| --- |
| language: en |
| tags: |
| - text-classification |
| - sentiment-analysis |
| - roberta |
| - pytorch |
| datasets: |
| - glue |
| - imdb |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: sentiment-classifier |
| results: |
| - task: |
| type: text-classification |
| name: Sentiment Analysis |
| dataset: |
| name: SST-2 + IMDB |
| type: mixed |
| metrics: |
| - type: accuracy |
| value: 0.9292 |
| - type: f1 |
| value: 0.9413 |
| --- |
| |
| # RoBERTa Fine-Tuned for Sentiment Analysis |
|
|
| This model classifies English text as either **Positive 😊** or **Negative 😞**. |
|
|
| Fine-tuned from `roberta-base` on a combination of SST-2 (Stanford Sentiment Treebank) and IMDB movie reviews. |
|
|
| ## Performance |
|
|
| | Metric | Score | |
| |----------|--------| |
| | Accuracy | 0.9292 (92.92%) | |
| | F1 Score | 0.9413 | |
|
|
| *Evaluated on 20,000 held-out IMDB test samples.* |
|
|
| ## How to Use |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline( |
| "text-classification", |
| model="samandar1105/sentiment-classifier" |
| ) |
| |
| result = classifier("This movie was absolutely fantastic!") |
| print(result) |
| # [{'label': 'positive', 'score': 0.998}] |
| ``` |
|
|
| ## Labels |
|
|
| | ID | Label | Meaning | |
| |----|----------|---------| |
| | 0 | negative | Negative sentiment | |
| | 1 | positive | Positive sentiment | |
|
|
| ## Training Details |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | Base model | roberta-base | |
| | Training data | SST-2 (67K) + IMDB (25K) = 92K samples | |
| | Epochs | 4 | |
| | Batch size | 32 | |
| | Learning rate | 2e-5 | |
| | Max sequence length | 256 | |
| | Warmup ratio | 0.1 | |
| | Weight decay | 0.01 | |
|
|
| ## Limitations |
|
|
| - Trained on English text only |
| - Optimized for movie/review-style text |
| - Binary only (positive / negative) — no neutral class |
|
|
