Task:

Given a set of input keywords, generate a corresponding text output for a section in the legal domain.

Dataset:

We used the Contract Understanding Atticus Dataset (CUAD).
It is a corpus of 13,000+ labels in 510 commercial legal contracts.
They have been manually labeled under the supervision of experienced lawyers to identify 41 types of legal clauses (e.g. licenses, warranty, governing law, insurance, etc…).

Workflow:

![alt text](https://github.com/vikramNU/Practicum/raw/main/Screenshot%202022-06-09%20210134.jpg)

You can connect me at vikram984511@gmail.com
{}
transformers pytorch t5 text2text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Task:

Given a set of input keywords, generate a corresponding text output for a section in the legal domain.

Dataset:

We used the Contract Understanding Atticus Dataset (CUAD).
It is a corpus of 13,000+ labels in 510 commercial legal contracts.
They have been manually labeled under the supervision of experienced lawyers to identify 41 types of legal clauses (e.g. licenses, warranty, governing law, insurance, etc…).

Workflow:

!alt text

You can connect me at vikram984511@URL
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

passage: TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
