Instructions to use YakovElm/IntelDAOS10_ElectraModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS10_ElectraModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS10_ElectraModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS10_ElectraModel") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS10_ElectraModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/IntelDAOS10_ElectraModel: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS10_ElectraModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/IntelDAOS10_ElectraModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/IntelDAOS10_ElectraModel/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- ec92614f7adb8e6c57006081526caedea174d92c8f81a2dfafd8a07758237243
- Size of remote file:
- 438 MB
- SHA256:
- f977722b95ce4e8506c1489654e01f8ee05575830dc1a79660c587c210440ff5
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