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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install torch transformers scikit-learn wandb accelerate tqdm\n",
"from IPython.display import clear_output\n",
"clear_output(wait=True)\n",
"print(\".\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!apt-get update\n",
"!apt-get install zstd\n",
"!tar --use-compress-program=unzstd -xvf bert_streamed_dataset.tar.zst\n",
"clear_output(wait=True)\n",
"print(\".\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments\n",
"from sklearn.model_selection import train_test_split\n",
"from tqdm import tqdm\n",
"import wandb\n",
"import json\n",
"\n",
"# Initialize W&B\n",
"wandb.init(project=\"distilbert-ai-text-classification\")\n",
"\n",
"# Check if MPS is available and set the device\n",
"device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
"print(device)\n",
"\n",
"# Load pre-trained DistilBERT tokenizer and model\n",
"tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
"model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)\n",
"model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the JSONL dataset\n",
"data = []\n",
"total_num_of_lines = 0\n",
"with open('bert_reddit_vs_synth_writing_prompts.jsonl', 'r') as infile:\n",
" for line in tqdm(infile, desc=\"Checking dataset size\"):\n",
" total_num_of_lines += 1\n",
"\n",
"with open('bert_reddit_vs_synth_writing_prompts.jsonl', 'r') as infile:\n",
" for line in tqdm(infile, desc=\"Loading dataset\", total=total_num_of_lines):\n",
" data.append(json.loads(line))\n",
"\n",
"# Extract texts and labels\n",
"print(\"Extracting texts and labels\")\n",
"texts = [entry['text'] for entry in data]\n",
"labels = [entry['label'] for entry in data]\n",
"\n",
"# Tokenize the text\n",
"print(\"Tokenizing text\")\n",
"inputs = tokenizer(texts, padding=True, truncation=True, return_tensors=\"pt\")\n",
"\n",
"# Move input tensors to the device\n",
"print(\"Moving input tensors\")\n",
"inputs = {key: val for key, val in inputs.items()}\n",
"\n",
"# Split the data into training and validation sets\n",
"print(\"Splitting data into train and validation\")\n",
"train_inputs, val_inputs, train_labels, val_labels = train_test_split(\n",
" inputs['input_ids'], labels, test_size=0.2, random_state=42)\n",
"\n",
"train_attention_masks, val_attention_masks, _, _ = train_test_split(\n",
" inputs['attention_mask'], labels, test_size=0.2, random_state=42)\n",
"\n",
"# Create a PyTorch dataset\n",
"class TextDataset(torch.utils.data.Dataset):\n",
" def __init__(self, input_ids, attention_masks, labels):\n",
" self.input_ids = input_ids\n",
" self.attention_masks = attention_masks\n",
" self.labels = labels\n",
"\n",
" def __len__(self):\n",
" return len(self.labels)\n",
"\n",
" def __getitem__(self, idx):\n",
" return {\n",
" 'input_ids': self.input_ids[idx],\n",
" 'attention_mask': self.attention_masks[idx],\n",
" 'labels': torch.tensor(self.labels[idx])\n",
" }\n",
"\n",
"print(\"Creating pytorch datasets\")\n",
"train_dataset = TextDataset(train_inputs, train_attention_masks, train_labels)\n",
"val_dataset = TextDataset(val_inputs, val_attention_masks, val_labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Reduce eval set to X examples to speed up training\n",
"NUM_OF_EVAL_EXAMPLES = 1000\n",
"val_inputs_subset = val_inputs[:NUM_OF_EVAL_EXAMPLES]\n",
"val_attention_masks_subset = val_attention_masks[:NUM_OF_EVAL_EXAMPLES]\n",
"val_labels_subset = val_labels[:NUM_OF_EVAL_EXAMPLES]\n",
"\n",
"# Create a TextDataset with only X examples\n",
"val_dataset = Textdataset(val_inputs_subset, val_attention_masks_subset, val_labels_subset)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define the training arguments\n",
"training_args = TrainingArguments(\n",
" output_dir='./distil-bert-train-results', \n",
" num_train_epochs=3, \n",
" per_device_train_batch_size=16, \n",
" per_device_eval_batch_size=16, \n",
" warmup_steps=500, # number of warmup steps for learning rate scheduler\n",
" weight_decay=0.01, \n",
" logging_dir='./logs', \n",
" logging_steps=10, \n",
" report_to=\"wandb\", \n",
" evaluation_strategy=\"steps\", # Evaluate every logging step\n",
" eval_steps=100, # Evaluate every 10 steps\n",
" fp16=True,\n",
")\n",
"\n",
"# Create the Trainer\n",
"trainer = Trainer(\n",
" model=model, # the instantiated 🤗 Transformers model to be trained\n",
" args=training_args, # training arguments, defined above\n",
" train_dataset=train_dataset, # training dataset\n",
" eval_dataset=val_dataset # evaluation dataset\n",
")\n",
"\n",
"# Train the model\n",
"trainer.train()\n",
"\n",
"# Save the model\n",
"model.save_pretrained('./distil-bert-train-final-result')\n",
"\n",
"# Finish the W&B run\n",
"wandb.finish()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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