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---
language: fr
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'
---
# m0_flat_ner_ocr_cmbert_io
## Introduction
This model is a fine-tuned verion from [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** on a nested NER Paris trade directories dataset.
## Dataset
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |2|Geographical feature
## Experiment parameter
* Pretrained-model : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Dataset : noisy (Pero OCR)
* Tagging format : IO
* Recognised entities : All (flat entities)
## Load model from the HuggingFace
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("m0_flat_ner_ocr_cmbert_io")
model = AutoModelForTokenClassification.from_pretrained("m0_flat_ner_ocr_cmbert_io")
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