---
language: fr
datasets:
- nlpso/m0_fine_tuning_ocr_cmbert_io
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 [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|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 |Geographical feature

## Experiment parameter

* Pretrained-model : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* 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("nlpso/m0_flat_ner_ocr_cmbert_io")
model = AutoModelForTokenClassification.from_pretrained("nlpso/m0_flat_ner_ocr_cmbert_io")