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---
language: fr
datasets:
- nlpso/m2m3_fine_tuning_ref_ptrn_cmbert_iob2
tag: token-classification
widget:
- text: 'Duflot, loueur de carrosses, r. de Paradis-
505
Poissonnière, 22.'
example_title: 'Noisy entry #1'
- text: 'Duſour el Besnard, march, de bois à bruler,
quai de la Tournelle, 17. etr. des Fossés-
SBernard. 11.
Dí'
example_title: 'Noisy entry #2'
- text: 'Dufour (Charles), épicier, r. St-Denis
☞
332'
example_title: 'Ground-truth entry #1'
---
# m3_hierarchical_ner_ref_ptrn_cmbert_iob2
## Introduction
This model is a fine-tuned verion from [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) 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 : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Dataset : ground-truth
* Tagging format : IOB2
* Recognised entities : 'All'
## Load model from the Hugging Face
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2")
|