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--- |
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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-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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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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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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