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
Download eval_results.json from junzai/demo: direct link, hf CLI and curl.
- Browser
- Download file 308 Bytes
-
https://huggingface.co/junzai/demo/resolve/main/eval_results.json
- Command line
-
hf download hf://junzai/demo/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/junzai/demo/resolve/main/eval_results.json
308 Bytes
| { | |
| "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 | |
| } |