Roberta Base On Cuad

🚀 roberta-base-on-cuad 模型卡片

本模型專為法律文檔的問答任務而設計，藉助先進的技術架構，能有效處理法律文本，為法律專業人士和相關從業者提供準確的問答服務。

🚀 快速開始

使用以下代碼開始使用該模型：

from transformers import AutoTokenizer, AutoModelForQuestionAnswering

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

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

✨ 主要特性

• 專為法律文檔問答任務設計，能精準處理法律文本。
• 基於 RoBERTa 架構，具有強大的語言理解能力。

📚 詳細文檔

模型詳情

模型描述

使用場景

直接使用

此模型可用於法律文檔的問答任務。

訓練詳情

閱讀論文 An Open Source Contractual Language Understanding Application Using Machine Learning ，獲取有關訓練過程、數據集預處理和評估的詳細信息。

訓練數據

更多信息請參閱 CUAD 數據集卡片。

訓練過程

• 預處理：待補充更多信息。
• 速度、大小、時間：待補充更多信息。

評估

測試數據、因素和指標

• 測試數據：更多信息請參閱 CUAD 數據集卡片。
• 因素：待補充更多信息。
• 指標：待補充更多信息。

結果

待補充更多信息。

模型檢查

• 硬件類型：待補充更多信息。
• 使用時長：待補充更多信息。
• 雲服務提供商：待補充更多信息。
• 計算區域：待補充更多信息。
• 碳排放：待補充更多信息。

技術規格

模型架構和目標

待補充更多信息。

計算基礎設施

• 硬件：使用了 Google Colab Pro 的 V100/P100。
• 軟件：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.",
}

模型卡片作者

Mohammed Rakib 與 Ezi Ozoani 以及 Hugging Face 團隊合作完成。

模型卡片聯繫方式

待補充更多信息。

📄 許可證

本模型採用 MIT 許可證。