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--- |
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language: be |
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tags: |
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- embeddings |
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- glove |
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- cc100 |
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license: cc-by-sa-4.0 |
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--- |
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# CC100 GloVe Embeddings for BE Language |
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## Model Description |
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- **Language:** be |
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- **Embedding Algorithm:** GloVe (Global Vectors for Word Representation) |
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- **Vocabulary Size:** 887866 |
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- **Vector Dimensions:** 300 |
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- **Training Data:** CC100 dataset |
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## Training Information |
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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. |
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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. |
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## Usage |
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These embeddings can be used for various NLP tasks such as text classification, named entity recognition, and as input features for neural networks. |
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## Citation |
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If you use these embeddings in your research, please cite: |
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```bibtex |
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@misc{gurgurov2024lowremrepositorywordembeddings, |
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title={LowREm: A Repository of Word Embeddings for 87 Low-Resource Languages Enhanced with Multilingual Graph Knowledge}, |
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author={Daniil Gurgurov and Rishu Kumar and Simon Ostermann}, |
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year={2024}, |
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eprint={2409.18193}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2409.18193}, |
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} |
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``` |
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## License |
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These embeddings are released under the [CC-BY-SA 4.0 License](https://creativecommons.org/licenses/by-sa/4.0/). |
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