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"cells": [
{
"cell_type": "markdown",
"source": [
"# 1. Why DistilBERT for Sentiment Analysis?\n",
" * DistilBERT is a smaller, faster version of BERT, making it ideal for tasks like sentiment analysis.\n",
" * Pre-trained DistilBERT models are available on Hugging Face, making fine-tuning or direct usage simple."
],
"metadata": {
"id": "CGQdwiYGm511"
}
},
{
"cell_type": "markdown",
"source": [
"# 2. Installation\n",
"Make sure to install the necessary libraries:"
],
"metadata": {
"id": "uwSZWzgbnGfd"
}
},
{
"cell_type": "code",
"source": [
"!pip install transformers datasets torch"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KRRmircrnEhK",
"outputId": "08e56b6c-7d63-45e3-8ea9-6285a0a8c9d1"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.46.2)\n",
"Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (3.1.0)\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.1+cu121)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.26.2)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n",
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"Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.11.2)\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) (1.16.0)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"For GPU support, ensure PyTorch with CUDA is installed. Verify GPU access:"
],
"metadata": {
"id": "-rc5d3NRnKv0"
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"print(torch.cuda.is_available()) # Should return True"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hMWUFe9GnJza",
"outputId": "b0564f1d-c732-43c3-e5f2-716f863dd3dd"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"True\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 3. Code Example: Sentiment Analysis with DistilBERT\n",
"Below is a script to fine-tune DistilBERT on the IMDB dataset:"
],
"metadata": {
"id": "SJkoOIA6nOuO"
}
},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset\n",
"from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments"
],
"metadata": {
"id": "N8cWm1N3nNCA"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Load IMDB dataset\n",
"dataset = load_dataset(\"imdb\")\n",
"\n",
"# Load tokenizer and model\n",
"tokenizer = DistilBertTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
"model = DistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2).to(device)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0P2mprxYnhfh",
"outputId": "fd16d410-f7e5-40f0-c2bf-944c5b4506cb"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Tokenize dataset\n",
"def tokenize_function(examples):\n",
" return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)"
],
"metadata": {
"id": "dPLkBXdlnl4f"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"tokenized_datasets = dataset.map(tokenize_function, batched=True)"
],
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"text/plain": [
"Saving the dataset (0/1 shards): 0%| | 0/25000 [00:00<?, ? examples/s]"
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{
"output_type": "display_data",
"data": {
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"data": {
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Tokenized dataset saved to 'tokenized_imdb_dataset'\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Prepare datasets for PyTorch\n",
"tokenized_datasets = tokenized_datasets.remove_columns([\"text\"])\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
"tokenized_datasets.set_format(\"torch\")"
],
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{
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"Map: 0%| | 0/25000 [00:00<?, ? examples/s]"
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{
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"Map: 0%| | 0/25000 [00:00<?, ? examples/s]"
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"Map: 0%| | 0/50000 [00:00<?, ? examples/s]"
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},
{
"cell_type": "code",
"source": [
"train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(10000)) # Smaller subset for quick training\n",
"test_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(2000))\n"
],
"metadata": {
"id": "NGRG1vTnnqPG"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Define training arguments\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./results\",\n",
" evaluation_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" learning_rate=2e-5,\n",
" per_device_train_batch_size=16,\n",
" per_device_eval_batch_size=16,\n",
" num_train_epochs=3,\n",
" weight_decay=0.01,\n",
" logging_dir='./logs',\n",
" logging_steps=10,\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"accuracy\",\n",
" logging_first_step=True\n",
")\n",
"\n",
"# Define Trainer\n",
"def compute_metrics(eval_pred):\n",
" from sklearn.metrics import accuracy_score\n",
" logits, labels = eval_pred\n",
" predictions = torch.argmax(torch.tensor(logits), dim=-1)\n",
" return {\"accuracy\": accuracy_score(labels, predictions)}\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=test_dataset,\n",
" compute_metrics=compute_metrics,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HUsRVlPSoGHM",
"outputId": "3e65f501-1696-49cb-8d35-c8fe55abf41a"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Train model and capture training history\n",
"train_results = trainer.train()\n",
"trainer.save_model()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 460
},
"id": "v_G55NNxoJZ_",
"outputId": "e51c9762-4044-4984-ce85-a925f18a45e6"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n"
]
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{
