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.
- Entity Extraction/Classification (ner)
- 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.
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