Instructions to use textgain/News2Topic-T5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use textgain/News2Topic-T5-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="textgain/News2Topic-T5-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("textgain/News2Topic-T5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("textgain/News2Topic-T5-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use textgain/News2Topic-T5-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "textgain/News2Topic-T5-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textgain/News2Topic-T5-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/textgain/News2Topic-T5-base

SGLang

How to use textgain/News2Topic-T5-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "textgain/News2Topic-T5-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textgain/News2Topic-T5-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "textgain/News2Topic-T5-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textgain/News2Topic-T5-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use textgain/News2Topic-T5-base with Docker Model Runner:
```
docker model run hf.co/textgain/News2Topic-T5-base
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

News2Topic-T5-base

Model Details

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

Uses

The News2Topic 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-T5-base")

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

Training Details

The News2Topic T5-base model was trained on a 21K sample of the "Newsroom" dataset (https://lil.nlp.cornell.edu/newsroom/index.html) annotated with synthetic data generated by GPT-3.5-turbo

The model was trained for 3 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} }

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