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" newScript.onload = resolve;\n",
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" newScript.src = url;\n",
" });\n",
" }\n",
" loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
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" iframe.style.cssText = \"width:0;height:0;border:none\"\n",
" document.body.appendChild(iframe)\n",
" const handshake = new Postmate({\n",
" container: iframe,\n",
" url: 'https://wandb.ai/authorize'\n",
" });\n",
" const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
" handshake.then(function(child) {\n",
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" clearTimeout(timeout)\n",
" resolve(data)\n",
" });\n",
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"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
"wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" ··········\n"
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"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m API key must be 40 characters long, yours was 17\n"
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},
{
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" newScript.onload = resolve;\n",
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" newScript.src = url;\n",
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" }\n",
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" iframe.style.cssText = \"width:0;height:0;border:none\"\n",
" document.body.appendChild(iframe)\n",
" const handshake = new Postmate({\n",
" container: iframe,\n",
" url: 'https://wandb.ai/authorize'\n",
" });\n",
" const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
" handshake.then(function(child) {\n",
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" clearTimeout(timeout)\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
"wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
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{
"name": "stdout",
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"text": [
" ··········\n"
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
]
},
{
"output_type": "display_data",
"data": {
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"text/html": [
"Tracking run with wandb version 0.18.7"
]
},
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{
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"data": {
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"text/html": [
"Run data is saved locally in <code>/content/wandb/run-20241205_115349-y2go0ta1</code>"
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{
"output_type": "display_data",
"data": {
"text/plain": [
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"text/html": [
"Syncing run <strong><a href='https://wandb.ai/cscmu-scicamp66/huggingface/runs/y2go0ta1' target=\"_blank\">./results</a></strong> to <a href='https://wandb.ai/cscmu-scicamp66/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br/>"
]
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"metadata": {}
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{
"output_type": "display_data",
"data": {
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"<IPython.core.display.HTML object>"
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"text/html": [
" View project at <a href='https://wandb.ai/cscmu-scicamp66/huggingface' target=\"_blank\">https://wandb.ai/cscmu-scicamp66/huggingface</a>"
]
},
"metadata": {}
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{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
" View run at <a href='https://wandb.ai/cscmu-scicamp66/huggingface/runs/y2go0ta1' target=\"_blank\">https://wandb.ai/cscmu-scicamp66/huggingface/runs/y2go0ta1</a>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
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"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1875' max='1875' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1875/1875 25:21, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.203000</td>\n",
" <td>0.249249</td>\n",
" <td>0.910500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.119500</td>\n",
" <td>0.237566</td>\n",
" <td>0.925000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.239300</td>\n",
" <td>0.298314</td>\n",
" <td>0.923500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# !zip -r \"/content/drive/MyDrive/Colab Notebooks/ SentimentAnalysis/results.zip\" results/"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wra1iu4gz2q7",
"outputId": "ec022fee-bf2f-4834-d611-2c7be85775c2"
},
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"updating: results/ (stored 0%)\n",
"updating: results/config.json (deflated 46%)\n",
"updating: results/checkpoint-625/ (stored 0%)\n",
"updating: results/checkpoint-625/config.json (deflated 46%)\n",
"updating: results/checkpoint-625/trainer_state.json (deflated 80%)\n",
"updating: results/checkpoint-625/scheduler.pt (deflated 56%)\n",
"updating: results/checkpoint-625/optimizer.pt (deflated 15%)\n",
"updating: results/checkpoint-625/model.safetensors (deflated 8%)\n",
"updating: results/checkpoint-625/training_args.bin (deflated 51%)\n",
"updating: results/checkpoint-625/rng_state.pth (deflated 25%)\n",
"updating: results/model.safetensors (deflated 8%)\n",
"updating: results/training_args.bin (deflated 51%)\n",
"updating: results/checkpoint-1875/ (stored 0%)\n",
"updating: results/checkpoint-1875/config.json (deflated 46%)\n",
"updating: results/checkpoint-1875/trainer_state.json (deflated 82%)\n",
"updating: results/checkpoint-1875/scheduler.pt (deflated 56%)\n",
"updating: results/checkpoint-1875/optimizer.pt (deflated 15%)\n",
