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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b124b966837b7944f1a562a848b686a297c10dc8eabffc0ae9622abf64cbb8b9
- Size of remote file:
- 5.18 kB
- SHA256:
- 8fbf8840c452a16e095495499bc8bbbf42493da6ca534687064110197a66d60e
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