sparknlp.annotator.classifier_dl.mpnet_for_sequence_classification#

Classes#

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

MPNetForSequenceClassification can load MPNet 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:

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

The default model is "mpnet_sequence_classifier_ukr_message", 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	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

maxSentenceLength

Max sentence length to process, by default 128

coalesceSentences

Instead of 1 class per sentence (if inputCols is sentence) output 1 class per document by averaging probabilities in all sentences, by default False.

activation

Whether to calculate logits via Softmax or Sigmoid, by default “softmax”.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> document = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> sequenceClassifier = MPNetForSequenceClassification \
...     .pretrained() \
...     .setInputCols(["document", "token"]) \
...     .setOutputCol("label")
>>> data = spark.createDataFrame([
...     ["I love driving my car."],
...     ["The next bus will arrive in 20 minutes."],
...     ["pineapple on pizza is the worst 🤮"],
... ]).toDF("text")
>>> pipeline = Pipeline().setStages([document, tokenizer, sequenceClassifier])
>>> pipelineModel = pipeline.fit(data)
>>> results = pipelineModel.transform(data)
>>> results.select("label.result").show()
+--------------------+
|              result|
+--------------------+
|     [TRANSPORT/CAR]|
|[TRANSPORT/MOVEMENT]|
|              [FOOD]|
+--------------------+

getClasses()[source]#

Returns labels used to train this model

setCoalesceSentences(value)[source]#

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: true)

Parameters:

valuebool

If the output of all sentences will be averaged to one output

static loadSavedModel(folder, spark_session)[source]#

Loads a locally saved model.

Parameters:

folderstr

Folder of the saved model

spark_sessionpyspark.sql.SparkSession

The current SparkSession

Returns:

MPNetForSequenceClassification

The restored model

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

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “MPNet_base_sequence_classifier_imdb”

langstr, optional

Language of the pretrained model, by default “en”

remote_locstr, optional

Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

MPNetForSequenceClassification

The restored model