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README.md

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+
+---
+tags:
+  - embeddings
+  - glove
+  - cc100
+license: cc-by-sa-4.0
+---
+        
+# CC100 GloVe Embeddings for MY_ZAW Language
+
+## Model Description
+- **Language:** my_zaw
+- **Embedding Algorithm:** GloVe (Global Vectors for Word Representation)
+- **Vocabulary Size:** 795001
+- **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{gurgurov2024lowremrepositorywordembeddings,
+      title={LowREm: A Repository of Word Embeddings for 87 Low-Resource Languages Enhanced 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/).