---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: ssc-aln-model
  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. -->

# ssc-aln-model

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8647
- Cer: 0.5679
- Wer: 0.9768

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Cer    | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 2.6542        | 0.1027 | 100  | 2.7873          | 0.9127 | 1.0    |
| 2.7312        | 0.2054 | 200  | 2.6739          | 0.8102 | 0.9999 |
| 2.7166        | 0.3082 | 300  | 2.3973          | 0.8100 | 0.9998 |
| 2.6442        | 0.4109 | 400  | 2.5701          | 0.7773 | 0.9863 |
| 2.6282        | 0.5136 | 500  | 2.4967          | 0.7596 | 1.0    |
| 2.6793        | 0.6163 | 600  | 2.4465          | 0.8274 | 0.9988 |
| 2.5982        | 0.7191 | 700  | 2.4531          | 0.7070 | 0.9893 |
| 2.5929        | 0.8218 | 800  | 2.3973          | 0.7647 | 0.9991 |
| 2.6211        | 0.9245 | 900  | 2.3430          | 0.7394 | 0.9911 |
| 2.5614        | 1.0267 | 1000 | 2.2116          | 0.6708 | 0.9887 |
| 2.5421        | 1.1294 | 1100 | 2.1762          | 0.7062 | 0.9970 |
| 2.5272        | 1.2322 | 1200 | 2.1483          | 0.6747 | 0.9907 |
| 2.4457        | 1.3349 | 1300 | 2.1416          | 0.6783 | 0.9754 |
| 2.4582        | 1.4376 | 1400 | 2.1515          | 0.6323 | 0.9812 |
| 2.5182        | 1.5403 | 1500 | 2.1518          | 0.6933 | 0.9828 |
| 2.545         | 1.6430 | 1600 | 2.1046          | 0.6844 | 0.9948 |
| 2.4768        | 1.7458 | 1700 | 2.0930          | 0.6794 | 0.9971 |
| 2.437         | 1.8485 | 1800 | 2.0755          | 0.6974 | 0.9977 |
| 2.4652        | 1.9512 | 1900 | 2.0531          | 0.6387 | 0.9852 |
| 2.4666        | 2.0534 | 2000 | 2.0942          | 0.6326 | 0.9725 |
| 2.4098        | 2.1561 | 2100 | 2.1318          | 0.7399 | 0.9999 |
| 2.295         | 2.2589 | 2200 | 2.0930          | 0.6261 | 0.9975 |
| 2.3255        | 2.3616 | 2300 | 2.0553          | 0.6080 | 0.9830 |
| 2.3362        | 2.4643 | 2400 | 2.0664          | 0.6241 | 0.9820 |
| 2.324         | 2.5670 | 2500 | 2.0415          | 0.6090 | 0.9839 |
| 2.3254        | 2.6697 | 2600 | 2.0766          | 0.5845 | 0.9765 |
| 2.3232        | 2.7725 | 2700 | 2.0245          | 0.6318 | 0.9836 |
| 2.2821        | 2.8752 | 2800 | 1.9850          | 0.6249 | 0.9870 |
| 2.2661        | 2.9779 | 2900 | 1.9709          | 0.6247 | 0.9770 |
| 2.2066        | 3.0801 | 3000 | 2.0029          | 0.5864 | 0.9691 |
| 2.1706        | 3.1828 | 3100 | 1.9698          | 0.5725 | 0.9681 |
| 2.1382        | 3.2856 | 3200 | 1.9499          | 0.5990 | 0.9759 |
| 2.2142        | 3.3883 | 3300 | 1.9464          | 0.6189 | 0.9825 |
| 2.2512        | 3.4910 | 3400 | 1.9367          | 0.6020 | 0.9843 |
| 2.1671        | 3.5937 | 3500 | 1.9393          | 0.5939 | 0.9799 |
| 2.2047        | 3.6965 | 3600 | 1.9381          | 0.5728 | 0.9700 |
| 2.1303        | 3.7992 | 3700 | 1.9116          | 0.5683 | 0.9722 |
| 2.1517        | 3.9019 | 3800 | 1.9412          | 0.5383 | 0.9495 |
| 2.2205        | 4.0041 | 3900 | 1.8760          | 0.5827 | 0.9780 |
| 2.07          | 4.1068 | 4000 | 1.9216          | 0.5793 | 0.9768 |
| 2.049         | 4.2096 | 4100 | 1.9057          | 0.5595 | 0.9694 |
| 2.057         | 4.3123 | 4200 | 1.9335          | 0.5549 | 0.9664 |
| 2.0582        | 4.4150 | 4300 | 1.9117          | 0.5552 | 0.9675 |
| 2.0678        | 4.5177 | 4400 | 1.8778          | 0.5699 | 0.9767 |
| 2.0643        | 4.6204 | 4500 | 1.8775          | 0.5704 | 0.9778 |
| 1.9829        | 4.7232 | 4600 | 1.8712          | 0.5704 | 0.9780 |
| 2.0293        | 4.8259 | 4700 | 1.8655          | 0.5577 | 0.9695 |
| 2.0133        | 4.9286 | 4800 | 1.8647          | 0.5679 | 0.9768 |


### Framework versions

- Transformers 4.57.2
- Pytorch 2.9.1+cu128
- Datasets 3.6.0
- Tokenizers 0.22.0