Instructions to use OneclickAI/RNN_test_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use OneclickAI/RNN_test_Model 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://OneclickAI/RNN_test_Model") - Notebooks
- Google Colab
- Kaggle
Download config.json from OneclickAI/RNN_test_Model: direct link, hf CLI and curl.
- Browser
- Download file 500 Bytes
-
https://huggingface.co/OneclickAI/RNN_test_Model/resolve/main/config.json
- Command line
-
hf download hf://OneclickAI/RNN_test_Model/config.json
-
curl -L -o config.json https://huggingface.co/OneclickAI/RNN_test_Model/resolve/main/config.json
500 Bytes
| { | |
| "model_type": "SimpleRNN_for_Sequence_Classification", | |
| "embedding_layer": { | |
| "input_dim": 10000, | |
| "output_dim": 32 | |
| }, | |
| "rnn_layer": { | |
| "type": "SimpleRNN", | |
| "units": 32 | |
| }, | |
| "classifier_head": { | |
| "units": 1, | |
| "activation": "sigmoid" | |
| }, | |
| "preprocessing": { | |
| "max_sequence_length": 256, | |
| "padding_type": "pre" | |
| }, | |
| "training_details": { | |
| "loss_function": "binary_crossentropy", | |
| "optimizer": "adam", | |
| "metrics": ["accuracy"] | |
| } | |
| } |