from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="Kapilydv6/my-first-sentiment-model")

result = classifier("This movie was amazing!")
print(result)
# [{'label': 'POSITIVE', 'score': 0.99}]

Training Details

Base model: distilbert-base-uncased
Dataset: IMDB (2000 training samples)
Epochs: 3
Framework: PyTorch + Hugging Face Transformers

Limitations

This is a beginner project trained on a small subset of IMDB reviews. For production use, train on the full dataset with more epochs.

Training Results Explained

📌 What Happened Step by Step

1. Dataset Loaded

Downloaded the full IMDB dataset (25,000 train + 25,000 test reviews)
Used only:
- 2,000 samples for training
- 500 samples for testing
This was done as per configuration

2. Model Loaded — "MISSING / UNEXPECTED" Warnings

These warnings are normal and expected:

UNEXPECTED keys (vocab layers)
- DistilBERT was originally trained for masked language modeling
- These layers are not needed for classification
MISSING keys (classifier, pre_classifier)
- These are new layers added for the sentiment task
- They are randomly initialized and learned during training

3. Training (3 Epochs — ~1h 46min on CPU)

Epoch	Train Loss	Eval Accuracy	Eval F1
1	~0.51	85.0%	84.96%
2	~0.26	86.6%	86.55%
3	~0.16	87.4%	87.39%

✅ Loss decreased (0.60 → 0.15) → Model is learning
✅ Accuracy increased each epoch → Model is improving
✅ Best model (Epoch 3) was automatically selected and saved

4. Final Accuracy

87.4% accuracy
Strong performance considering only 2,000 training samples
Using the full dataset could reach ~93%+ accuracy

5. Test Predictions

Review	Prediction	Confidence
"Absolutely fantastic!"	POSITIVE	97.9%
"Terrible waste of time"	NEGATIVE	96.7%
"It was okay, nothing special"	NEGATIVE	75.3%

💡 Insight:

The third review is ambiguous
A human might label it neutral
The model predicts NEGATIVE with lower confidence, which is reasonable

⚠️ Warnings to Ignore

Symlinks warning
- Harmless Windows limitation
- Only affects disk usage
LayerNorm gamma/beta → weight/bias
- Due to PyTorch naming changes
- No impact on results

✅ Summary

Model trained successfully
Performance improved across epochs
Achieved strong accuracy with limited data
Predictions are confident and reasonable
Warnings are expected and safe to ignore