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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Recompense/product-pricer-bilstm") - Notebooks
- Google Colab
- Kaggle
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RMSLE = \sqrt{ \frac{1}{n} \sum_{i=1}^{n} \bigl(\log(1 + \hat{y}_i) - \log(1 + y_i)\bigr)^2 }
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- **Test RMSLE:** 0.
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RMSLE = \sqrt{ \frac{1}{n} \sum_{i=1}^{n} \bigl(\log(1 + \hat{y}_i) - \log(1 + y_i)\bigr)^2 }
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- **Test RMSLE:** 0.51 on held-out validation set hit 90.4% on validation set with margin of $40
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