Description
Pretrained BertSentenceEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.sent_turkish_medium_bert_uncased is a Turkish model originally trained by ytu-ce-cosmos.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
sentenceDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx") \
.setInputCols(["document"]) \
.setOutputCol("sentence")
embeddings = BertSentenceEmbeddings.pretrained("sent_turkish_medium_bert_uncased","tr") \
.setInputCols(["sentence"]) \
.setOutputCol("embeddings")
pipeline = Pipeline().setStages([documentAssembler, sentenceDL, embeddings])
data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val embeddings = BertSentenceEmbeddings.pretrained("sent_turkish_medium_bert_uncased","tr")
.setInputCols(Array("sentence"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDL, embeddings))
val data = Seq("I love spark-nlp").toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
| Model Name: | sent_turkish_medium_bert_uncased |
| Compatibility: | Spark NLP 5.5.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence] |
| Output Labels: | [embeddings] |
| Language: | tr |
| Size: | 157.4 MB |
References
https://huggingface.co/ytu-ce-cosmos/turkish-medium-bert-uncased