Description
Pretrained XLMRobertaForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. xlm-roberta-large-uncased-mit-movie-trivia is a English model originally trained by tner.
Predicted Entities
actor, origin, plot, award, character name, relationship, opinion, director, genre, soundtrack, quote, date
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
token_classifier = XlmRoBertaForTokenClassification.pretrained("xlmroberta_ner_large_uncased_mit_movie_trivia","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["document", "token", "ner"])\
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, token_classifier, ner_converter])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCols(Array("text"))
.setOutputCols(Array("document"))
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val token_classifier = XlmRoBertaForTokenClassification.pretrained("xlmroberta_ner_large_uncased_mit_movie_trivia","en")
.setInputCols(Array("document", "token"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("document", "token', "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, token_classifier, ner_converter))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.ner.xlmr_roberta.trivia_movie.uncased_large").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | xlmroberta_ner_large_uncased_mit_movie_trivia |
| Compatibility: | Spark NLP 4.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 1.7 GB |
| Case sensitive: | false |
| Max sentence length: | 256 |
References
- https://huggingface.co/tner/xlm-roberta-large-uncased-mit-movie-trivia
- https://github.com/asahi417/tner