Summarization
Transformers
Safetensors
Russian
t5
text2text-generation
fred-t5
russian
research
rahvusarhiiv
Eval Results (legacy)
text-generation-inference
Instructions to use Rahvusarhiiv/ru_summariser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rahvusarhiiv/ru_summariser 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="Rahvusarhiiv/ru_summariser")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Rahvusarhiiv/ru_summariser") model = AutoModelForSeq2SeqLM.from_pretrained("Rahvusarhiiv/ru_summariser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe8b2a95d1222f99b6c1e18c174c563aba31da59d25524617f11a0c69c12358d
- Size of remote file:
- 5.36 kB
- SHA256:
- 8647eb21ee7c408ee2bdf2369cd8bd1ad4c55aab24a33c383213ba965682a442
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