Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- distilbert/distilbert-base-uncased
+---
+# DistilBERT Sentiment Analysis Model
+
+This model is a fine-tuned version of **DistilBERT** for sentiment analysis on the **IMDb** dataset. It classifies movie reviews as either **positive** or **negative** based on the text content.
+
+## Model Details
+
+- **Model Type**: DistilBERT (a smaller and faster variant of BERT)
+- **Task**: Sentiment Analysis
+- **Dataset**: IMDb dataset containing movie reviews with labels (positive/negative)
+- **Fine-Tuned On**: IMDb dataset
+
+## Model Performance
+
+This model was fine-tuned on the IMDb dataset for sentiment classification, achieving good performance for binary sentiment classification tasks (positive/negative).
+
+## Usage
+
+To use this model, you can load it from the Hugging Face Model Hub using the `transformers` library:
+
+```python
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline('sentiment-analysis', model='dorukan/distil-bert-imdb')
+
+# Example usage
+result = classifier("This movie was amazing!")
+print(result)
+```
+
+This will output a sentiment prediction for the given text.
+
+
+## License
+
+This model is licensed under the MIT License. For more information, see the LICENSE file.
+
+## Acknowledgments
+
+- **DistilBERT**: A smaller version of BERT, created by the Hugging Face team.
+- **IMDb Dataset**: A collection of movie reviews used for sentiment classification, widely used in NLP tasks.
+
+You can find more details about the model at the [Hugging Face model page](https://huggingface.co/dorukan/distil-bert-imdb).