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:
- 4b806970d14b56f03ff8bbdf7c472eb17781f5cb2f81f4171eb3292835e2161b
- Size of remote file:
- 268 MB
- SHA256:
- 0f32b8303d84e7cf8ade9b8f961e8fb66e298b3bf1cbd6fe7bc2887e1a1469f1
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