Datasets:

mteb
/

ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification

text string	label int64
The term “Confidential Information” as used herein means all nonpublic information relating to the Subject Matter that is disclosed by either party, its Affiliates (as defined below), or their agents (where applicable, collectively referred to as the “Disclosing Party”), directly or indirectly, in writing, orally or by inspection of premises or tangible objects to the other party (the “Recipient”) that is:	1
4. The undertakings in clauses 2 and 3 above apply to all of the information disclosed by the Discloser to the Recipient, regardless of the way or form in which it is disclosed or recorded but they do not apply to:	1
1. The confidential, proprietary and trade secret information of the Disclosing Party (hereinafter "Confidential Information") provided hereunder is any and all information, in whatever form (including electronic data) that will be disclosed to facilitate the potential transaction of business (related to the provision of SAP Services to HRM) between the Parties, including, but not limited to, portions or parts of the November 1, 2012 Agreement (including any schedules or appendices) between the Province of Nova Scotia and IBM, each Party's disclosure of intellectual property, techniques, sketches, drawings, models, inventions, know-how, processes, apparatus, equipment, algorithms, software programs, software source documents, and formulae related to the current, future and proposed products, documents and services, research, experimental work, development, design details and specifications, engineering, financial information, procurement requirements, purchasing, manufacturing, licensing, sales or service customer lists, business forecasts, sales and merchandizing, and marketing plans and information.	1
1. Disclosure: Recipient agrees not to disclose and the Discloser agrees to let the Recipient have the access to the Confidential Information as identified and reduced in writing or provided verbally or in any other way not reduced in writing at the time of such disclosure of the information.	1
Confidential Information shall mean the following: a) all such information, of any kind whatsoever (whether in oral, written or electronic form, and including, but not limited to, technical, commercial, financial, accounting, legal and administrative information) pertaining to the Sale of the Munt and the Sellers as may be provided to the Disclosee and their responsible managers, officers, employees, shareholders, members of the Board of Directors and advisors (including financial, legal and tax advisors and auditors) (“Representatives”), by the Sellers, their advisors or their representatives;	0
(a) “Confidential Information” means any proprietary information that is disclosed in writing by Disclosing Party (defined herein) to Receiving Party (defined herein) and is duly and recognizably marked “Confidential” on each document / sheet.	0
All Confidential Information received from the disclosing party shall be in tangible form.	0
11. Nothing contained in the Agreement shall be construed as granting any rights under any patent, trademark or copyright, by license or otherwise, protecting any Information subject to this Agreement, and that this Agreement does not create a partnership, joint venture or other legal relationship between the Parties.	0

ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification

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 Confidential Information may include verbally conveyed information.


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(["ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification"])
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("ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 139,
        "number_of_characters": 73080,
        "number_texts_intersect_with_train": 0,
        "min_text_length": 76,
        "average_text_length": 525.7553956834532,
        "max_text_length": 1931,
        "unique_text": 139,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 68
            },
            "0": {
                "count": 71
            }
        }
    },
    "train": {
        "num_samples": 8,
        "number_of_characters": 3300,
        "number_texts_intersect_with_train": null,
        "min_text_length": 92,
        "average_text_length": 412.5,
        "max_text_length": 1120,
        "unique_text": 8,
        "unique_labels": 2,
        "labels": {
            "1": {
                "count": 4
            },
            "0": {
                "count": 4
            }
        }
    }
}