Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. autotrain-livedoor_news-732022289 is a Japanese model originally trained by jurader.
Predicted Entities
livedoor-homme, movie-enter, smax, kaden-channel, it-life-hack, dokujo-tsushin, sports-watch, topic-news, peachy
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_autotrain_livedoor_news_732022289","ja") \
.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_autotrain_livedoor_news_732022289","ja")
.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_autotrain_livedoor_news_732022289 |
| Compatibility: | Spark NLP 4.3.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | ja |
| Size: | 417.4 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
- https://huggingface.co/jurader/autotrain-livedoor_news-732022289