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 training_args.bin from christinacdl/clickbait_binary_detection_DeBERTa: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/christinacdl/clickbait_binary_detection_DeBERTa/resolve/main/training_args.bin
- Command line
-
hf download hf://christinacdl/clickbait_binary_detection_DeBERTa/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/christinacdl/clickbait_binary_detection_DeBERTa/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 73ef0e81c00b73088605a30be8f15997ee4b771bb70fc718ededf74c4fd52600
- Size of remote file:
- 3.64 kB
- SHA256:
- 01524491409e611f0c01436b42b1c3baf65abdbf8120d54fbc08a15134c21b6f
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