Instructions to use Eimhin03/outout_model_29Jan26_TestingGeneralisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eimhin03/outout_model_29Jan26_TestingGeneralisation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Eimhin03/outout_model_29Jan26_TestingGeneralisation")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Eimhin03/outout_model_29Jan26_TestingGeneralisation") model = AutoModelForSpeechSeq2Seq.from_pretrained("Eimhin03/outout_model_29Jan26_TestingGeneralisation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Eimhin03/outout_model_29Jan26_TestingGeneralisation: direct link, hf CLI and curl.
- Browser
- Download file 1.5 kB
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https://huggingface.co/Eimhin03/outout_model_29Jan26_TestingGeneralisation/resolve/main/README.md
- Command line
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hf download hf://Eimhin03/outout_model_29Jan26_TestingGeneralisation/README.md
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curl -L -o README.md https://huggingface.co/Eimhin03/outout_model_29Jan26_TestingGeneralisation/resolve/main/README.md
1.5 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: Eimhin03/outout_model | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: outout_model_29Jan26_TestingGeneralisation | |
| 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. --> | |
| # outout_model_29Jan26_TestingGeneralisation | |
| This model is a fine-tuned version of [Eimhin03/outout_model](https://huggingface.co/Eimhin03/outout_model) on an unknown dataset. | |
| ## 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: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.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_steps: 200 | |
| - training_steps: 1 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| | |
| | No log | 0.0020 | 1 | 3.8299 | 101.7722 | | |
| ### Framework versions | |
| - Transformers 5.0.1.dev0 | |
| - Pytorch 2.9.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |