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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