Datasets:

mteb
/

ContractNLISharingWithEmployeesLegalBenchClassification

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; The Receiving Party agrees to use the same degree of protection it uses for its own trade secret information, and in no event less than reasonable efforts, to prevent and protect the Confidential Information, or any part thereof, from disclosure to any person other than the Receiving Party's employees having a need for disclosure in connection with the Receiving Party's authorized use of the Confidential Information.	1
Recipient agrees to limit disclosure of Confidential Information to employees and employees of Affiliates having a specific need to know such Confidential Information for the Purpose and in the case of Affiliates only to the extent that such Affiliate is under obligation to hold such information in confidence and is made aware of these terms and conditions.	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
____________________ , agrees that, in consideration for being shown or told about certain trade secrets or property belonging to Navidec, Incorporated, ____________________, shall not disclose or cause to be disclosed, disseminated or distributed any information concerning said trade secret or property to any person, entity, business or other individual or company without the prior written permission of Navidec, Incorporated.	0
"Confidential Information" includes, without limitation, information in tangible or intangible form relating to and/or including released or unreleased Disclosing Party software or hardware products, the marketing or promotion of any Disclosing Party product, Disclosing Party's business policies or practices, and information received from others that Disclosing Party is obligated to treat as confidential.	0
Proprietary Information does not include, however, information that (iv) was independently developed by the Receiving Party or any of its Representatives without reference to the	0
7. Confidential Information shall not include information which is: f. Independently developed by or for the Receiving Party.	0

ContractNLISharingWithEmployeesLegalBenchClassification

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 of Receiving Party's employees.


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(["ContractNLISharingWithEmployeesLegalBenchClassification"])
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("ContractNLISharingWithEmployeesLegalBenchClassification")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 170,
        "number_of_characters": 93267,
        "number_texts_intersect_with_train": 0,
        "min_text_length": 87,
        "average_text_length": 548.6294117647059,
        "max_text_length": 2493,
        "unique_text": 170,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 88
            },
            "0": {
                "count": 82
            }
        }
    },
    "train": {
        "num_samples": 8,
        "number_of_characters": 2680,
        "number_texts_intersect_with_train": null,
        "min_text_length": 126,
        "average_text_length": 335.0,
        "max_text_length": 706,
        "unique_text": 8,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 4
            },
            "0": {
                "count": 4
            }
        }
    }
}