distilbert-imdb-sentiment

This is distilbert-base-uncased fine-tuned for binary sentiment classification (positive/negative) on the IMDB movie reviews dataset.

Training details

• Base model: distilbert-base-uncased
• Dataset: stanfordnlp/imdb, full train split (25,000 examples)
• Epochs: 3
• Max sequence length: 256 (padding + truncation)
• Evaluation: full test split (25,000 examples), evaluated after every epoch

Results

Epoch	Train loss	Test loss	Test accuracy
1	0.302	0.283	88.5%
2	0.157	0.366	89.2%
3	0.068	0.385	91.2%

Final test accuracy: 91.2%

Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

repo_id = "Niophy/distilbert-imdb-sentiment"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
model.eval()

id2label = {0: "negative", 1: "positive"}

sentence = "This movie was absolutely fantastic"
inputs = tokenizer(sentence, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
    logits = model(**inputs).logits
predicted_id = torch.argmax(logits, dim=-1).item()

print(id2label[predicted_id])

Limitations

Test loss rises after epoch 1 even as accuracy keeps improving, indicating mild overfitting by epoch 3. Training for more epochs without regularization (e.g. weight decay, early stopping) is unlikely to help much beyond this point.