Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. Poem_Qafiyah_Detection is a Arabic model originally trained by Yah216.
Predicted Entities
ؤ, ح, م, ل, ه, ز, د, ء, غ, ي, ص, ف, ذ, خ, ث, ج, ن, هـ, ط, س, طن, ى, ب, ت, لا, ش, ر, ا, ع, ض, ك, و, هن, ق, ظ
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_poem_qafiyah_detection","ar") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier])
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("document")
.setOutputCol("token")
val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_poem_qafiyah_detection","ar")
.setInputCols(Array("document", "token"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
| Model Name: | bert_sequence_classifier_poem_qafiyah_detection |
| Compatibility: | Spark NLP 4.3.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | ar |
| Size: | 467.1 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
- https://huggingface.co/Yah216/Poem_Qafiyah_Detection