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---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
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: 0.1603
- Wer: 0.1145
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.0491 | 0.47 | 600 | 0.3405 | 0.4429 |
| 0.1667 | 0.95 | 1200 | 0.2590 | 0.3481 |
| 0.1213 | 1.42 | 1800 | 0.2217 | 0.3055 |
| 0.1021 | 1.9 | 2400 | 0.1943 | 0.2839 |
| 0.0822 | 2.37 | 3000 | 0.1861 | 0.2341 |
| 0.0739 | 2.85 | 3600 | 0.1681 | 0.2302 |
| 0.062 | 3.32 | 4200 | 0.1669 | 0.2065 |
| 0.0543 | 3.8 | 4800 | 0.1727 | 0.2115 |
| 0.0434 | 4.27 | 5400 | 0.1581 | 0.1826 |
| 0.0378 | 4.74 | 6000 | 0.1544 | 0.1963 |
| 0.0349 | 5.22 | 6600 | 0.1415 | 0.1680 |
| 0.0266 | 5.69 | 7200 | 0.1504 | 0.1607 |
| 0.0226 | 6.17 | 7800 | 0.1471 | 0.1485 |
| 0.0186 | 6.64 | 8400 | 0.1435 | 0.1456 |
| 0.0163 | 7.12 | 9000 | 0.1415 | 0.1331 |
| 0.0117 | 7.59 | 9600 | 0.1413 | 0.1309 |
| 0.0119 | 8.07 | 10200 | 0.1618 | 0.1214 |
| 0.0076 | 8.54 | 10800 | 0.1545 | 0.1194 |
| 0.0068 | 9.02 | 11400 | 0.1553 | 0.1204 |
| 0.0038 | 9.49 | 12000 | 0.1584 | 0.1177 |
| 0.0036 | 9.96 | 12600 | 0.1603 | 0.1145 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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