Instructions to use CHINUKI/keyword with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CHINUKI/keyword with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="CHINUKI/keyword")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("CHINUKI/keyword") model = AutoModelForAudioClassification.from_pretrained("CHINUKI/keyword", device_map="auto") - Notebooks
- Google Colab
- Kaggle
keyword
This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6077
- Accuracy: 1.0
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 6 | 1.8717 | 0.2632 |
| No log | 2.0 | 12 | 1.7142 | 0.3158 |
| No log | 3.0 | 18 | 1.4633 | 0.6316 |
| No log | 4.0 | 24 | 1.2702 | 0.7895 |
| No log | 5.0 | 30 | 1.1273 | 0.9474 |
| No log | 6.0 | 36 | 0.8993 | 1.0 |
| No log | 7.0 | 42 | 0.7751 | 1.0 |
| No log | 8.0 | 48 | 0.6874 | 1.0 |
| No log | 9.0 | 54 | 0.6359 | 1.0 |
| No log | 10.0 | 60 | 0.6077 | 1.0 |
Framework versions
- Transformers 5.18.0
- Pytorch 2.11.0+cpu
- Datasets 4.8.5
- Tokenizers 0.23.2
- Downloads last month
- 16
Model tree for CHINUKI/keyword
Base model
Kkonjeong/wav2vec2-base-korean