File size: 3,027 Bytes
8686e20 3fc1ef5 8686e20 3fc1ef5 |
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 |
---
license: apache-2.0
base_model: NbAiLab/nb-whisper-medium-verbatim
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: nb-whisper-medium-karelian-CodeSwitching
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. -->
# nb-whisper-medium-karelian-CodeSwitching
This model is a fine-tuned version of [NbAiLab/nb-whisper-medium-verbatim](https://huggingface.co/NbAiLab/nb-whisper-medium-verbatim) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5439
- Wer: 0.2585
- Cer: 0.0714
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 0.2467 | 1.1351 | 500 | 0.5664 | 0.3488 | 0.0895 |
| 0.0718 | 2.2701 | 1000 | 0.5562 | 0.3166 | 0.0819 |
| 0.0513 | 3.4052 | 1500 | 0.5366 | 0.2997 | 0.0798 |
| 0.0377 | 4.5403 | 2000 | 0.5430 | 0.2815 | 0.0730 |
| 0.0339 | 5.6754 | 2500 | 0.5444 | 0.2906 | 0.0755 |
| 0.0263 | 6.8104 | 3000 | 0.5439 | 0.2757 | 0.0735 |
| 0.0182 | 7.9455 | 3500 | 0.5474 | 0.2754 | 0.0741 |
| 0.0141 | 9.0806 | 4000 | 0.5625 | 0.2808 | 0.0758 |
| 0.0117 | 10.2157 | 4500 | 0.5537 | 0.2662 | 0.0716 |
| 0.0122 | 11.3507 | 5000 | 0.5610 | 0.2703 | 0.0726 |
| 0.0118 | 12.4858 | 5500 | 0.5557 | 0.2686 | 0.0720 |
| 0.0075 | 13.6209 | 6000 | 0.5522 | 0.2673 | 0.0711 |
| 0.0069 | 14.7560 | 6500 | 0.5576 | 0.2764 | 0.0745 |
| 0.0072 | 15.8910 | 7000 | 0.5562 | 0.2676 | 0.0705 |
| 0.0085 | 17.0261 | 7500 | 0.5474 | 0.2713 | 0.0868 |
| 0.0041 | 18.1612 | 8000 | 0.5493 | 0.2639 | 0.0716 |
| 0.0041 | 19.2963 | 8500 | 0.5493 | 0.2612 | 0.0712 |
| 0.0041 | 20.4313 | 9000 | 0.5449 | 0.2554 | 0.0699 |
| 0.004 | 21.5664 | 9500 | 0.5444 | 0.2591 | 0.0708 |
| 0.0028 | 22.7015 | 10000 | 0.5439 | 0.2585 | 0.0714 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|