m3_hierarchical_ner_ocr_ptrn_cmbert_iob2
Introduction
This model is a fine-tuned verion from HueyNemud/das22-10-camembert_pretrained for nested NER task on a nested NER Paris trade directories dataset.
Dataset
| Abbreviation | Entity group (level) | Description |
|---|---|---|
| O | 1 & 2 | Outside of a named entity |
| PER | 1 | Person or company name |
| ACT | 1 & 2 | Person or company professional activity |
| TITREH | 2 | Military or civil distinction |
| DESC | 1 | Entry full description |
| TITREP | 2 | Professionnal reward |
| SPAT | 1 | Address |
| LOC | 2 | Street name |
| CARDINAL | 2 | Street number |
| FT | 2 | Geographical feature |
Experiment parameter
- Pretrained-model : HueyNemud/das22-10-camembert_pretrained
- Dataset : noisy (Pero OCR)
- Tagging format : IOB2
- Recognised entities : 'All'
Load model from the Hugging Face
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2")
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