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:
- 2649b649e2376b2bc2724c3f3e72e32049168d8683e0e943fc4f8ad4650eb5c0
- Size of remote file:
- 268 MB
- SHA256:
- e702b4dd8c5520240e6cdc3e642480bd0656236a12234f71d4ed93cfd0c8be25
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