---
base_model: lnxdx/21_2500_1e-4_hp-mehrdad
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: 2_1000_1e-5_hp-mehrdad
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 2_1000_1e-5_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 on ShEMO train set: 0.6809
- Loss on ShEMO dev set:   0.6591
- WER on ShEMO train set:          27.41
- WER on ShEMO dev set:            31.37 (Why not 31.36?)
- WER on Common Voice 13 test set: 19.26

## 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: 1e-05
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7624        | 0.62  | 100  | 0.6708          | 0.3236 |
| 0.78          | 1.25  | 200  | 0.6668          | 0.3245 |
| 0.7856        | 1.88  | 300  | 0.6600          | 0.3274 |
| 0.7239        | 2.5   | 400  | 0.6672          | 0.3233 |
| 0.7311        | 3.12  | 500  | 0.6748          | 0.3143 |
| 0.7408        | 3.75  | 600  | 0.6518          | 0.3248 |
| 0.713         | 4.38  | 700  | 0.6587          | 0.3178 |
| 0.7068        | 5.0   | 800  | 0.6600          | 0.3172 |
| 0.6938        | 5.62  | 900  | 0.6598          | 0.3157 |
| 0.6809        | 6.25  | 1000 | 0.6591          | 0.3137 |


### Framework versions

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