sparknlp.annotator.classifier_dl.deberta_for_token_classification#

Contains classes for DeBertaForTokenClassification.

Module Contents#

Classes#

DeBertaForTokenClassification

DeBertaForTokenClassification can load DeBERTa v2&v3 Models with a token

class DeBertaForTokenClassification(classname='com.johnsnowlabs.nlp.annotators.classifier.dl.DeBertaForTokenClassification', java_model=None)[source]#

DeBertaForTokenClassification can load DeBERTa v2&v3 Models 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.

Pretrained models can be loaded with pretrained() of the companion object:

>>> embeddings = DeBertaForTokenClassification.pretrained() \
...     .setInputCols(["token", "document"]) \
...     .setOutputCol("label")

The default model is "deberta_v3_xsmall_token_classifier_conll03", if no name is provided.

For available pretrained models please see the Models Hub.

To see which models are compatible and how to import them see Import Transformers into Spark NLP 🚀.

Input Annotation types

Output Annotation type

DOCUMENT, TOKEN

NAMED_ENTITY

Parameters:
batchSize

Batch size. Large values allows faster processing but requires more memory, by default 8

caseSensitive

Whether to ignore case in tokens for embeddings matching, by default True

configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

maxSentenceLength

Max sentence length to process, by default 128

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> tokenClassifier = DeBertaForTokenClassification.pretrained() \
...     .setInputCols(["token", "document"]) \
...     .setOutputCol("label") \
...     .setCaseSensitive(True)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     tokenizer,
...     tokenClassifier
... ])
>>> data = spark.createDataFrame([["John Lenon was born in London and lived in Paris. My name is Sarah and I live in London"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("label.result").show(truncate=False)
+------------------------------------------------------------------------------------+
|result                                                                              |
+------------------------------------------------------------------------------------+
|[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
+------------------------------------------------------------------------------------+
getClasses()[source]#

Returns labels used to train this model

setConfigProtoBytes(b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters:
bList[int]

ConfigProto from tensorflow, serialized into byte array

static loadSavedModel(folder, spark_session)[source]#

Loads a locally saved model.

Parameters:
folderstr

Folder of the saved model spark_session : pyspark.sql.SparkSession The current SparkSession

Returns:
DeBertaForTokenClassification

The restored model

static pretrained(name='deberta_v3_xsmall_token_classifier_conll03', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters:
namestr, optional

Name of the pretrained model, by default “deberta_v3_xsmall_token_classifier_conll03” lang : str, optional Language of the pretrained model, by default “en” remote_loc : str, optional Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:
DeBertaForTokenClassification

The restored model