Instructions to use scasutt/Prototype_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/Prototype_training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/Prototype_training")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/Prototype_training") model = AutoModelForCTC.from_pretrained("scasutt/Prototype_training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from scasutt/Prototype_training: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/scasutt/Prototype_training/resolve/main/README.md
- Command line
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hf download hf://scasutt/Prototype_training/README.md
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curl -L -o README.md https://huggingface.co/scasutt/Prototype_training/resolve/main/README.md
1.47 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: Prototype_training | |
| 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. --> | |
| # Prototype_training | |
| This model is a fine-tuned version of [scasutt/Prototype_training](https://huggingface.co/scasutt/Prototype_training) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3719 | |
| - Wer: 0.4626 | |
| ## 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: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 1000 | |
| - num_epochs: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 0.3853 | 1.47 | 100 | 0.3719 | 0.4626 | | |
| | 0.3867 | 2.94 | 200 | 0.3719 | 0.4626 | | |
| ### Framework versions | |
| - Transformers 4.11.3 | |
| - Pytorch 1.10.0+cu111 | |
| - Datasets 1.17.0 | |
| - Tokenizers 0.10.3 | |