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---
library_name: transformers
license: apache-2.0
base_model: imrajeshkr/distilhubert-finetuned-speech_commands
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- precision
- recall
- f1
model-index:
- name: distilhubert-finetuned-speech_commands
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: test
args: default
metrics:
- name: Precision
type: precision
value: 0.9759184555734861
- name: Recall
type: recall
value: 0.9749126053876208
- name: F1
type: f1
value: 0.9749296122020006
---
<!-- 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. -->
# distilhubert-finetuned-speech_commands
This model is a fine-tuned version of [imrajeshkr/distilhubert-finetuned-speech_commands](https://huggingface.co/imrajeshkr/distilhubert-finetuned-speech_commands) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0934
- Precision: 0.9759
- Recall: 0.9749
- F1: 0.9749
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.2713 | 1.0 | 1216 | 0.2523 | 0.9172 | 0.9267 | 0.9166 |
| 0.137 | 2.0 | 2432 | 0.1119 | 0.9685 | 0.9667 | 0.9664 |
| 0.0295 | 3.0 | 3648 | 0.0977 | 0.9726 | 0.9703 | 0.9701 |
| 0.0037 | 4.0 | 4864 | 0.0956 | 0.9743 | 0.9733 | 0.9732 |
| 0.052 | 5.0 | 6080 | 0.0934 | 0.9759 | 0.9749 | 0.9749 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.2.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|