---
base_model: lnxdx/B4_1000_1e-5_hp-myself-2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: C2_1000_1e-5_hp-myself-2
  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. -->

# C2_1000_1e-5_hp-myself-2

This model is a fine-tuned version of [lnxdx/B4_1000_1e-5_hp-myself-2](https://huggingface.co/lnxdx/B4_1000_1e-5_hp-myself-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss on ShEMO train set: 0.7065
- Loss on ShEMO dev set:   0.6634
- WER on ShEMO train set:          26.52
- WER on ShEMO dev set:            30.87
- WER on Common Voice 13 test set: 19.43

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7564        | 0.62  | 100  | 0.6705          | 0.3131 |
| 0.7761        | 1.25  | 200  | 0.6664          | 0.3140 |
| 0.7722        | 1.88  | 300  | 0.6573          | 0.3137 |
| 0.7035        | 2.5   | 400  | 0.6627          | 0.3157 |
| 0.7026        | 3.12  | 500  | 0.6834          | 0.3107 |
| 0.7213        | 3.75  | 600  | 0.6561          | 0.3169 |
| 0.6996        | 4.38  | 700  | 0.6664          | 0.3096 |
| 0.7146        | 5.0   | 800  | 0.6593          | 0.3148 |
| 0.7071        | 5.62  | 900  | 0.6646          | 0.3125 |
| 0.7065        | 6.25  | 1000 | 0.6634          | 0.3107 |


### Framework versions

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