flan_t5_large_summarization_v2

Flan-T5 Large Fine-Tuned on EFRA Dataset

This is a fine-tuned version of Flan-T5 Large on the EFRA dataset for summarizing legal documents related to food regulations and policies.

Model Description

Flan-T5 is a sequence-to-sequence model trained for text-to-text tasks. This fine-tuned version is specifically optimized for summarizing legal text in the domain of food legislation, regulatory requirements, and compliance documents.

Fine-Tuning Details

• Base Model: google/flan-t5-large
• Dataset: EFRA (a curated dataset of legal documents in the food domain)
• Objective: Summarization of legal documents
• Framework: Hugging Face Transformers

Applications

This model is suitable for:

• Summarizing legal texts in the food domain
• Extracting key information from lengthy regulatory documents
• Assisting legal professionals and food companies in understanding compliance requirements

Example Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("giuid/flan_t5_large_summarization_v2")
tokenizer = AutoTokenizer.from_pretrained("giuid/flan_t5_large_summarization_v2")

# Input text
input_text = "Your lengthy legal document text here..."

# Tokenize and generate summary
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(inputs.input_ids, max_length=150, num_beams=5, early_stopping=True)

# Decode summary
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(summary)