---
language: en
datasets:
  - efra
license: apache-2.0
tags:
  - summarization
  - flan-t5
  - legal
  - food
model_type: t5
pipeline_tag: text2text-generation
---

# Flan-T5 Large Fine-Tuned on EFRA Dataset

This is a fine-tuned version of [Flan-T5 Large](https://huggingface.co/google/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](https://huggingface.co/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

```python
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)