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#made by gpt
from datasets import load_dataset
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch
# Load a small dataset (IMDB with just a few samples for quick testing)
dataset = load_dataset("imdb", split='train[:2%]').train_test_split(test_size=0.2)
# Tokenizer and model
tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
# Tokenize the dataset
def tokenize(batch):
return tokenizer(batch['text'], padding=True, truncation=True)
tokenized_dataset = dataset.map(tokenize, batched=True)
tokenized_dataset = tokenized_dataset.rename_column("label", "labels")
# Training arguments
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
num_train_epochs=1,
logging_steps=10,
save_steps=10,
report_to="none"
)
# Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["test"]
)
# Train the model
trainer.train()
# Save model
trainer.save_model("my-simple-sentiment-model")
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