Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Wing4/roberta-base-klue-ynat-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wing4/roberta-base-klue-ynat-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wing4/roberta-base-klue-ynat-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Wing4/roberta-base-klue-ynat-classification") model = AutoModelForSequenceClassification.from_pretrained("Wing4/roberta-base-klue-ynat-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Wing4/roberta-base-klue-ynat-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Wing4/roberta-base-klue-ynat-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://Wing4/roberta-base-klue-ynat-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Wing4/roberta-base-klue-ynat-classification/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- cbc68c841f89458dbbeee7b3006a81e858852ea00049f975489e78154426c5bd
- Size of remote file:
- 5.43 kB
- SHA256:
- e8d520faf56c82a49f7be265459c47e36022381835aa6740ee8291c5bb8b28d1
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