Instructions to use YakovElm/IntelDAOS15Classic_MSE_Sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS15Classic_MSE_Sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS15Classic_MSE_Sample")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS15Classic_MSE_Sample") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS15Classic_MSE_Sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS15Classic_MSE_Sample: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS15Classic_MSE_Sample/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS15Classic_MSE_Sample/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS15Classic_MSE_Sample/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 6aaa2a211436c3e2d60a2ff43d2e6705687dadcef5e8b3b444fa909d658fcfe9
- Size of remote file:
- 438 MB
- SHA256:
- 879524f123f1d547104daec2f79a2a9d8d7f6a1559175ff375952cfa16c2e62c
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