Description
Pretrained named entity recognition deep learning model for molecular biology-related terms. It is the version of ner_cellular model augmented with langtest library.
| test_type | before fail_count | after fail_count | before pass_count | after pass_count | minimum pass_rate | before pass_rate | after pass_rate |
|---|---|---|---|---|---|---|---|
| add_ocr_typo | 1240 | 311 | 738 | 1667 | 70% | 37% | 84% |
| lowercase | 840 | 437 | 1153 | 1556 | 70% | 58% | 78% |
| titlecase | 1404 | 466 | 589 | 1527 | 70% | 30% | 77% |
| uppercase | 1788 | 575 | 205 | 1418 | 70% | 10% | 71% |
| weighted average | 5272 | 1789 | 2685 | 6168 | 70% | 33.74% | 77.52% |
Predicted Entities
DNA, Cell_type, Cell_line, RNA, Protein
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
cellular_ner = MedicalNerModel.pretrained("ner_cellular_langtest", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, cellular_ner, ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
result = model.transform(spark.createDataFrame([['Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.']]).toDF("text"))
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val celular_ner = MedicalNerModel.pretrained("ner_cellular_langtest", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, word_embeddings, cellular_ner, ner_converter))
val data = Seq("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""").toDS().toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-------------------------------------------+---------+
|chunk |ner_label|
+-------------------------------------------+---------+
|intracellular signaling proteins |protein |
|human T-cell leukemia virus type 1 promoter|DNA |
|Tax |protein |
|Tax-responsive element 1 |DNA |
|cyclic AMP-responsive members |protein |
|CREB/ATF family |protein |
|transcription factors |protein |
|Tax |protein |
|Tax-responsive element 1 |DNA |
|TRE-1 |DNA |
|lacZ gene |DNA |
|CYC1 promoter |DNA |
|TRE-1 |DNA |
|cyclic AMP response element-binding protein|protein |
|CREB |protein |
|CREB |protein |
|GAL4 activation domain |protein |
|GAD |protein |
|reporter gene |DNA |
|Tax |protein |
+-------------------------------------------+---------+
Model Information
| Model Name: | ner_cellular_langtest |
| Compatibility: | Healthcare NLP 5.1.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.5 MB |
References
Trained on the JNLPBA corpus containing more than 2.404 publication abstracts with ‘embeddings_clinical’.
Benchmarking
label precision recall f1-score support
B-DNA 0.81 0.79 0.80 1026
B-RNA 0.79 0.90 0.84 87
B-cell_line 0.79 0.74 0.77 457
B-cell_type 0.77 0.81 0.79 843
B-protein 0.86 0.90 0.88 3630
I-DNA 0.88 0.87 0.87 1776
I-RNA 0.76 0.99 0.86 127
I-cell_line 0.80 0.75 0.78 819
I-cell_type 0.76 0.86 0.81 1185
I-protein 0.85 0.86 0.85 3069
micro-avg 0.83 0.85 0.84 13019
macro-avg 0.81 0.85 0.82 13019
weighted-avg 0.83 0.85 0.84 13019