135 lines
3.5 KiB
Markdown
135 lines
3.5 KiB
Markdown
---
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language: en
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datasets:
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- conll2003
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widget:
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- text: "My name is jean-baptiste and I live in montreal"
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- text: "My name is clara and I live in berkeley, california."
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- text: "My name is wolfgang and I live in berlin"
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train-eval-index:
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- config: conll2003
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task: token-classification
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task_id: entity_extraction
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splits:
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eval_split: validation
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col_mapping:
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tokens: tokens
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ner_tags: tags
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license: mit
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---
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# roberta-large-ner-english: model fine-tuned from roberta-large for NER task
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## Introduction
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[roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset.
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Model was validated on emails/chat data and outperformed other models on this type of data specifically.
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In particular the model seems to work better on entity that don't start with an upper case.
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## Training data
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Training data was classified as follow:
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Abbreviation|Description
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-|-
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O |Outside of a named entity
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MISC |Miscellaneous entity
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PER |Person’s name
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ORG |Organization
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LOC |Location
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In order to simplify, the prefix B- or I- from original conll2003 was removed.
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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:
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Train | Validation
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-|-
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17494 | 3250
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## How to use roberta-large-ner-english with HuggingFace
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##### Load roberta-large-ner-english and its sub-word tokenizer :
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```python
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from transformers import AutoTokenizer, AutoModelForTokenClassification
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tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
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model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
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##### Process text sample (from wikipedia)
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from transformers import pipeline
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nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
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nlp("Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne to develop and sell Wozniak's Apple I personal computer")
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[{'entity_group': 'ORG',
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'score': 0.99381506,
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'word': ' Apple',
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'start': 0,
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'end': 5},
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{'entity_group': 'PER',
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'score': 0.99970853,
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'word': ' Steve Jobs',
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'start': 29,
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'end': 39},
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{'entity_group': 'PER',
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'score': 0.99981767,
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'word': ' Steve Wozniak',
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'start': 41,
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'end': 54},
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{'entity_group': 'PER',
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'score': 0.99956465,
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'word': ' Ronald Wayne',
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'start': 59,
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'end': 71},
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{'entity_group': 'PER',
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'score': 0.9997918,
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'word': ' Wozniak',
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'start': 92,
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'end': 99},
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{'entity_group': 'MISC',
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'score': 0.99956393,
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'word': ' Apple I',
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'start': 102,
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'end': 109}]
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```
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## Model performances
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Model performances computed on conll2003 validation dataset (computed on the tokens predictions)
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entity|precision|recall|f1
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-|-|-|-
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PER|0.9914|0.9927|0.9920
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ORG|0.9627|0.9661|0.9644
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LOC|0.9795|0.9862|0.9828
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MISC|0.9292|0.9262|0.9277
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Overall|0.9740|0.9766|0.9753
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On private dataset (email, chat, informal discussion), computed on word predictions:
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entity|precision|recall|f1
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-|-|-|-
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PER|0.8823|0.9116|0.8967
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ORG|0.7694|0.7292|0.7487
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LOC|0.8619|0.7768|0.8171
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By comparison on the same private dataset, Spacy (en_core_web_trf-3.2.0) was giving:
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entity|precision|recall|f1
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-|-|-|-
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PER|0.9146|0.8287|0.8695
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ORG|0.7655|0.6437|0.6993
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LOC|0.8727|0.6180|0.7236
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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:
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https://medium.com/@jean-baptiste.polle/lstm-model-for-email-signature-detection-8e990384fefa
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