Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. nb-bert-base-mnli
is a Norwegian model originally trained by NbAiLab
.
Predicted Entities
neutral
, contradiction
, entailment
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_nb_base_mnli","no") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, sequenceClassifier])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_nb_base_mnli","no")
.setInputCols(Array("document", "token"))
.setOutputCol("ner")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Model Information
Model Name: | bert_sequence_classifier_nb_base_mnli |
Compatibility: | Spark NLP 4.3.1+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [ner] |
Language: | no |
Size: | 668.9 MB |
Case sensitive: | true |
Max sentence length: | 128 |
References
- https://huggingface.co/NbAiLab/nb-bert-base-mnli
- https://arxiv.org/abs/1909.00161
- https://colab.research.google.com/gist/peregilk/769b5150a2f807219ab8f15dd11ea449/nbailab-mnli-norwegian-demo.ipynb
- https://github.com/NBAiLab/notram