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README.md
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# Odia Word Embeddings
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This project implements and compares different word embedding models (Word2Vec and GloVe) for the Odia language. The models are trained on a combination of Odia literature and Wikipedia data.
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## Project Structure
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```
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βββ data/
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β βββ raw/ # Raw data files (not in repo)
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β βββ processed/ # Processed data files (not in repo)
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β βββ interim/ # Intermediate data files (not in repo)
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βββ models/ # Trained models (not in repo)
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βββ notebooks/ # Jupyter notebooks for analysis
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βββ scripts/ # Utility scripts
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βββ src/ # Source code
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βββ data/ # Data processing modules
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βββ models/ # Model training modules
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```
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## Data and Models
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Due to size limitations, the data and model files are hosted on Hugging Face:
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### Data
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All data is available in the [odia-word-embeddings-data](https://huggingface.co/datasets/your-username/odia-word-embeddings-data) dataset:
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#### Raw Data
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- `odia_wiki_scraped.txt` - Scraped Odia Wikipedia articles
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- `odia_literature.txt` - Odia literature corpus
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#### Processed Data
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- `odia_wiki_scraped.csv` - Processed Wikipedia articles
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- `odia_literature.csv` - Processed literature corpus
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#### Additional Data Used
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- Monolingual Odia corpus from [OdiEnCorp 1.0](https://github.com/odiencorp/OdiEnCorp) (used for training)
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### Models
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All models are available in the [odia-word-embeddings](https://huggingface.co/VanshajR/odia-word-embeddings) repository:
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- Word2Vec Model
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- GloVe Model
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## Setup
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1. Clone the repository:
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```bash
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git clone https://github.com/VanshajR/Odia-Word-Embeddings.git
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cd Odia-Word-Embeddings
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```
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2. Create and activate a virtual environment:
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```bash
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python -m venv venv
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source venv/bin/activate # On Windows: venv\Scripts\activate
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```
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3. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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4. Download data and models:
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```bash
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# Install huggingface-hub
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pip install huggingface-hub
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# Login to Hugging Face
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huggingface-cli login
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# Download data
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huggingface-cli download VanshajR/odia-word-embeddings-data --local-dir data/
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# Download models
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huggingface-cli VanshajR/odia-word-embeddings --local-dir models/
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```
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## Usage
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### Training Models
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1. Train Word2Vec:
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```bash
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python scripts/train_word2vec.py
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```
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2. Train GloVe:
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```bash
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python scripts/train_glove.py
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```
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### Evaluating Models
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Run the evaluate_embeddings.ipynb notebook
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## Contributing
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Contributions are welcome! Please feel free to submit a Pull Request.
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## License
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This project is licensed under the MIT License - see the LICENSE file for details.
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## Acknowledgments
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- OdiEnCorp team for providing the monolingual Odia corpus, check it out [here](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-2879)
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- Wikipedia for their public articles
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