Instructions to use YakovElm/IntelDAOS20Classic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS20Classic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS20Classic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS20Classic") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS20Classic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS20Classic: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS20Classic/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS20Classic/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS20Classic/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 0a1b9517b6cf1267f32049cc418010f5b8ac93a407fbe4f19c87990915474be1
- Size of remote file:
- 438 MB
- SHA256:
- 31b744dedf51bb7e1215c9e6b781ae0adeaa8a19995636f3fe2a4a233871c257
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