xtreme_s_w2v2_t5lephone-small_minds14.en-US
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the GOOGLE/XTREME_S - MINDS14.EN-US dataset. It achieves the following results on the evaluation set:
- Loss: 1.5203
- F1: 0.7526
- Accuracy: 0.7518
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 150.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 2.589 | 3.95 | 20 | 2.6401 | 0.0108 | 0.0816 |
| 2.5223 | 7.95 | 40 | 2.6493 | 0.0339 | 0.0816 |
| 2.5085 | 11.95 | 60 | 2.6236 | 0.0539 | 0.1028 |
| 2.1252 | 15.95 | 80 | 2.5006 | 0.1458 | 0.1667 |
| 1.3711 | 19.95 | 100 | 2.2712 | 0.2344 | 0.2837 |
| 1.5092 | 23.95 | 120 | 2.0599 | 0.3631 | 0.3936 |
| 0.4962 | 27.95 | 140 | 1.8475 | 0.4881 | 0.4894 |
| 0.4169 | 31.95 | 160 | 1.8262 | 0.5358 | 0.5142 |
| 0.1579 | 35.95 | 180 | 1.6481 | 0.5967 | 0.6028 |
| 0.0927 | 39.95 | 200 | 1.4470 | 0.6748 | 0.6560 |
| 0.1363 | 43.95 | 220 | 1.2725 | 0.6836 | 0.6879 |
| 0.1324 | 47.95 | 240 | 1.4330 | 0.6653 | 0.6702 |
| 0.0294 | 51.95 | 260 | 1.2978 | 0.7079 | 0.7163 |
| 0.0326 | 55.95 | 280 | 1.3869 | 0.6823 | 0.6879 |
| 0.0444 | 59.95 | 300 | 1.5764 | 0.7051 | 0.6986 |
| 0.0527 | 63.95 | 320 | 2.2013 | 0.5899 | 0.5851 |
| 0.1542 | 67.95 | 340 | 1.5203 | 0.7053 | 0.6986 |
| 0.0127 | 71.95 | 360 | 1.7149 | 0.7105 | 0.7128 |
| 0.0105 | 75.95 | 380 | 1.2471 | 0.7853 | 0.7837 |
| 0.009 | 79.95 | 400 | 1.5720 | 0.7065 | 0.7057 |
| 0.0081 | 83.95 | 420 | 1.9395 | 0.6656 | 0.6702 |
| 0.2345 | 87.95 | 440 | 1.5704 | 0.7408 | 0.7411 |
| 0.0076 | 91.95 | 460 | 1.4706 | 0.7554 | 0.7589 |
| 0.0064 | 95.95 | 480 | 1.5746 | 0.7491 | 0.7518 |
| 0.3105 | 99.95 | 500 | 1.6824 | 0.7273 | 0.7376 |
| 0.0058 | 103.95 | 520 | 1.3799 | 0.7474 | 0.7624 |
| 0.0055 | 107.95 | 540 | 1.4086 | 0.7350 | 0.7518 |
| 0.0051 | 111.95 | 560 | 1.2832 | 0.7874 | 0.7979 |
| 0.0052 | 115.95 | 580 | 1.3474 | 0.7752 | 0.7801 |
| 0.0046 | 119.95 | 600 | 1.6125 | 0.7451 | 0.7482 |
| 0.0044 | 123.95 | 620 | 1.5927 | 0.7486 | 0.7518 |
| 0.0044 | 127.95 | 640 | 1.5551 | 0.7487 | 0.7518 |
| 0.0041 | 131.95 | 660 | 1.5117 | 0.7631 | 0.7660 |
| 0.0041 | 135.95 | 680 | 1.5210 | 0.7577 | 0.7624 |
| 0.0041 | 139.95 | 700 | 1.5145 | 0.7655 | 0.7660 |
| 0.004 | 143.95 | 720 | 1.5053 | 0.7665 | 0.7660 |
| 0.004 | 147.95 | 740 | 1.5203 | 0.7526 | 0.7518 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1