Instructions to use YakovElm/IntelDAOS20Classic_MSE_Sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS20Classic_MSE_Sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS20Classic_MSE_Sample")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS20Classic_MSE_Sample") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS20Classic_MSE_Sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS20Classic_MSE_Sample: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS20Classic_MSE_Sample/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS20Classic_MSE_Sample/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS20Classic_MSE_Sample/resolve/main/tf_model.h5
438 MB
- Xet hash:
- cef79f7a5b93d8fe25722ce1806c90d5d8945cad22ad6d933e9f8e62b953b359
- Size of remote file:
- 438 MB
- SHA256:
- 7e6781b2463209b28df876f3e338a4c093af1a8e73956dad03b8dd24acd13cf2
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