Transformers
PyTorch
JAX
Safetensors
Russian
t5
text2text-generation
normalization
denoising autoencoder
russian
text-generation-inference
Instructions to use cointegrated/rut5-small-normalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rut5-small-normalizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cointegrated/rut5-small-normalizer") model = AutoModelForSeq2SeqLM.from_pretrained("cointegrated/rut5-small-normalizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from cointegrated/rut5-small-normalizer: direct link, hf CLI and curl.
- Browser
- Download file 259 MB
-
https://huggingface.co/cointegrated/rut5-small-normalizer/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://cointegrated/rut5-small-normalizer/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/cointegrated/rut5-small-normalizer/resolve/main/flax_model.msgpack
259 MB
- Xet hash:
- c2030790e79a5134fcc9f3f6d60f0abb827b6c7ce0edc6eab9aaa3d4479da218
- Size of remote file:
- 259 MB
- SHA256:
- 37e1ed46f19e45cb06289b0a2626ab2a1b9a6c2c53ddc723da62c7cd8f2731cf
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