
# Span NLI BERT (large)

This is a **BERT-large** model ([`bert-large-uncased-whole-word-masking`][2]) fine-tuned on the [**ContractNLI**][3] dataset (non-disclosure agreements) with the **Span NLI BERT** model architecture, 
from [*ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts* (Koreeda and Manning, 2021)][1].

For a hypothesis, the **Span NLI BERT** model predicts NLI labels and identifies evidence for documents as premises. 
Spans of documents should be pre-annotated; evidence is always full sentences or items in an enumerated list in the document. 

For details of the architecture and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 
This model is fine-tuned according to the recommended hyperparameters in the Appendix of the paper, 
some of which differ from the hyperparameters in `data/conf_large.yml` in their repo. 

ArXiv: <https://arxiv.org/abs/2110.01799>

[1]: https://aclanthology.org/2021.findings-emnlp.164/
[2]: https://huggingface.co/bert-large-uncased-whole-word-masking
[3]: https://stanfordnlp.github.io/contract-nli/
[4]: https://github.com/stanfordnlp/contract-nli-bert
{"language": ["en"], "pipeline_tag": "text-classification"}
transformers pytorch tensorboard bert text-classification en arxiv:2110.01799 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #en #arxiv-2110.01799 #endpoints_compatible #region-us 


# Span NLI BERT (large)

This is a BERT-large model (['bert-large-uncased-whole-word-masking'][2]) fine-tuned on the [ContractNLI][3] dataset (non-disclosure agreements) with the Span NLI BERT model architecture, 
from [*ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts* (Koreeda and Manning, 2021)][1].

For a hypothesis, the Span NLI BERT model predicts NLI labels and identifies evidence for documents as premises. 
Spans of documents should be pre-annotated; evidence is always full sentences or items in an enumerated list in the document. 

For details of the architecture and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 
This model is fine-tuned according to the recommended hyperparameters in the Appendix of the paper, 
some of which differ from the hyperparameters in 'data/conf_large.yml' in their repo. 

ArXiv: <URL

[1]: URL
[2]: URL
[3]: URL
[4]: URL
# Span NLI BERT (large)

This is a BERT-large model (['bert-large-uncased-whole-word-masking'][2]) fine-tuned on the [ContractNLI][3] dataset (non-disclosure agreements) with the Span NLI BERT model architecture, 
from [*ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts* (Koreeda and Manning, 2021)][1].

For a hypothesis, the Span NLI BERT model predicts NLI labels and identifies evidence for documents as premises. 
Spans of documents should be pre-annotated; evidence is always full sentences or items in an enumerated list in the document. 

For details of the architecture and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 
This model is fine-tuned according to the recommended hyperparameters in the Appendix of the paper, 
some of which differ from the hyperparameters in 'data/conf_large.yml' in their repo. 

ArXiv: <URL

[1]: URL
[2]: URL
[3]: URL
[4]: URL
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #en #arxiv-2110.01799 #endpoints_compatible #region-us 
 # Span NLI BERT (large)

This is a BERT-large model (['bert-large-uncased-whole-word-masking'][2]) fine-tuned on the [ContractNLI][3] dataset (non-disclosure agreements) with the Span NLI BERT model architecture, 
from [*ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts* (Koreeda and Manning, 2021)][1].

For a hypothesis, the Span NLI BERT model predicts NLI labels and identifies evidence for documents as premises. 
Spans of documents should be pre-annotated; evidence is always full sentences or items in an enumerated list in the document. 

For details of the architecture and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 
This model is fine-tuned according to the recommended hyperparameters in the Appendix of the paper, 
some of which differ from the hyperparameters in 'data/conf_large.yml' in their repo. 

ArXiv: <URL

[1]: URL
[2]: URL
[3]: URL
[4]: URL
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #en #arxiv-2110.01799 #endpoints_compatible #region-us 
# Span NLI BERT (large)

This is a BERT-large model (['bert-large-uncased-whole-word-masking'][2]) fine-tuned on the [ContractNLI][3] dataset (non-disclosure agreements) with the Span NLI BERT model architecture, 
from [*ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts* (Koreeda and Manning, 2021)][1].

For a hypothesis, the Span NLI BERT model predicts NLI labels and identifies evidence for documents as premises. 
Spans of documents should be pre-annotated; evidence is always full sentences or items in an enumerated list in the document. 

For details of the architecture and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 
This model is fine-tuned according to the recommended hyperparameters in the Appendix of the paper, 
some of which differ from the hyperparameters in 'data/conf_large.yml' in their repo. 

ArXiv: <URL

[1]: URL
[2]: URL
[3]: URL
[4]: URL