Instructions to use Leonardolin/MLM_NTCIR_att_sup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Leonardolin/MLM_NTCIR_att_sup with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Leonardolin/MLM_NTCIR_att_sup")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Leonardolin/MLM_NTCIR_att_sup") model = AutoModelForSequenceClassification.from_pretrained("Leonardolin/MLM_NTCIR_att_sup", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Leonardolin/MLM_NTCIR_att_sup: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/Leonardolin/MLM_NTCIR_att_sup/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Leonardolin/MLM_NTCIR_att_sup/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Leonardolin/MLM_NTCIR_att_sup/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- 75ff2d6c4e73b838b81347d68ed74c8ea469ebcf433633da3482dd756b22a286
- Size of remote file:
- 409 MB
- SHA256:
- 34fcfb1fb2c848b3cd4338c3631a12cbb4f8f2be00dbf24777d75f3662785018
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