Instructions to use tiya1012/smishdistilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/smishdistilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/smishdistilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/smishdistilbert") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/smishdistilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/smishdistilbert: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/tiya1012/smishdistilbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/smishdistilbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/smishdistilbert/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 19c2961d1a3664612e0852529f0822715694c02417adfd7fe6f1a37495f30b75
- Size of remote file:
- 268 MB
- SHA256:
- 3b18164dbd2fff3dc20eca24e90972d9e505dc1b9522cda14071ce69ea17e56e
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