Instructions to use whitedevil0089devil/Cyber_Bot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whitedevil0089devil/Cyber_Bot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="whitedevil0089devil/Cyber_Bot")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("whitedevil0089devil/Cyber_Bot") model = AutoModelForSequenceClassification.from_pretrained("whitedevil0089devil/Cyber_Bot") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_global_step": 200, | |
| "best_metric": 0.9943516121440339, | |
| "best_model_checkpoint": "/content/drive/MyDrive/model/roberta_model/checkpoint-200", | |
| "epoch": 0.09128251939753537, | |
| "eval_steps": 200, | |
| "global_step": 200, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.00045641259698767686, | |
| "grad_norm": 10.173584938049316, | |
| "learning_rate": 0.0, | |
| "loss": 1.9445, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.045641259698767686, | |
| "grad_norm": 1.0232760906219482, | |
| "learning_rate": 5.5872291904218926e-06, | |
| "loss": 1.2613, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.09128251939753537, | |
| "grad_norm": 0.08294256031513214, | |
| "learning_rate": 1.1288483466362599e-05, | |
| "loss": 0.0782, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 0.09128251939753537, | |
| "eval_accuracy": 0.9943516121440339, | |
| "eval_f1_macro": 0.24929195185272598, | |
| "eval_f1_weighted": 0.9915354168771638, | |
| "eval_loss": 0.041581232100725174, | |
| "eval_runtime": 27.4281, | |
| "eval_samples_per_second": 154.914, | |
| "eval_steps_per_second": 9.698, | |
| "step": 200 | |
| } | |
| ], | |
| "logging_steps": 100, | |
| "max_steps": 8764, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 4, | |
| "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 | |
| } | |