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fine_tuned_t5_summarizer

Summarization
Transformers
Safetensors
t5
text2text-generation
legal-documents
text-generation-inference
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metadata
library_name: transformers
tags:
  - summarization
  - legal-documents
  - t5

Model Card for Fine-Tuned T5 Summarizer

This model is a fine-tuned version of the T5 base model, designed for summarizing legal texts into concise short and long summaries. It enables efficient processing of complex legal cases, facilitating quick insights and detailed analysis.

Model Details

Model Description

This is the model card for the fine-tuned T5 summarizer developed for legal case summaries. It has been specifically optimized to process long legal documents and generate two types of summaries:

  • Short Summaries: Concise highlights for quick review.

  • Long Summaries: Detailed insights for deeper analysis.

  • Developed by: Manjunatha Inti

  • Funded by: Self-funded

  • Shared by: Manjunatha Inti

  • Model type: Fine-tuned Transformer for Summarization

  • Language(s) (NLP): English

  • License: Apache 2.0

  • Finetuned from model: T5-base

Model Sources

Uses

Direct Use

The model can be directly used to summarize legal case texts. It works best with English legal documents.

Downstream Use

The model can be integrated into:

  • Legal document management systems.
  • AI tools for legal research and compliance.

Out-of-Scope Use

  • Use on non-legal documents without additional fine-tuning.
  • Summarization in languages other than English.

Bias, Risks, and Limitations

Bias

The model may reflect biases present in the training data, such as jurisdictional focus or societal biases inherent in the dataset.

Risks

  • Critical legal details might be omitted.
  • The model's output should not replace expert legal opinions.

Recommendations

  • Outputs should always be reviewed by a legal expert.
  • Avoid using for legal tasks where complete precision is mandatory.

How to Get Started with the Model 

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "manjunathainti/fine_tuned_t5_summarizer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example Input
input_text = "Insert a legal case description here."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

# Generate Summary
summary_ids = model.generate(input_ids, max_length=150, num_beams=4, length_penalty=2.0)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print("Generated Summary:", summary)