Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use junzai/demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junzai/demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="junzai/demo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("junzai/demo") model = AutoModelForSequenceClassification.from_pretrained("junzai/demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 308 Bytes
450bcb1 | 1 2 3 4 5 6 7 8 9 10 11 | {
"epoch": 1.0,
"eval_accuracy": 0.8284313725490197,
"eval_combined_score": 0.8550940646528882,
"eval_f1": 0.8817567567567567,
"eval_loss": 0.4022885262966156,
"eval_runtime": 48.0829,
"eval_samples": 408,
"eval_samples_per_second": 8.485,
"eval_steps_per_second": 1.061
} |