Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use pepppper/roberta-base-klue-ynat-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pepppper/roberta-base-klue-ynat-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pepppper/roberta-base-klue-ynat-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pepppper/roberta-base-klue-ynat-classification") model = AutoModelForSequenceClassification.from_pretrained("pepppper/roberta-base-klue-ynat-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pepppper/roberta-base-klue-ynat-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/pepppper/roberta-base-klue-ynat-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://pepppper/roberta-base-klue-ynat-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pepppper/roberta-base-klue-ynat-classification/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 86b0619c37946efca923a4102ca819ef09b351aa741ea909c17e802165217287
- Size of remote file:
- 5.43 kB
- SHA256:
- 1c65e38607fd6427ad7a4aaca0a476d2018a8e979f22b1c11690a31c60938fc7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.