The wikipedia-sections-models implement the idea from Ein Dor et al., 2018, Learning Thematic Similarity Metric Using Triplet Networks.
It was trained with a triplet-loss: The anchor and the positive example were sentences from the same section from an wikipedia article, for example, from the History section of the London article. The negative example came from a different section from the same article, for example, from the Education section of the London article.
Dataset
We use dataset from Ein Dor et al., 2018, Learning Thematic Similarity Metric Using Triplet Networks.
See examples/training_wikipedia_sections.py for how to train on this dataset.
Pre-trained models
We provide the following pre-trained models:
bert-base-wikipedia-sections-mean-tokens: 80.42% accuracy on test set.
You can use them in the following way:
