roberta-base-on-cuad

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]

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# Model Card Authors [optional]

Mohammed Rakib in collaboration with Ezi Ozoani and the Hugging Face team

# Model Card Contact

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# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad")

model = AutoModelForQuestionAnswering.from_pretrained("Rakib/roberta-base-on-cuad")