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roberta-base on a combination of SST-2 (Stanford Sentiment Treebank) and IMDB movie reviews.| Metric | Score |
|---|---|
| Accuracy | 0.9292 (92.92%) |
| F1 Score | 0.9413 |
1from transformers import pipeline
2
3classifier = pipeline(
4 "text-classification",
5 model="samandar1105/sentiment-classifier"
6)
7
8result = classifier("This movie was absolutely fantastic!")
9print(result)
10# [{'label': 'positive', 'score': 0.998}]| ID | Label | Meaning |
|---|---|---|
| 0 | negative | Negative sentiment |
| 1 | positive | Positive sentiment |
| 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 |