Harsha901/tinybert-imdb-sentiment-analysis-model

#

📦 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)

🚀 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}]