https://github.com/charlesyuan02/named_entity_recognition

Utilizing Spacy and Tensorflow to train custom Named Entity Recognizers.
https://github.com/charlesyuan02/named_entity_recognition

conll-2003 named-entity-recognition ner nlp spacy transformer

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Utilizing Spacy and Tensorflow to train custom Named Entity Recognizers.

• Host: GitHub
• URL: https://github.com/charlesyuan02/named_entity_recognition
• Owner: CharlesYuan02
• License: mit
• Created: 2021-11-27T03:33:15.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-12-11T21:11:21.000Z (about 3 years ago)
• Last Synced: 2024-10-31T18:26:45.372Z (about 2 months ago)
• Topics: conll-2003, named-entity-recognition, ner, nlp, spacy, transformer
• Language: Python
• Homepage:
• Size: 19.9 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Named Entity Recognizer

#### (named_entity_recognition.py)

Earlier today, I was browsing through the list of Thesis Projects from past fourth-year EngScis, and one topic in particular stood out to me. Perhaps it was because I didn't know anything about it, but the words "Named Entity Recognition" remained stuck in my head for the rest of the day. As such, I decided to search up what it was and how to do it. Turns out, it's not actually too difficult. Subsequently, I used Spacy to train a custom Named Entity Recognizer to classify words from the light novel series Eighty Six.

#### Update 1 (spacy_conll2003.py):

I wondered what it would take to train an NER model from scratch, so I did just that using the CoNLL-2003 benchmark dataset. Unlike the fine-tuned model used for Eighty Six, this one is trained from scratch. 

#### Update 2 (transformer_conll2003.py):

Of course, I can't conclude this project without testing out my own deep learning model. So I trained a transformer to compare with Spacy's model, and it actually did perform better! It ended up detecting all the entities in the text, along with identifying them with the correct labels.

## Prerequisites

```

datasets==1.16.1

python==3.8.0

spacy==3.2.0

```

## Dataset

The dataset I used was CoNLL-2003, a named entity recognition dataset released as a part of CoNLL-2003 shared task: language-independent named entity recognition. The data was taken from the Reuters Corpus, which consists of Reuters news stories between August 1996 and August 1997.

## Results, with Pretrained Model and Custom Examples

### Before Training



### After Training



## Results, Training From Scratch with CoNLL-2003 Dataset

#### Precision: 46.2% 

#### Recall: 51.7% 

#### F1 Score: 48.8% 

### Before Training



### After Training



## Results, Transformer Model with CoNLL-2003 Dataset

#### Precision: 69.02% 

#### Recall: 65.58%

#### F1 Score: 67.26%





## Notes

* There is a weird bug with Spacy, where you can train, save, and load your model just fine.

* But then when you try to load your saved model without training first, it doesn't work.

* I'm not too sure why...

## Acknowledgements

* Varun Singh, for writing this Transformer tutorial

* Shrivarsheni, for writing this article on the basics of training Spacy NER models

* Asato Asato, for writing the amazing series that is Eighty Six