Description
This model was imported from Hugging Face and it’s been trained to detect time-related terminology, leveraging RoBERTa embeddings and RobertaForTokenClassification for NER purposes.
Predicted Entities
Period, Year, Calendar-Interval, Month-Of-Year, Day-Of-Month, Day-Of-Week, Hour-Of-Day, Minute-Of-Hour, Number, Second-Of-Minute, Time-Zone, Part-Of-Day, Season-Of-Year, AMPM-Of-Day, Part-Of-Week, Week-Of-Year, Two-Digit-Year, Sum, Difference, Union, Intersection, Every-Nth, This, Last, Next, Before, After, Between, NthFromStart, NthFromEnd, Frequency, Modifier
Live Demo Open in Colab Download Copy S3 URI
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
tokenClassifier = RoBertaForTokenClassification.pretrained("roberta_token_classifier_timex_semeval", "en"))\
.setInputCols(["sentence",'token'])\
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, tokenClassifier, ner_converter])
empty_data = spark.createDataFrame([[""]]).toDF("text")
model = nlpPipeline.fit(empty_data)
text = """Model training was started at 22:12C and it took 3 days from Tuesday to Friday."""
result = model.transform(spark.createDataFrame([[text]]).toDF("text"))
val documentAssembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val tokenClassifier = RoBertaForTokenClassification.pretrained("roberta_token_classifier_timex_semeval", "en"))
.setInputCols(Array("sentence","token"))
.setOutputCol("ner")
ner_converter = NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDetector, tokenizer, tokenClassifier, ner_converter))
val example = Seq.empty["Model training was started at 22:12C and it took 3 days from Tuesday to Friday."].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("en.ner.time").predict("""Model training was started at 22:12C and it took 3 days from Tuesday to Friday.""")
Results
+-------+-----------------+
|chunk |ner_label |
+-------+-----------------+
|22:12C |Period |
|3 |Number |
|days |Calendar-Interval|
|Tuesday|Day-Of-Week |
|to |Between |
|Friday |Day-Of-Week |
+-------+-----------------+
Model Information
| Model Name: | roberta_token_classifier_timex_semeval |
| Compatibility: | Spark NLP 3.3.4+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 439.5 MB |
| Case sensitive: | true |
| Max sentense length: | 256 |