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---
license: mit
tags:
- generated_from_trainer
metrics:
- wer
base_model: facebook/w2v-bert-2.0
model-index:
- name: w2v-bert-2.0-malayalam_mixeddataset_thre
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-malayalam_mixeddataset_thre
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: 0.1709
- Wer: 0.1197
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1907 | 0.47 | 600 | 0.3890 | 0.4765 |
| 0.1663 | 0.95 | 1200 | 0.2528 | 0.3528 |
| 0.1207 | 1.42 | 1800 | 0.2176 | 0.2849 |
| 0.1017 | 1.9 | 2400 | 0.2021 | 0.2625 |
| 0.0833 | 2.37 | 3000 | 0.2032 | 0.2456 |
| 0.076 | 2.85 | 3600 | 0.1880 | 0.2376 |
| 0.0625 | 3.32 | 4200 | 0.1946 | 0.2247 |
| 0.0552 | 3.8 | 4800 | 0.1701 | 0.2247 |
| 0.0441 | 4.27 | 5400 | 0.1627 | 0.1759 |
| 0.0392 | 4.74 | 6000 | 0.1629 | 0.1829 |
| 0.0362 | 5.22 | 6600 | 0.1723 | 0.1605 |
| 0.0278 | 5.69 | 7200 | 0.1600 | 0.1665 |
| 0.0248 | 6.17 | 7800 | 0.1557 | 0.1446 |
| 0.0197 | 6.64 | 8400 | 0.1524 | 0.1505 |
| 0.0176 | 7.12 | 9000 | 0.1580 | 0.1339 |
| 0.0129 | 7.59 | 9600 | 0.1528 | 0.1411 |
| 0.0125 | 8.07 | 10200 | 0.1502 | 0.1299 |
| 0.0076 | 8.54 | 10800 | 0.1711 | 0.1189 |
| 0.0076 | 9.02 | 11400 | 0.1689 | 0.1237 |
| 0.0041 | 9.49 | 12000 | 0.1708 | 0.1227 |
| 0.0041 | 9.96 | 12600 | 0.1709 | 0.1197 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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