Word Embeddings for Japanese (japanese_cc_300d)

Description

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.

Predicted Entities

Download

How to use

import sparknlp
from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline

documentAssembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

sentence = SentenceDetector() \
    .setInputCols(["document"]) \
    .setOutputCol("sentence")

word_segmenter = WordSegmenterModel.pretrained("wordseg_gsd_ud", "ja") \
    .setInputCols(["sentence"]) \
    .setOutputCol("token")

pipeline = Pipeline().setStages([
    documentAssembler,
    sentence,
    word_segmenter,
    embeddings
])

data = spark.createDataFrame([["宮本茂氏は、日本の任天堂のゲームプロデューサーです。"]]).toDF("text")
model = pipeline.fit(data)
result = model.transform(data)
result.selectExpr("explode(arrays_zip(embeddings.result, embeddings.embeddings))").show()
import spark.implicits._
import com.johnsnowlabs.nlp.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.{SentenceDetector, WordSegmenterModel}
import com.johnsnowlabs.nlp.embeddings.WordEmbeddingsModel
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val sentence = new SentenceDetector()
  .setInputCols("document")
  .setOutputCol("sentence")

val word_segmenter = WordSegmenterModel.pretrained("wordseg_gsd_ud", "ja")
  .setInputCols("sentence")
  .setOutputCol("token")

val embeddings = WordEmbeddingsModel.pretrained("japanese_cc_300d", "ja")
  .setInputCols("sentence", "token")
  .setOutputCol("embeddings")

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  sentence,
  word_segmenter,
  embeddings
))

val data = Seq("宮本茂氏は、日本の任天堂のゲームプロデューサーです。").toDF("text")
val model = pipeline.fit(data)
val result = model.transform(data)
result.selectExpr("explode(arrays_zip(embeddings.result, embeddings.embeddings))").show()

Results

+---------------------------+
|                        col|
+---------------------------+
|     [宮本, [0.1944, 0.4...|
|      [茂, [-0.079, 0.09...|
|      [氏, [-0.1053, 0.1...|
|      [は, [0.0732, -0.0...|
|      [、, [0.0571, -0.0...|
|     [日本, [0.1844, 0.0...|
|      [の, [0.0109, -0.0...|
|     [任天, [0.0, 0.0, 0...|
|      [堂, [-0.1972, 0.0...|
|      [の, [0.0109, -0.0...|
|    [ゲーム, [0.013, 0.0...|
|[プロデューサー, [-0.010...|
|     [です, [0.0036, -0....|
|      [。, [0.069, -0.01...|
+---------------------------+

Model Information

Model Name: japanese_cc_300d
Type: embeddings
Compatibility: Spark NLP 3.2.2+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [embeddings]
Language: ja
Case sensitive: false
Dimension: 300

Data Source

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