Instructions to use breadlicker45/test-class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use breadlicker45/test-class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="breadlicker45/test-class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("breadlicker45/test-class") model = AutoModelForSequenceClassification.from_pretrained("breadlicker45/test-class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6754c50dcb0be645d2ea0fa4b49cb95dd47f2f2344822abd80cd91fc7af8d5ee
- Size of remote file:
- 268 MB
- SHA256:
- 78c9671961067a29bbe9940cb875b56190a5b83262b5f25b1fbe9bebc0891ebd
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