Instructions to use whitedevil0089devil/cyber_bot1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whitedevil0089devil/cyber_bot1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="whitedevil0089devil/cyber_bot1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("whitedevil0089devil/cyber_bot1") model = AutoModelForSequenceClassification.from_pretrained("whitedevil0089devil/cyber_bot1") - Notebooks
- Google Colab
- Kaggle
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| { | |
| "best_global_step": 200, | |
| "best_metric": 0.9943516121440339, | |
| "best_model_checkpoint": "/content/drive/MyDrive/model/roberta_model/checkpoint-200", | |
| "epoch": 0.041084634346754315, | |
| "eval_steps": 200, | |
| "global_step": 200, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.00020542317173377156, | |
| "grad_norm": Infinity, | |
| "learning_rate": 0.0, | |
| "loss": 1.5291, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.020542317173377157, | |
| "grad_norm": 8.729037284851074, | |
| "learning_rate": 1.3278576317590692e-06, | |
| "loss": 1.4449, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.041084634346754315, | |
| "grad_norm": 0.5065429210662842, | |
| "learning_rate": 2.69678302532512e-06, | |
| "loss": 0.4001, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 0.041084634346754315, | |
| "eval_accuracy": 0.9943516121440339, | |
| "eval_f1_macro": 0.24929195185272598, | |
| "eval_f1_weighted": 0.9915354168771638, | |
| "eval_loss": 0.047249794006347656, | |
| "eval_runtime": 27.6034, | |
| "eval_samples_per_second": 153.93, | |
| "eval_steps_per_second": 9.636, | |
| "step": 200 | |
| } | |
| ], | |
| "logging_steps": 100, | |
| "max_steps": 14604, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 200, | |
| "stateful_callbacks": { | |
| "EarlyStoppingCallback": { | |
| "args": { | |
| "early_stopping_patience": 3, | |
| "early_stopping_threshold": 0.001 | |
| }, | |
| "attributes": { | |
| "early_stopping_patience_counter": 0 | |
| } | |
| }, | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 631483541913600.0, | |
| "train_batch_size": 16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |