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:
- b78979dc449d96381266f02e20baffe73a7e30e4e141162e2412cf95de3f731f
- Size of remote file:
- 819 Bytes
- SHA256:
- bfbea48ee125cf583cb4787f9e8e0800324b1332f7c6549fdf4413b4a73c8a5c
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