

# BERT-large fine-tuned on CUAD

This is a **BERT-large** model ([`bert-large-uncased-whole-word-masking`][2]) fine-tuned on the [**CUAD**][3] dataset 
from [*CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review* (Hendrycks et al., 2021)][1], with the **BertforQuestionAnswering** model architecture.

The questions ask for information often found in contracts; 
the model would return the relevant text string and its starting index in the given document if the information exists.
The CUAD dataset is in SQuAD 2.0 format.

For details of the dataset and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 

[1]: https://arxiv.org/abs/2103.06268
[2]: https://huggingface.co/bert-large-uncased-whole-word-masking
[3]: https://www.atticusprojectai.org/cuad
[4]: https://github.com/TheAtticusProject/cuad
{"language": ["en"], "datasets": ["cuad", "theatticusproject/cuad"], "pipeline_tag": "question-answering"}
transformers pytorch bert question-answering en dataset:cuad dataset:theatticusproject/cuad arxiv:2103.06268 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #en #dataset-cuad #dataset-theatticusproject/cuad #arxiv-2103.06268 #endpoints_compatible #region-us 



# BERT-large fine-tuned on CUAD

This is a BERT-large model (['bert-large-uncased-whole-word-masking'][2]) fine-tuned on the [CUAD][3] dataset 
from [*CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review* (Hendrycks et al., 2021)][1], with the BertforQuestionAnswering model architecture.

The questions ask for information often found in contracts; 
the model would return the relevant text string and its starting index in the given document if the information exists.
The CUAD dataset is in SQuAD 2.0 format.

For details of the dataset and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 

[1]: URL
[2]: URL
[3]: URL
[4]: URL
# BERT-large fine-tuned on CUAD

This is a BERT-large model (['bert-large-uncased-whole-word-masking'][2]) fine-tuned on the [CUAD][3] dataset 
from [*CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review* (Hendrycks et al., 2021)][1], with the BertforQuestionAnswering model architecture.

The questions ask for information often found in contracts; 
the model would return the relevant text string and its starting index in the given document if the information exists.
The CUAD dataset is in SQuAD 2.0 format.

For details of the dataset and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 

[1]: URL
[2]: URL
[3]: URL
[4]: URL
TAGS
#transformers #pytorch #bert #question-answering #en #dataset-cuad #dataset-theatticusproject/cuad #arxiv-2103.06268 #endpoints_compatible #region-us 
 # BERT-large fine-tuned on CUAD

This is a BERT-large model (['bert-large-uncased-whole-word-masking'][2]) fine-tuned on the [CUAD][3] dataset 
from [*CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review* (Hendrycks et al., 2021)][1], with the BertforQuestionAnswering model architecture.

The questions ask for information often found in contracts; 
the model would return the relevant text string and its starting index in the given document if the information exists.
The CUAD dataset is in SQuAD 2.0 format.

For details of the dataset and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 

[1]: URL
[2]: URL
[3]: URL
[4]: URL
TAGS
#transformers #pytorch #bert #question-answering #en #dataset-cuad #dataset-theatticusproject/cuad #arxiv-2103.06268 #endpoints_compatible #region-us 
# BERT-large fine-tuned on CUAD

This is a BERT-large model (['bert-large-uncased-whole-word-masking'][2]) fine-tuned on the [CUAD][3] dataset 
from [*CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review* (Hendrycks et al., 2021)][1], with the BertforQuestionAnswering model architecture.

The questions ask for information often found in contracts; 
the model would return the relevant text string and its starting index in the given document if the information exists.
The CUAD dataset is in SQuAD 2.0 format.

For details of the dataset and usage of the relevant training/testing scripts, check out the paper and their [Github repo][4]. 

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