Model Card for roberta-base-on-cuad
Model Details
Model Description
- Developed by: Mohammed Rakib
- Shared by [Optional]: More information needed
- Model type: Question Answering
- Language(s) (NLP): en
- License: MIT
- Related Models:
- Parent Model: RoBERTa
- Resources for more information:
- GitHub Repo: defactolaw
- Associated Paper: An Open Source Contractual Language Understanding Application Using Machine Learning
Uses
Direct Use
This model can be used for the task of Question Answering on Legal Documents.
Training Details
Read: An Open Source Contractual Language Understanding Application Using Machine Learning for detailed information on training procedure, dataset preprocessing and evaluation.
Training Data
See CUAD dataset card for more information.
Training Procedure
Preprocessing
More information needed
Speeds, Sizes, Times
More information needed
Evaluation
Testing Data, Factors & Metrics
Testing Data
See CUAD dataset card for more information.
Factors
Metrics
More information needed
Results
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Model Examination
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- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
More information needed
Compute Infrastructure
More information needed
Hardware
Used V100/P100 from Google Colab Pro
Software
Python, Transformers
Citation
BibTeX:
@inproceedings{nawar-etal-2022-open,
title = "An Open Source Contractual Language Understanding Application Using Machine Learning",
author = "Nawar, Afra and
Rakib, Mohammed and
Hai, Salma Abdul and
Haq, Sanaulla",
booktitle = "Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lateraisse-1.6",
pages = "42--50",
abstract = "Legal field is characterized by its exclusivity and non-transparency. Despite the frequency and relevance of legal dealings, legal documents like contracts remains elusive to non-legal professionals for the copious usage of legal jargon. There has been little advancement in making legal contracts more comprehensible. This paper presents how Machine Learning and NLP can be applied to solve this problem, further considering the challenges of applying ML to the high length of contract documents and training in a low resource environment. The largest open-source contract dataset so far, the Contract Understanding Atticus Dataset (CUAD) is utilized. Various pre-processing experiments and hyperparameter tuning have been carried out and we successfully managed to eclipse SOTA results presented for models in the CUAD dataset trained on RoBERTa-base. Our model, A-type-RoBERTa-base achieved an AUPR score of 46.6{\%} compared to 42.6{\%} on the original RoBERT-base. This model is utilized in our end to end contract understanding application which is able to take a contract and highlight the clauses a user is looking to find along with it{'}s descriptions to aid due diligence before signing. Alongside digital, i.e. searchable, contracts the system is capable of processing scanned, i.e. non-searchable, contracts using tesseract OCR. This application is aimed to not only make contract review a comprehensible process to non-legal professionals, but also to help lawyers and attorneys more efficiently review contracts.",
}
Glossary [optional]
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More Information [optional]
More information needed
Model Card Authors [optional]
Mohammed Rakib in collaboration with Ezi Ozoani and the Hugging Face team
Model Card Contact
More information needed
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad")
model = AutoModelForQuestionAnswering.from_pretrained("Rakib/roberta-base-on-cuad")