Instructions to use Indramal/Calender-Event-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Indramal/Calender-Event-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Indramal/Calender-Event-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Indramal/Calender-Event-Classification") model = AutoModelForSequenceClassification.from_pretrained("Indramal/Calender-Event-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.681374192237854, | |
| "best_model_checkpoint": "event_cat/checkpoint-30", | |
| "epoch": 1.0, | |
| "global_step": 30, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.92, | |
| "eval_loss": 0.681374192237854, | |
| "eval_runtime": 0.3288, | |
| "eval_samples_per_second": 152.084, | |
| "eval_steps_per_second": 12.167, | |
| "step": 30 | |
| } | |
| ], | |
| "max_steps": 600, | |
| "num_train_epochs": 20, | |
| "total_flos": 28528168388886.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |