| language: en | |
| pipeline_tag: text-classification | |
| tags: | |
| - sentiment-analysis | |
| # 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 | 0.8868 | | |
| | F1 | 0.89 | | |
| ## 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. | |