Description
This model annotates named entities in a text, that can be used to find features such as names of people, places, and organizations in the BIO format. The model does not read words directly but instead reads word embeddings, which represent words as points such that more semantically similar words are closer together.
This model uses the pre-trained glove_840B_300 embeddings model from WordEmbeddings annotator as an input, so be sure to use the same embeddings in the pipeline.
Predicted Entities
Dates-DT, Locations-LC, Organizations-OG, Persons-PS, Time-TI.
Live Demo Open in Colab Download Copy S3 URI
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
word_segmenter = WordSegmenterModel.pretrained("wordseg_kaist_ud", "ko")\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("glove_840B_300", "xx")\
.setInputCols("document", "token") \
.setOutputCol("embeddings")
ner = NerDLModel.pretrained("ner_kmou_glove_840B_300d", "ko") \
.setInputCols(["document", "token", "embeddings"]) \
.setOutputCol("ner")
...
pipeline = Pipeline(stages=[document_assembler, sentence_detector, word_segmenter, embeddings, ner, ner_converter])
example = spark.createDataFrame([['라이프니츠 의 주도 로 베를린 에 세우 어 지 ㄴ 베를린 과학아카데미']], ["text"])
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val word_segmenter = WordSegmenterModel.pretrained("wordseg_kaist_ud", "ko")
.setInputCols(Array("sentence"))
.setOutputCol("token")
val embeddings = WordEmbeddingsModel.pretrained("glove_840B_300", "xx")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val ner = NerDLModel.pretrained("ner_kmou_glove_840B_300d", "ko")
.setInputCols(Array("document", "token", "embeddings"))
.setOutputCol("ner")
...
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, word_segmenter, embeddings, ner, ner_converter))
val data = Seq("라이프니츠 의 주도 로 베를린 에 세우 어 지 ㄴ 베를린 과학아카데미").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["라이프니츠 의 주도 로 베를린 에 세우 어 지 ㄴ 베를린 과학아카데미"]
ner_df = nlu.load('ko.ner').predict(text)
ner_df
Results
+------------+----+
|token |ner |
+------------+----+
|라이프니츠 |B-PS|
|베를린 |B-OG|
|과학아카데미 |I-OG|
+------------+----+
Model Information
| Model Name: | ner_kmou_glove_840B_300d |
| Type: | ner |
| Compatibility: | Spark NLP 2.7.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | ko |
Data Source
The model was trained by the Korea Maritime and Ocean University NLP data set.
Benchmarking
| ner_tag | precision | recall | f1-score | support |
|:------------:|:---------:|:------:|:--------:|:-------:|
| B-DT | 0.95 | 0.29 | 0.44 | 132 |
| B-LC | 0.00 | 0.00 | 0.00 | 166 |
| B-OG | 1.00 | 0.06 | 0.11 | 149 |
| B-PS | 0.86 | 0.13 | 0.23 | 287 |
| B-TI | 0.50 | 0.05 | 0.09 | 20 |
| I-DT | 0.94 | 0.36 | 0.52 | 164 |
| I-LC | 0.00 | 0.00 | 0.00 | 4 |
| I-OG | 1.00 | 0.08 | 0.15 | 25 |
| I-PS | 1.00 | 0.08 | 0.15 | 12 |
| I-TI | 0.50 | 0.10 | 0.17 | 10 |
| O | 0.94 | 1.00 | 0.97 | 12830 |
| accuracy | 0.94 | 13799 | | |
| macro avg | 0.70 | 0.20 | 0.26 | 13799 |
| weighted avg | 0.93 | 0.94 | 0.92 | 13799 |