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
- ADJ
- ADP
- ADV
- AUX
- CCONJ
- DET
- NOUN
- NUM
- PART
- PRON
- PROPN
- PUNCT
- VERB
- X
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")
pos = PerceptronModel.pretrained("pos_afribooms", "af")\
.setInputCols(["document", "token"])\
.setOutputCol("pos")
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
posTagger
])
example = spark.createDataFrame([['Die kodes wat gebruik word , moet duidelik en verstaanbaar vir leerders en ouers wees .']], ["text"])
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val pos = PerceptronModel.pretrained("pos_afribooms", "af")
.setInputCols(Array("document", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer ,pos))
val data = Seq("Die kodes wat gebruik word , moet duidelik en verstaanbaar vir leerders en ouers wees .").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = [""Die kodes wat gebruik word , moet duidelik en verstaanbaar vir leerders en ouers wees .""]
token_df = nlu.load('af.pos.afribooms').predict(text)
token_df
Results
+---------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------+
|text |result |
+---------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------+
|Die kodes wat gebruik word , moet duidelik en verstaanbaar vir leerders en ouers wees .|[DET, NOUN, PRON, VERB, AUX, PUNCT, AUX, ADJ, CCONJ, ADJ, ADP, NOUN, CCONJ, NOUN, AUX, PUNCT]|
+---------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------+
Model Information
| Model Name: | pos_afribooms |
| Compatibility: | Spark NLP 2.7.5+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [pos] |
| Language: | af |
Data Source
The model was trained on the Universal Dependencies data set.
Benchmarking
| | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| ADJ | 0.60 | 0.67 | 0.63 | 665 |
| ADP | 0.76 | 0.78 | 0.77 | 1299 |
| ADV | 0.74 | 0.69 | 0.72 | 523 |
| AUX | 0.85 | 0.83 | 0.84 | 663 |
| CCONJ | 0.71 | 0.71 | 0.71 | 380 |
| DET | 0.83 | 0.70 | 0.76 | 1014 |
| NOUN | 0.69 | 0.72 | 0.71 | 2025 |
| NUM | 0.76 | 0.76 | 0.76 | 42 |
| PART | 0.67 | 0.68 | 0.68 | 322 |
| PRON | 0.87 | 0.87 | 0.87 | 794 |
| PROPN | 0.82 | 0.73 | 0.77 | 156 |
| PUNCT | 0.68 | 0.70 | 0.69 | 877 |
| SCONJ | 0.85 | 0.85 | 0.85 | 210 |
| SYM | 0.87 | 0.88 | 0.87 | 142 |
| VERB | 0.69 | 0.72 | 0.70 | 889 |
| X | 0.35 | 0.14 | 0.20 | 64 |
| accuracy | | | 0.74 | 10065 |
| macro avg | 0.73 | 0.72 | 0.72 | 10065 |
| weighted avg | 0.74 | 0.74 | 0.74 | 10065 |