m1_ind_layers_ocr_ptrn_cmbert_iob2_level_1
Introduction
This model is a model that was fine-tuned 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 : level 1
Load model from the Hugging Face
**Warning 1 ** : this model only recognises level-1 entities of dataset. It has to be used with m1_ind_layers_ocr_ptrn_cmbert_iob2_level_2 to recognise nested entities level-2.
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_1")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_1")
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