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--- |
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language: ti |
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widget: |
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- text: "ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር" |
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datasets: |
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- TLMD |
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- NTC |
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metrics: |
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- f1 |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: tiroberta-base-pos |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.9562 |
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- name: Precision |
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type: precision |
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value: 0.9562 |
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- name: Recall |
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type: recall |
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value: 0.9562 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9562 |
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--- |
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# Tigrinya POS tagging with TiRoBERTa |
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This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co/fgaim/tiroberta) on the NTC-v1 dataset (Tedla et al. 2016). |
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## Training |
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### Hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10.0 |
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### Results |
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The model achieves the following results on the test set: |
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- Loss: 0.3194 |
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- Adj Precision: 0.9219 |
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- Adj Recall: 0.9335 |
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- Adj F1: 0.9277 |
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- Adj Number: 1670 |
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- Adv Precision: 0.8297 |
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- Adv Recall: 0.8554 |
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- Adv F1: 0.8423 |
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- Adv Number: 484 |
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- Con Precision: 0.9844 |
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- Con Recall: 0.9763 |
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- Con F1: 0.9804 |
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- Con Number: 972 |
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- Fw Precision: 0.7895 |
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- Fw Recall: 0.5357 |
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- Fw F1: 0.6383 |
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- Fw Number: 28 |
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- Int Precision: 0.6552 |
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- Int Recall: 0.7308 |
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- Int F1: 0.6909 |
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- Int Number: 26 |
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- N Precision: 0.9650 |
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- N Recall: 0.9662 |
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- N F1: 0.9656 |
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- N Number: 3992 |
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- Num Precision: 0.9747 |
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- Num Recall: 0.9665 |
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- Num F1: 0.9706 |
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- Num Number: 239 |
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- N Prp Precision: 0.9308 |
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- N Prp Recall: 0.9447 |
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- N Prp F1: 0.9377 |
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- N Prp Number: 470 |
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- N V Precision: 0.9854 |
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- N V Recall: 0.9736 |
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- N V F1: 0.9794 |
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- N V Number: 416 |
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- Pre Precision: 0.9722 |
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- Pre Recall: 0.9625 |
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- Pre F1: 0.9673 |
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- Pre Number: 907 |
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- Pro Precision: 0.9448 |
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- Pro Recall: 0.9236 |
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- Pro F1: 0.9341 |
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- Pro Number: 445 |
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- Pun Precision: 1.0 |
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- Pun Recall: 0.9994 |
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- Pun F1: 0.9997 |
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- Pun Number: 1607 |
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- Unc Precision: 1.0 |
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- Unc Recall: 0.875 |
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- Unc F1: 0.9333 |
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- Unc Number: 16 |
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- V Precision: 0.8780 |
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- V Recall: 0.9231 |
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- V F1: 0.9 |
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- V Number: 78 |
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- V Aux Precision: 0.9685 |
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- V Aux Recall: 0.9878 |
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- V Aux F1: 0.9780 |
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- V Aux Number: 654 |
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- V Ger Precision: 0.9388 |
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- V Ger Recall: 0.9571 |
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- V Ger F1: 0.9479 |
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- V Ger Number: 513 |
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- V Imf Precision: 0.9634 |
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- V Imf Recall: 0.9497 |
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- V Imf F1: 0.9565 |
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- V Imf Number: 914 |
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- V Imv Precision: 0.8793 |
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- V Imv Recall: 0.7286 |
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- V Imv F1: 0.7969 |
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- V Imv Number: 70 |
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- V Prf Precision: 0.8960 |
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- V Prf Recall: 0.9082 |
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- V Prf F1: 0.9020 |
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- V Prf Number: 294 |
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- V Rel Precision: 0.9678 |
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- V Rel Recall: 0.9538 |
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- V Rel F1: 0.9607 |
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- V Rel Number: 757 |
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- Overall Precision: 0.9562 |
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- Overall Recall: 0.9562 |
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- Overall F1: 0.9562 |
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- Overall Accuracy: 0.9562 |
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### Framework versions |
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- Transformers 4.12.0.dev0 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.13.3 |
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- Tokenizers 0.10.3 |
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## Citation |
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If you use this model in your product or research, please cite as follows: |
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``` |
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@article{Fitsum2021TiPLMs, |
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author={Fitsum Gaim and Wonsuk Yang and Jong C. Park}, |
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title={Monolingual Pre-trained Language Models for Tigrinya}, |
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year=2021, |
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publisher={WiNLP 2021/EMNLP 2021} |
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} |
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``` |
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## References |
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``` |
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Tedla, Y., Yamamoto, K. & Marasinghe, A. 2016. |
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Tigrinya Part-of-Speech Tagging with Morphological Patterns and the New Nagaoka Tigrinya Corpus. |
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International Journal Of Computer Applications 146 pp. 33-41 (2016). |
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``` |
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