| datasets: | |
| - imdb | |
| language: en | |
| license: apache-2.0 | |
| metrics: | |
| - accuracy | |
| - f1 | |
| pipeline_tag: text-classification | |
| tags: | |
| - sentiment-analysis | |
| - text-classification | |
| - distilbert | |
| # sentiment-tutorial | |
| Fine-tuned distilbert-base-uncased for binary sentiment classification. | |
| ## Intended Use | |
| Classify English text as positive or negative. | |
| ## Training Procedure | |
| - Base model: distilbert-base-uncased | |
| - Epochs: 2 | |
| - Learning rate: 2e-5 | |
| - Batch size: 32 | |
| - Max length: 128 | |
| ## Evaluation Results | |
| Accuracy: 0.870 | |
| Precision: 0.879 | |
| Recall: 0.858 | |
| F1: 0.868 | |
| ## Limitations | |
| - Binary classification only | |
| - English only | |
| - Movie reviews domain | |
| ## Usage | |
| from transformers import pipeline | |
| classifier = pipeline( | |
| "sentiment-analysis", | |
| model="ayesha9f/sentiment-tutorial" | |
| ) | |
| classifier("This was a great experience!") | |