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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DeadBeast/korscm-mBERT") model = AutoModelForSequenceClassification.from_pretrained("DeadBeast/korscm-mBERT") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Error:"language" with value "korean" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
Korean-mBERT
This model is a fine-tune checkpoint of mBERT-base-cased over Hugging Face Kore_Scm dataset for Text classification.
How to use?
Task: binary-classification
- LABEL_1: Sarcasm (Sarcasm means tweets contains sarcasm)
- LABEL_0: Not Sarcasm (Not Sarcasm means tweets do not contain sarcasm)
Click on Use in Transformers!
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