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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("junzai/demo") model = AutoModelForSequenceClassification.from_pretrained("junzai/demo") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.0, | |
| "global_step": 459, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "step": 459, | |
| "total_flos": 241272837765120.0, | |
| "train_loss": 0.5285058779913875, | |
| "train_runtime": 1821.1561, | |
| "train_samples_per_second": 2.014, | |
| "train_steps_per_second": 0.252 | |
| } | |
| ], | |
| "max_steps": 459, | |
| "num_train_epochs": 1, | |
| "total_flos": 241272837765120.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |