Instructions to use froggydood/deberta-stance-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use froggydood/deberta-stance-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="froggydood/deberta-stance-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("froggydood/deberta-stance-detection") model = AutoModelForSequenceClassification.from_pretrained("froggydood/deberta-stance-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download datasets/test.csv from froggydood/deberta-stance-detection: direct link, hf CLI and curl.
- Browser
- Download file 503 kB
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https://huggingface.co/froggydood/deberta-stance-detection/resolve/main/datasets/test.csv
- Command line
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hf download hf://froggydood/deberta-stance-detection/datasets/test.csv
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curl -L -o test.csv https://huggingface.co/froggydood/deberta-stance-detection/resolve/main/datasets/test.csv
503 kB
File too large to display, you can check the raw version instead.