Instructions to use Lakoc/ED_small_cv_en_deeper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lakoc/ED_small_cv_en_deeper with Transformers:
# Load model directly from transformers import JointCTCAttentionEncoderDecoder model = JointCTCAttentionEncoderDecoder.from_pretrained("Lakoc/ED_small_cv_en_deeper", dtype="auto") - Notebooks
- Google Colab
- Kaggle
ED_small_cv_en_deeper
This model is a fine-tuned version of on the common_voice_13_0 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: 0.001
- train_batch_size: 256
- eval_batch_size: 64
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15000
- num_epochs: 50.0
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.0+rocm5.6
- Datasets 2.18.0
- Tokenizers 0.15.2
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