Update README.md

README.md

@@ -10,4 +10,52 @@ metrics:
 - precision
 base_model:
 - google-bert/bert-base-uncased
----
+---
+
+# Fine-Tuned BERT for IMDB Sentiment Classification
+
+![Hugging Face Model](https://huggingface.co/front/assets/huggingface_logo-noborder.svg)
+
+## Model Description
+This is a fine-tuned version of [BERT-Base-Uncased](https://huggingface.co/google-bert/bert-base-uncased) for binary sentiment classification on the [IMDB dataset](https://huggingface.co/datasets/stanfordnlp/imdb). The model is trained to classify movie reviews as either **positive** or **negative**.
+
+## Model Details
+- **Base Model**: [BERT-Base-Uncased](https://huggingface.co/google-bert/bert-base-uncased)
+- **Dataset**: [IMDB Movie Reviews](https://huggingface.co/datasets/stanfordnlp/imdb)
+- **Languages**: English (`en`)
+- **Fine-tuning Epochs**: 3
+- **Batch Size**: 8
+- **Evaluation Metrics**: Accuracy, Precision, Recall
+- **License**: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+## Usage
+### Load the Model
+```python
+from transformers import BertForSequenceClassification, BertTokenizer
+
+model_name = "kparkhade/Fine-tuned-BERT-Imdb"
+
+model = BertForSequenceClassification.from_pretrained(model_name)
+tokenizer = BertTokenizer.from_pretrained(model_name)
+```
+
+### Inference Example
+```python
+from transformers import pipeline
+
+sentiment_pipeline = pipeline("text-classification", model=model_name)
+result = sentiment_pipeline("The movie was absolutely fantastic! I loved it.")
+print(result)
+```
+
+## Citation
+If you use this model, please cite:
+@article{devlin2019bert,
+  title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
+  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
+  journal={arXiv preprint arXiv:1810.04805},
+  year={2019}
+}
+
+## License
+This model is released under the Apache 2.0 License.