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
-
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
metadata
library_name: transformers
license: apache-2.0
base_model: Eimhin03/outout_model
tags:
- generated_from_trainer
model-index:
- name: outout_model_29Jan26_TestingGeneralisation
results: []
outout_model_29Jan26_TestingGeneralisation
This model is a fine-tuned version of 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