Description
This model contains a deep bidirectional transformer trained on Wikipedia and the BookCorpus in Romanian Language. The details are described in the paper “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”.
Predicted Entities
How to use
...
embeddings = BertEmbeddings.pretrained("bert_base_cased", "ro") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
result = pipeline_model.transform(spark.createDataFrame([['I love NLP']], ["text"]))
...
val embeddings = BertEmbeddings.pretrained("bert_base_cased", "ro")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
val data = Seq("I love NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
Generates 768 dimensional embeddings vectors per token
Model Information
| Model Name: | bert_base_cased |
| Compatibility: | Spark NLP 3.2.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | ro |
| Case sensitive: | true |
Benchmarking
This model is imported from https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1