Datasets:

mteb
/

ContractNLILimitedUseLegalBenchClassification

text string	label int64
The Parties agree to use the Confidential Information solely in connection with the Transaction and not for any purpose other than as authorized by this Agreement without the prior written consent of an authorized representative of the Parties. The Parties agree to ensure that all copyright products, such as source codes, system design data/architecture, and other proprietary information, disclosed during the engagement is protected from any misuse, alteration, modification, sharing, or transfer to any party without express permission of the Discloser. Such data is provided for the sole use of the Parties, and limited for the use under this agreement. The Receiving Party agrees not to use the Confidential Information in any way, or to manufacture or test any product embodying Confidential Information, except for the purpose set forth above.	1
2. The Recipient undertakes not to use the Confidential Information for any purpose except the Purpose, without first obtaining the written agreement of the Discloser.	1
3. The Receiving Party agrees that Confidential Information shall be used only for the purposes of facilitating the business relationship between the Parties.	1
2.1 No Use: Recipient agrees not to use the Confidential Information in any way or under any circumstances share the same, in writing or through any other means, with any Third Party.	1
4. Recipient may use the Confidential Information solely for evaluation purposes in connection with Recipient business discussions with Discloser. 6. Freedom of Use. Notwithstanding anything to the contrary, Recipient shall be free to use for any purposes the Residuals resulting from access to or work with Discloser's Confidential Information. However, the foregoing does not give Recipient the right to disclose (except as set forth in Section 3) the financial, statistical, or personnel information or the business plans of Discloser, and the foregoing shall not be deemed to grant to either party a license under the other party's copyright or patents. The term "Residuals" means information which may be retained in non-tangible form by person who have had access to the Confidential Information, including without limitation general ideas, concepts, know-how or techniques contained therein. Recipient certifies that the Confidential Information will only be used for the purposes expressly stated herein and will not be rented, leased, sold, sublicensed, assigned, or otherwise transferred.	0
Further, the Receiving Party shall be free to use for any purpose the residuals resulting from access to or work with the Confidential Information of the Disclosing Party, provided that the Receiving Party shall not disclose the Confidential Information except as expressly permitted pursuant to the terms of this Agreement. The term "residuals" means information in intangible form, which is retained in memory by persons who have had access to the Confidential Information, including ideas, concepts, know-how or techniques contained therein. Furthermore, except as otherwise provided herein or in a separate subsequent written agreement between the parties, the receiver of the Feedback shall be free to use, disclose, reproduce, license or otherwise distribute, and exploit the Feedback provided to it as it sees fit, entirely without obligation or restriction of any kind on account of intellectual property rights or otherwise.	0
(iii) either Party will be free to use for any purpose the residuals resulting from access to or work with Confidential Information disclosed hereunder.	0
Nothing in this Agreement shall be construed to limit either party’s ability to use “residuals” relating to the Evaluation Material of the other party. The term “residuals” shall mean information included in the Evaluation Material in nontangible form (i.e., not written or other documentary form, including tape or disk), which is incidentally retained in the memories of employees of either party who have had access to the Evaluation Material, including ideas, know-how, or techniques contained therein, and where the source of the Evaluation Material has become remote (e.g., as a result of the passage of time or the employee’s subsequent exposure to information of a similar nature from other sources) that the employee in good faith believes that it is not Evaluation Material.	0

ContractNLILimitedUseLegalBenchClassification

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 shall not use any Confidential Information for any purpose other than the purposes stated in Agreement.


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(["ContractNLILimitedUseLegalBenchClassification"])
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("ContractNLILimitedUseLegalBenchClassification")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 208,
        "number_of_characters": 84762,
        "number_texts_intersect_with_train": 0,
        "min_text_length": 80,
        "average_text_length": 407.50961538461536,
        "max_text_length": 1672,
        "unique_text": 208,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 97
            },
            "0": {
                "count": 111
            }
        }
    },
    "train": {
        "num_samples": 8,
        "number_of_characters": 4349,
        "number_texts_intersect_with_train": null,
        "min_text_length": 154,
        "average_text_length": 543.625,
        "max_text_length": 1104,
        "unique_text": 8,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 4
            },
            "0": {
                "count": 4
            }
        }
    }
}