Instructions to use bdpc/test_twowayloss_implementation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/test_twowayloss_implementation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/test_twowayloss_implementation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/test_twowayloss_implementation") model = AutoModelForSequenceClassification.from_pretrained("bdpc/test_twowayloss_implementation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 046734471f04df7a559032fc435cbcc04e50744b4bcf9a105197d8e5b4e48f87
- Size of remote file:
- 438 MB
- SHA256:
- 29130670126c2a2cdcb027a0ee715e650fb5bdf6e33246b9d381d6528bba3788
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