Description
Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. marathi-bert is a Marathi model orginally trained by l3cube-pune.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("bert_embeddings_marathi_bert","mr") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["मला स्पार्क एनएलपी आवडते"]]).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("bert_embeddings_marathi_bert","mr")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("मला स्पार्क एनएलपी आवडते").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("mr.embed.marathi_bert").predict("""मला स्पार्क एनएलपी आवडते""")
Model Information
| Model Name: | bert_embeddings_marathi_bert |
| Compatibility: | Spark NLP 3.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | mr |
| Size: | 668.0 MB |
| Case sensitive: | true |
References
- https://huggingface.co/l3cube-pune/marathi-bert
- https://github.com/l3cube-pune/MarathiNLP
- https://arxiv.org/abs/2202.01159