Datasets:

mteb
/

ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification

text string	label int64
The obligations and restrictions imposed by this Agreement will not apply to any information that: c. was received by the Recipient without breach of this Agreement from a third party without restriction as to the use and disclosure of the Discloser's Confidential Information; or,	1
Recipient shall have no obligation of confidentiality with respect to any information which: 5.3. is rightfully acquired from others who did not obtain it under obligation of confidentiality; or Disclosing Party understands that Recipient may develop or have developed information internally, or receive or have received information from other parties that is similar to the Confidential Information.	1
2. The term Confidential Information shall not include information, which is: (b) rightfully received from a third party with no duty of confidentiality; or	1
Receiver may disclose Confidential Information if the same: (d) becomes known to Receiver on a non-confidential basis from a source other than VIDAR without breach of this Agreement by Receiver;	1
Notwithstanding the return or destruction of the Evaluation Material, each party and its Representatives will continue to be bound by its obligations of confidentiality and other obligations hereunder for a period ending on the second anniversary of the Effective Date.	0
The obligations accruing prior to termination as set forth herein, shall, however, survive the termination of this Agreement for a period of two years.	0
4. The receiving Party hereby covenants that, during the validity period of this Agreement and for a period of five (5) years after its end or its termination, the Proprietary Information received from the disclosing Party shall: (e) neither be copied, nor otherwise reproduced nor duplicated in whole or in part where such copying, reproduction or duplication have not been specifically authorized in writing by the disclosing Party.	0
Therefore, the parties agree that a) Recipient may disclose Confidential Information to its Subsidiaries without Discloser’s consent, if and to the extent such disclosure is required in order to fulfill the Purpose; and b) a disclosure to or by a party’s respective Subsidiaries shall be considered as disclosure to or by the respective party; and	0

ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification

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 acquire information similar to Confidential Information from a third party.


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(["ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification"])
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("ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 178,
        "number_of_characters": 76077,
        "number_texts_intersect_with_train": 0,
        "min_text_length": 87,
        "average_text_length": 427.3988764044944,
        "max_text_length": 1903,
        "unique_text": 178,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 89
            },
            "0": {
                "count": 89
            }
        }
    },
    "train": {
        "num_samples": 8,
        "number_of_characters": 2241,
        "number_texts_intersect_with_train": null,
        "min_text_length": 152,
        "average_text_length": 280.125,
        "max_text_length": 435,
        "unique_text": 8,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 4
            },
            "0": {
                "count": 4
            }
        }
    }
}