Instructions to use vietdata/vietnamese-content-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vietdata/vietnamese-content-cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vietdata/vietnamese-content-cls")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vietdata/vietnamese-content-cls") model = AutoModelForSequenceClassification.from_pretrained("vietdata/vietnamese-content-cls") - Notebooks
- Google Colab
- Kaggle
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README.md
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This is a fine-tuned version of NlpHUST/vibert4news-base-cased model for news genre classification. The training data is 3864 vietnamese news summaries in WikiMulti dataset.
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<b>Labels</b>:
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0-> Agriculture, food, and drink
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1-> Albums
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This is a fine-tuned version of NlpHUST/vibert4news-base-cased model for news genre classification. The training data is 3864 vietnamese news summaries in WikiMulti dataset.
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<b>Labels</b>:
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0-> Agriculture, food, and drink
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1-> Albums
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