Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use hyunkookim/roberta-base-klue-ynat-classification-using-hg_api-epoch_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hyunkookim/roberta-base-klue-ynat-classification-using-hg_api-epoch_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hyunkookim/roberta-base-klue-ynat-classification-using-hg_api-epoch_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hyunkookim/roberta-base-klue-ynat-classification-using-hg_api-epoch_2") model = AutoModelForSequenceClassification.from_pretrained("hyunkookim/roberta-base-klue-ynat-classification-using-hg_api-epoch_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b54e2aee34f39927a77857647aeede83c399dba333c291497d41f7ceaa72c7b
- Size of remote file:
- 443 MB
- SHA256:
- b4f3bc912d5db85160d78dbbb7dc08d024ae76c985708fbac4f8f4014740e3fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.