Instructions to use dinhhung1508/vit5-base-test-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dinhhung1508/vit5-base-test-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dinhhung1508/vit5-base-test-text-classification", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dinhhung1508/vit5-base-test-text-classification") model = AutoModelForSequenceClassification.from_pretrained("dinhhung1508/vit5-base-test-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb0d3d5a000bebd114c8f0a77a8cf86ed5c7f6a5f02fd93923b1b5addf2af1d0
- Size of remote file:
- 820 kB
- SHA256:
- 59986b62f9f0b90edafb9b073ea7b93d21114a5841219a1ea2399ade73f729c6
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