`sparknlp_jsl.annotator.re.zero_shot_relation_extraction`#

Module Contents#

Classes#

ZeroShotRelationExtractionModel

ZeroShotRelationExtractionModel implements zero shot binary relations extraction by utilizing BERT transformer

class ZeroShotRelationExtractionModel(classname='com.johnsnowlabs.nlp.annotators.re.ZeroShotRelationExtractionModel', java_model=None)#

Bases: sparknlp_jsl.annotator.classification.medical_bert_for_sequence_classification.MedicalBertForSequenceClassification, sparknlp_jsl.common.HasEngine

ZeroShotRelationExtractionModel implements zero shot binary relations extraction by utilizing BERT transformer models trained on the NLI (Natural Language Inference) task. The model inputs consists of documents/sentences and paired NER chunks, usually obtained by RENerChunksFilter. The definitions of relations which are extracted is given by a dictionary structures, specifying a set of statements regarding the relationship of named entities. These statements are automatically appended to each document in the dataset and the NLI model is used to determine whether a particular relationship between entities.

Input Annotation types	Output Annotation type
`CHUNK, DOCUMENT`	`CATEGORY`

Parameters:

relationalCategories –

A dictionary with definitions of relational categories. The keys of dictionary are the relation labels and the values are lists of hypothesis templates. For example:

>>>      {"CURE": [
>>>            "{TREATMENT, DRUG} cures {PROBLEM}."
>>>            ],
>>>        "IMPROVE": [
>>>            "{TREATMENT} improves {PROBLEM}.",
>>>            "{TREATMENT} cures {PROBLEM}."
>>>            ]}

predictionThreshold – Minimal confidence score to encode a relation (Default: 0.5f)
multiLabel – Whether or not a pair of entities can be categorized by multiple relations (Default: False)

Examples

>>> documentAssembler = DocumentAssembler()
>>> documenter = sparknlp.DocumentAssembler()     ...     .setInputCol("text")     ...     .setOutputCol("document")
>>> tokenizer = sparknlp.annotators.Tokenizer()     ...     .setInputCols(["document"])     ...     .setOutputCol("tokens")
>>> sentencer = sparknlp.annotators.SentenceDetector()    ...     .setInputCols(["document"])    ...     .setOutputCol("sentences")
>>> words_embedder = sparknlp.annotators.WordEmbeddingsModel()     ...     .pretrained("embeddings_clinical", "en", "clinical/models")     ...     .setInputCols(["sentences", "tokens"])     ...     .setOutputCol("embeddings")
>>> pos_tagger = sparknlp.annotators.PerceptronModel()     ...     .pretrained("pos_clinical", "en", "clinical/models")     ...     .setInputCols(["sentences", "tokens"])     ...     .setOutputCol("pos_tags")
>>> ner_tagger = MedicalNerModel()     ...     .pretrained("ner_clinical", "en", "clinical/models")     ...     .setInputCols(["sentences", "tokens", "embeddings"])     ...     .setOutputCol("ner_tags")
>>> ner_converter = sparknlp.annotators.NerConverter()     ...     .setInputCols(["sentences", "tokens", "ner_tags"])     ...     .setOutputCol("ner_chunks")
>>> dependency_parser = sparknlp.annotators.DependencyParserModel()     ...     .pretrained("dependency_conllu", "en")     ...     .setInputCols(["document", "pos_tags", "tokens"])     ...     .setOutputCol("dependencies")
>>> re_ner_chunk_filter = RENerChunksFilter()     ...     .setRelationPairs(["problem-test","problem-treatment"])     ...     .setMaxSyntacticDistance(4)    ...     .setDocLevelRelations(False)    ...     .setInputCols(["ner_chunks", "dependencies"])     ...     .setOutputCol("re_ner_chunks")
>>> re_model = ZeroShotRelationExtractionModel     ...     .load("/tmp/spark_sbert_zero_shot")     ...     .setRelationalCategories({
...         "CURE": ["{TREATMENT} cures {PROBLEM}."],
...         "IMPROVE": ["{TREATMENT} improves {PROBLEM}.", "{TREATMENT} cures {PROBLEM}."],
...         "REVEAL": ["{TEST} reveals {PROBLEM}."]})    ...     .setMultiLabel(False)    ...     .setInputCols(["re_ner_chunks", "sentences"])     ...     .setOutputCol("relations")
>>> data = spark.createDataFrame(
...     [["Paracetamol can alleviate headache or sickness. An MRI test can be used to find cancer."]]
...     ).toDF("text")
>>> results = sparknlp.base.Pipeline()     ...     .setStages([documenter, tokenizer, sentencer, words_embedder, pos_tagger, ner_tagger, ner_converter,
...         dependency_parser, re_ner_chunk_filter, re_model])     ...     .fit(data)     ...     .transform(data)     >>> results    ...     .selectExpr("explode(relations) as relation")    ...     .selectExpr("relation.result", "relation.metadata.confidence")    ...     .show(truncate=False)
+-------+----------+
|result |confidence|
+-------+----------+
|REVEAL |0.9760039 |
|IMPROVE|0.98819494|
|IMPROVE|0.9929625 |
+-------+----------+

caseSensitive#

coalesceSentences#

configProtoBytes#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

maxSentenceLength#

multiLabel#

name = 'MedicalBertForSequenceClassification'#

negativeRelationships#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'category'#

outputCol#

predictionThreshold#

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 the list of entities which are recognized

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.

Parameters:

folder (str) – Folder of the saved model
spark_session (pyspark.sql.SparkSession) – The current SparkSession

Returns:

The restored model

Return type:

ZeroShotRelationExtractionModel

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:

MedicalBertForSequenceClassification

static pretrained(name='zero_shot_re', lang='en', remote_loc='clinical/models')#

Download a pre-trained ZeroShotRelationExtractionModel.

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 ZeroShotRelationExtractionModel.

Return type:

ZeroShotRelationExtractionModel

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

setNegativeRelationships(relations: list)#

Set the list of relational categories which serve as negative examples and are not included in the output annotations

Parameters:: relations (List[str]) –

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()#

setRelationalCategories(categories)#

Set definitions of relational categories

Parameters:: categories (dict[str, list[str]]) –

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 dataset
params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

write() → JavaMLWriter#: Returns an MLWriter instance for this ML instance.

sparknlp_jsl.annotator.re.zero_shot_relation_extraction#

Module Contents#

Classes#

`sparknlp_jsl.annotator.re.zero_shot_relation_extraction`#