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") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2298d6777535c5ad26680fb06388e676a76c88655834b4947b34e74c63e377bd
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size 497853980
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