Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use gigauser/kcbert_nsmc_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gigauser/kcbert_nsmc_tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gigauser/kcbert_nsmc_tuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gigauser/kcbert_nsmc_tuning") model = AutoModelForSequenceClassification.from_pretrained("gigauser/kcbert_nsmc_tuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gigauser/kcbert_nsmc_tuning: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/gigauser/kcbert_nsmc_tuning/resolve/main/training_args.bin
- Command line
-
hf download hf://gigauser/kcbert_nsmc_tuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gigauser/kcbert_nsmc_tuning/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 8d9d0e7851d466329f76b1d1b49032365626e7c7674685c0c749493104d53621
- Size of remote file:
- 5.18 kB
- SHA256:
- 493720855fd0ecab33454a43891957666a575437f47ee5e2c2ddf1309637c534
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