File size: 2,085 Bytes
1b660f9 47d4365 1b660f9 c753418 1b660f9 47d4365 c753418 1b660f9 c753418 1b660f9 c753418 1b660f9 |
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 |
---
library_name: transformers
license: apache-2.0
base_model: google/umt5-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: t5-asr-CV16
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. -->
# t5-asr-CV16
This model is a fine-tuned version of [google/umt5-small](https://huggingface.co/google/umt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6678
- Wer: 0.7639
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 128
- total_train_batch_size: 4096
- optimizer: Use 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_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 1.8105 | 1.9694 | 48 | 0.7812 | 0.8528 |
| 1.6752 | 3.9694 | 96 | 0.7174 | 0.8285 |
| 1.6146 | 5.9694 | 144 | 0.7357 | 0.8215 |
| 1.3847 | 7.9694 | 192 | 0.6796 | 0.8172 |
| 1.2792 | 9.9694 | 240 | 0.6601 | 0.7841 |
| 1.2129 | 11.9694 | 288 | 0.6540 | 0.7764 |
| 1.279 | 13.9694 | 336 | 0.6792 | 0.7837 |
| 1.1706 | 15.9694 | 384 | 0.6695 | 0.7888 |
| 1.0348 | 17.9694 | 432 | 0.6931 | 0.7948 |
| 0.9335 | 19.9694 | 480 | 0.6678 | 0.7639 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu121
- Datasets 2.17.1
- Tokenizers 0.21.0
|