Update to PyTorch v1.1.0.

v0.1.3 (2017-12-14)

Added

• Goodbooks dataset.

Changed

• Raise ValueError if loss becomes NaN or 0.
• Updated to work with PyTorch 0.3.0.

Added

• spotlight.layers.BloomEmbedding: bloom embedding layers that reduce the number of parameters required by hashing embedding indices into some fixed smaller dimensionality, following Serrà, Joan, and Alexandros Karatzoglou. "Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks."
• sequence_mrr_score now accepts an option that excludes previously seen items from scoring.

Changed

• optimizer arguments is now optimizer_func. It accepts a function that takes a single argument (list of model parameters) and return a PyTorch optimizer (thanks to Ethan Rosenthal).
• fit calls will resume from previous model state when called repeatedly (Ethan Rosenthal).
• Updated to work with PyTorch v0.2.0.

Fixed

• Factorization predict APIs now work as advertised in the documentation.