---
tags:
- embeddings
- glove
- cc100
license: cc-by-sa-4.0
language:
- my
---
        
# CC100 GloVe Embeddings for MY Language

## Model Description
- **Language:** my
- **Embedding Algorithm:** GloVe (Global Vectors for Word Representation)
- **Vocabulary Size:** 226710
- **Vector Dimensions:** 300
- **Training Data:** CC100 dataset

## Training Information
We trained GloVe embeddings using the original C code. The model was trained by stochastically sampling nonzero elements from the co-occurrence matrix, over 100 iterations, to produce 300-dimensional vectors. We used a context window of ten words to the left and ten words to the right. Words with fewer than 5 co-occurrences were excluded for languages with over 1 million tokens in the training data, and the threshold was set to 2 for languages with smaller datasets.

We used data from CC100 for training the static word embeddings. We set xmax = 100, α = 3/4, and used AdaGrad optimization with an initial learning rate of 0.05.

## Usage
These embeddings can be used for various NLP tasks such as text classification, named entity recognition, and as input features for neural networks.

## Citation
If you use these embeddings in your research, please cite:

```bibtex
@misc{gurgurov2024gremlinrepositorygreenbaseline,
      title={GrEmLIn: A Repository of Green Baseline Embeddings for 87 Low-Resource Languages Injected with Multilingual Graph Knowledge}, 
      author={Daniil Gurgurov and Rishu Kumar and Simon Ostermann},
      year={2024},
      eprint={2409.18193},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.18193}, 
}
```

## License
These embeddings are released under the [CC-BY-SA 4.0 License](https://creativecommons.org/licenses/by-sa/4.0/).