Description
“ DeBertaForTokenClassification can load DeBERTA Models v2 and v3 with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
deberta_token_classifer_v3_base_food is a fine-tuned DeBERTa model that is ready to be used for Token Classification task such as Named Entity Recognition and it achieves state-of-the-art performance.
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols(['document'])\
.setOutputCol('token')
tokenClassifier = DeBertaForTokenClassification.pretrained("deberta_token_classifer_v3_base_food", "en")\
.setInputCols(["document", "token"])\
.setOutputCol("ner")\
.setCaseSensitive(True)\
.setMaxSentenceLength(512)
# since output column is IOB/IOB2 style, NerConverter can extract entities
ner_converter = NerConverter()\
.setInputCols(['document', 'token', 'ner'])\
.setOutputCol('entities')
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
tokenClassifier,
ner_converter
])
example = spark.createDataFrame([['I really liked that movie!']]).toDF("text")
result = pipeline.fit(example).transform(example)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val tokenClassifier = DeBertaForTokenClassification.pretrained("deberta_token_classifer_v3_base_food", "en")
.setInputCols("document", "token")
.setOutputCol("ner")
.setCaseSensitive(true)
.setMaxSentenceLength(512)
// since output column is IOB/IOB2 style, NerConverter can extract entities
val ner_converter = NerConverter()
.setInputCols("document", "token", "ner")
.setOutputCol("entities")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, tokenClassifier, ner_converter))
val example = Seq("I really liked that movie!").toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
Model Information
| Model Name: | deberta_token_classifer_v3_base_food |
| Compatibility: | Spark NLP 5.5.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, document] |
| Output Labels: | [label] |
| Language: | en |
| Size: | 609.7 MB |
| Case sensitive: | true |