---
base_model: lnxdx/21_2500_1e-4_hp-mehrdad
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: 23_3000_1e-4_hp-mehrdad
  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. -->

# 23_3000_1e-4_hp-mehrdad

This model is a fine-tuned version of [lnxdx/21_2500_1e-4_hp-mehrdad](https://huggingface.co/lnxdx/21_2500_1e-4_hp-mehrdad) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9137
- Wer: 0.2932

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7626        | 0.62  | 100  | 0.6772          | 0.3216 |
| 0.7702        | 1.25  | 200  | 0.6705          | 0.3178 |
| 0.7605        | 1.88  | 300  | 0.6425          | 0.3175 |
| 0.6617        | 2.5   | 400  | 0.7113          | 0.3029 |
| 0.661         | 3.12  | 500  | 0.7173          | 0.3020 |
| 0.6037        | 3.75  | 600  | 0.6833          | 0.3478 |
| 0.5343        | 4.38  | 700  | 0.8272          | 0.2959 |
| 0.5096        | 5.0   | 800  | 0.8571          | 0.2973 |
| 0.4471        | 5.62  | 900  | 0.9498          | 0.2909 |
| 0.4393        | 6.25  | 1000 | 0.9137          | 0.2932 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0