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https://github.com/metriccoders/metriccoders_indic_models
This is the Metric Coders Model Hub that contains the fastest growing indic ML models.
https://github.com/metriccoders/metriccoders_indic_models
keras machine-learning nlp sentiment-analysis sklearn tensorflow
Last synced: 3 days ago
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This is the Metric Coders Model Hub that contains the fastest growing indic ML models.
- Host: GitHub
- URL: https://github.com/metriccoders/metriccoders_indic_models
- Owner: metriccoders
- License: mit
- Created: 2024-01-20T15:25:15.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2024-01-22T14:48:14.000Z (10 months ago)
- Last Synced: 2024-10-10T18:11:09.034Z (28 days ago)
- Topics: keras, machine-learning, nlp, sentiment-analysis, sklearn, tensorflow
- Homepage:
- Size: 238 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# Indian Languages Machine Learning Repository
Welcome to the Indian Languages Machine Learning Repository of Metric Coders! This repository is dedicated to providing machine learning models and datasets for various Indian languages. Whether you're working on natural language processing, sentiment analysis, or language translation, we aim to support your projects with resources tailored to Indian languages. For now, we support Kannada language.
## Models
### Sentiment Analysis
1. **Kannada Sentiment Analysis Model**
- Description: A pre-trained sentiment analysis model for Kannada language text.
- Usage: Achieve sentiment classification on Kannada text data.
## Datasets
### Multilingual Text Corpus
1. **Indian Languages Text Corpus**
- Description: A diverse collection of text data for Kannada language
- Usage: Suitable for training and evaluating language models, sentiment analysis, and other language-related tasks.
## How to Use
1. Clone the repository:
```bash
git clone https://github.com/metriccoders/metriccoders_indic_models.git
cd metriccoders_indic_models
```
2. Explore the `models` directory for pre-trained models and the `datasets` directory for datasets.
3. Refer to individual model or dataset directories for specific usage instructions and examples.
## Contributions
Contributions are welcome! If you have models, datasets, or improvements to existing resources, please feel free to open an issue or submit a pull request.
## License
This repository is licensed under the MIT License - see the [LICENSE](LICENSE.md) file for details.