Model Card for roberta-base-on-cuad

模型详情

模型描述

• 开发者：Mohammed Rakib
• 共享者[可选]：需要更多信息
• 模型类型：问答
• 语言：英语
• 许可证：MIT
• 相关模型：
  • 父模型：RoBERTa
• 更多信息资源：
  • GitHub Repo： defactolaw
  • 相关论文： An Open Source Contractual Language Understanding Application Using Machine Learning

应用场景

直接使用

此模型可用于法律文件的问答任务。

训练细节

阅读 An Open Source Contractual Language Understanding Application Using Machine Learning 以获取有关训练过程、数据集预处理和评估的详细信息。

训练数据

有关更多信息，请参阅 CUAD dataset card 。

训练过程

预处理

需要更多信息

速度、大小、时间

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评估

测试数据、因素和度量标准

测试数据

有关更多信息，请参阅 CUAD dataset card 。

因素

度量标准

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结果

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模型审查

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• 硬件类型：需要更多信息
• 使用小时数：需要更多信息
• 云提供商：需要更多信息
• 计算区域：需要更多信息
• 碳排放量：需要更多信息

技术规格[可选]

模型架构和目标

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计算基础设施

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硬件

使用了来自Google Colab Pro的V100/P100

软件

Python，Transformers

引用

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.",
}

术语表[可选]

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更多信息[可选]

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模型卡作者[可选]

Mohammed Rakib与Ezi Ozoani和Hugging Face团队合作

模型卡联系方式

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如何开始使用该模型

使用以下代码开始使用该模型。

from transformers import AutoTokenizer, AutoModelForQuestionAnswering
 
tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad")
 
model = AutoModelForQuestionAnswering.from_pretrained("Rakib/roberta-base-on-cuad")