Instructions to use avacaondata/maria-exist22-task1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avacaondata/maria-exist22-task1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avacaondata/maria-exist22-task1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avacaondata/maria-exist22-task1") model = AutoModelForSequenceClassification.from_pretrained("avacaondata/maria-exist22-task1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.8825914161641272, | |
| "best_model_checkpoint": "/mnt/d/models_iberlef22/exist22/task1/models_es/retrained_BSC-TeMU/checkpoint-322", | |
| "epoch": 3.0, | |
| "global_step": 483, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "learning_rate": 5.2402087303147016e-05, | |
| "loss": 0.3659, | |
| "step": 161 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_f1-score": 0.8762599995046685, | |
| "eval_loss": 0.32265210151672363, | |
| "eval_precision": 0.8820436657141424, | |
| "eval_recall": 0.874809413732048, | |
| "eval_runtime": 0.7263, | |
| "eval_samples_per_second": 786.163, | |
| "eval_steps_per_second": 12.391, | |
| "eval_support": 571, | |
| "step": 161 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "learning_rate": 2.6201043651573508e-05, | |
| "loss": 0.1328, | |
| "step": 322 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_f1-score": 0.8825914161641272, | |
| "eval_loss": 0.3923543691635132, | |
| "eval_precision": 0.8826217642007116, | |
| "eval_recall": 0.8834780149518001, | |
| "eval_runtime": 0.7091, | |
| "eval_samples_per_second": 805.279, | |
| "eval_steps_per_second": 12.693, | |
| "eval_support": 571, | |
| "step": 322 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "learning_rate": 1.627394015625684e-07, | |
| "loss": 0.026, | |
| "step": 483 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_f1-score": 0.8790866823182801, | |
| "eval_loss": 0.5471494793891907, | |
| "eval_precision": 0.8791191264875475, | |
| "eval_recall": 0.8799675388550069, | |
| "eval_runtime": 0.7205, | |
| "eval_samples_per_second": 792.548, | |
| "eval_steps_per_second": 12.492, | |
| "eval_support": 571, | |
| "step": 483 | |
| } | |
| ], | |
| "max_steps": 483, | |
| "num_train_epochs": 3, | |
| "total_flos": 1012319785497600.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |