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:
# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 293b20064641ef555656c3dce7fb9ab24fdb86dc2e7b21a66b6a4e369ab88e2d
- Size of remote file:
- 1.2 GB
- SHA256:
- 70a494d1815ea0a5b28a6ab154f51adcc9dd62dc9eb3e30885bec778db58e285
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