File size: 2,164 Bytes
5768838 |
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 |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-5
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. -->
# libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-5
This model is a fine-tuned version of [rohitp1/libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-3](https://huggingface.co/rohitp1/libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 30.3050
- Wer: 0.2650
## 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: 0.002
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 239.4865 | 1.12 | 400 | 31.0836 | 0.2908 |
| 210.2046 | 2.25 | 800 | 29.7831 | 0.2742 |
| 195.0478 | 3.37 | 1200 | 28.8794 | 0.2636 |
| 188.8096 | 4.49 | 1600 | 28.7458 | 0.2600 |
| 183.6592 | 5.62 | 2000 | 29.1159 | 0.2573 |
| 181.5025 | 6.74 | 2400 | 29.0081 | 0.2564 |
| 181.9954 | 7.86 | 2800 | 28.7132 | 0.2588 |
| 181.8548 | 8.99 | 3200 | 29.3207 | 0.2630 |
| 186.2524 | 10.11 | 3600 | 29.9119 | 0.2593 |
| 188.3838 | 11.24 | 4000 | 30.2963 | 0.2627 |
| 192.2623 | 12.36 | 4400 | 30.3050 | 0.2650 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0
|