Description
Pretrained DistilBERT Classification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. distilbert-base-uncased-newspop-student is a Spanish model originally trained by mrm8488.
Predicted Entities
palestine, obama, microsoft, economy
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq = DistilBertForSequenceClassification.pretrained("distilbert_classifier_base_uncased_newspop_student","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq])
data = spark.createDataFrame([["PUT YOUR STRING HERE."]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val seq = DistilBertForSequenceClassification.pretrained("distilbert_classifier_base_uncased_newspop_student","en")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq))
val data = Seq("PUT YOUR STRING HERE.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.distil_bert.news.uncased_base").predict("""PUT YOUR STRING HERE.""")
Model Information
| Model Name: | distilbert_classifier_base_uncased_newspop_student |
| Compatibility: | Spark NLP 4.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 249.8 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
https://huggingface.co/mrm8488/distilbert-base-uncased-newspop-student