Description
“
ALBERT Model 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.
How to use
document_assembler = DocumentAssembler() .setInputCol('text') .setOutputCol('document')
tokenizer = Tokenizer() .setInputCols(['document']) .setOutputCol('token')
tokenClassifier = AlbertForTokenClassification .pretrained('albert_token_classifier_base_by_hooshvarelab', 'en') .setInputCols(['token', 'document']) .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([['My name is John!']]).toDF("text")
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val tokenClassifier = AlbertForTokenClassification.pretrained("albert_token_classifier_base_by_hooshvarelab", "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.empty["My name is John!"].toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
Model Information
| Model Name: | albert_token_classifier_base_by_hooshvarelab |
| Compatibility: | Spark NLP 5.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, document] |
| Output Labels: | [label] |
| Language: | en |
| Size: | 41.9 MB |
| Case sensitive: | true |