Instructions to use YakovElm/IntelDAOS20Classic_MSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS20Classic_MSE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS20Classic_MSE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS20Classic_MSE") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS20Classic_MSE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS20Classic_MSE: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS20Classic_MSE/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS20Classic_MSE/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS20Classic_MSE/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 72cacdf6a62bcf5425170b65273c4346729abc87c4f5ab330e49430fa40d1bbc
- Size of remote file:
- 438 MB
- SHA256:
- 1d25ca378265d252fd97eb1b2010d94e1b49f29dde7c1ef5f9e25eed3bb29b83
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