Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. autotrain-Arabic_Poetry_by_Subject-920730230 is a Arabic model originally trained by zenkri.
Predicted Entities
اعتذار, فراق, رحمة, هجاء, صبر, مدح, رومنسيه, حزينه, ذم, عتاب, عدل, شوق, ابتهال, دينية, نصيحة, جود, عامه, رثاء, حكمة, المعلقات, وطنيه, سياسية, قصيره, غزل, الاناشيد
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_autotrain_arabic_poetry_by_subject_920730230","ar") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCols(Array("text"))
.setOutputCols(Array("document"))
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_autotrain_arabic_poetry_by_subject_920730230","ar")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | bert_classifier_autotrain_arabic_poetry_by_subject_920730230 |
| Compatibility: | Spark NLP 4.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | ar |
| Size: | 467.2 MB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
- https://huggingface.co/zenkri/autotrain-Arabic_Poetry_by_Subject-920730230