Instructions to use Devishetty100/redmoon-gibberishdetective with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devishetty100/redmoon-gibberishdetective with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Devishetty100/redmoon-gibberishdetective")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Devishetty100/redmoon-gibberishdetective") model = AutoModelForSequenceClassification.from_pretrained("Devishetty100/redmoon-gibberishdetective") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 2.6642088414519094e-05, | |
| "best_model_checkpoint": "redmoon-gibberishdetective/checkpoint-95", | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 95, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 1.0, | |
| "eval_loss": 2.6642088414519094e-05, | |
| "eval_runtime": 0.6769, | |
| "eval_samples_per_second": 1112.483, | |
| "eval_steps_per_second": 35.458, | |
| "step": 95 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 285, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 99747951187968.0, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |