Instructions to use mbouilloud/unbalanced_deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbouilloud/unbalanced_deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mbouilloud/unbalanced_deberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mbouilloud/unbalanced_deberta") model = AutoModelForSequenceClassification.from_pretrained("mbouilloud/unbalanced_deberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mbouilloud/unbalanced_deberta: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/mbouilloud/unbalanced_deberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mbouilloud/unbalanced_deberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mbouilloud/unbalanced_deberta/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 69959ecf23f33391d297116e8eb5ede820e65d07d758fc4eed375f1db109e282
- Size of remote file:
- 557 MB
- SHA256:
- ef6a5506ecb212549feaeab1a142b2ffab198f7adcac627bc27aca2d9e822d3f
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