File size: 5,369 Bytes
49583eb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
---
tags:
- generated_from_trainer
model-index:
- name: aradia-ctc-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# aradia-ctc-v1
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7171
- Wer: 0.3331
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.22 | 100 | 5.1889 | 1.0 |
| No log | 0.43 | 200 | 3.1129 | 1.0 |
| No log | 0.65 | 300 | 3.0503 | 1.0 |
| No log | 0.87 | 400 | 3.0279 | 1.0 |
| 6.2756 | 1.09 | 500 | 2.9965 | 1.0 |
| 6.2756 | 1.3 | 600 | 2.3618 | 0.9993 |
| 6.2756 | 1.52 | 700 | 1.2715 | 0.8758 |
| 6.2756 | 1.74 | 800 | 0.9971 | 0.7156 |
| 6.2756 | 1.96 | 900 | 0.8927 | 0.6382 |
| 1.712 | 2.17 | 1000 | 0.8252 | 0.5926 |
| 1.712 | 2.39 | 1100 | 0.7794 | 0.5434 |
| 1.712 | 2.61 | 1200 | 0.7557 | 0.5092 |
| 1.712 | 2.83 | 1300 | 0.7347 | 0.5203 |
| 1.712 | 3.04 | 1400 | 0.7189 | 0.4929 |
| 0.9305 | 3.26 | 1500 | 0.6820 | 0.4595 |
| 0.9305 | 3.48 | 1600 | 0.6792 | 0.4504 |
| 0.9305 | 3.69 | 1700 | 0.6596 | 0.4442 |
| 0.9305 | 3.91 | 1800 | 0.6756 | 0.4432 |
| 0.9305 | 4.13 | 1900 | 0.6663 | 0.4392 |
| 0.737 | 4.35 | 2000 | 0.6479 | 0.4372 |
| 0.737 | 4.56 | 2100 | 0.6353 | 0.4203 |
| 0.737 | 4.78 | 2200 | 0.6251 | 0.4088 |
| 0.737 | 5.0 | 2300 | 0.6209 | 0.4177 |
| 0.737 | 5.22 | 2400 | 0.6639 | 0.4094 |
| 0.6247 | 5.43 | 2500 | 0.6408 | 0.3970 |
| 0.6247 | 5.65 | 2600 | 0.6373 | 0.3932 |
| 0.6247 | 5.87 | 2700 | 0.6411 | 0.3928 |
| 0.6247 | 6.09 | 2800 | 0.6378 | 0.3897 |
| 0.6247 | 6.3 | 2900 | 0.6396 | 0.3929 |
| 0.5443 | 6.52 | 3000 | 0.6544 | 0.3864 |
| 0.5443 | 6.74 | 3100 | 0.6218 | 0.3786 |
| 0.5443 | 6.96 | 3200 | 0.6200 | 0.3784 |
| 0.5443 | 7.17 | 3300 | 0.6157 | 0.3791 |
| 0.5443 | 7.39 | 3400 | 0.6317 | 0.3798 |
| 0.4845 | 7.61 | 3500 | 0.6540 | 0.3771 |
| 0.4845 | 7.83 | 3600 | 0.6436 | 0.3670 |
| 0.4845 | 8.04 | 3700 | 0.6335 | 0.3695 |
| 0.4845 | 8.26 | 3800 | 0.6579 | 0.3610 |
| 0.4845 | 8.48 | 3900 | 0.6170 | 0.3613 |
| 0.4279 | 8.69 | 4000 | 0.6523 | 0.3617 |
| 0.4279 | 8.91 | 4100 | 0.6349 | 0.3577 |
| 0.4279 | 9.13 | 4200 | 0.6344 | 0.3673 |
| 0.4279 | 9.35 | 4300 | 0.6215 | 0.3641 |
| 0.4279 | 9.56 | 4400 | 0.6513 | 0.3608 |
| 0.3825 | 9.78 | 4500 | 0.6386 | 0.3605 |
| 0.3825 | 10.0 | 4600 | 0.6724 | 0.3549 |
| 0.3825 | 10.22 | 4700 | 0.6776 | 0.3602 |
| 0.3825 | 10.43 | 4800 | 0.6739 | 0.3544 |
| 0.3825 | 10.65 | 4900 | 0.6688 | 0.3557 |
| 0.3477 | 10.87 | 5000 | 0.6674 | 0.3564 |
| 0.3477 | 11.09 | 5100 | 0.6786 | 0.3476 |
| 0.3477 | 11.3 | 5200 | 0.6818 | 0.3478 |
| 0.3477 | 11.52 | 5300 | 0.6874 | 0.3470 |
| 0.3477 | 11.74 | 5400 | 0.6993 | 0.3424 |
| 0.3101 | 11.96 | 5500 | 0.6950 | 0.3404 |
| 0.3101 | 12.17 | 5600 | 0.6872 | 0.3406 |
| 0.3101 | 12.39 | 5700 | 0.6846 | 0.3424 |
| 0.3101 | 12.61 | 5800 | 0.7051 | 0.3405 |
| 0.3101 | 12.83 | 5900 | 0.7051 | 0.3378 |
| 0.2859 | 13.04 | 6000 | 0.6955 | 0.3403 |
| 0.2859 | 13.26 | 6100 | 0.7115 | 0.3390 |
| 0.2859 | 13.48 | 6200 | 0.7074 | 0.3384 |
| 0.2859 | 13.69 | 6300 | 0.7002 | 0.3376 |
| 0.2859 | 13.91 | 6400 | 0.7171 | 0.3360 |
| 0.2714 | 14.13 | 6500 | 0.7193 | 0.3341 |
| 0.2714 | 14.35 | 6600 | 0.7132 | 0.3347 |
| 0.2714 | 14.56 | 6700 | 0.7184 | 0.3353 |
| 0.2714 | 14.78 | 6800 | 0.7171 | 0.3331 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|