stanfordnlp/imdb
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Fine-tuned distilbert-base-uncased for binary sentiment classification (0 = negative, 1 = positive).
| Metric | Score |
|---|---|
| Accuracy | 0.877 |
| Precision | 0.869 |
| Recall | 0.887 |
| F1 | 0.878 |
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="Hannia67/my-sentiment-analyzer")
result = classifier("This was a great experience!")
print(result)