Instructions to use YakovElm/IntelDAOS5_ElectraModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS5_ElectraModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS5_ElectraModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS5_ElectraModel") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS5_ElectraModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/IntelDAOS5_ElectraModel: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS5_ElectraModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/IntelDAOS5_ElectraModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/IntelDAOS5_ElectraModel/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 1f630c704bbdf3d7a01931ff7c86d961280bc8c39fb21ed8bb556800c4481b46
- Size of remote file:
- 438 MB
- SHA256:
- a9f20f6841bdf6c49c2708aa2fcdc93ebd369f6c85dd061a1c71abc5e9a47d17
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