🚀 Model Card for roberta-base-on-cuad
This model card provides detailed information about the roberta-base-on-cuad model, which is designed for question-answering tasks on legal documents.
🚀 Quick Start
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")
✨ Features
- Question Answering: The model can be used for question-answering tasks on legal documents.
📦 Installation
The model can be installed using the transformers library. You can install it via pip:
pip install transformers
📚 Documentation
Model Details
- Developed by: Mohammed Rakib
- Model type: Question Answering
- Language(s) (NLP): en
- License: MIT
- Related Models:
- Resources for more information:
| Property |
Details |
| Model Type |
Question Answering |
| Training Data |
See CUAD dataset card for more information. |
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 the 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
More information needed.
Metrics
More information needed.
Results
More information needed.
Model Examination
- 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
Model Architecture and Objective
More information needed.
Compute Infrastructure
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.",
}
Model Card Authors
Mohammed Rakib in collaboration with Ezi Ozoani and the Hugging Face team.
Model Card Contact
More information needed.
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