File size: 2,922 Bytes
1044e28 | 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 | ---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: iteboshi-small
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. -->
# iteboshi-small
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9635
- Wer: 94.6252
- Cer: 50.3003
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|
| 1.1451 | 1.1013 | 1000 | 1.3127 | 98.0009 | 52.9954 |
| 0.6729 | 2.2026 | 2000 | 0.9211 | 94.9364 | 36.0076 |
| 0.4087 | 3.3040 | 3000 | 0.8170 | 91.4757 | 33.0592 |
| 0.303 | 4.4053 | 4000 | 0.7996 | 95.9642 | 35.4912 |
| 0.211 | 5.5066 | 5000 | 0.7910 | 90.7685 | 39.8708 |
| 0.1389 | 6.6079 | 6000 | 0.8133 | 91.2588 | 46.3311 |
| 0.0864 | 7.7093 | 7000 | 0.8312 | 92.6638 | 39.9178 |
| 0.0729 | 8.8106 | 8000 | 0.8530 | 91.6172 | 50.7434 |
| 0.0381 | 9.9119 | 9000 | 0.8698 | 91.5700 | 47.7159 |
| 0.028 | 11.0132 | 10000 | 0.8864 | 92.1452 | 54.3756 |
| 0.0142 | 12.1145 | 11000 | 0.8988 | 93.2107 | 53.6414 |
| 0.0131 | 13.2159 | 12000 | 0.9192 | 92.8053 | 46.2153 |
| 0.0088 | 14.3172 | 13000 | 0.9230 | 93.8142 | 54.3103 |
| 0.0092 | 15.4185 | 14000 | 0.9310 | 94.0311 | 53.1192 |
| 0.0069 | 16.5198 | 15000 | 0.9370 | 93.8802 | 51.9775 |
| 0.0023 | 17.6211 | 16000 | 0.9437 | 94.2386 | 49.6876 |
| 0.0026 | 18.7225 | 17000 | 0.9495 | 94.0971 | 51.5929 |
| 0.0016 | 19.8238 | 18000 | 0.9531 | 94.1631 | 51.0942 |
| 0.0015 | 20.9251 | 19000 | 0.9608 | 94.7006 | 50.9125 |
| 0.0012 | 22.0264 | 20000 | 0.9635 | 94.6252 | 50.3003 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|