Instructions to use karths/binary_classification_train_process with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_process with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_process")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_process") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_process") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 5.0, | |
| "eval_steps": 500, | |
| "global_step": 1970, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.27, | |
| "learning_rate": 4.9800000000000004e-05, | |
| "loss": 0.037, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 2.54, | |
| "learning_rate": 3.717472061010918e-05, | |
| "loss": 0.056, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 3.81, | |
| "learning_rate": 1.1721825255787072e-05, | |
| "loss": 0.0333, | |
| "step": 1500 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 1970, | |
| "num_train_epochs": 5, | |
| "save_steps": 500, | |
| "total_flos": 1.66809571757568e+16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |