
# camembert-ner: model fine-tuned from camemBERT for NER task.

## Introduction

[camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset.
Model was trained on wikiner-fr dataset (~170 634  sentences).
Model was validated on emails/chat data and overperformed other models on this type of data specifically. 
In particular the model seems to work better on entity that don't start with an upper case.

## Training data
Training data was classified as follow:

Abbreviation|Description
-|-
O |Outside of a named entity
MISC |Miscellaneous entity
PER |Person’s name
ORG |Organization
LOC |Location


## How to use camembert-ner with HuggingFace

##### Load camembert-ner and its sub-word tokenizer :

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner")
model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner")


##### Process text sample (from wikipedia)

from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("Apple est créée le 1er avril 1976 dans le garage de la maison d'enfance de Steve Jobs à Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wayne14, puis constituée sous forme de société le 3 janvier 1977 à l'origine sous le nom d'Apple Computer, mais pour ses 30 ans et pour refléter la diversification de ses produits, le mot « computer » est retiré le 9 janvier 2015.")


[{'entity_group': 'ORG',
  'score': 0.9472818374633789,
  'word': 'Apple',
  'start': 0,
  'end': 5},
 {'entity_group': 'PER',
  'score': 0.9838564991950989,
  'word': 'Steve Jobs',
  'start': 74,
  'end': 85},
 {'entity_group': 'LOC',
  'score': 0.9831605950991312,
  'word': 'Los Altos',
  'start': 87,
  'end': 97},
 {'entity_group': 'LOC',
  'score': 0.9834540486335754,
  'word': 'Californie',
  'start': 100,
  'end': 111},
 {'entity_group': 'PER',
  'score': 0.9841555754343668,
  'word': 'Steve Jobs',
  'start': 115,
  'end': 126},
 {'entity_group': 'PER',
  'score': 0.9843501806259155,
  'word': 'Steve Wozniak',
  'start': 127,
  'end': 141},
 {'entity_group': 'PER',
  'score': 0.9841533899307251,
  'word': 'Ronald Wayne',
  'start': 144,
  'end': 157},
 {'entity_group': 'ORG',
  'score': 0.9468960364659628,
  'word': 'Apple Computer',
  'start': 243,
  'end': 257}]

```


## Model performances (metric: seqeval)

Overall

precision|recall|f1
-|-|-
0.8859|0.8971|0.8914

By entity

entity|precision|recall|f1
-|-|-|-
PER|0.9372|0.9598|0.9483 
ORG|0.8099|0.8265|0.8181
LOC|0.8905|0.9005|0.8955
MISC|0.8175|0.8117|0.8146




For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails:
https://medium.com/@jean-baptiste.polle/lstm-model-for-email-signature-detection-8e990384fefa

{"language": "fr", "license": "mit", "datasets": ["Jean-Baptiste/wikiner_fr"], "widget": [{"text": "Je m'appelle jean-baptiste et je vis \u00e0 montr\u00e9al"}, {"text": "george washington est all\u00e9 \u00e0 washington"}]}
transformers pytorch onnx safetensors camembert token-classification fr dataset:Jean-Baptiste/wikiner_fr license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

camembert-ner: model fine-tuned from camemBERT for NER task.
============================================================


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


[camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset.
Model was trained on wikiner-fr dataset (~170 634 sentences).
Model was validated on emails/chat data and overperformed other models on this type of data specifically.
In particular the model seems to work better on entity that don't start with an upper case.


Training data
-------------


Training data was classified as follow:



How to use camembert-ner with HuggingFace
-----------------------------------------


##### Load camembert-ner and its sub-word tokenizer :


Model performances (metric: seqeval)
------------------------------------


Overall


precision: 0.8859, recall: 0.8971, f1: 0.8914


By entity



For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails:
URL



##### Load camembert-ner and its sub-word tokenizer :


Model performances (metric: seqeval)
------------------------------------


Overall


precision: 0.8859, recall: 0.8971, f1: 0.8914


By entity



For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails:
URL
TAGS
#transformers #pytorch #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ##### Load camembert-ner and its sub-word tokenizer :


Model performances (metric: seqeval)
------------------------------------


Overall


precision: 0.8859, recall: 0.8971, f1: 0.8914


By entity



For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails:
URL
passage: TAGS
#transformers #pytorch #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
##### Load camembert-ner and its sub-word tokenizer :


Model performances (metric: seqeval)
------------------------------------


Overall


precision: 0.8859, recall: 0.8971, f1: 0.8914


By entity



For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails:
URL