Instructions to use Bgeorge/model_dialect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bgeorge/model_dialect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Bgeorge/model_dialect")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Bgeorge/model_dialect") model = AutoModelForAudioClassification.from_pretrained("Bgeorge/model_dialect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: model_dialect | |
| results: [] | |
| <!-- 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. --> | |
| # model_dialect | |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.8038 | |
| - Accuracy: 0.7113 | |
| ## 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: 4e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 128 | |
| - 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: 16 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-------:|:----:|:---------------:|:--------:| | |
| | 6.4219 | 0.9455 | 13 | 1.5899 | 0.2610 | | |
| | 6.2904 | 1.9636 | 27 | 1.4556 | 0.4550 | | |
| | 5.4442 | 2.9818 | 41 | 1.2566 | 0.5219 | | |
| | 5.0752 | 4.0 | 55 | 1.1670 | 0.5566 | | |
| | 4.748 | 4.9455 | 68 | 1.0790 | 0.5958 | | |
| | 4.2202 | 5.9636 | 82 | 1.0372 | 0.6120 | | |
| | 4.0075 | 6.9818 | 96 | 0.9833 | 0.6397 | | |
| | 3.5847 | 8.0 | 110 | 0.9311 | 0.6721 | | |
| | 3.3304 | 8.9455 | 123 | 0.9242 | 0.6420 | | |
| | 3.2199 | 9.9636 | 137 | 0.8707 | 0.6928 | | |
| | 2.9659 | 10.9818 | 151 | 0.8680 | 0.6767 | | |
| | 2.8954 | 12.0 | 165 | 0.8357 | 0.6952 | | |
| | 2.6402 | 12.9455 | 178 | 0.8325 | 0.7021 | | |
| | 2.4812 | 13.9636 | 192 | 0.8158 | 0.6998 | | |
| | 2.4249 | 14.9818 | 206 | 0.8042 | 0.7090 | | |
| | 2.4249 | 15.1273 | 208 | 0.8038 | 0.7113 | | |
| ### Framework versions | |
| - Transformers 4.46.0 | |
| - Pytorch 2.4.0 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.0 | |