---
license: mit
language:
- en
pipeline_tag: text2text-generation
---
# News2Topic-V2-Flan-T5-base

## Model Details

- **Model type:** Text-to-Text Generation
- **Language(s) (NLP):**  English
- **License:** MIT License
- **Finetuned from model:** FLAN-T5 Base Model (Google AI)

## Uses

The News2Topic Flan T5-base model is designed for automatic generation of topic names from news articles or news-like text. It can be integrated into news aggregation platforms, content management systems, or used for enhancing news browsing and searching experiences by providing concise topics.

## How to Get Started with the Model

```
from transformers import pipeline

pipe = pipeline("text2text-generation", model="textgain/News2Topic-V2-Flan-T5-base")

news_text = "Your news text here."
print(pipe(news_text))
```

## Training Details

The News2Topic V2 Flan T5-base model was trained on a 20K sample of the "Newsroom" dataset (https://lil.nlp.cornell.edu/newsroom/index.html), annotated with data generated by a fine-tuned GPT-3.5-turbo on synthetic curated data.

The model was trained for 10 epochs, with a learning rate of 0.00001, a maximum sequence length of 512, and a training batch size of 12.

## Citation

**BibTeX:**
```
@article{Kosar_DePauw_Daelemans_2024,
title={Comparative Evaluation of Topic Detection: Humans vs. LLMs}, volume={13},
url={https://www.clinjournal.org/clinj/article/view/173}, journal={Computational Linguistics in the Netherlands Journal},
author={Kosar, Andriy and De Pauw, Guy and Daelemans, Walter},
year={2024},
month={Mar.},
pages={91–120} }
```