Instructions to use AmitMY/signwriting-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmitMY/signwriting-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AmitMY/signwriting-clip")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("AmitMY/signwriting-clip") model = AutoModel.from_pretrained("AmitMY/signwriting-clip") - Notebooks
- Google Colab
- Kaggle
signwriting-clip
This model was trained from scratch 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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