|
--- |
|
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" |
|
train-eval-index: |
|
- config: conll2003 |
|
task: token-classification |
|
task_id: entity_extraction |
|
splits: |
|
eval_split: validation |
|
col_mapping: |
|
tokens: tokens |
|
ner_tags: tags |
|
license: mit |
|
|
|
--- |
|
|
|
# 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 : |
|
|
|
```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 |
|
|
|
|
|
|
|
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 |
|
|
