|
|
|
--- |
|
language: fy |
|
tags: |
|
- embeddings |
|
- glove |
|
- cc100 |
|
license: cc-by-sa-4.0 |
|
--- |
|
|
|
# CC100 GloVe Embeddings for FY Language |
|
|
|
## Model Description |
|
- **Language:** fy |
|
- **Embedding Algorithm:** GloVe (Global Vectors for Word Representation) |
|
- **Vocabulary Size:** 128611 |
|
- **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/). |
|
|
