Instructions to use Leonardolin/mt5-NTCIR17-3classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Leonardolin/mt5-NTCIR17-3classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Leonardolin/mt5-NTCIR17-3classification") model = AutoModelForSeq2SeqLM.from_pretrained("Leonardolin/mt5-NTCIR17-3classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Leonardolin/mt5-NTCIR17-3classification: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/Leonardolin/mt5-NTCIR17-3classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Leonardolin/mt5-NTCIR17-3classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Leonardolin/mt5-NTCIR17-3classification/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 2acc274684f6ee7fe8483fa44697133127dfa780b1bf671fd0bdf4929355e4ec
- Size of remote file:
- 1.2 GB
- SHA256:
- d8294904963a57227d058941d25d7517a429df671257ec0e22313f7b3cd19bff
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