---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-300m-ft-soft-skill
  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. -->

# wav2vec2-300m-ft-soft-skill

This model is a fine-tuned version of [glob-asr/xls-r-es-test-lm](https://huggingface.co/glob-asr/xls-r-es-test-lm) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7447
- Accuracy: 0.6827
- F1 Micro: 0.3514
- F1 Macro: 0.6827
- Precision Micro: 0.6827

## 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: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|
| 0.823         | 0.51  | 100  | 0.6821          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.7122        | 1.02  | 200  | 0.6767          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6706        | 1.52  | 300  | 0.6768          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.7096        | 2.03  | 400  | 0.6791          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6909        | 2.54  | 500  | 0.6780          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6861        | 3.05  | 600  | 0.6779          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6842        | 3.55  | 700  | 0.6773          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6887        | 4.06  | 800  | 0.6764          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6766        | 4.57  | 900  | 0.6803          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6964        | 5.08  | 1000 | 0.6819          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6515        | 5.58  | 1100 | 0.6788          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6608        | 6.09  | 1200 | 0.6864          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6171        | 6.6   | 1300 | 0.6980          | 0.7589   | 0.2876   | 0.7589   | 0.7589          |
| 0.6292        | 7.11  | 1400 | 0.7172          | 0.7386   | 0.3119   | 0.7386   | 0.7386          |
| 0.6015        | 7.61  | 1500 | 0.6988          | 0.7462   | 0.3212   | 0.7462   | 0.7462          |
| 0.6236        | 8.12  | 1600 | 0.7493          | 0.6954   | 0.3432   | 0.6954   | 0.6954          |
| 0.5643        | 8.63  | 1700 | 0.7250          | 0.7107   | 0.3466   | 0.7107   | 0.7107          |
| 0.6134        | 9.14  | 1800 | 0.7561          | 0.6751   | 0.3565   | 0.6751   | 0.6751          |
| 0.5642        | 9.64  | 1900 | 0.7447          | 0.6827   | 0.3514   | 0.6827   | 0.6827          |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.8.1+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1