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:
- 97c37ebbce21b6b994cbc801791b57b61800e2c74c26f60f607c235a289058a2
- Size of remote file:
- 3.9 kB
- SHA256:
- 44fd55eb52ea09651a394f3a3ce0d81c859e25ede1542ca3ed7f9172b38d7b25
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