metadata
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: superb_ks_42
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: superb
type: superb
config: ks
split: validation
args: ks
metrics:
- name: Accuracy
type: accuracy
value: 0.6209179170344219
superb_ks_42
This model is a fine-tuned version of facebook/hubert-base-ls960 on the superb dataset. It achieves the following results on the evaluation set:
- Loss: -9.8017
- Accuracy: 0.6209
- Test Accuracy: 0.6209
- Df Accuracy: 0.1395
- Unlearn Overall Accuracy: 0.7407
- Unlearn Time: 472.7725
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Overall Accuracy | Unlearn Overall Accuracy | Time |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 128 | -9.8017 | 0.1395 | 0.7407 | 0.7407 | -1 |
| No log | 2.0 | 256 | -22.2995 | 0.1395 | 0.7407 | 0.7407 | -1 |
| No log | 3.0 | 384 | -37.2667 | 0.1395 | 0.7407 | 0.7407 | -1 |
| No log | 4.0 | 512 | -53.5139 | 0.1395 | 0.7407 | 0.7407 | -1 |
| No log | 5.0 | 640 | -69.6990 | 0.1395 | 0.7407 | 0.7407 | -1 |
| No log | 6.0 | 768 | -84.6761 | 0.1395 | 0.7407 | 0.7407 | -1 |
| No log | 7.0 | 896 | -97.3327 | 0.1395 | 0.7407 | 0.7407 | -1 |
| -112.9256 | 8.0 | 1024 | -106.9770 | 0.1395 | 0.7407 | 0.7407 | -1 |
| -112.9256 | 9.0 | 1152 | -113.0233 | 0.1395 | 0.7407 | 0.7407 | -1 |
| -112.9256 | 10.0 | 1280 | -115.0864 | 0.1395 | 0.7407 | 0.7407 | -1 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2