Instructions to use Recompense/product-pricer-bilstm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Recompense/product-pricer-bilstm 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://Recompense/product-pricer-bilstm") - Notebooks
- Google Colab
- Kaggle
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Verified signature## Predicts Prices given amazon `appliances` description with 0.5(RMSLE) value $$ RMSLE = \sqrt{ \frac{1}{n} \sum_{i=1}^{n} \left( \log(1 + \hat{y}_i) - \log(1 + y_i) \right)^2 } $$ trained on the `Recompense/amazon-appliances-lite-data`7563aba -
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