Instructions to use khs1218/checkpoint-150 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khs1218/checkpoint-150 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="khs1218/checkpoint-150")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("khs1218/checkpoint-150") model = AutoModelForAudioClassification.from_pretrained("khs1218/checkpoint-150") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_global_step": 150, | |
| "best_metric": 0.5294117647058824, | |
| "best_model_checkpoint": "./result/audio_cls1/checkpoint-150", | |
| "epoch": 10.0, | |
| "eval_steps": 500, | |
| "global_step": 150, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.09243697478991597, | |
| "eval_loss": 2.6456968784332275, | |
| "eval_runtime": 11.0273, | |
| "eval_samples_per_second": 10.791, | |
| "eval_steps_per_second": 1.36, | |
| "step": 15 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.13445378151260504, | |
| "eval_loss": 2.597277879714966, | |
| "eval_runtime": 10.8643, | |
| "eval_samples_per_second": 10.953, | |
| "eval_steps_per_second": 1.381, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.24369747899159663, | |
| "eval_loss": 2.5225844383239746, | |
| "eval_runtime": 10.8906, | |
| "eval_samples_per_second": 10.927, | |
| "eval_steps_per_second": 1.377, | |
| "step": 45 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "eval_accuracy": 0.2773109243697479, | |
| "eval_loss": 2.4095823764801025, | |
| "eval_runtime": 10.8614, | |
| "eval_samples_per_second": 10.956, | |
| "eval_steps_per_second": 1.381, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.36134453781512604, | |
| "eval_loss": 2.2868471145629883, | |
| "eval_runtime": 10.7023, | |
| "eval_samples_per_second": 11.119, | |
| "eval_steps_per_second": 1.402, | |
| "step": 75 | |
| }, | |
| { | |
| "epoch": 6.0, | |
| "eval_accuracy": 0.42016806722689076, | |
| "eval_loss": 2.189913034439087, | |
| "eval_runtime": 10.7854, | |
| "eval_samples_per_second": 11.033, | |
| "eval_steps_per_second": 1.391, | |
| "step": 90 | |
| }, | |
| { | |
| "epoch": 7.0, | |
| "eval_accuracy": 0.44537815126050423, | |
| "eval_loss": 2.121011972427368, | |
| "eval_runtime": 10.7606, | |
| "eval_samples_per_second": 11.059, | |
| "eval_steps_per_second": 1.394, | |
| "step": 105 | |
| }, | |
| { | |
| "epoch": 8.0, | |
| "eval_accuracy": 0.453781512605042, | |
| "eval_loss": 2.0561814308166504, | |
| "eval_runtime": 10.5823, | |
| "eval_samples_per_second": 11.245, | |
| "eval_steps_per_second": 1.417, | |
| "step": 120 | |
| }, | |
| { | |
| "epoch": 9.0, | |
| "eval_accuracy": 0.5126050420168067, | |
| "eval_loss": 2.0251529216766357, | |
| "eval_runtime": 10.5788, | |
| "eval_samples_per_second": 11.249, | |
| "eval_steps_per_second": 1.418, | |
| "step": 135 | |
| }, | |
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.5294117647058824, | |
| "eval_loss": 2.0155346393585205, | |
| "eval_runtime": 10.6039, | |
| "eval_samples_per_second": 11.222, | |
| "eval_steps_per_second": 1.415, | |
| "step": 150 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 150, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 10, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 3.4226545962877677e+17, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |