readme: ---
  language: en
  datasets:
  - conll2003
  widget:
  - text: "My name is jean-baptiste and I live in montreal"
  - text: "My name is clara and I live in berkeley, california."
  - text: "My name is wolfgang and I live in berlin"
  
  ---
  
  # roberta-large-ner-english: model fine-tuned from roberta-large for NER task
  
  ## Introduction
  
  [roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset. 
  Model was validated on emails/chat data and outperformed 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
  
  In order to simplify, the prefix B- or I- from original conll2003 was removed.
  I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size:
  
  Train | Validation 
  -|-
  17494 | 3250
  
  ## 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/roberta-large-ner-english")
  model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
  
  
  ##### Process text sample (from wikipedia)
  
  from transformers import pipeline
  
  nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
  nlp("Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne to develop and sell Wozniak's Apple I personal computer")
  
  
  [{'entity_group': 'ORG',
    'score': 0.99381506,
    'word': ' Apple',
    'start': 0,
    'end': 5},
   {'entity_group': 'PER',
    'score': 0.99970853,
    'word': ' Steve Jobs',
    'start': 29,
    'end': 39},
   {'entity_group': 'PER',
    'score': 0.99981767,
    'word': ' Steve Wozniak',
    'start': 41,
    'end': 54},
   {'entity_group': 'PER',
    'score': 0.99956465,
    'word': ' Ronald Wayne',
    'start': 59,
    'end': 71},
   {'entity_group': 'PER',
    'score': 0.9997918,
    'word': ' Wozniak',
    'start': 92,
    'end': 99},
   {'entity_group': 'MISC',
    'score': 0.99956393,
    'word': ' Apple I',
    'start': 102,
    'end': 109}]
  ```
  
  
  ## Model performances 
  
  Model performances computed on conll2003 validation dataset (computed on the tokens predictions)
  
  entity|precision|recall|f1
  -|-|-|-
  PER|0.9914|0.9927|0.9920 
  ORG|0.9627|0.9661|0.9644
  LOC|0.9795|0.9862|0.9828
  MISC|0.9292|0.9262|0.9277
  Overall|0.9740|0.9766|0.9753
  
  
  On private dataset (email, chat, informal discussion), computed on word predictions:
  
  entity|precision|recall|f1
  -|-|-|-
  PER|0.8823|0.9116|0.8967
  ORG|0.7694|0.7292|0.7487
  LOC|0.8619|0.7768|0.8171
  
  By comparison on the same private dataset, Spacy (en_core_web_trf-3.2.0) was giving:
  
  entity|precision|recall|f1
  -|-|-|-
  PER|0.9146|0.8287|0.8695
  ORG|0.7655|0.6437|0.6993
  LOC|0.8727|0.6180|0.7236
  
  
  