Word Embeddings for Hebrew (hebrew_cc_300d)


This model is trained on Common Crawl and Wikipedia using fastText. It is trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives.

The model gives 300 dimensional vector outputs per token. The output vectors map words into a meaningful space where the distance between the vectors is related to semantic similarity of words.

These embeddings can be used in multiple tasks like semantic word similarity, named entity recognition, sentiment analysis, and classification.


How to use

Use as part of a pipeline after tokenization.

embeddings = WordEmbeddingsModel.pretrained("hebrew_cc_300d", "he") \
        .setInputCols(["document", "token"]) \
val embeddings = WordEmbeddingsModel.pretrained("hebrew_cc_300d", "he")
        .setInputCols(Array("document", "token")) 


The model gives 300 dimensional Word2Vec feature vector outputs per token.

Model Information

Model Name: hebrew_cc_300d
Type: embeddings
Compatibility: Spark NLP 2.7.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [word_embeddings]
Language: he
Case sensitive: false
Dimension: 300

Data Source

This model is imported from https://fasttext.cc/docs/en/crawl-vectors.html