Instructions to use monideep2255/batch_size_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monideep2255/batch_size_8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="monideep2255/batch_size_8")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("monideep2255/batch_size_8") model = AutoModelForCTC.from_pretrained("monideep2255/batch_size_8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: batch_size_8 | |
| 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. --> | |
| # batch_size_8 | |
| This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 3.5425 | |
| ## 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: 5e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 16 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 200 | |
| - num_epochs: 30 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 22.7791 | 6.67 | 200 | 4.0094 | | |
| | 3.7834 | 13.33 | 400 | 3.6360 | | |
| | 3.6483 | 20.0 | 600 | 3.6478 | | |
| | 3.59 | 26.67 | 800 | 3.5425 | | |
| ### Framework versions | |
| - Transformers 4.28.0 | |
| - Pytorch 2.0.1 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.2 | |