File size: 2,894 Bytes
d8a6a96 a7d6832 d8a6a96 5867026 d8a6a96 3f61b30 5867026 ea19a79 5867026 d8a6a96 bcb19a1 d8a6a96 3f61b30 5867026 3f61b30 d8a6a96 3f61b30 5867026 bcb19a1 d8a6a96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
---
language: fr
datasets:
- Jean-Baptiste/wikiner_fr
widget:
- text: "Je m'appelle jean-baptiste et je vis à montréal"
---
# 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.
## 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, grouped_entities=True)
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)
Global
```
'precision': 0.8859
'recall': 0.8971
'f1': 0.8914
```
By entity
```
'LOC': {'precision': 0.8905576596578294,
'recall': 0.900554675118859,
'f1': 0.8955282684352223},
'MISC': {'precision': 0.8175627240143369,
'recall': 0.8117437722419929,
'f1': 0.8146428571428571},
'ORG': {'precision': 0.8099480326651819,
'recall': 0.8265151515151515,
'f1': 0.8181477315335584},
'PER': {'precision': 0.9372509960159362,
'recall': 0.959812321501428,
'f1': 0.9483975005039308}
```
|