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	---
	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).
	```