Instructions to use tiya1012/swmh4_mtb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/swmh4_mtb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/swmh4_mtb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/swmh4_mtb") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/swmh4_mtb") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 128ff31153fc50ba33e34083eae517097ec1fca588a2cddb980fcdc84aa77d24
- Size of remote file:
- 438 MB
- SHA256:
- 1c2f23d5737e114da8eb1d1da977c2d32f2b2daa13f330b0e2914de475f31cae
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