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 roberta-large-ner-english with HuggingFace
Load roberta-large-ner-english and its sub-word tokenizer :
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 |
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