Instructions to use YakovElm/IntelDAOS5Classic_MSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS5Classic_MSE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS5Classic_MSE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS5Classic_MSE") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS5Classic_MSE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS5Classic_MSE: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS5Classic_MSE/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS5Classic_MSE/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS5Classic_MSE/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 0ae531f5bcc174a00d116015d2e34db7814d409fbe3c27ef721d94fbb0098b02
- Size of remote file:
- 438 MB
- SHA256:
- 314e7e4e2fd73b8ae94bc01016e9cfe67646a49c2314d5ddf68181c7f25b100e
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