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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ksgr5566/demo_classification") model = AutoModelForSequenceClassification.from_pretrained("ksgr5566/demo_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload metrics.json with huggingface_hub
Browse files- metrics.json +1 -1
metrics.json
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{"test_loss": 0.
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{"test_loss": 0.6965702772140503, "test_accuracy": 0.5, "test_f1": 0.6666666666666666, "test_precision": 0.5, "test_recall": 1.0, "test_runtime": 2.7372, "test_samples_per_second": 3.653, "test_steps_per_second": 3.653}
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