Description
Pretrained Electra Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. electra-base-turkish-mc4-uncased-generator is a Turkish model orginally trained by dbmdz.
Predicted Entities
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_uncased_generator","tr") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["Spark NLP'yi seviyorum"]]).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 embeddings = BertEmbeddings.pretrained("electra_embeddings_electra_base_turkish_mc4_uncased_generator","tr")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("Spark NLP'yi seviyorum").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("tr.embed.electra.uncased_base").predict("""Spark NLP'yi seviyorum""")
Model Information
| Model Name: | electra_embeddings_electra_base_turkish_mc4_uncased_generator |
| Compatibility: | Spark NLP 5.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [embeddings] |
| Language: | tr |
| Size: | 130.0 MB |
| Case sensitive: | false |