Instructions to use anaghasavit/sample3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anaghasavit/sample3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anaghasavit/sample3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anaghasavit/sample3") model = AutoModelForSequenceClassification.from_pretrained("anaghasavit/sample3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anaghasavit/sample3: direct link, hf CLI and curl.
- Browser
- Download file 62.5 MB
-
https://huggingface.co/anaghasavit/sample3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://anaghasavit/sample3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anaghasavit/sample3/resolve/main/pytorch_model.bin
62.5 MB
- Xet hash:
- 2318d5f13f3905c59c45fadfab534d6f276f6b62292d56683f1cc94a25a70f38
- Size of remote file:
- 62.5 MB
- SHA256:
- 3441a90aee6142ee9c294e1149cfc80eeffa4a4650b1c579a56a163cddd0cd54
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