readme: ---
  language: ti
  widget:
  - text: "ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር"
  datasets:
  - TLMD
  - NTC
  metrics:
  - f1
  - precision
  - recall
  - accuracy
  model-index:
  - name: tiroberta-base-pos
    results:
    - task:
        name: Token Classification
        type: token-classification
      metrics:
      - name: F1
        type: f1
        value: 0.9562
      - name: Precision
        type: precision
        value: 0.9562
      - name: Recall
        type: recall
        value: 0.9562
      - name: Accuracy
        type: accuracy
        value: 0.9562
  ---
  
  
  # Tigrinya POS tagging with TiRoBERTa
  
  This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co/fgaim/tiroberta) on the NTC-v1 dataset (Tedla et al. 2016).
  
  ## 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
  
  The model achieves the following results on the test 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
  
  
  ## Citation
  
  If you use this model in your product or research, please cite as follows:
  
  ```
  @article{Fitsum2021TiPLMs,
    author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
    title={Monolingual Pre-trained Language Models for Tigrinya},
    year=2021,
    publisher={WiNLP 2021/EMNLP 2021}
  }
  ```
  
  
  ## References
  
  ```
  Tedla, Y., Yamamoto, K. & Marasinghe, A. 2016.
  Tigrinya Part-of-Speech Tagging with Morphological Patterns and the New Nagaoka Tigrinya Corpus.
  International Journal Of Computer Applications 146 pp. 33-41 (2016).
  ```
  