Instructions to use YakovElm/IntelDAOS10Classic_MSE_Sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS10Classic_MSE_Sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS10Classic_MSE_Sample")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS10Classic_MSE_Sample") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS10Classic_MSE_Sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS10Classic_MSE_Sample: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS10Classic_MSE_Sample/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS10Classic_MSE_Sample/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS10Classic_MSE_Sample/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 5f2d85b8226c93cc410a744b63d22f1b6bca2a8c2c6eefbbccb9c68e0ddf13c7
- Size of remote file:
- 438 MB
- SHA256:
- a60097e1360769f4efcef0c401661b4a5427cbe511ba0a9f806a141b8d752860
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