Instructions to use Mitradn/all-3data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mitradn/all-3data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Mitradn/all-3data")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Mitradn/all-3data") model = AutoModelForMultimodalLM.from_pretrained("Mitradn/all-3data") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 1.9628836545324768, | |
| "best_model_checkpoint": "./3d-whisper-finetune/checkpoint-200", | |
| "epoch": 0.7322654462242563, | |
| "eval_steps": 25, | |
| "global_step": 200, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.09, | |
| "learning_rate": 4.000000000000001e-06, | |
| "loss": 5.9984, | |
| "step": 25 | |
| }, | |
| { | |
| "epoch": 0.09, | |
| "eval_loss": 3.8559463024139404, | |
| "eval_runtime": 636.0081, | |
| "eval_samples_per_second": 0.821, | |
| "eval_steps_per_second": 0.41, | |
| "eval_wer": 75.05353319057816, | |
| "step": 25 | |
| }, | |
| { | |
| "epoch": 0.18, | |
| "learning_rate": 9e-06, | |
| "loss": 2.6581, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.18, | |
| "eval_loss": 1.592560887336731, | |
| "eval_runtime": 601.8767, | |
| "eval_samples_per_second": 0.867, | |
| "eval_steps_per_second": 0.434, | |
| "eval_wer": 6.138472519628837, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.27, | |
| "learning_rate": 9.200000000000002e-06, | |
| "loss": 1.2735, | |
| "step": 75 | |
| }, | |
| { | |
| "epoch": 0.27, | |
| "eval_loss": 1.0036380290985107, | |
| "eval_runtime": 570.3747, | |
| "eval_samples_per_second": 0.915, | |
| "eval_steps_per_second": 0.458, | |
| "eval_wer": 4.211277658815132, | |
| "step": 75 | |
| }, | |
| { | |
| "epoch": 0.37, | |
| "learning_rate": 8.2e-06, | |
| "loss": 0.6543, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.37, | |
| "eval_loss": 0.5222607851028442, | |
| "eval_runtime": 637.2958, | |
| "eval_samples_per_second": 0.819, | |
| "eval_steps_per_second": 0.41, | |
| "eval_wer": 9.243397573162026, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.46, | |
| "learning_rate": 7.2000000000000005e-06, | |
| "loss": 0.2469, | |
| "step": 125 | |
| }, | |
| { | |
| "epoch": 0.46, | |
| "eval_loss": 0.1941457986831665, | |
| "eval_runtime": 586.186, | |
| "eval_samples_per_second": 0.891, | |
| "eval_steps_per_second": 0.445, | |
| "eval_wer": 13.49036402569593, | |
| "step": 125 | |
| }, | |
| { | |
| "epoch": 0.55, | |
| "learning_rate": 6.200000000000001e-06, | |
| "loss": 0.1311, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 0.55, | |
| "eval_loss": 0.15886355936527252, | |
| "eval_runtime": 608.108, | |
| "eval_samples_per_second": 0.858, | |
| "eval_steps_per_second": 0.429, | |
| "eval_wer": 3.675945753033547, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 0.64, | |
| "learning_rate": 5.2e-06, | |
| "loss": 0.101, | |
| "step": 175 | |
| }, | |
| { | |
| "epoch": 0.64, | |
| "eval_loss": 0.16113096475601196, | |
| "eval_runtime": 582.3936, | |
| "eval_samples_per_second": 0.896, | |
| "eval_steps_per_second": 0.448, | |
| "eval_wer": 1.9985724482512492, | |
| "step": 175 | |
| }, | |
| { | |
| "epoch": 0.73, | |
| "learning_rate": 4.2000000000000004e-06, | |
| "loss": 0.0586, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 0.73, | |
| "eval_loss": 0.11285543441772461, | |
| "eval_runtime": 573.3964, | |
| "eval_samples_per_second": 0.91, | |
| "eval_steps_per_second": 0.455, | |
| "eval_wer": 1.9628836545324768, | |
| "step": 200 | |
| } | |
| ], | |
| "logging_steps": 25, | |
| "max_steps": 300, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 2, | |
| "save_steps": 50, | |
| "total_flos": 3.265935704064e+18, | |
| "train_batch_size": 2, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |