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README.md

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+---
+language: en
+license: apache-2.0
+base_model: distilbert/distilbert-base-uncased
+tags:
+  - text-classification
+  - sentiment-analysis
+  - distilbert
+datasets:
+  - imdb
+metrics:
+  - loss
+---
+
+# DistilBERT Sentiment Classifier (IMDB)
+
+Fine-tuned [distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) for binary sentiment classification (positive/negative) on a subset of the IMDB movie review dataset.
+
+## Model Details
+
+- **Base model:** distilbert/distilbert-base-uncased
+- **Task:** Sentiment analysis (binary classification)
+- **Labels:** `0` = negative, `1` = positive
+- **Max sequence length:** 128 tokens
+
+## Training
+
+| Hyperparameter | Value |
+|---|---|
+| Dataset | IMDB (500 samples, 80/20 split) |
+| Epochs | 2 |
+| Batch size | 8 |
+| Learning rate | 5e-5 (linear decay) |
+
+**Final eval loss:** 0.0008
+
+## Usage
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="chinmaygarde/hello")
+classifier("This movie was absolutely fantastic!")
+# [{'label': 'LABEL_1', 'score': 0.999}]
+```