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---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-large-xls-r-300m-dm32
results: []
pipeline_tag: audio-classification
---
<!-- 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-large-xls-r-300m-dm32
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4880
- Accuracy: 0.7917
## Model description
More information needed
## Intended uses & limitations
Used for detecting Alzheimer's disease given voice samples
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 22
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 2.3448 | 34 | 0.6847 | 0.5833 |
| No log | 4.6897 | 68 | 0.6828 | 0.5833 |
| No log | 7.0345 | 102 | 0.6775 | 0.5833 |
| 0.3495 | 9.3793 | 136 | 0.6757 | 0.5833 |
| 0.3495 | 11.7241 | 170 | 0.6739 | 0.5833 |
| 0.3495 | 14.0690 | 204 | 0.6081 | 0.6875 |
| 0.3335 | 16.4138 | 238 | 0.5084 | 0.7917 |
| 0.3335 | 18.7586 | 272 | 0.4868 | 0.8125 |
| 0.3335 | 21.1034 | 306 | 0.4880 | 0.7917 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3 |