tensorflow/models

The Skip-Thoughts model is a sentence encoder. It learns to encode input sentences into a fixed-dimensional vector representation that is useful for many tasks, for example to detect paraphrases or to classify whether a product review is positive or negative. See the Skip-Thought Vectors paper for details of the model architecture and more example applications. A trained Skip-Thoughts model will encode similar sentences nearby each other in the embedding vector space. The following examples show the nearest neighbor by cosine similarity of some sentences from the movie review dataset.