"updating: results/checkpoint-1875/model.safetensors (deflated 8%)\n",
"updating: results/checkpoint-1875/training_args.bin (deflated 51%)\n",
"updating: results/checkpoint-1875/rng_state.pth (deflated 25%)\n",
"updating: results/checkpoint-1250/ (stored 0%)\n",
"updating: results/checkpoint-1250/config.json (deflated 46%)\n",
"updating: results/checkpoint-1250/trainer_state.json (deflated 82%)\n",
"updating: results/checkpoint-1250/scheduler.pt (deflated 55%)\n",
"updating: results/checkpoint-1250/optimizer.pt (deflated 15%)\n",
"updating: results/checkpoint-1250/model.safetensors (deflated 8%)\n",
"updating: results/checkpoint-1250/training_args.bin (deflated 51%)\n",
"updating: results/checkpoint-1250/rng_state.pth (deflated 25%)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Evaluate\n",
"eval_results = trainer.evaluate()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 37
},
"id": "pIJNPwN0oiWD",
"outputId": "a1d84d41-5f50-41fc-e194-513a072c132a"
},
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='125' max='125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [125/125 00:30]\n",
" </div>\n",
" "
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Visualization: Extracting training logs\n",
"history = trainer.state.log_history\n",
"\n",
"# Separate out losses and accuracies\n",
"train_loss = [log['loss'] for log in history if 'loss' in log]\n",
"eval_loss = [log['eval_loss'] for log in history if 'eval_loss' in log]\n",
"eval_acc = [log['eval_accuracy'] for log in history if 'eval_accuracy' in log]\n"
],
"metadata": {
"id": "pM3ZUxjJoevN"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt"
],
"metadata": {
"id": "DICQJyrJw17j"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Plot training and validation loss\n",
"plt.figure(figsize=(12, 6))\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(range(1, len(train_loss) + 1), train_loss, label='Training Loss')\n",
"plt.plot(range(1, len(eval_loss) + 1), eval_loss, label='Validation Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Loss')\n",
"plt.title('Training and Validation Loss')\n",
"plt.legend()\n",
"\n",
"# Plot validation accuracy\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot(range(1, len(eval_acc) + 1), eval_acc, label='Validation Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Accuracy')\n",
"plt.title('Validation Accuracy')\n",
"plt.legend()\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 329
},
"id": "kZjqIzHxok7M",
"outputId": "c30c8cd6-e73c-4658-817c-f01041056dbc"
},
"execution_count": 27,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x600 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Print evaluation results\n",
"print(\"Evaluation Results:\", eval_results)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "c7wiU1BcnYtU",
"outputId": "8edcd7eb-cfab-496a-f2b9-a8af04d59118"
},
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Evaluation Results: {'eval_loss': 0.23756597936153412, 'eval_accuracy': 0.925, 'eval_runtime': 30.5137, 'eval_samples_per_second': 65.544, 'eval_steps_per_second': 4.097, 'epoch': 3.0}\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"plt.savefig(\"training_history.png\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "ts774p2soz1U",
"outputId": "21fd45d8-33b4-416a-9a15-4461345c708c"
},
"execution_count": 26,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 0 Axes>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# 4. Loading a Model and Tokenizer\n",
"\n",
"Here’s the code to load the saved model:"
],
"metadata": {
"id": "aPbOUrIHxrWD"
}
},
{
"cell_type": "code",
"source": [
"from transformers import DistilBertForSequenceClassification, DistilBertTokenizer\n",
"\n",
"# Path to the directory where the model was saved\n",
"model_dir = \"results\"\n",
"\n",
"# Load the model\n",
"model = DistilBertForSequenceClassification.from_pretrained(model_dir)\n",
"\n",
"# Load the tokenizer\n",
"tokenizer = DistilBertTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
"\n",
"print(\"Model and tokenizer loaded successfully!\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a8jB90YPxsEx",
"outputId": "48b06633-5b5e-4b41-ec8c-a3497db7e25c"
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model and tokenizer loaded successfully!\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# 5. Using the Loaded Model\n",
"\n",
"Once the model is loaded, you can use it for tasks like sentiment analysis or other predictions.\n",
"\n",
"Example: Sentiment Analysis"
],
"metadata": {
"id": "tlLIZ34fyvNd"
}
},
{
"cell_type": "code",
"source": [
"# Example text for prediction\n",
"text = \"I love this movie, it was amazing!\"\n",
"\n",
"# Tokenize the input text\n",
"inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=512)\n",
"\n",
"# Move model to GPU if available\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"model.to(device)\n",
"\n",
"# Run the model and get predictions\n",
"with torch.no_grad():\n",
" outputs = model(**inputs.to(device))\n",
"\n",
"# Get predicted class (since it's binary classification, class 0 or 1)\n",
"predicted_class = torch.argmax(outputs.logits, dim=-1).item()\n",
"\n",
"# Display result\n",
"if predicted_class == 1:\n",
" print(f\"Prediction: Positive sentiment\")\n",
"else:\n",
" print(f\"Prediction: Negative sentiment\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hoUm7wZgxwov",
"outputId": "4e67dbe6-6b69-469d-aacf-3357c832ec3e"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Prediction: Positive sentiment\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "nMTqA_cNy9fC"
},
"execution_count": null,
"outputs": []
}
]
} |