Text Classification
Transformers
Safetensors
Korean
English
roberta
korean
decision-model
system-one
typed-decisions
choice
noul
score
classification
reranker
zero-shot-classification
nli
klue
kobest
relation-extraction
calibration
text-embeddings-inference
Instructions to use mmetamong/ko-decision-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmetamong/ko-decision-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mmetamong/ko-decision-roberta-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mmetamong/ko-decision-roberta-large") model = AutoModelForSequenceClassification.from_pretrained("mmetamong/ko-decision-roberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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