transformers pytorch albert text-classification en dataset:ACCORD-NLP/CODE-ACCORD-Relations license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- ACCORD-NLP/CODE-ACCORD-Relations
language:
- en
license: apache-2.0
---

# ACCORD-NLP

ACCORD-NLP is a Natural Language Processing (NLP) framework developed by the [ACCORD](https://accordproject.eu/) 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)

**re-albert-large** is an ALBERT large model fine-tuned for relation classification using [CODE-ACCORD relations](https://huggingface.co/datasets/ACCORD-NLP/CODE-ACCORD-Relations) 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)

```python
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)

```python
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](https://github.com/Accord-Project/accord-nlp) GitHub repository.