Instructions to use karths/binary_classification_train_automation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_automation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_automation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_automation") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_automation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-695/trainer_state.json from karths/binary_classification_train_automation: direct link, hf CLI and curl.
- Browser
- Download file 533 Bytes
-
https://huggingface.co/karths/binary_classification_train_automation/resolve/main/checkpoint-695/trainer_state.json
- Command line
-
hf download hf://karths/binary_classification_train_automation/checkpoint-695/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/karths/binary_classification_train_automation/resolve/main/checkpoint-695/trainer_state.json
533 Bytes
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 5.0, | |
| "eval_steps": 500, | |
| "global_step": 695, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 3.6, | |
| "learning_rate": 4.9800000000000004e-05, | |
| "loss": 0.0296, | |
| "step": 500 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 695, | |
| "num_train_epochs": 5, | |
| "save_steps": 500, | |
| "total_flos": 5876253804380160.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |