Instructions to use martin-ha/text_image_dual_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use martin-ha/text_image_dual_encoder with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://martin-ha/text_image_dual_encoder") - Notebooks
- Google Colab
- Kaggle
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:
- optimizer: {'name': 'AdamW', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay': 0.001, 'exclude_from_weight_decay': None}
- training_precision: float32
Training Metrics
Model history needed
Model Plot
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