Instructions to use DeadBeast/korscm-mBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeadBeast/korscm-mBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DeadBeast/korscm-mBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DeadBeast/korscm-mBERT") model = AutoModelForSequenceClassification.from_pretrained("DeadBeast/korscm-mBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from DeadBeast/korscm-mBERT: direct link, hf CLI and curl.
- Browser
- Download file 1.96 MB
-
https://huggingface.co/DeadBeast/korscm-mBERT/resolve/main/tokenizer.json
- Command line
-
hf download hf://DeadBeast/korscm-mBERT/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/DeadBeast/korscm-mBERT/resolve/main/tokenizer.json
1.96 MB
File too large to display, you can check the raw version instead.