Instructions to use whitedevil0089devil/Cyber_bot_squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whitedevil0089devil/Cyber_bot_squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="whitedevil0089devil/Cyber_bot_squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("whitedevil0089devil/Cyber_bot_squad2") model = AutoModelForSequenceClassification.from_pretrained("whitedevil0089devil/Cyber_bot_squad2") - 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": 0.450112528132033, | |
| "eval_steps": 150, | |
| "global_step": 600, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.0007501875468867217, | |
| "grad_norm": 27.303098678588867, | |
| "learning_rate": 0.0, | |
| "loss": 1.1906, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.037509377344336084, | |
| "grad_norm": 17.193105697631836, | |
| "learning_rate": 8.997188378631678e-07, | |
| "loss": 1.0245, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.07501875468867217, | |
| "grad_norm": 0.9530491232872009, | |
| "learning_rate": 1.8369259606373011e-06, | |
| "loss": 0.2468, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.11252813203300825, | |
| "grad_norm": 0.649541974067688, | |
| "learning_rate": 2.7741330834114345e-06, | |
| "loss": 0.0702, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 0.11252813203300825, | |
| "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": 0.038926418870687485, | |
| "eval_runtime": 35.4512, | |
| "eval_samples_per_second": 119.855, | |
| "eval_steps_per_second": 3.752, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 0.15003750937734434, | |
| "grad_norm": 0.23891082406044006, | |
| "learning_rate": 3.7113402061855674e-06, | |
| "loss": 0.0472, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 0.18754688672168043, | |
| "grad_norm": 1.910960078239441, | |
| "learning_rate": 4.6485473289597e-06, | |
| "loss": 0.0478, | |
| "step": 250 | |
| }, | |
| { | |
| "epoch": 0.2250562640660165, | |
| "grad_norm": 0.1880768984556198, | |
| "learning_rate": 5.585754451733834e-06, | |
| "loss": 0.0412, | |
| "step": 300 | |
| }, | |
| { | |
| "epoch": 0.2250562640660165, | |
| "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": 0.040430303663015366, | |
| "eval_runtime": 34.879, | |
| "eval_samples_per_second": 121.821, | |
| "eval_steps_per_second": 3.813, | |
| "step": 300 | |
| }, | |
| { | |
| "epoch": 0.2625656414103526, | |
| "grad_norm": 0.09085190296173096, | |
| "learning_rate": 6.522961574507966e-06, | |
| "loss": 0.04, | |
| "step": 350 | |
| }, | |
| { | |
| "epoch": 0.30007501875468867, | |
| "grad_norm": 0.05724379047751427, | |
| "learning_rate": 7.4601686972821e-06, | |
| "loss": 0.0287, | |
| "step": 400 | |
| }, | |
| { | |
| "epoch": 0.33758439609902474, | |
| "grad_norm": 0.2768031358718872, | |
| "learning_rate": 8.397375820056232e-06, | |
| "loss": 0.0374, | |
| "step": 450 | |
| }, | |
| { | |
| "epoch": 0.33758439609902474, | |
| "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": 0.033962853252887726, | |
| "eval_runtime": 34.8673, | |
| "eval_samples_per_second": 121.862, | |
| "eval_steps_per_second": 3.814, | |
| "step": 450 | |
| }, | |
| { | |
| "epoch": 0.37509377344336087, | |
| "grad_norm": 1.1712861061096191, | |
| "learning_rate": 9.334582942830366e-06, | |
| "loss": 0.0351, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.41260315078769694, | |
| "grad_norm": 0.24754193425178528, | |
| "learning_rate": 1.0271790065604499e-05, | |
| "loss": 0.042, | |
| "step": 550 | |
| }, | |
| { | |
| "epoch": 0.450112528132033, | |
| "grad_norm": 1.4196994304656982, | |
| "learning_rate": 1.1208997188378632e-05, | |
| "loss": 0.0544, | |
| "step": 600 | |
| }, | |
| { | |
| "epoch": 0.450112528132033, | |
| "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": 0.03701222687959671, | |
| "eval_runtime": 35.0215, | |
| "eval_samples_per_second": 121.325, | |
| "eval_steps_per_second": 3.798, | |
| "step": 600 | |
| } | |
| ], | |
| "logging_steps": 50, | |
| "max_steps": 10664, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 8, | |
| "save_steps": 150, | |
| "stateful_callbacks": { | |
| "EarlyStoppingCallback": { | |
| "args": { | |
| "early_stopping_patience": 4, | |
| "early_stopping_threshold": 0.0005 | |
| }, | |
| "attributes": { | |
| "early_stopping_patience_counter": 3 | |
| } | |
| }, | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 1.01037366706176e+16, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |