Description
Pretrained XLMRobertaForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. tner-xlm-roberta-large-all-english is a English model originally trained by asahi417.
Predicted Entities
time, corporation, ordinal number, cardinal number, rna, geopolitical area, protein, product, percent, dna, disease, cell line, law, other, date, chemical, event, work of art, cell type, location, language, quantity, facility, organization, group, money, person
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
token_classifier = XlmRoBertaForTokenClassification.pretrained("xlmroberta_ner_tner_large_all_english","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_tner_large_all_english","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.large").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | xlmroberta_ner_tner_large_all_english |
| Compatibility: | Spark NLP 4.1.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 1.8 GB |
| Case sensitive: | true |
| Max sentence length: | 256 |
References
- https://huggingface.co/asahi417/tner-xlm-roberta-large-all-english
- https://github.com/asahi417/tner