Instructions to use snunlp/KR-FinBert-SC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use snunlp/KR-FinBert-SC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="snunlp/KR-FinBert-SC")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("snunlp/KR-FinBert-SC") model = AutoModelForSequenceClassification.from_pretrained("snunlp/KR-FinBert-SC", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from snunlp/KR-FinBert-SC: direct link, hf CLI and curl.
- Browser
- Download file 406 MB
-
https://huggingface.co/snunlp/KR-FinBert-SC/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://snunlp/KR-FinBert-SC/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/snunlp/KR-FinBert-SC/resolve/main/pytorch_model.bin
406 MB
- Xet hash:
- b3477b0563c3803d49b81ee05c31dffde765897854491d4f4a7220a62ed1e598
- Size of remote file:
- 406 MB
- SHA256:
- d335a3aeee23df209a2c828ddb2bf16342127b77e54e51a67012c902a21e7b14
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