Instructions to use YakovElm/IntelDAOS5Classic_MSE_Sample_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS5Classic_MSE_Sample_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS5Classic_MSE_Sample_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS5Classic_MSE_Sample_2") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS5Classic_MSE_Sample_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS5Classic_MSE_Sample_2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS5Classic_MSE_Sample_2/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS5Classic_MSE_Sample_2/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS5Classic_MSE_Sample_2/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 26b7c6a0e3922e714e57db2db9cdc188a573806cdd9cdb8d5921f93c8593de1f
- Size of remote file:
- 438 MB
- SHA256:
- 0ccb599ac9e1d32e7e88e0b05737df52a14e1de03aad1fa7fd2302896e5133de
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