Instructions to use christinacdl/clickbait_binary_detection_DeBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use christinacdl/clickbait_binary_detection_DeBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="christinacdl/clickbait_binary_detection_DeBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("christinacdl/clickbait_binary_detection_DeBERTa") model = AutoModelForSequenceClassification.from_pretrained("christinacdl/clickbait_binary_detection_DeBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from christinacdl/clickbait_binary_detection_DeBERTa: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/christinacdl/clickbait_binary_detection_DeBERTa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://christinacdl/clickbait_binary_detection_DeBERTa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/christinacdl/clickbait_binary_detection_DeBERTa/resolve/main/pytorch_model.bin
1.74 GB
- Xet hash:
- 5fb7a4fde20b3890c69c6435b3546da22f3d2a293013738f896f51bbd914d84b
- Size of remote file:
- 1.74 GB
- SHA256:
- c5542be2af567edcb272a974ba207386817eabcee2f9c197d526c72d0824285a
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