train.py

"""
Finetuning stage 1:
1. Download ONLY DESCRIPTIONS for some patents from the USPTO.
2. Use the descriptions to train a model on a small set of patents.

### cursor said this (below)
3. Use the model to generate descriptions for the same set of patents.
4. Compare the generated descriptions to the original descriptions.
5. Use the comparisons to train a model on the entire set of patents.
"""

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer

if __name__ == "__main__":
    raw_datasets = load_dataset("imdb")
    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
    def tokenize_function(examples):
        return tokenizer(examples["text"], padding="max_length", truncation=True)
    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
    small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) 
    small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000)) 
    # full_train_dataset = tokenized_datasets["train"]
    # full_eval_dataset = tokenized_datasets["test"]
    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
    training_args = TrainingArguments("test_trainer")
    trainer = Trainer(
        model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset
    )
    trainer.train()