ACCORD-NLP/ner-bert-large

ACCORD-NLP

ACCORD-NLP is a Natural Language Processing (NLP) framework developed by the ACCORD project to facilitate Automated Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector. It consists of several pre-trained/fine-tuned machine learning models to perform the following information extraction tasks from regulatory text.

1. Entity Extraction/Classification (ner)
2. Relation Extraction/Classification (re)

ner-bert-large is a BERT large (cased) model fine-tuned for sequence labelling/entity classification using CODE-ACCORD entities dataset.

Installation

From Source

git clone https://github.com/Accord-Project/accord-nlp.git
cd accord-nlp
pip install -r requirements.txt

From pip

pip install accord-nlp

Using Pre-trained Models

Entity Extraction/Classification (ner)

from accord_nlp.text_classification.ner.ner_model import NERModel

model = NERModel('roberta', 'ACCORD-NLP/ner-roberta-large')
predictions, raw_outputs = model.predict(['The gradient of the passageway should not exceed five per cent.'])
print(predictions)

Relation Extraction/Classification (re)

from accord_nlp.text_classification.relation_extraction.re_model import REModel

model = REModel('roberta', 'ACCORD-NLP/re-roberta-large')
predictions, raw_outputs = model.predict(['The <e1>gradient<\e1> of the passageway should not exceed <e2>five per cent</e2>.'])
print(predictions)

For more details, please refer to the ACCORD-NLP GitHub repository.