Datasets:

mteb
/

ContractNLISharingWithThirdPartiesLegalBenchClassification

text string	label int64
The Parties will: a. limit disclosure of any Confidential Information to its directors, officers, employees, agents or representatives (collectively "Representatives") who have a need to know such Confidential Information in connection with the Transaction, and only for that purpose;	1
The Disclosee will procure that prior to the disclosure to any other person (including any professional advisor) of any Confidential Information, such other person is made aware of the provisions of this Agreement and the fact that the Disclosee will be liable.	1
Each Party shall be responsible for any breach of this Agreement by such Party, its employees, agents, officials, representatives or consultants acting within the scope of their engagement or employment.	1
An employee, director, officer, manager, member, partner, affiliate, associate, agent, attorney, accountant, consultant, banker, business adviser, financial adviser, scientific adviser or technical adviser of Receiver may become a party to this Agreement by signing a counterpart hereof, a copy of which shall be provided to VIDAR within five days of signature.	1
Recipient will not disclose or permit access to Confidential Information to contract workers, consultants or contractors of Recipient or its Affiliates unless authorized by Disclosing Party in writing and on condition that such persons are bound by obligations of confidentiality inuring to the benefit of Disclosing Party and its Affiliates at least as restrictive as these terms and conditions.	0
Subject to the terms of the NDA the Receiving Party hereby undertakes to the Disclosing Party: c) that it shall give access to the Confidential Information only,to those of its employees who need access to the Confidential Information for LOA purposes and will ensure that such employees adhere to the obligations and restrictions contained in the NDA;	0
WHEREAS, in order for the Contractor to perform the work required under the Contract, it will be necessary for the State at times to provide the Contractor and the Contractor’s employees, agents, and subcontractors (collectively the “Contractor’s Personnel”) with access to certain information the State deems confidential information (the “Confidential Information”). 3. If the Contractor intends to disseminate any portion of the Confidential Information to non-employee agents who are assisting in the Contractor’s performance of the Contract or will otherwise have a role in performing any aspect of the Contract, the Contractor shall first obtain the written consent of the State to any such dissemination.	0
In the absence of the Disclosing Party’s prior written consent, the Receiving Party shall not produce nor disclose the Confidential Information, or any part thereof, to any third party.	0

ContractNLISharingWithThirdPartiesLegalBenchClassification

An MTEB dataset

Massive Text Embedding Benchmark

This task is a subset of ContractNLI, and consists of determining whether a clause from an NDA clause provides that the Receiving Party may share some Confidential Information with some third-parties (including consultants, agents and professional advisors).


Task category	t2c
Domains	Legal, Written
Reference	https://huggingface.co/datasets/nguha/legalbench

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_tasks(["ContractNLISharingWithThirdPartiesLegalBenchClassification"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repitory.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.


@misc{guha2023legalbench,
  archiveprefix = {arXiv},
  author = {Neel Guha and Julian Nyarko and Daniel E. Ho and Christopher Ré and Adam Chilton and Aditya Narayana and Alex Chohlas-Wood and Austin Peters and Brandon Waldon and Daniel N. Rockmore and Diego Zambrano and Dmitry Talisman and Enam Hoque and Faiz Surani and Frank Fagan and Galit Sarfaty and Gregory M. Dickinson and Haggai Porat and Jason Hegland and Jessica Wu and Joe Nudell and Joel Niklaus and John Nay and Jonathan H. Choi and Kevin Tobia and Margaret Hagan and Megan Ma and Michael Livermore and Nikon Rasumov-Rahe and Nils Holzenberger and Noam Kolt and Peter Henderson and Sean Rehaag and Sharad Goel and Shang Gao and Spencer Williams and Sunny Gandhi and Tom Zur and Varun Iyer and Zehua Li},
  eprint = {2308.11462},
  primaryclass = {cs.CL},
  title = {LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models},
  year = {2023},
}

@article{koreeda2021contractnli,
  author = {Koreeda, Yuta and Manning, Christopher D},
  journal = {arXiv preprint arXiv:2110.01799},
  title = {ContractNLI: A dataset for document-level natural language inference for contracts},
  year = {2021},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("ContractNLISharingWithThirdPartiesLegalBenchClassification")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 180,
        "number_of_characters": 93112,
        "number_texts_intersect_with_train": 0,
        "min_text_length": 65,
        "average_text_length": 517.2888888888889,
        "max_text_length": 1976,
        "unique_text": 180,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 71
            },
            "0": {
                "count": 109
            }
        }
    },
    "train": {
        "num_samples": 8,
        "number_of_characters": 2763,
        "number_texts_intersect_with_train": null,
        "min_text_length": 186,
        "average_text_length": 345.375,
        "max_text_length": 713,
        "unique_text": 8,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 4
            },
            "0": {
                "count": 4
            }
        }
    }
}