📦 TinyBERT IMDB Sentiment Analysis Model
This is a fine-tuned TinyBERT model for binary sentiment classification on a 5,000-sample subset of the IMDB dataset. It predicts whether a movie review is positive or negative.
🧠 Model Details
- Base model:
huawei-noah/TinyBERT_General_4L_312D - Task: Sentiment Classification (Binary)
- Dataset: 4,000 training + 1,000 test samples from IMDB
- Tokenizer:
AutoTokenizer.from_pretrained('huawei-noah/TinyBERT_General_4L_312D') - Max length: 300 tokens
- Batch size: 64
- Training framework: Hugging Face
Trainer - Device: A100 GPU
📊 Evaluation Metrics
📊 Evaluation Metrics (on 1,000-sample test set)
| Metric | Value |
|---|---|
| Accuracy | 88.02% |
| Evaluation Loss | 0.2962 |
| Runtime | 30.9 sec |
| Samples per Second | 485 |
🚀 How to Use
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="Harsha901/tinybert-imdb-sentiment-analysis-model"
)
result = classifier("This movie was absolutely amazing!")
print(result) # [{'label': 'LABEL_1', 'score': 0.98}]