Instructions to use sachaarbonel/bert-italian-cased-finetuned-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sachaarbonel/bert-italian-cased-finetuned-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sachaarbonel/bert-italian-cased-finetuned-pos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sachaarbonel/bert-italian-cased-finetuned-pos") model = AutoModelForTokenClassification.from_pretrained("sachaarbonel/bert-italian-cased-finetuned-pos") - Notebooks
- Google Colab
- Kaggle
| language: it | |
| datasets: | |
| - xtreme | |
| # Italian-Bert (Italian Bert) + POS ππ· | |
| This model is a fine-tuned on [xtreme udpos Italian](https://huggingface.co/nlp/viewer/?dataset=xtreme&config=udpos.Italian) version of [Bert Base Italian](https://huggingface.co/dbmdz/bert-base-italian-cased) for **POS** downstream task. | |
| ## Details of the downstream task (POS) - Dataset | |
| - [Dataset: xtreme udpos Italian](https://huggingface.co/nlp/viewer/?dataset=xtreme&config=udpos.Italian) π | |
| | Dataset | # Examples | | |
| | ---------------------- | ----- | | |
| | Train | 716 K | | |
| | Dev | 85 K | | |
| - [Fine-tune on NER script provided by @stefan-it](https://raw.githubusercontent.com/stefan-it/fine-tuned-berts-seq/master/scripts/preprocess.py) | |
| - Labels covered: | |
| ``` | |
| ADJ | |
| ADP | |
| ADV | |
| AUX | |
| CCONJ | |
| DET | |
| INTJ | |
| NOUN | |
| NUM | |
| PART | |
| PRON | |
| PROPN | |
| PUNCT | |
| SCONJ | |
| SYM | |
| VERB | |
| X | |
| ``` | |
| ## Metrics on evaluation set π§Ύ | |
| | Metric | # score | | |
| | :------------------------------------------------------------------------------------: | :-------: | | |
| | F1 | **97.25** | |
| | Precision | **97.15** | | |
| | Recall | **97.36** | | |
| ## Model in action π¨ | |
| Example of usage | |
| ```python | |
| 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 Γ¨ 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 π | |
| > Created by [Sacha Arbonel/@sachaarbonel](https://twitter.com/sachaarbonel) | [LinkedIn](https://www.linkedin.com/in/sacha-arbonel) | |
| > Made with <span style="color: #e25555;">♥</span> in Paris | |