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:
- 880189414aa9008d747dabc7152eb4af296e4aa833389a5a1b5f61d006d65a10
- Size of remote file:
- 3.9 kB
- SHA256:
- 7d83f45d8599c42eb99699532982a4da788a47d0733d7fd36af302345552c5aa
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