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:
- 52f0ad7fa8c2833827b487f56cdf19aa439741f33d03a9ec3d71ccaf0f99fd6e
- Size of remote file:
- 5.21 MB
- SHA256:
- ea5eb073a9d7ed6b78f41e55b75ac31c16570bdd1a81982709fd09302e9f7b5b
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