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.
Predicted Entities
Breaks text in sentences.
Live Demo Open in Colab Copy S3 URI
How to use
documenter = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentencerDL = SentenceDetectorDLModel\
.pretrained("sentence_detector_dl_healthcare","en","clinical/models") \
.setInputCols(["document"]) \
.setOutputCol("sentences")
text = """He was given boluses of MS04 with some effect, he has since been placed on a PCA - he take 80mg of oxycontin at home, his PCA dose is ~ 2 the morphine dose of the oxycontin, he has also received ativan for anxiety.Repleted with 20 meq kcl po, 30 mmol K-phos iv and 2 gms mag so4 iv.
Size: Prostate gland measures 10x1.1x 4.9 cm (LS x AP x TS). Estimated volume is
51.9 ml. , and is mildly enlarged in size.Normal delineation pattern of the prostate gland is preserved.
"""
sd_model = LightPipeline(PipelineModel(stages=[documenter, sentencerDL]))
result = sd_model.fullAnnotate(text)
val documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val model = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val pipeline = new Pipeline().setStages(Array(documenter, model))
val data = Seq("""He was given boluses of MS04 with some effect, he has since been placed on a PCA - he take 80mg of oxycontin at home, his PCA dose is ~ 2 the morphine dose of the oxycontin, he has also received ativan for anxiety.Repleted with 20 meq kcl po, 30 mmol K-phos iv and 2 gms mag so4 iv.
Size: Prostate gland measures 10x1.1x 4.9 cm (LS x AP x TS). Estimated volume is
51.9 ml. , and is mildly enlarged in size.Normal delineation pattern of the prostate gland is preserved.""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.detect_sentence.clinical").predict("""He was given boluses of MS04 with some effect, he has since been placed on a PCA - he take 80mg of oxycontin at home, his PCA dose is ~ 2 the morphine dose of the oxycontin, he has also received ativan for anxiety.Repleted with 20 meq kcl po, 30 mmol K-phos iv and 2 gms mag so4 iv.
Size: Prostate gland measures 10x1.1x 4.9 cm (LS x AP x TS). Estimated volume is
51.9 ml. , and is mildly enlarged in size.Normal delineation pattern of the prostate gland is preserved.
""")
Results
| | sentences |
|---:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | He was given boluses of MS04 with some effect, he has since been placed on a PCA - he take 80mg of oxycontin at home, his PCA dose is ~ 2 the morphine dose of the oxycontin, he has also received ativan for anxiety. |
| 1 | Repleted with 20 meq kcl po, 30 mmol K-phos iv and 2 gms mag so4 iv. |
| 2 | Size: Prostate gland measures 10x1.1x 4.9 cm (LS x AP x TS). |
| 3 | Estimated volume is |
| | 51.9 ml. , and is mildly enlarged in size. |
| 4 | Normal delineation pattern of the prostate gland is preserved. |
Model Information
| Model Name: | sentence_detector_dl_healthcare |
| Compatibility: | Healthcare NLP 3.2.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document] |
| Output Labels: | [sentences] |
| Language: | en |
Data Source
Healthcare SDDL model is trained on in-house domain specific data.
Benchmarking
label Accuracy Recall Prec F1
0 0.98 1.00 0.96 0.98