Description
Pretrained RoBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta-base-finetuned-sst2
is a English model originally trained by aristotletan
.
Predicted Entities
analogous event
, disposal
, appointment of receiver
, event or events
, repudiation
, non payment
, assets
, others
, breach of obligations
, cross default
, winding up
, nationalisation
, judgement
, composition and arrangement
, jeopardy
, insolvency
, revocation of license
, legal proceedings
, cessation of business
, invalidity
, misrepresentation
, creditor control
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = RoBertaForSequenceClassification.pretrained("roberta_classifier_aristotletan_base_finetuned_sst2","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])
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 seq_classifier = RoBertaForSequenceClassification.pretrained("roberta_classifier_aristotletan_base_finetuned_sst2","en")
.setInputCols(Array("document", "token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, seq_classifier))
val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.roberta.base_finetuned.by_aristotletan").predict("""PUT YOUR STRING HERE""")
Model Information
Model Name: | roberta_classifier_aristotletan_base_finetuned_sst2 |
Compatibility: | Spark NLP 5.2.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [class] |
Language: | en |
Size: | 427.5 MB |
Case sensitive: | true |
Max sentence length: | 256 |
References
References
- https://huggingface.co/aristotletan/roberta-base-finetuned-sst2