Summarization
Transformers
PyTorch
Safetensors
Russian
English
t5
text2text-generation
dialogue-summarization
Eval Results (legacy)
text-generation-inference
Instructions to use d0rj/rut5-base-summ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0rj/rut5-base-summ with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="d0rj/rut5-base-summ")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("d0rj/rut5-base-summ") model = AutoModelForSeq2SeqLM.from_pretrained("d0rj/rut5-base-summ", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_args.bin from d0rj/rut5-base-summ: direct link, hf CLI and curl.
- Browser
- Download file 4.02 kB
-
https://huggingface.co/d0rj/rut5-base-summ/resolve/main/training_args.bin
- Command line
-
hf download hf://d0rj/rut5-base-summ/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/d0rj/rut5-base-summ/resolve/main/training_args.bin
4.02 kB
- Xet hash:
- 855ce42436a04ba865b2ccb8e3d53eb5c99ab8874b4944d740edf31e10afe7d8
- Size of remote file:
- 4.02 kB
- SHA256:
- 79b3ae3254693486a92191dff610a3c61a53c199e02ca91223e766d772dcefb8
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