sachaarbonel/bert-italian-cased-finetuned-pos
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Italian-Bert (Italian Bert) + POS ๐๐ท
This model is a fine-tuned on xtreme udpos Italian version of Bert Base Italian for POS downstream task.
Details of the downstream task (POS) - Dataset
- Labels covered:
ADJ
ADP
ADV
AUX
CCONJ
DET
INTJ
NOUN
NUM
PART
PRON
PROPN
PUNCT
SCONJ
SYM
VERB
XMetrics on evaluation set ๐งพ
Model in action ๐จ
Example of usage
from transformers import pipeline
nlp_pos = pipeline(
"ner",
model="sachaarbonel/bert-italian-cased-finetuned-pos",
tokenizer=(
'sachaarbonel/bert-spanish-cased-finetuned-pos',
{"use_fast": False}
))
text = 'Roma รจ la Capitale d'Italia.'
nlp_pos(text)
'''
Output:
--------
[{'entity': 'PROPN', 'index': 1, 'score': 0.9995346665382385, 'word': 'roma'},
{'entity': 'AUX', 'index': 2, 'score': 0.9966597557067871, 'word': 'e'},
{'entity': 'DET', 'index': 3, 'score': 0.9994786977767944, 'word': 'la'},
{'entity': 'NOUN',
'index': 4,
'score': 0.9995198249816895,
'word': 'capitale'},
{'entity': 'ADP', 'index': 5, 'score': 0.9990894198417664, 'word': 'd'},
{'entity': 'PART', 'index': 6, 'score': 0.57159024477005, 'word': "'"},
{'entity': 'PROPN',
'index': 7,
'score': 0.9994804263114929,
'word': 'italia'},
{'entity': 'PUNCT', 'index': 8, 'score': 0.9772886633872986, 'word': '.'}]
'''Yeah! Not too bad ๐
Created by Sacha Arbonel/@sachaarbonel | LinkedIn
Made with <span style="color: #e25555;">♥</span> in Paris
