Instructions to use JLake310/kobart-base-topic-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JLake310/kobart-base-topic-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JLake310/kobart-base-topic-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JLake310/kobart-base-topic-classification") model = AutoModelForSequenceClassification.from_pretrained("JLake310/kobart-base-topic-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3936d32920f2a997940f49e92be7b0a9b2128b60ae710c8ca5138169554aee49
- Size of remote file:
- 498 MB
- SHA256:
- e38502de6eedae7e5428c2e75eb33ab9887ce6d576b279a5c8594c669f290b77
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