Instructions to use ksgr5566/demo_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ksgr5566/demo_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ksgr5566/demo_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ksgr5566/demo_classification") model = AutoModelForSequenceClassification.from_pretrained("ksgr5566/demo_classification") - Notebooks
- Google Colab
- Kaggle
Upload metrics.json with huggingface_hub
Browse files- metrics.json +1 -0
metrics.json
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{"test_loss": 0.30951207876205444, "test_accuracy": 0.9, "test_f1": 0.888888888888889, "test_precision": 1.0, "test_recall": 0.8, "test_runtime": 0.1994, "test_samples_per_second": 50.158, "test_steps_per_second": 50.158}
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