@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]
More information needed
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.
Click to expand
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad")
model = AutoModelForQuestionAnswering.from_pretrained("Rakib/roberta-base-on-cuad")
Runs of Rakib roberta-base-on-cuad on huggingface.co
5.3K
Total runs
-441
24-hour runs
339
3-day runs
199
7-day runs
-567
30-day runs
More Information About roberta-base-on-cuad huggingface.co Model
roberta-base-on-cuad huggingface.co is an AI model on huggingface.co that provides roberta-base-on-cuad's model effect (), which can be used instantly with this Rakib roberta-base-on-cuad model. huggingface.co supports a free trial of the roberta-base-on-cuad model, and also provides paid use of the roberta-base-on-cuad. Support call roberta-base-on-cuad model through api, including Node.js, Python, http.
roberta-base-on-cuad huggingface.co is an online trial and call api platform, which integrates roberta-base-on-cuad's modeling effects, including api services, and provides a free online trial of roberta-base-on-cuad, you can try roberta-base-on-cuad online for free by clicking the link below.
Rakib roberta-base-on-cuad online free url in huggingface.co:
roberta-base-on-cuad is an open source model from GitHub that offers a free installation service, and any user can find roberta-base-on-cuad on GitHub to install. At the same time, huggingface.co provides the effect of roberta-base-on-cuad install, users can directly use roberta-base-on-cuad installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
roberta-base-on-cuad install url in huggingface.co:
