
# 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")

{"language": "fr", "datasets": ["nlpso/m0_fine_tuning_ocr_cmbert_io"], "tag": "token-classification", "widget": [{"text": "Duflot, loueur de carrosses, r. de Paradis-\u2029505\u2029Poissonni\u00e8re, 22.", "example_title": "Noisy entry #1"}, {"text": "Du\u017four el Besnard, march, de bois \u00e0 bruler,\u2029quai de la Tournelle, 17. etr. des Foss\u00e9s-\u2029SBernard. 11.\u2029D\u00ed", "example_title": "Noisy entry #2"}, {"text": "Dufour (Charles), \u00e9picier, r. St-Denis\u2029\u261e\u2029332", "example_title": "Ground-truth entry #1"}]}
transformers pytorch camembert token-classification fr dataset:nlpso/m0_fine_tuning_ocr_cmbert_io autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #token-classification #fr #dataset-nlpso/m0_fine_tuning_ocr_cmbert_io #autotrain_compatible #endpoints_compatible #region-us 

m0\_flat\_ner\_ocr\_cmbert\_io
==============================


Introduction
------------


This model is a fine-tuned verion from Jean-Baptiste/camembert-ner for nested NER task on a nested NER Paris trade directories dataset.


Dataset
-------



Experiment parameter
--------------------


* Pretrained-model : 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")



TAGS
#transformers #pytorch #camembert #token-classification #fr #dataset-nlpso/m0_fine_tuning_ocr_cmbert_io #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #camembert #token-classification #fr #dataset-nlpso/m0_fine_tuning_ocr_cmbert_io #autotrain_compatible #endpoints_compatible #region-us 
