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+# DistilBERT Base Cased - Text Processing Model
+
+This repository contains a Jupyter notebook demonstrating the use of DistilBERT, a distilled version of BERT (Bidirectional Encoder Representations from Transformers), for masked language modeling and text embedding generation.
+
+## Overview
+
+DistilBERT is a smaller, faster, and lighter version of BERT that retains 97% of BERT's language understanding while being 60% faster and 40% smaller in size. This project demonstrates both the cased and uncased variants of DistilBERT.
+
+## Features
+
+- **Fill-Mask Pipeline**: Uses DistilBERT to predict masked tokens in sentences
+- **Word Embeddings**: Generates contextual word embeddings for text processing
+- **GPU Support**: Configured to run on CUDA-enabled GPUs for faster inference
+- **Easy Integration**: Simple examples using Hugging Face Transformers library
+
+## Requirements
+
+- Python 3.7+
+- PyTorch
+- Transformers library
+- CUDA-compatible GPU (optional, but recommended)
+
+## Installation
+
+Install the required dependencies:
+
+```bash
+pip install -U transformers
+```
+
+For GPU support, ensure you have PyTorch with CUDA installed:
+
+```bash
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+```
+
+## Usage
+
+### Fill-Mask Task
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("fill-mask", model="distilbert/distilbert-base-cased")
+result = pipe("Hello I'm a [MASK] model.")
+
+for candidate in result:
+    print(candidate)
+```
+
+### Generating Word Embeddings
+
+```python
+from transformers import DistilBertTokenizer, DistilBertModel
+
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertModel.from_pretrained("distilbert-base-uncased")
+
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+
+# Access the embeddings
+embeddings = output.last_hidden_state
+```
+
+### Direct Model Loading
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-cased")
+model = AutoModelForMaskedLM.from_pretrained("distilbert/distilbert-base-cased")
+```
+
+## Notebook Contents
+
+The [Distilbert-base-cased.ipynb](Distilbert-base-cased.ipynb) notebook includes:
+
+1. **Installation**: Setting up the Transformers library
+2. **Pipeline Usage**: High-level API for fill-mask tasks
+3. **Direct Model Loading**: Lower-level API for custom implementations
+4. **Embedding Generation**: Creating contextual word embeddings
+5. **Token Visualization**: Inspecting tokenization results
+
+## Models Used
+
+- **distilbert-base-cased**: DistilBERT model trained on cased English text
+- **distilbert-base-uncased**: DistilBERT model trained on lowercased English text
+
+Model pages:
+- [distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased)
+- [distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
+
+## Example Output
+
+When running the fill-mask task with "Hello I'm a [MASK] model.", the model predicts:
+
+1. fashion (15.75%)
+2. professional (6.04%)
+3. role (2.56%)
+4. celebrity (1.94%)
+5. model (1.73%)
+
+## Use Cases
+
+- **Text Classification**: Sentiment analysis, topic classification
+- **Named Entity Recognition**: Identifying entities in text
+- **Question Answering**: Building QA systems
+- **Text Embeddings**: Feature extraction for downstream tasks
+- **Language Understanding**: Transfer learning for NLP tasks
+
+## Performance
+
+DistilBERT offers an excellent trade-off between performance and efficiency:
+
+- **Speed**: 60% faster than BERT
+- **Size**: 40% smaller than BERT
+- **Performance**: Retains 97% of BERT's capabilities
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## Issues
+
+If the code snippets do not work, please open an issue on:
+- [Model Repository](https://huggingface.co/distilbert/distilbert-base-cased)
+- [Hugging Face.js](https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries-snippets.ts)
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Acknowledgments
+
+- **Hugging Face**: For the Transformers library and pre-trained models
+- **DistilBERT Authors**: Sanh et al. for the DistilBERT research and implementation
+
+## References
+
+- [DistilBERT Paper](https://arxiv.org/abs/1910.01108)
+- [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers/index)
+- [DistilBERT Model Card](https://huggingface.co/distilbert/distilbert-base-cased)
+
+## Contact
+
+For questions or feedback, please open an issue in this repository.