Instructions to use anaghasavit/samplemodel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anaghasavit/samplemodel2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anaghasavit/samplemodel2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anaghasavit/samplemodel2") model = AutoModelForSequenceClassification.from_pretrained("anaghasavit/samplemodel2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from anaghasavit/samplemodel2: direct link, hf CLI and curl.
- Browser
- Download file 17.6 MB
-
https://huggingface.co/anaghasavit/samplemodel2/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://anaghasavit/samplemodel2@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/anaghasavit/samplemodel2/resolve/refs%2Fpr%2F1/pytorch_model.bin
17.6 MB
- Xet hash:
- 41c385fa8443f7bd438c4871bab9d2738426e6eabe7d7d6a7d7346653df438da
- Size of remote file:
- 17.6 MB
- SHA256:
- 3fc7a4ed87d487e2aa875f153f702311b7357199693be2a5965a4cc5375c5d03
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