Embeddings trained on CONLL2017 Corpora (conll2017-embeddings) - Part 2

The embeddings were trained with finalfrontier on the CONLL2017 corpora with more than 100m tokens. For all languages embeddings, were trained with the skip- and structgram algorithms and contain subword ngrams. All embeddings are stored in the finalfusion format and can be used an processed with tools provided by the finalfusion ecosystem.

• N-Gram range (inclusive): 3 - 6
• Number of hashing buckets: 2^21
• Hashing function: FNV-1a
• Window size: 10
• Negative Samples: 5
• Dimensions: 300
• Minimum Token Frequency: 30