Instructions to use YakovElm/Apache5_DeBERTaV3Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Apache5_DeBERTaV3Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Apache5_DeBERTaV3Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Apache5_DeBERTaV3Model") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Apache5_DeBERTaV3Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/Apache5_DeBERTaV3Model: direct link, hf CLI and curl.
- Browser
- Download file 796 MB
-
https://huggingface.co/YakovElm/Apache5_DeBERTaV3Model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/Apache5_DeBERTaV3Model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/Apache5_DeBERTaV3Model/resolve/main/pytorch_model.bin
796 MB
- Xet hash:
- cc0a78b507353d7c76343bc6840b5e1dbcaae3c276aae7def29a7b8d55c746f9
- Size of remote file:
- 796 MB
- SHA256:
- ee34e9ca7725478a8ad577fb4da7a292acd9203cc4f70925675229027c29fd8f
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