MLOps IMDB Sentiment Analysis Model ---‐----------------------------------
Model Description
Fine-tuned distilbert-base-uncased for binary sentiment classification on IMDB movie reviews.
Training Details
- Base Model: distilbert-base-uncased
- Dataset: IMDB Movie Reviews (50,000 samples)
- Task: Binary Text Classification
- Platform: Kaggle GPU T4 x2
Performance (run-v2 - Best Model)
| Metric | Score |
|---|---|
| Accuracy | 91.70% |
| F1 Score | 91.70% |
| Validation Loss | 0.7424 |
Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 5e-5 |
| Epochs | 3 |
| Batch Size | 16 |
| Max Length | 256 |
Experiment Comparison
| Run | Learning Rate | Accuracy | F1 |
|---|---|---|---|
| run-v1 | 3e-5 | 91.54% | 91.53% |
| run-v2 | 5e-5 | 91.70% | 91.70% |
Usage
from transformers import pipeline classifier = pipeline('text-classification', model='Atreyee-Halder/mlops-imdb-sentiment') result = classifier("This movie was absolutely amazing!") print(result)
Labels
- 0 = negative
- 1 = positive
Project Links
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