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_mtg", "te") \
.setInputCols(["document", "token"]) \
.setOutputCol("pos")
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
posTagger
])
example = spark.createDataFrame([['ఆయన వస్తున్నారా , లేదా ?']], ["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_mtg", "te")
.setInputCols(Array("document", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector,tokenizer, pos))
val data = Seq("ఆయన వస్తున్నారా , లేదా ?").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = [""ఆయన వస్తున్నారా , లేదా ?""]
token_df = nlu.load('te.pos.mtg').predict(text)
token_df
Results
+------------------------+--------------------------------+
|text |result |
+------------------------+--------------------------------+
|ఆయన వస్తున్నారా , లేదా ?|[PRON, VERB, PUNCT, VERB, PUNCT]|
+------------------------+--------------------------------+
Model Information
| Model Name: | pos_mtg |
| Compatibility: | Spark NLP 2.7.5+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [pos] |
| Language: | te |
Data Source
The model was trained on the Universal Dependencies data set.
Benchmarking
| | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| ADJ | 0.50 | 0.40 | 0.44 | 5 |
| ADP | 0.75 | 0.43 | 0.55 | 7 |
| ADV | 0.78 | 0.68 | 0.72 | 31 |
| CCONJ | 0.00 | 0.00 | 0.00 | 1 |
| DET | 0.89 | 0.89 | 0.89 | 18 |
| INTJ | 0.00 | 0.00 | 0.00 | 0 |
| NOUN | 0.82 | 0.76 | 0.79 | 171 |
| NUM | 0.83 | 0.42 | 0.56 | 12 |
| PART | 0.00 | 0.00 | 0.00 | 2 |
| PRON | 0.88 | 0.93 | 0.91 | 122 |
| PROPN | 0.69 | 0.86 | 0.77 | 21 |
| PUNCT | 0.99 | 0.99 | 0.99 | 165 |
| SCONJ | 0.71 | 1.00 | 0.83 | 5 |
| VERB | 0.84 | 0.92 | 0.88 | 161 |
| accuracy | | | 0.87 | 721 |
| macro avg | 0.62 | 0.59 | 0.59 | 721 |
| weighted avg | 0.87 | 0.87 | 0.86 | 721 |