| language: | |
| - ru | |
| - uk | |
| - be | |
| - kk | |
| - az | |
| - hy | |
| - ka | |
| - he | |
| - en | |
| - de | |
| - multilingual | |
| tags: | |
| - language classification | |
| - text segmentation | |
| datasets: | |
| - open_subtitles | |
| - tatoeba | |
| - oscar | |
| # RoBERTa for Multilabel Language Segmentation | |
| ## Training | |
| RoBERTa fine-tuned on small parts of Open Subtitles, Oscar and Tatoeba datasets (~9k samples per language). | |
| Implemented heuristic algorithm for multilingual training data creation with generation of target masks- https://github.com/n1kstep/lang-classifier | |
| | data source | language | | |
| |-----------------|----------------| | |
| | open_subtitles | ka, he, en, de | | |
| | oscar | be, kk, az, hu | | |
| | tatoeba | ru, uk | | |
| ## Validation | |
| The metrics obtained from validation on the another part of dataset (~1k samples per language). | |
| | Validation Loss | Precision | Recall | F1-Score | Accuracy | | |
| |-----------------|-----------|----------|----------|----------| | |
| | 0.029172 | 0.919623 | 0.933586 | 0.926552 | 0.991883 | |