README.md

metadata

language:
  - en
library_name: transformers
license: cc-by-nc-4.0
datasets:
  - NebulaSense/Legal_Clause_Instructions

Model Card for ContractAssist model

Instruction tuned FlanT5-XXL on Legal Clauses data generated via ChatGPT. The model is capable for generating and/or modifying the Legal Clauses.

Model Details

Model Description

• Developed by: Jaykumar Kasundra, Shreyans Dhankhar

• Model type: Language model

• Language(s) (NLP): en

• License: other

• Resources for more information:

  • Associated Paper ----> Details to be added soon!!

Uses

Prompt

Click to expand

{
    "prompt_input": (
        "Below is an instruction that describes a task, paired with an input that provides further context. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
    ),
    "prompt_no_input": (
        "Below is an instruction that describes a task. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Response:\n"
    ),
} 

Running the model on a GPU in 8bit

Click to expand

# pip install accelerate peft bitsandbytes
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel,PeftConfig
peft_model_id = 'NebulaSense/ContractAssist'

peft_config = PeftConfig.from_pretrained(peft_model_id) 

model =  AutoModelForSeq2SeqLM.from_pretrained(peft_config.base_model_name_or_path, device_map="auto",load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, peft_model_id)
model.eval()

Example

Click to expand

### Instruction:
Create a legal exhibit for the list of confidential information for Non-Disclosure Agreement (NDA) in Employment industry

### Input:

### Response:

Exhibit A: List of Confidential Information
The following information is considered confidential and proprietary to the Company:
1. Trade secrets, including but not limited to, formulas, patterns, compilations, programs, devices, methods, techniques, and processes, and any other information that is not generally known to the public.
2. Customer lists, including personal information, financial information, and other information related to the customer's business.
3. Marketing and sales strategies, including customer preferences, buying habits, and sales forecasts.
4. Financial information, including budgets, forecasts, and financial statements.
5. Intellectual property, including patents, trademarks, copyrights, trade names, and service marks.
6. Any other information designated as confidential by the Company in writing.
The Employee agrees to maintain the confidentiality of all such information and not to disclose it to any third party without the prior written consent of the Company.
The employee further agrees not to use any such information for any purpose other than as necessary to perform their duties for the Company, except as required by law.
This Exhibited List of Information is incorporated into and made a part of the Non-Disclosure Agreement between the Company and the Employee.

Direct Use

The model can directly be used to generate/modify legal clauses and help assist in drafting contracts. It likely works best on english language.

Compute Infrastructure

Amazon SageMaker Training Job.

Hardware

1 x 24GB NVIDIA A10G

Software

Transformers, PEFT, BitsandBytes

Citation

BibTeX: ---> Details to be added soon!!

Model Card Authors

Jaykumar Kasundra, Shreyans Dhankhar