" [-0.1312, 0.1507],\n",
" [-0.5558, 0.4219],\n",
" [-0.4881, 0.3416],\n",
" [-0.6623, 0.4497],\n",
" [-0.5963, 0.4848],\n",
" [-0.5053, 0.3500],\n",
" [-0.1152, 0.1482],\n",
" [-0.6302, 0.3531],\n",
" [-0.6268, 0.4978],\n",
" [-0.4811, 0.2927],\n",
" [ 0.0057, 0.1694],\n",
" [-0.6268, 0.3306],\n",
" [-0.5859, 0.4029],\n",
" [-0.3552, 0.2425],\n",
" [-0.5622, 0.4161],\n",
" [-0.7670, 0.5203],\n",
" [-0.6624, 0.5146],\n",
" [-0.6089, 0.4091],\n",
" [-0.4992, 0.2702]]), hidden_states=None, attentions=None)\n"
]
}
],
"source": [
"import torch\n",
"model.eval()\n",
"with torch.no_grad():\n",
" print(model(**one_batch))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/huggingface/lib/python3.10/site-packages/transformers/optimization.py:391: 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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2106\n"
]
}
],
"source": [
"from transformers import AdamW\n",
"from transformers import get_scheduler\n",
"\n",
"# Define the optimizer here\n",
"optimizer = AdamW(model.parameters(), lr = 5e-5)\n",
"\n",
"# Define the learning rate scheduler here\n",
"num_epochs = 2\n",
"num_training_steps = num_epochs * len(train_dataloader)\n",
"lr_scheduler = get_scheduler(\n",
" \"linear\",\n",
" optimizer=optimizer,\n",
" num_warmup_steps=0,\n",
" num_training_steps=num_training_steps,\n",
")\n",
"print(num_training_steps)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Use GPU if available\n",
"device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
"model.to(device);"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 50%|█████ | 1054/2106 [03:48<13:25, 1.31it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Metrics at end of epoch 0:\n",
"{'accuracy': 0.9288990825688074}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████▉| 2105/2106 [07:35<00:00, 4.98it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Metrics at end of epoch 1:\n",
"{'accuracy': 0.926605504587156}\n"
]
}
],
"source": [
"from tqdm.auto import tqdm\n",
"import evaluate\n",
"progress_bar = tqdm(range(num_training_steps))\n",
"\n",
"for epoch_id in range(num_epochs):\n",
"\n",
" # Train for one epoch\n",
" model.train()\n",
" for batch in train_dataloader:\n",
" batch = {k: v.to(device) for k, v in batch.items()}\n",
" outputs = model(**batch)\n",
" outputs.loss.backward()\n",
"\n",
" optimizer.step()\n",
" lr_scheduler.step()\n",
" optimizer.zero_grad()\n",
" progress_bar.update(1)\n",
"\n",
" # Evaluate at the end of epoch\n",
" model.eval()\n",
" # # For MRPC\n",
" # metric = evaluate.load(\"glue\", \"mrpc\")\n",
"\n",
" # For SST2\n",
" metric = evaluate.load(\"glue\", \"sst2\")\n",
"\n",
" with torch.no_grad():\n",
" for batch in eval_dataloader:\n",
" batch = {k: v.to(device) for k, v in batch.items()}\n",
" outputs = model(**batch)\n",
" logits = outputs.logits\n",
" predictions = logits.argmax(dim = -1)\n",
" metric.add_batch(predictions = predictions, references = batch[\"labels\"])\n",
" m = metric.compute()\n",
"\n",
" print(f\"Metrics at end of epoch {epoch_id}:\\n{m}\")\n"
]
}
],
"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",
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