Instructions to use Aanchan/psst_model_cer_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aanchan/psst_model_cer_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Aanchan/psst_model_cer_3")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Aanchan/psst_model_cer_3") model = AutoModelForCTC.from_pretrained("Aanchan/psst_model_cer_3") - Notebooks
- Google Colab
- Kaggle
psst_model_cer_3
This model is a fine-tuned version of Aanchan/psst_model_cer_2 on the None 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: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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