Description
Pretrained Part of Speech model, uploaded to Hugging Face, adapted and imported into Spark NLP. chinese-roberta-base-upos is a Chinese model orginally trained by KoichiYasuoka.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer() \
.setInputCols("sentence") \
.setOutputCol("token")
tokenClassifier = BertForTokenClassification.pretrained("bert_pos_chinese_roberta_base_upos","zh") \
.setInputCols(["sentence", "token"]) \
.setOutputCol("pos")
pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, tokenClassifier])
data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val tokenClassifier = BertForTokenClassification.pretrained("bert_pos_chinese_roberta_base_upos","zh")
.setInputCols(Array("sentence", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(documentAssembler,sentenceDetector, tokenizer, tokenClassifier))
val data = Seq("I love Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("zh.pos.chinese_roberta_base_upos").predict("""I love Spark NLP""")
Model Information
| Model Name: | bert_pos_chinese_roberta_base_upos |
| Compatibility: | Spark NLP 3.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | zh |
| Size: | 381.8 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
- https://huggingface.co/KoichiYasuoka/chinese-roberta-base-upos
- https://universaldependencies.org/u/pos/
- https://github.com/KoichiYasuoka/esupar