<commit_before>---
layout: model
title: Word Embeddings for Japanese (dutch_cc_300d)
author: <NAME>
name: dutch_cc_300d
date: 2021-10-04
tags: [nl, embeddings, open_source]
task: Embeddings
language: nl
edition: Spark NLP 3.3.0
spark_version: 3.0
supported: true
article_header:
  type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This model is trained on Common Crawl and Wikipedia dataset for Dutch language 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.

## Predicted Entities
<commit_msg>Update 2021-10-04-dutch_cc_300d_nl.md<commit_after>---
layout: model
title: Word Embeddings for Dutch (dutch_cc_300d)
author: <NAME>
name: dutch_cc_300d
date: 2021-10-04
tags: [nl, embeddings, open_source]
task: Embeddings
language: nl
edition: Spark NLP 3.3.0
spark_version: 3.0
supported: true
article_header:
  type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This model is trained on Common Crawl and Wikipedia dataset for Dutch language 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.

## Predicted Entities

{'fraction_non_alphanumeric': 0.06041986687147977, 'fraction_numerical': 0.03533026113671275, 'mean_word_length': 5.068322981366459, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3134927', 'n_tokens_mistral': 602, 'n_tokens_neox': 531, 'n_words': 257}