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| 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 |
1from transformers import AutoTokenizer, AutoModelForTokenClassification
2
3tokenizer = AutoTokenizer.from_pretrained("nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1")
4model = AutoModelForTokenClassification.from_pretrained("nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1")
5