

 [ElhBERTeu](https://huggingface.co/orai-nlp/ElhBERTeu)  model finetuned for POS tagging on Universal Dependencies 1.2 corpus (eiec). 

Results on test set:

* accuracy = 0.9793222286042504
* f1 = 0.9712429365605912
* loss = 0.12006692268620504
* precision = 0.9708653494328576
* recall = 0.9716208175035475


Tagset: 
[ADJ, ADP, ADV, AUX, CONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SYM, VERB, X]


Finetuning details: 10 epochs, batch size 32, learning rate  3e-5.

{"language": ["eu"], "license": "cc-by-nc-4.0", "tags": ["POS tagging"], "datasets": ["universal_dependencies"], "metrics": ["accuracy", "f1"], "pipeline_tag": "token-classification"}
transformers pytorch bert token-classification POS tagging eu dataset:universal_dependencies license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #POS tagging #eu #dataset-universal_dependencies #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 



 ElhBERTeu  model finetuned for POS tagging on Universal Dependencies 1.2 corpus (eiec). 

Results on test set:

* accuracy = 0.9793222286042504
* f1 = 0.9712429365605912
* loss = 0.12006692268620504
* precision = 0.9708653494328576
* recall = 0.9716208175035475


Tagset: 
[ADJ, ADP, ADV, AUX, CONJ, DET, INTJ, NOUN, NUM, PART, PRON, PROPN, PUNCT, SYM, VERB, X]


Finetuning details: 10 epochs, batch size 32, learning rate  3e-5.

TAGS
#transformers #pytorch #bert #token-classification #POS tagging #eu #dataset-universal_dependencies #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 
