| language: | |
| - hi | |
| - en | |
| - multilingual | |
| license: cc-by-4.0 | |
| tags: | |
| - hi | |
| - en | |
| - codemix | |
| datasets: | |
| - L3Cube-HingCorpus | |
| ## HingBERT | |
| HingBERT is a Hindi-English code-mixed BERT model trained on roman text. It is a base BERT model fine-tuned on L3Cube-HingCorpus. | |
| <br> | |
| [dataset link] (https://github.com/l3cube-pune/code-mixed-nlp) | |
| More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398) | |
| ``` | |
| @inproceedings{nayak-joshi-2022-l3cube, | |
| title = "{L}3{C}ube-{H}ing{C}orpus and {H}ing{BERT}: A Code Mixed {H}indi-{E}nglish Dataset and {BERT} Language Models", | |
| author = "Nayak, Ravindra and Joshi, Raviraj", | |
| booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference", | |
| month = jun, | |
| year = "2022", | |
| address = "Marseille, France", | |
| publisher = "European Language Resources Association", | |
| url = "https://aclanthology.org/2022.wildre-1.2", | |
| pages = "7--12", | |
| } | |
| ``` |