transcribe-monkey
This model is a fine-tuned version of superb/wav2vec2-base-superb-sid on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2787
- Accuracy: 0.9677
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1226 | 1.0 | 212 | 0.4222 | 0.9516 |
| 0.2144 | 2.0 | 424 | 0.2416 | 0.9516 |
| 0.0888 | 3.0 | 636 | 0.2240 | 0.9677 |
| 0.0004 | 4.0 | 848 | 0.3074 | 0.9677 |
| 0.0204 | 5.0 | 1060 | 0.2787 | 0.9677 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
- 6
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for jdalegonzalez/transcribe-monkey
Base model
superb/wav2vec2-base-superb-sid