model_bert

Sentiment classifier (positive / negative) fine-tuned from distilbert-base-uncased.

Group members: Gumoshabe Sandra and Keeya Tracy Dataset: stanfordnlp/imdb

Results on the test set

Metric	Score
Accuracy	0.9090
F1	0.9090

Example predictions

1. "Terrible experience, I want my money back." -> Negative
2. "This is boring" -> Negative
3. "Absolutely wonderful movie with great acting!" -> Positive

Limitations

Trained on English movie reviews only; struggles with sarcasm and highly complex or mixed language text.