Instructions to use Azma-AI/deberta-base-multi-label-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azma-AI/deberta-base-multi-label-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Azma-AI/deberta-base-multi-label-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Azma-AI/deberta-base-multi-label-classifier") model = AutoModelForSequenceClassification.from_pretrained("Azma-AI/deberta-base-multi-label-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Azma-AI/deberta-base-multi-label-classifier: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/Azma-AI/deberta-base-multi-label-classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Azma-AI/deberta-base-multi-label-classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Azma-AI/deberta-base-multi-label-classifier/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- c0fb6ff7fac835f10d74c1b68e28e9945843a9bdfeea9e6578d1c1dab37bbb5c
- Size of remote file:
- 738 MB
- SHA256:
- 2e28518532d77001e5030110c5761ec75499fc5fad1a7e81045c7ce0ba0a86e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.