| --- | |
| language: ti | |
| widget: | |
| - text: "ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር" | |
| datasets: | |
| - tiposd_sera.py | |
| model-index: | |
| - name: tipos-tiroberta | |
| results: [] | |
| --- | |
| # Tigrinya POS tagging with TiRoBERTa | |
| This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co//content/tiroberta) on the NTC tiposd dataset. | |
| ## Training | |
| ### Hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 5e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10.0 | |
| ### Results | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3194 | |
| - Adj Precision: 0.9219 | |
| - Adj Recall: 0.9335 | |
| - Adj F1: 0.9277 | |
| - Adj Number: 1670 | |
| - Adv Precision: 0.8297 | |
| - Adv Recall: 0.8554 | |
| - Adv F1: 0.8423 | |
| - Adv Number: 484 | |
| - Con Precision: 0.9844 | |
| - Con Recall: 0.9763 | |
| - Con F1: 0.9804 | |
| - Con Number: 972 | |
| - Fw Precision: 0.7895 | |
| - Fw Recall: 0.5357 | |
| - Fw F1: 0.6383 | |
| - Fw Number: 28 | |
| - Int Precision: 0.6552 | |
| - Int Recall: 0.7308 | |
| - Int F1: 0.6909 | |
| - Int Number: 26 | |
| - N Precision: 0.9650 | |
| - N Recall: 0.9662 | |
| - N F1: 0.9656 | |
| - N Number: 3992 | |
| - Num Precision: 0.9747 | |
| - Num Recall: 0.9665 | |
| - Num F1: 0.9706 | |
| - Num Number: 239 | |
| - N Prp Precision: 0.9308 | |
| - N Prp Recall: 0.9447 | |
| - N Prp F1: 0.9377 | |
| - N Prp Number: 470 | |
| - N V Precision: 0.9854 | |
| - N V Recall: 0.9736 | |
| - N V F1: 0.9794 | |
| - N V Number: 416 | |
| - Pre Precision: 0.9722 | |
| - Pre Recall: 0.9625 | |
| - Pre F1: 0.9673 | |
| - Pre Number: 907 | |
| - Pro Precision: 0.9448 | |
| - Pro Recall: 0.9236 | |
| - Pro F1: 0.9341 | |
| - Pro Number: 445 | |
| - Pun Precision: 1.0 | |
| - Pun Recall: 0.9994 | |
| - Pun F1: 0.9997 | |
| - Pun Number: 1607 | |
| - Unc Precision: 1.0 | |
| - Unc Recall: 0.875 | |
| - Unc F1: 0.9333 | |
| - Unc Number: 16 | |
| - V Precision: 0.8780 | |
| - V Recall: 0.9231 | |
| - V F1: 0.9 | |
| - V Number: 78 | |
| - V Aux Precision: 0.9685 | |
| - V Aux Recall: 0.9878 | |
| - V Aux F1: 0.9780 | |
| - V Aux Number: 654 | |
| - V Ger Precision: 0.9388 | |
| - V Ger Recall: 0.9571 | |
| - V Ger F1: 0.9479 | |
| - V Ger Number: 513 | |
| - V Imf Precision: 0.9634 | |
| - V Imf Recall: 0.9497 | |
| - V Imf F1: 0.9565 | |
| - V Imf Number: 914 | |
| - V Imv Precision: 0.8793 | |
| - V Imv Recall: 0.7286 | |
| - V Imv F1: 0.7969 | |
| - V Imv Number: 70 | |
| - V Prf Precision: 0.8960 | |
| - V Prf Recall: 0.9082 | |
| - V Prf F1: 0.9020 | |
| - V Prf Number: 294 | |
| - V Rel Precision: 0.9678 | |
| - V Rel Recall: 0.9538 | |
| - V Rel F1: 0.9607 | |
| - V Rel Number: 757 | |
| - Overall Precision: 0.9562 | |
| - Overall Recall: 0.9562 | |
| - Overall F1: 0.9562 | |
| - Overall Accuracy: 0.9562 | |
| ### Framework versions | |
| - Transformers 4.12.0.dev0 | |
| - Pytorch 1.9.0+cu111 | |
| - Datasets 1.13.3 | |
| - Tokenizers 0.10.3 | |