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README.md

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-#
+#
+
+# 📦 TinyBERT IMDB Sentiment Analysis Model
+
+This is a fine-tuned [TinyBERT](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) model for binary **sentiment classification** on a 5,000-sample subset of the [IMDB dataset](https://huggingface.co/datasets/imdb).
+It predicts whether a movie review is **positive** or **negative**.
+
+## 🧠 Model Details
+
+- **Base model:** [`huawei-noah/TinyBERT_General_4L_312D`](https://huggingface.co/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}]