Instructions to use yacoubagbane/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yacoubagbane/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="yacoubagbane/checkpoints")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("yacoubagbane/checkpoints") model = AutoModelForCTC.from_pretrained("yacoubagbane/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: checkpoints | |
| 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. --> | |
| # checkpoints | |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 3.1084 | |
| - Wer: 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 | |
| - lr_scheduler_warmup_steps: 500 | |
| - num_epochs: 30 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:------:|:----:|:---------------:|:---:| | |
| | 5.4567 | 0.7962 | 500 | 3.1191 | 1.0 | | |
| | 3.0371 | 1.5924 | 1000 | 3.1471 | 1.0 | | |
| | 3.0352 | 2.3885 | 1500 | 3.1423 | 1.0 | | |
| | 3.0396 | 3.1847 | 2000 | 3.1056 | 1.0 | | |
| | 3.0361 | 3.9809 | 2500 | 3.1074 | 1.0 | | |
| | 3.0391 | 4.7771 | 3000 | 3.1084 | 1.0 | | |
| ### Framework versions | |
| - Transformers 5.13.1 | |
| - Pytorch 2.11.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |