Summarization
Transformers
Safetensors
Russian
mt5
text2text-generation
summarizer
text-generation-inference
Instructions to use sarahai/ru-sum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sarahai/ru-sum 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="sarahai/ru-sum")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sarahai/ru-sum") model = AutoModelForSeq2SeqLM.from_pretrained("sarahai/ru-sum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sarahai/ru-sum: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/sarahai/ru-sum/resolve/main/tokenizer.json
- Command line
-
hf download hf://sarahai/ru-sum/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sarahai/ru-sum/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 749ee03d51b402ba7c368d32edcb951844290b4bfb52726023d1b09180d01b26
- Size of remote file:
- 16.3 MB
- SHA256:
- a07b565503d390625405fce2f3a4069be2364b0e398a4907ab059dd14caeb7c2
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