Model Details
Full Model IDd0rj/rut5-base-summ
Pipeline / Tasksummarization
Librarytransformers
Downloads (all-time)27.2K
Likes27
Last Modified10/5/2023
Author / Orgd0rj
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("summarization", model="d0rj/rut5-base-summ")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerspytorchsafetensorst5text2text-generationsummarizationdialogue-summarizationruendataset:d0rj/samsum-rudataset:IlyaGusev/gazetadataset:zjkarina/matreshkadataset:rcp-meetings/rudialogsum_v2dataset:GEM/wiki_linguadataset:mlsummodel-indextext-generation-inferenceendpoints_compatibleregion:us
More Summarization Models
See all →🚀 Use This Model
Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models📝 Task: Summarization
This model is designed for the Summarization task. Explore more models for this use case.
All Summarization Models →📊 Popularity
⬇ Downloads27.2K
❤️ Community Likes27
🛠️ Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.