Instructions to use whitedevil0089devil/Cyber_bot_Squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whitedevil0089devil/Cyber_bot_Squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="whitedevil0089devil/Cyber_bot_Squad")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("whitedevil0089devil/Cyber_bot_Squad") model = AutoModelForSequenceClassification.from_pretrained("whitedevil0089devil/Cyber_bot_Squad") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_global_step": 150, | |
| "best_metric": 0.9915354168771638, | |
| "best_model_checkpoint": "/content/drive/MyDrive/model/Roberta_Squad/roberta_Squad/checkpoint-150", | |
| "epoch": 0.028134671293257058, | |
| "eval_steps": 150, | |
| "global_step": 150, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.00018756447528838038, | |
| "grad_norm": 36.069454193115234, | |
| "learning_rate": 0.0, | |
| "loss": 2.0268, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.00937822376441902, | |
| "grad_norm": 36.57353591918945, | |
| "learning_rate": 1.5000000000000002e-07, | |
| "loss": 1.9985, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.01875644752883804, | |
| "grad_norm": 32.89529800415039, | |
| "learning_rate": 3.03125e-07, | |
| "loss": 1.7123, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.028134671293257058, | |
| "grad_norm": 23.408254623413086, | |
| "learning_rate": 4.5937500000000005e-07, | |
| "loss": 1.0907, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 0.028134671293257058, | |
| "eval_accuracy": 0.9943516121440339, | |
| "eval_f1_macro": 0.24929195185272598, | |
| "eval_f1_min": 0.0, | |
| "eval_f1_std": 0.4317863265269357, | |
| "eval_f1_weighted": 0.9915354168771638, | |
| "eval_loss": 0.6054947972297668, | |
| "eval_runtime": 36.4079, | |
| "eval_samples_per_second": 116.705, | |
| "eval_steps_per_second": 14.612, | |
| "step": 150 | |
| } | |
| ], | |
| "logging_steps": 50, | |
| "max_steps": 31992, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 6, | |
| "save_steps": 150, | |
| "stateful_callbacks": { | |
| "EarlyStoppingCallback": { | |
| "args": { | |
| "early_stopping_patience": 4, | |
| "early_stopping_threshold": 0.0005 | |
| }, | |
| "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": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |