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Model Details

Instruction fine tuned Flan-T5 on Contracts

Model Description

This model is fine-tuned using Alpaca like instructions. The base data for instruction fine-tuning is a legal corpus with fields like Titles , agreement date, party name, and addresses.

There are many type of models trained on above DataSet (DataSet will be released soon for the community) An encoder-decoder architecture like Flan-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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Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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Uses

Just like any ChatGPT equivalent model (For Contracts Domain)

Direct Use

Downstream Use [optional]

Out-of-Scope Use

Bias, Risks, and Limitations

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)
>>> ### Example 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))
>>> ### Example 2
>>> 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))
>>> ### Example 3
>>> prompt =  """ ### Instruction: \n\n what is agreement date ### Input: \n\n 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

Training Data

DataSet will be released soon for the community

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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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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Technical Specifications [optional]

Model Architecture and Objective

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Hardware

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Software

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Citation [optional]

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Model Card Authors [optional]

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Model Card Contact

https://github.com/scholarly360