Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bounti is a Turkish model originally trained by akoksal.
Predicted Entities
positive, neutral, negative
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bounti","tr") \
.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_bounti","tr")
.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)
import nlu
nlu.load("tr.classify.bert.by_akoksal").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | bert_classifier_bounti |
| Compatibility: | Spark NLP 4.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | tr |
| Size: | 692.3 MB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
- https://huggingface.co/akoksal/bounti
- https://ieeexplore.ieee.org/document/9477814
- https://github.com/boun-tabi/BounTi-Turkish-Sentiment-Analysis