Instructions to use NlpHUST/t5-small-vi-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NlpHUST/t5-small-vi-summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("NlpHUST/t5-small-vi-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("NlpHUST/t5-small-vi-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NlpHUST/t5-small-vi-summarization: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/NlpHUST/t5-small-vi-summarization/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NlpHUST/t5-small-vi-summarization/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NlpHUST/t5-small-vi-summarization/resolve/main/flax_model.msgpack
1.2 GB
- Xet hash:
- 293b20064641ef555656c3dce7fb9ab24fdb86dc2e7b21a66b6a4e369ab88e2d
- Size of remote file:
- 1.2 GB
- SHA256:
- 70a494d1815ea0a5b28a6ab154f51adcc9dd62dc9eb3e30885bec778db58e285
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