tielectra-small-pos

Tigrinya POS tagging with TiELECTRA

This model is a fine-tuned version of TiELECTRA on the NTC-v1 dataset (Tedla et al. 2016).

Basic usage

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).