Instructions to use Deadbody42/Qna_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deadbody42/Qna_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Deadbody42/Qna_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Deadbody42/Qna_classifier") model = AutoModelForSequenceClassification.from_pretrained("Deadbody42/Qna_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Deadbody42/Qna_classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Deadbody42/Qna_classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://Deadbody42/Qna_classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Deadbody42/Qna_classifier/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 780230a9be9a15e9985e9877965813488b5f88acf2dcadd021f83e0e7dd7769d
- Size of remote file:
- 5.3 kB
- SHA256:
- 2953b41cf8b2b6ddce579c374cf415b924d003f2ef76bda1c5dc30e3357c4be8
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