Description
This pretrained pipeline is built on the top of ner_chemd_clinical model.
Predicted Entities
MULTIPLE, TRIVIAL, SYSTEMATIC, FORMULA, FAMILY, IDENTIFIER, ABBREVIATION
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
text = '''Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
val text = "Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition."
val result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-----------------------------------|--------:|------:|:-------------|-------------:|
| 0 | Lystabactins | 65 | 76 | FAMILY | 0.9841 |
| 1 | lystabactins A, B, and C | 278 | 301 | MULTIPLE | 0.813429 |
| 2 | amino acid | 392 | 401 | FAMILY | 0.74585 |
| 3 | lystabactins | 426 | 437 | FAMILY | 0.8007 |
| 4 | serine | 455 | 460 | TRIVIAL | 0.9924 |
| 5 | Ser | 463 | 465 | FORMULA | 0.9999 |
| 6 | asparagine | 469 | 478 | TRIVIAL | 0.9795 |
| 7 | Asn | 481 | 483 | FORMULA | 0.9999 |
| 8 | formylated/hydroxylated ornithines | 491 | 524 | FAMILY | 0.50085 |
| 9 | FOHOrn | 527 | 532 | FORMULA | 0.509 |
| 10 | dihydroxy benzoic acid | 536 | 557 | SYSTEMATIC | 0.6346 |
| 11 | amino acid | 602 | 611 | FAMILY | 0.4204 |
| 12 | 4,8-diamino-3-hydroxyoctanoic acid | 614 | 647 | SYSTEMATIC | 0.9124 |
| 13 | LySta | 650 | 654 | ABBREVIATION | 0.9193 |
Model Information
| Model Name: | ner_chemd_clinical_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel