Instructions to use tiya1012/SMISHROBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/SMISHROBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/SMISHROBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/SMISHROBERT") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/SMISHROBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/SMISHROBERT: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/tiya1012/SMISHROBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/SMISHROBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/SMISHROBERT/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- db3ad8ab02d156cb20df7f1a0be6662ceabbcbc6ea531b435134a8a25ded9d37
- Size of remote file:
- 499 MB
- SHA256:
- 48f170c0a0eeee5fc38e9f8d5647d964b418d93c6d67abc870d09c9d0d40d489
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