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
- NN
- SYM
- NNP
- VM
- INTF
- JJ
- QF
- CC
- NST
- PSP
- QC
- DEM
- RDP
- PRP
- NEG
- WQ
- RB
- VAUX
- UT
- XC
- RP
- QO
- BM
- NNC
- PPR
- INJ
- CL
- UNK
Live Demo Open in Colab Download Copy S3 URI
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols(["document"]) \
.setOutputCol("token")
posTagger = PerceptronModel.pretrained("pos_msri", "bn") \
.setInputCols(["document", "token"]) \
.setOutputCol("pos")
pipeline = Pipeline(stages=[document_assembler, tokenizer, posTagger])
example = spark.createDataFrame([['জন স্নো ল্যাবস থেকে হ্যালো! ']], ["text"])
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val pos = PerceptronModel.pretrained("pos_msri", "bn")
.setInputCols(Array("document", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, pos))
val data = Seq("জন স্নো ল্যাবস থেকে হ্যালো! ").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = [""জন স্নো ল্যাবস থেকে হ্যালো! ""]
token_df = nlu.load('bn.pos').predict(text)
token_df
Results
token pos
0 জন NN
1 স্নো NN
2 ল্যাবস NN
3 থেকে PSP
4 হ্যালো JJ
5 ! SYM
Model Information
| Model Name: | pos_msri |
| Compatibility: | Spark NLP 3.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [pos] |
| Language: | bn |