---
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.