Instructions to use EshAhm/Fake-Citation-Detector-V2-scibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EshAhm/Fake-Citation-Detector-V2-scibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EshAhm/Fake-Citation-Detector-V2-scibert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EshAhm/Fake-Citation-Detector-V2-scibert") model = AutoModelForSequenceClassification.from_pretrained("EshAhm/Fake-Citation-Detector-V2-scibert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from EshAhm/Fake-Citation-Detector-V2-scibert: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/EshAhm/Fake-Citation-Detector-V2-scibert/resolve/main/training_args.bin
- Command line
-
hf download hf://EshAhm/Fake-Citation-Detector-V2-scibert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EshAhm/Fake-Citation-Detector-V2-scibert/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 27e0bc5a53e7232f2fee2ff65995977a07f40ece05db697367a59a7f212f7a90
- Size of remote file:
- 5.27 kB
- SHA256:
- 5cb97128b6899d664a012ba7265fcb0c33d83a35bbdd68a2d530725a8514abf1
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