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---
license: mit
tags:
- generated_from_trainer
base_model: facebook/w2v-bert-2.0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-tamil-gpu-custom_clean_v2
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. -->
# w2v-bert-2.0-tamil-gpu-custom_clean_v2
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1042
- Wer: 0.1892
## 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: 2.5356e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.9949 | 0.25 | 300 | 0.5158 | 0.6736 |
| 0.4051 | 0.5 | 600 | 0.1858 | 0.3035 |
| 0.2789 | 0.76 | 900 | 0.1670 | 0.2730 |
| 0.2352 | 1.01 | 1200 | 0.1479 | 0.2594 |
| 0.1988 | 1.26 | 1500 | 0.1428 | 0.2464 |
| 0.1879 | 1.51 | 1800 | 0.1388 | 0.2391 |
| 0.1757 | 1.76 | 2100 | 0.1244 | 0.2412 |
| 0.1683 | 2.01 | 2400 | 0.1278 | 0.2231 |
| 0.1475 | 2.27 | 2700 | 0.1231 | 0.2240 |
| 0.1438 | 2.52 | 3000 | 0.1219 | 0.2192 |
| 0.1422 | 2.77 | 3300 | 0.1216 | 0.2128 |
| 0.1337 | 3.02 | 3600 | 0.1145 | 0.2087 |
| 0.1212 | 3.27 | 3900 | 0.1131 | 0.2061 |
| 0.1195 | 3.52 | 4200 | 0.1159 | 0.2147 |
| 0.1169 | 3.78 | 4500 | 0.1111 | 0.2083 |
| 0.1162 | 4.03 | 4800 | 0.1116 | 0.2058 |
| 0.1073 | 4.28 | 5100 | 0.1138 | 0.2114 |
| 0.1011 | 4.53 | 5400 | 0.1103 | 0.2057 |
| 0.1008 | 4.78 | 5700 | 0.1096 | 0.2018 |
| 0.1016 | 5.03 | 6000 | 0.1045 | 0.2008 |
| 0.092 | 5.29 | 6300 | 0.1104 | 0.2008 |
| 0.0889 | 5.54 | 6600 | 0.1079 | 0.2005 |
| 0.0936 | 5.79 | 6900 | 0.1036 | 0.2026 |
| 0.0888 | 6.04 | 7200 | 0.1106 | 0.2109 |
| 0.0836 | 6.29 | 7500 | 0.1115 | 0.2103 |
| 0.0807 | 6.54 | 7800 | 0.1104 | 0.2045 |
| 0.0807 | 6.8 | 8100 | 0.1051 | 0.2039 |
| 0.0784 | 7.05 | 8400 | 0.1067 | 0.1947 |
| 0.0719 | 7.3 | 8700 | 0.1051 | 0.1957 |
| 0.0735 | 7.55 | 9000 | 0.1084 | 0.1894 |
| 0.0715 | 7.8 | 9300 | 0.1029 | 0.1916 |
| 0.0732 | 8.05 | 9600 | 0.1059 | 0.1894 |
| 0.0673 | 8.31 | 9900 | 0.1053 | 0.1890 |
| 0.0642 | 8.56 | 10200 | 0.1042 | 0.1879 |
| 0.0669 | 8.81 | 10500 | 0.1039 | 0.1877 |
| 0.0665 | 9.06 | 10800 | 0.1043 | 0.1881 |
| 0.0606 | 9.31 | 11100 | 0.1027 | 0.1870 |
| 0.0615 | 9.56 | 11400 | 0.1046 | 0.1887 |
| 0.0602 | 9.82 | 11700 | 0.1042 | 0.1892 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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