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| Metric | Score |
|---|---|
| Accuracy | 0.894 |
| F1 Score | 0.893 |
| ROC-AUC | 0.960 |
| Precision | 0.884 |
| Recall | 0.902 |
| Property | Value |
|---|---|
| Base model | distilbert-base-uncased |
| Task | Binary text classification |
| Labels | NEGATIVE (0), POSITIVE (1) |
| Max token length | 256 |
| Training samples | 5,000 (IMDB subset) |
| Epochs | 2 |
| Batch size | 16 |
| Learning rate | 2e-5 |
| Framework | HuggingFace Transformers + Trainer API |
| Experiment tracking | MLflow |
1from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
2
3MODEL_PATH = "amarshiv86/sentiment-analysis-imdb-model"
4
5tokenizer = AutoTokenizer.from_pretrained(f"{MODEL_PATH}/model")
6model = AutoModelForSequenceClassification.from_pretrained(f"{MODEL_PATH}/model")
7
8clf = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, truncation=True)
9
10results = clf([
11 "This movie was absolutely fantastic, loved every minute!",
12 "Terrible film, complete waste of time.",
13])
14
15for r in results:
16 print(f"{r['label']} — {r['score']:.1%} confidence")src/ or params.yaml changes:GitHub Push
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GitHub Actions
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prepare.py → train.py → evaluate.py
↓ ↓
model files metrics.json
confusion_matrix.png
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HuggingFace Hub (this repo)amarshiv86/sentiment-analysis-imdb-model
├── model/
│ ├── model.safetensors # fine-tuned weights (268 MB)
│ ├── config.json # model architecture config
│ ├── tokenizer.json # tokenizer vocab
│ └── tokenizer_config.json # tokenizer settings
├── artifacts/
│ └── confusion_matrix.png # evaluation plot
└── metrics.json # latest eval metrics