Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.actionable_policy_bert_base_multilingual_uncased_f1_weighted is a Multilingual model originally trained by faodl.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
sequenceClassifier = BertForSequenceClassification.pretrained("actionable_policy_bert_base_multilingual_uncased_f1_weighted","xx") \
.setInputCols(["documents","token"]) \
.setOutputCol("class")
pipeline = Pipeline().setStages([documentAssembler, tokenizer, sequenceClassifier])
data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCols("text")
.setOutputCols("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val sequenceClassifier = BertForSequenceClassification.pretrained("actionable_policy_bert_base_multilingual_uncased_f1_weighted", "xx")
.setInputCols(Array("documents","token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
val data = Seq("I love spark-nlp").toDS.toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)
Model Information
| Model Name: | actionable_policy_bert_base_multilingual_uncased_f1_weighted |
| Compatibility: | Spark NLP 5.5.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | xx |
| Size: | 627.7 MB |
References
https://huggingface.co/faodl/actionable-policy-bert-base-multilingual-uncased-f1_weighted