license: cc-by-sa-4.0
language:
- en
tags:
- contracts
- legal
- document ai
Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
This model is fine-tuned using Alpaca like instructions. The base data for instruction fine-tuning was a legal corpus with fields like agreement date, party name, and addresses.
An encoder-decoder architecture like flag T5 is used because the author found it to be better than a decoder only architecture given the same number of parameters.
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Uses
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model_name = "scholarly360/contracts-extraction-flan-t5-base" model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name)
##1 prompt = """ what kind of clause is "Neither Party shall be liable to the other for any abatement of Charges, delay or non-performance of its obligations under the Services Agreement arising from any cause or causes beyond its reasonable control (a "Force Majeure Event") including, without limitation" """ inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) ##2 prompt = """ what are the multiple parties in "This COLLABORATION AGREEMENT (“Agreement”) dated November 14, 2002, is made by and between ZZZ, INC., a Delaware corporation, and having its principal office at 901 Gateway Boulevard, South San Francisco, California 9999 (ZZZZ), and MAJJO GROUP LIMITED, a United Kingdom corporation, and having its principal office at Majjo House, Berkeley Avenue, Greenford, Middlesex, UB6 0NN, United Kingdom (MAJJ). Both may be referred to as a “Party” or together, the “Parties”." """ inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) ##3 prompt = """ what is agreement date in "This COLLABORATION AGREEMENT (“Agreement”) dated November 14, 2002, is made by and between ZZZ, INC., a Delaware corporation" """
inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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Training Details
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Training Procedure
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Evaluation
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Summary
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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