sparknlp_jsl.annotator.classification.medical_bert_for_sequence_classification
#
Module Contents#
Classes#
MedicalBertForTokenClassifier can load Bert Models with sequence classification/regression head on top |
- class MedicalBertForSequenceClassification(classname='com.johnsnowlabs.nlp.annotators.classification.MedicalBertForSequenceClassification', java_model=None)#
Bases:
sparknlp_jsl.common.AnnotatorModelInternal
,sparknlp_jsl.common.HasCaseSensitiveProperties
,sparknlp_jsl.common.HasBatchedAnnotate
,sparknlp_jsl.common.HasEngine
MedicalBertForTokenClassifier can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.
Pretrained models can be loaded with
pretrained()
of the companion object:For available pretrained models please see the Models Hub.
Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. 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
CATEGORY
- 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 = MedicalBertForSequenceClassification.pretrained() \ ... .setInputCols(["token", "document"]) \ ... .setOutputCol("label") \ ... .setCaseSensitive(True) >>> pipeline = Pipeline().setStages([ ... documentAssembler, ... tokenizer, ... tokenClassifier ... ]) >>> data = spark.createDataFrame([["I felt a bit drowsy and had blurred vision after taking Aspirin."]]).toDF("text") >>> result = pipeline.fit(data).transform(data) >>> result.select("label.result").show(truncate=False)
- caseSensitive#
- coalesceSentences#
- configProtoBytes#
- getter_attrs = []#
- inputAnnotatorTypes#
- inputCols#
- lazyAnnotator#
- maxSentenceLength#
- name = 'MedicalBertForSequenceClassification'#
- optionalInputAnnotatorTypes = []#
- outputAnnotatorType = 'category'#
- outputCol#
- skipLPInputColsValidation = True#
- uid = ''#
- clear(param: pyspark.ml.param.Param) None #
Clears a param from the param map if it has been explicitly set.
- copy(extra: pyspark.ml._typing.ParamMap | None = None) JP #
Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.
- Parameters:
extra (dict, optional) – Extra parameters to copy to the new instance
- Returns:
Copy of this instance
- Return type:
JavaParams
- explainParam(param: str | Param) str #
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
- explainParams() str #
Returns the documentation of all params with their optionally default values and user-supplied values.
- extractParamMap(extra: pyspark.ml._typing.ParamMap | None = None) pyspark.ml._typing.ParamMap #
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
- Parameters:
extra (dict, optional) – extra param values
- Returns:
merged param map
- Return type:
dict
- getCaseSensitive()#
Gets whether to ignore case in tokens for embeddings matching.
- Returns:
Whether to ignore case in tokens for embeddings matching
- Return type:
bool
- getClasses()#
Returns labels used to train this model
- getInputCols()#
Gets current column names of input annotations.
- getLazyAnnotator()#
Gets whether Annotator should be evaluated lazily in a RecursivePipeline.
- getOrDefault(param: str) Any #
- getOrDefault(param: Param[T]) T
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
- getOutputCol()#
Gets output column name of annotations.
- getParam(paramName: str) Param #
Gets a param by its name.
- getParamValue(paramName)#
Gets the value of a parameter.
- Parameters:
paramName (str) – Name of the parameter
- hasDefault(param: str | Param[Any]) bool #
Checks whether a param has a default value.
- hasParam(paramName: str) bool #
Tests whether this instance contains a param with a given (string) name.
- inputColsValidation(value)#
- isDefined(param: str | Param[Any]) bool #
Checks whether a param is explicitly set by user or has a default value.
- isSet(param: str | Param[Any]) bool #
Checks whether a param is explicitly set by user.
- classmethod load(path: str) RL #
Reads an ML instance from the input path, a shortcut of read().load(path).
- static loadSavedModel(folder, spark_session)#
Loads a locally saved model :param folder: Folder of the saved model :type folder: str :param spark_session: The current SparkSession :type spark_session: pyspark.sql.SparkSession
- Returns:
The restored model
- Return type:
- static loadSavedModelOpenSource(bertForTokenClassifierPath, tfModelPath, spark_session)#
Loads a locally saved model.
- Parameters:
bertForTokenClassifierPath (str) – Folder of the bertForTokenClassifier
tfModelPath (str) – Folder taht contains the tf model
spark_session (pyspark.sql.SparkSession) – The current SparkSession
- Returns:
The restored model
- Return type:
- static pretrained(name='bert_sequence_classifier_ade', lang='en', remote_loc='clinical/models')#
Download a pre-trained MedicalBertForSequenceClassification.
- Parameters:
name (str) – Name of the pre-trained model.
lang (str) – Language of the pre-trained model.
remote_loc (str) – Remote location of the pre-trained model. If None, use the open-source location. Other values are “clinical/models”, “finance/models”, or “legal/models”.
- Returns:
A pre-trained MedicalBertForSequenceClassification.
- Return type:
- classmethod read()#
Returns an MLReader instance for this class.
- save(path: str) None #
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
- set(param: Param, value: Any) None #
Sets a parameter in the embedded param map.
- setCaseSensitive(value)#
Sets whether to ignore case in tokens for embeddings matching.
- Parameters:
value (bool) – Whether to ignore case in tokens for embeddings matching
- setCoalesceSentences(value)#
Instead of 1 class per sentence (if inputCols is ‘’’sentence’’’) output 1 class per document by averaging probabilities in all sentences. Due to max sequence length limit in almost all transformer models such as BERT (512 tokens), this parameter helps feeding all the sentences into the model and averaging all the probabilities for the entire document instead of probabilities per sentence. (Default: False)
- Parameters:
value (bool) – If the output of all sentences will be averaged to one output
- setConfigProtoBytes(b)#
Sets configProto from tensorflow, serialized into byte array.
- Parameters:
b (List[str]) – ConfigProto from tensorflow, serialized into byte array
- setForceInputTypeValidation(etfm)#
- setInputCols(*value)#
Sets column names of input annotations.
- Parameters:
*value (List[str]) – Input columns for the annotator
- setLazyAnnotator(value)#
Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
- Parameters:
value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline
- setMaxSentenceLength(value)#
Sets max sentence length to process, by default 128.
- Parameters:
value (int) – Max sentence length to process
- setOutputCol(value)#
Sets output column name of annotations.
- Parameters:
value (str) – Name of output column
- setParamValue(paramName)#
Sets the value of a parameter.
- Parameters:
paramName (str) – Name of the parameter
- setParams()#
- transform(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = None) pyspark.sql.dataframe.DataFrame #
Transforms the input dataset with optional parameters.
New in version 1.3.0.
- Parameters:
dataset (
pyspark.sql.DataFrame
) – input datasetparams (dict, optional) – an optional param map that overrides embedded params.
- Returns:
transformed dataset
- Return type:
- write() JavaMLWriter #
Returns an MLWriter instance for this ML instance.