Description
SentenceDetectorDL (SDDL) is based on a general-purpose neural network model for sentence boundary detection. The task of sentence boundary detection is to identify sentences within a text. Many natural language processing tasks take a sentence as an input unit, such as part-of-speech tagging, dependency parsing, named entity recognition or machine translation.
Live Demo Open in Colab Download Copy S3 URI
How to use
documenter = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentencerDL = SentenceDetectorDLModel\
.pretrained("sentence_detector_dl", "en") \
.setInputCols(["document"]) \
.setOutputCol("sentences")
sd_model = LightPipeline(PipelineModel(stages=[documenter, sentencerDL]))
sd_model.fullAnnotate("""John loves Mary.Mary loves Peter. Peter loves Helen .Helen loves John; Total: four people involved.""")
val documenter = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val model = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val pipeline = new Pipeline().setStages(Array(documenter, model))
val data = Seq("John loves Mary.Mary loves Peter. Peter loves Helen .Helen loves John; Total: four people involved.").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("sentence_detector").predict("""John loves Mary.Mary loves Peter. Peter loves Helen .Helen loves John; Total: four people involved.""")
Results
+---+------------------------------+
| 0 | John loves Mary. |
+---+------------------------------+
| 1 | Mary loves Peter |
+---+------------------------------+
| 2 | Peter loves Helen . |
+---+------------------------------+
| 3 | Helen loves John; |
+---+------------------------------+
| 4 | Total: four people involved. |
+---+------------------------------+
Model Information
| Model Name: | sentence_detector_dl |
| Compatibility: | Spark NLP 2.7.0+ |
| Edition: | Official |
| Input Labels: | [document] |
| Output Labels: | [sentences] |
| Language: | en |
Data Source
Please visit the repo for more information https://github.com/dbmdz/deep-eos
Benchmarking
label Accuracy Recall Prec F1
0 0.98 1.00 0.96 0.98