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

python

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}]