Instructions to use faisaltareque/XL-HeadTags-mT5-c5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use faisaltareque/XL-HeadTags-mT5-c5 with Transformers:

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

pipe = pipeline("text-generation", model="faisaltareque/XL-HeadTags-mT5-c5")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("faisaltareque/XL-HeadTags-mT5-c5")
model = AutoModel.from_pretrained("faisaltareque/XL-HeadTags-mT5-c5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use faisaltareque/XL-HeadTags-mT5-c5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "faisaltareque/XL-HeadTags-mT5-c5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "faisaltareque/XL-HeadTags-mT5-c5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/faisaltareque/XL-HeadTags-mT5-c5

SGLang

How to use faisaltareque/XL-HeadTags-mT5-c5 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 "faisaltareque/XL-HeadTags-mT5-c5" \
    --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": "faisaltareque/XL-HeadTags-mT5-c5",
		"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 "faisaltareque/XL-HeadTags-mT5-c5" \
        --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": "faisaltareque/XL-HeadTags-mT5-c5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use faisaltareque/XL-HeadTags-mT5-c5 with Docker Model Runner:
```
docker model run hf.co/faisaltareque/XL-HeadTags-mT5-c5
```

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

Model Card for Model ID

This is a Text-to-Text model for generating headlines and tags from news articles.

Model Details

Model Description

This model is a Text-to-Text model for generating headlines and tags from news articles. This is a mT5 model finetuned on the XL-HeadTags multilingual dataset (read more).

Model type: T5
Finetuned from model : mT5

Model Sources [optional]

Repository: XL-HeadTags
Paper: XL-HeadTags

Citation [optional]

@inproceedings{shohan-etal-2024-xl,
    title = "{XL}-{H}ead{T}ags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags",
    author = "Shohan, Faisal  and
      Nayeem, Mir Tafseer  and
      Islam, Samsul  and
      Akash, Abu Ubaida  and
      Joty, Shafiq",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.771",
    pages = "12991--13024"
}

Model Card Authors [optional]

Faisal Tareque Shohan (faisaltareque@hotmail.com)
Mir Tafseer Nayeem (mnayeem@ualberta.ca)
Samsul Islam (samsulratul98@gmail.com)
Abu Ubaida Akash (abu.ubaida.akash@usherbrooke.ca)
Shafiq Joty (sjoty@salesforce.com)

Model Card Contact

Faisal Tareque Shohan

Downloads last month: 2

Safetensors

Model size

0.4B params

Tensor type

F32

Dataset used to train faisaltareque/XL-HeadTags-mT5-c5

Paper for faisaltareque/XL-HeadTags-mT5-c5

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45