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Update 2023-07-04-leggen_contract_nli_en.md
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## Description

This is a text-to-text generation model (encode-decoder architecture) that has undergone fine-tuning on flan-t5 using the Contract-NLI dataset. The ContractNLI dataset is designed for document-level natural language inference (NLI) on contracts, aiming to streamline and expedite the contract review process. The objective of this task is to provide a system with a set of hypotheses, like "Some obligations of Agreement may survive termination," along with a contract, and task it with classifying whether each hypothesis is entailed, contradicted, or not mentioned (neutral) by the contract.
This is a text-to-text generation model (encode-decoder architecture) that has undergone fine-tuning on contract for Natural Language Inference on in-house curated dataset, aiming to streamline and expedite the contract review process. The objective of this task is to provide a system with a set of hypotheses, like "Some obligations of Agreement may survive termination," along with a contract, and task it with classifying whether each hypothesis is entailed, contradicted, or not mentioned (neutral) by the contract.

## Predicted Entities

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|Language:|en|
|Size:|1.6 GB|

## References

This model has been fine tuned using [Stanford ContractNLI](https://stanfordnlp.github.io/contract-nli/) dataset.

Reference:
```bibtex
@inproceedings{koreeda-manning-2021-contractnli-dataset,
title = "{C}ontract{NLI}: A Dataset for Document-level Natural Language Inference for Contracts",
author = "Koreeda, Yuta and Manning, Christopher",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.164",
pages = "1907--1919",
}
```

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