Instructions to use tarudesu/ViHateT5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarudesu/ViHateT5-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tarudesu/ViHateT5-base") model = AutoModelForSeq2SeqLM.from_pretrained("tarudesu/ViHateT5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from tarudesu/ViHateT5-base: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/tarudesu/ViHateT5-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://tarudesu/ViHateT5-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/tarudesu/ViHateT5-base/resolve/main/flax_model.msgpack
892 MB
- Xet hash:
- 6217f99471f1bcc9b202d04fa632fa408a9c1e27c8fde5a17a0997e110f00b9a
- Size of remote file:
- 892 MB
- SHA256:
- 8b2ffef5c62bd04de09f3bd0f94377fc4f1c2b682c3a04c038180567863e1ceb
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