Instructions to use ragarwal/deberta-v3-base-nli-mixer-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ragarwal/deberta-v3-base-nli-mixer-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ragarwal/deberta-v3-base-nli-mixer-binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ragarwal/deberta-v3-base-nli-mixer-binary") model = AutoModelForSequenceClassification.from_pretrained("ragarwal/deberta-v3-base-nli-mixer-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ragarwal/deberta-v3-base-nli-mixer-binary: direct link, hf CLI and curl.
- Browser
- Download file 21 Bytes
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https://huggingface.co/ragarwal/deberta-v3-base-nli-mixer-binary/resolve/5f0cd592f25796d004819b0097e59b6822e81419/README.md
- Command line
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hf download hf://ragarwal/deberta-v3-base-nli-mixer-binary@5f0cd592f25796d004819b0097e59b6822e81419/README.md
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curl -L -o README.md https://huggingface.co/ragarwal/deberta-v3-base-nli-mixer-binary/resolve/5f0cd592f25796d004819b0097e59b6822e81419/README.md
21 Bytes
metadata
license: mit