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:
- fa924085ecca42927e227198edd3bdf198c4370f7cdc8b5c7d930ca91d66a0db
- Size of remote file:
- 204 MB
- SHA256:
- 5283fdd81ffd1e2e6f8124726bc82cd1c2e1308ca149e396492cc4d27174c486
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