course/en/chapter3/section7.ipynb (4,968 lines of code) (raw):
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"# Install Pytorch & other libraries\n",
"%pip install -qqq torch torchvision setuptools scikit-learn\n",
"\n",
"# Install Hugging Face libraries\n",
"%pip install --upgrade datasets -qqq accelerate hf-transfer transformers"
]
},
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"source": [
"from datasets import load_dataset\n",
"\n",
"# Dataset id from huggingface.co/dataset\n",
"dataset_id = \"burtenshaw/PleIAs_common_corpus_code_classification\"\n",
"\n",
"# Load raw dataset\n",
"dataset = load_dataset(dataset_id)"
]
},
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"source": [
"print(len(dataset[\"train\"]))\n",
"print(dataset[\"train\"][0])"
]
},
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"source": [
"from transformers import AutoTokenizer\n",
"\n",
"# Model id to load the tokenizer\n",
"model_id = \"answerdotai/ModernBERT-base\"\n",
"\n",
"# Load Tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
"\n",
"# Tokenize helper function\n",
"def tokenize(batch):\n",
" return tokenizer(batch['text'], padding=True, truncation=True, return_tensors=\"pt\")\n",
"\n",
"# Tokenize dataset\n",
"tokenized_dataset = dataset.map(tokenize, batched=True, remove_columns=[\"text\"])\n",
"\n",
"tokenized_dataset[\"train\"].features.keys()\n",
"# dict_keys(['labels', 'input_ids', 'attention_mask'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3EJxH4YsVBcW"
},
"outputs": [],
"source": [
"from transformers import AutoModelForSequenceClassification\n",
"\n",
"# Model id to load the tokenizer\n",
"model_id = \"answerdotai/ModernBERT-base\"\n",
"\n",
"# Prepare model labels - useful for inference\n",
"labels = list(set(tokenized_dataset[\"train\"][\"labels\"]))\n",
"num_labels = len(labels)\n",
"label2id, id2label = dict(), dict()\n",
"for i, label in enumerate(labels):\n",
" label2id[label] = str(i)\n",
" id2label[str(i)] = label"
]
},
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"source": [
"# Download the model from huggingface.co/models\n",
"model = AutoModelForSequenceClassification.from_pretrained(\n",
" model_id, num_labels=num_labels, label2id=label2id, id2label=id2label,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Rb9Ab_ykVBfD"
},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.metrics import f1_score\n",
"\n",
"# Metric helper method\n",
"def compute_metrics(eval_pred):\n",
" predictions, labels = eval_pred\n",
" predictions = np.argmax(predictions, axis=1)\n",
" score = f1_score(\n",
" labels, predictions, labels=labels, pos_label=1, average=\"weighted\"\n",
" )\n",
" return {\"f1\": float(score) if score == 1 else score}\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MOgrfOJJVHSj"
},
"outputs": [],
"source": [
"from huggingface_hub import HfFolder\n",
"from transformers import Trainer, TrainingArguments\n",
"\n",
"# Define training args\n",
"training_args = TrainingArguments(\n",
" output_dir= \"ModernBERT-code-classifier\",\n",
" per_device_train_batch_size=1,\n",
" per_device_eval_batch_size=1,\n",
" gradient_accumulation_steps=16,\n",
" learning_rate=5e-5,\n",
" num_train_epochs=5,\n",
" bf16=True, # bfloat16 training\n",
" optim=\"adamw_torch_fused\", # improved optimizer\n",
" # logging & evaluation strategies\n",
" logging_strategy=\"steps\",\n",
" logging_steps=100,\n",
" eval_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" save_total_limit=2,\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"f1\",\n",
" # push to hub parameters\n",
" push_to_hub=True,\n",
" hub_strategy=\"every_save\",\n",
" hub_token=HfFolder.get_token(),\n",
" report_to=\"wandb\"\n",
")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lgQsi3tnXJU4"
},
"source": [
"# Overfitting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DiwCjMY2XJci"
},
"outputs": [],
"source": [
"limited_dataset = tokenized_dataset[\"train\"].select(range(100))\n",
"\n",
"# Create a Trainer instance\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=limited_dataset,\n",
" eval_dataset=tokenized_dataset[\"test\"],\n",
" compute_metrics=compute_metrics,\n",
")\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eZMjCx_1XiC0"
},
"outputs": [],
"source": [
"# clear memory\n",
"\n",
"import torch\n",
"torch.cuda.empty_cache()\n",
"\n",
"del trainer\n",
"del model\n",
"del limited_dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u86h29rsXJlY"
},
"source": [
"# Underfitting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zOZHoke2XJtk"
},
"outputs": [],
"source": [
"# define a low learning rate\n",
"training_args.learning_rate = 1e-7\n",
"\n",
"# Create a Trainer instance\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=limited_dataset,\n",
" eval_dataset=tokenized_dataset[\"test\"],\n",
" compute_metrics=compute_metrics,\n",
")\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2r3jOiQ1Xq-E"
},
"outputs": [],
"source": [
"# clear memory\n",
"\n",
"import torch\n",
"torch.cuda.empty_cache()\n",
"\n",
"del trainer\n",
"del model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2FQRRGOyXKAZ"
},
"source": [
"# Just right! π₯£"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QGL6NDFUXKI3"
},
"outputs": [],
"source": [
"# define a valid learning rate\n",
"training_args.learning_rate = 5e-5\n",
"\n",
"# Create a Trainer instance\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=limited_dataset,\n",
" eval_dataset=tokenized_dataset[\"test\"],\n",
" compute_metrics=compute_metrics,\n",
")\n",
"trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "R3pV0O3uhc2X"
},
"source": [
"# Inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9dSYxaH4VQRY"
},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"\n",
"# load model from huggingface.co/models using our repository id\n",
"classifier = pipeline(\n",
" task=\"text-classification\",\n",
" model=\"argilla/ModernBERT-domain-classifier\",\n",
" device=0,\n",
")\n",
"\n",
"sample = \"\"\"def add_numbers(a, b):\n",
" return a + b\"\"\"\n",
"\n",
"classifier(sample)\n"
]
}
],
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