121 lines
3.1 KiB
Markdown
121 lines
3.1 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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---
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# roberta-large-ner: model fine-tuned from roberta-large for NER task
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## Introduction
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[roberta-large-ner] is a 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 | 17494
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Validation | 3250
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## How to use camembert-ner with HuggingFace
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##### Load camembert-ner 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")
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model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner")
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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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```
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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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```
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On private dataset (email, chat, informal discussion), computed on word predictions:
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```
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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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```
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Spacy (en_core_web_trf-3.2.0) on the same private dataset was giving:
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```
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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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```
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