Description
A Part of Speech classifier predicts a grammatical label for every token in the input text. Implemented with an averaged perceptron architecture.
Predicted Entities
- ADP
- DET
- ADJ
- NOUN
- VERB
- PRON
- PROPN
- X
- PUNCT
- CCONJ
- NUM
- ADV
- AUX
- SCONJ
- PART
- INTJ
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")
tokenizer = Tokenizer() \
.setInputCols("sentence") \
.setOutputCol("token")
posTagger = PerceptronModel.pretrained("pos_ud_hdt", "de") \
.setInputCols(["document", "token"]) \
.setOutputCol("pos")
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
posTagger
])
data = spark.createDataFrame([["Hallo aus John Snow Labs! "]], ["text"])
result = pipeline.fit(data).transform(data)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val pos = PerceptronModel.pretrained("pos_ud_hdt", "de")
.setInputCols(Array("document", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, pos))
val data = Seq("Hallo aus John Snow Labs! ").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["Hallo aus John Snow Labs!"]
token_df = nlu.load('de.pos').predict(text)
token_df
Results
token pos
0 Hallo NOUN
1 aus ADP
2 John PROPN
3 Snow PROPN
4 Labs PROPN
5 ! PUNCT
Model Information
| Model Name: | pos_ud_hdt |
| Compatibility: | Spark NLP 3.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [pos] |
| Language: | de |