Instructions to use Bisher/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Bisher/test with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Bisher/test") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-base | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - minds14 | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: test | |
| results: | |
| - task: | |
| name: Audio Classification | |
| type: audio-classification | |
| dataset: | |
| name: minds14 | |
| type: minds14 | |
| config: en-US | |
| split: train | |
| args: en-US | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.0 | |
| <!-- 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. --> | |
| # test | |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.7187 | |
| - Accuracy: 0.0 | |
| ## 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: 3e-05 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 4 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 1.3128 | 1.0 | 1 | 2.7187 | 0.0 | | |
| ### Framework versions | |
| - Transformers 4.42.4 | |
| - Pytorch 2.3.1+cu121 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.19.1 | |