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: tielectra-small-pos
    results:
    - task:
        name: Token Classification
        type: token-classification
      metrics:
      - name: F1
        type: f1
        value: 0.9456
      - name: Precision
        type: precision
        value: 0.9456
      - name: Recall
        type: recall
        value: 0.9456
      - name: Accuracy
        type: accuracy
        value: 0.9456
  ---
  
  
  # Tigrinya POS tagging with TiELECTRA
  
  This model is a fine-tuned version of [TiELECTRA](https://huggingface.co/fgaim/tielectra-small) on the NTC-v1 dataset (Tedla et al. 2016).
  
  
  ## Basic usage
  
  ```python
  from transformers import pipeline
  
  ti_pos = pipeline("token-classification", model="fgaim/tielectra-small-pos")
  ti_pos("ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር")
  ```
  
  
  ## 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: 0.2236
  - Adj Precision: 0.9148
  - Adj Recall: 0.9192
  - Adj F1: 0.9170
  - Adj Number: 1670
  - Adv Precision: 0.8228
  - Adv Recall: 0.8058
  - Adv F1: 0.8142
  - Adv Number: 484
  - Con Precision: 0.9793
  - Con Recall: 0.9743
  - Con F1: 0.9768
  - Con Number: 972
  - Fw Precision: 0.5
  - Fw Recall: 0.3214
  - Fw F1: 0.3913
  - Fw Number: 28
  - Int Precision: 0.64
  - Int Recall: 0.6154
  - Int F1: 0.6275
  - Int Number: 26
  - N Precision: 0.9525
  - N Recall: 0.9587
  - N F1: 0.9556
  - N Number: 3992
  - Num Precision: 0.9825
  - Num Recall: 0.9372
  - Num F1: 0.9593
  - Num Number: 239
  - N Prp Precision: 0.9132
  - N Prp Recall: 0.9404
  - N Prp F1: 0.9266
  - N Prp Number: 470
  - N V Precision: 0.9667
  - N V Recall: 0.9760
  - N V F1: 0.9713
  - N V Number: 416
  - Pre Precision: 0.9645
  - Pre Recall: 0.9592
  - Pre F1: 0.9619
  - Pre Number: 907
  - Pro Precision: 0.9395
  - Pro Recall: 0.9079
  - Pro F1: 0.9234
  - Pro Number: 445
  - Pun Precision: 1.0
  - Pun Recall: 0.9994
  - Pun F1: 0.9997
  - Pun Number: 1607
  - Unc Precision: 0.9286
  - Unc Recall: 0.8125
  - Unc F1: 0.8667
  - Unc Number: 16
  - V Precision: 0.7609
  - V Recall: 0.8974
  - V F1: 0.8235
  - V Number: 78
  - V Aux Precision: 0.9581
  - V Aux Recall: 0.9786
  - V Aux F1: 0.9682
  - V Aux Number: 654
  - V Ger Precision: 0.9183
  - V Ger Recall: 0.9415
  - V Ger F1: 0.9297
  - V Ger Number: 513
  - V Imf Precision: 0.9473
  - V Imf Recall: 0.9442
  - V Imf F1: 0.9458
  - V Imf Number: 914
  - V Imv Precision: 0.8163
  - V Imv Recall: 0.5714
  - V Imv F1: 0.6723
  - V Imv Number: 70
  - V Prf Precision: 0.8927
  - V Prf Recall: 0.8776
  - V Prf F1: 0.8851
  - V Prf Number: 294
  - V Rel Precision: 0.9535
  - V Rel Recall: 0.9485
  - V Rel F1: 0.9510
  - V Rel Number: 757
  - Overall Precision: 0.9456
  - Overall Recall: 0.9456
  - Overall F1: 0.9456
  - Overall Accuracy: 0.9456
  
  ### Framework versions
  
  - Transformers 4.10.3
  - Pytorch 1.9.0+cu111
  - Datasets 1.10.2
  - Tokenizers 0.10.1
  
  
  ## 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).
  ```
  