Description
This model annotates named entities in a text, that can be used to find features such as names of people, places, and organizations. 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 bert_base_chinese embeddings model from BertEmbeddings annotator as an input, so be sure to use the same embeddings in the pipeline.
Predicted Entities
Persons-PER, Locations-LOC, Organizations-ORG.
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_large", "zh")\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained(name='bert_base_chinese', lang='zh')\
.setInputCols("document", "token") \
.setOutputCol("embeddings")
ner = NerDLModel.pretrained("ner_msra_bert_768d", "zh") \
.setInputCols(["document", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("entities")
pipeline = Pipeline(stages=[document_assembler, sentence_detector, word_segmenter, embeddings, ner, ner_converter])
example = spark.createDataFrame([['马云在浙江省杭州市出生，是阿里巴巴集团的主要创始人。']], ["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_large", "zh")
.setInputCols(Array("sentence"))
.setOutputCol("token")
val embeddings = BertEmbeddings.pretrained(name='bert_base_chinese', lang='zh')
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val ner = NerDLModel.pretrained("ner_msra_bert_768d", "zh")
.setInputCols("document", "token", "embeddings")
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols("sentence", "token", "ner")
.setOutputCol("entities")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, word_segmenter, embeddings, ner, ner_converter))
val data = Seq("马云在浙江省杭州市出生，是阿里巴巴集团的主要创始人。").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["""马云在浙江省杭州市出生，是阿里巴巴集团的主要创始人。"""]
ner_df = nlu.load('zh.ner.msra.bert_768D').predict(text, output_level='token')
ner_df
Results
+------------+---+
|token |ner|
+------------+---+
|马云 |PER|
|浙江省 |LOC|
|杭州市 |LOC|
|出生 |ORG|
|阿里巴巴集团|ORG|
|创始人 |PER|
+------------+---+
Model Information
| Model Name: | ner_msra_bert_768d |
| Type: | ner |
| Compatibility: | Spark NLP 2.7.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | zh |
Data Source
The model was trained on the MSRA (Levow, 2006) data set created by “Microsoft Research Asia”.
Benchmarking
| ner_tag | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| LOC | 0.97 | 0.97 | 0.97 | 2777 |
| O | 1.00 | 1.00 | 1.00 | 146826 |
| ORG | 0.88 | 0.99 | 0.93 | 1292 |
| PER | 0.97 | 0.97 | 0.97 | 1430 |
| accuracy | 1.00 | 152325 | | |
| macro avg | 0.95 | 0.98 | 0.97 | 152325 |
| weighted avg | 1.00 | 1.00 | 1.00 | 152325 |