Instructions to use bright1/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bright1/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bright1/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bright1/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("bright1/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e8c3bd8d57d879ab422803ae9ae95555dc0fa4cc28c33800d4eba40917ed7f7
- Size of remote file:
- 3.58 kB
- SHA256:
- 18b0f8e4458f6b2f94f06ca4d00211549f75d4d126032c82abebd7f29cb7221e
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