# Copyright 2017-2025 John Snow Labs
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"""Contains classes for ModernBertEmbeddings."""
from sparknlp.common import *
[docs]class ModernBertEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasMaxSentenceLengthLimit):
"""Token-level embeddings using ModernBERT.
ModernBERT is a modernized bidirectional encoder model that is 8x faster,
uses 5x less memory, and achieves better downstream performance than
traditional BERT models. ModernBERT incorporates modern improvements
including Flash Attention, unpadding, and GeGLU activation functions.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> embeddings = ModernBertEmbeddings.pretrained() \\
... .setInputCols(["token", "document"]) \\
... .setOutputCol("modernbert_embeddings")
The default model is ``"modernbert-base"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?task=Embeddings>`__.
For extended examples of usage, see the `Examples
<https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_bert.ipynb>`__.
To see which models are compatible and how to import them see
`Import Transformers into Spark NLP 🚀
<https://github.com/JohnSnowLabs/spark-nlp/discussions/5669>`_.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT, TOKEN`` ``WORD_EMBEDDINGS``
====================== ======================
Parameters
----------
batchSize
Size of every batch , by default 8
dimension
Number of embedding dimensions, by default 768
caseSensitive
Whether to ignore case in tokens for embeddings matching, by default False
maxSentenceLength
Max sentence length to process, by default 8192
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
References
----------
`Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Applications <https://arxiv.org/abs/2412.13663>`__
https://huggingface.co/answerdotai/ModernBERT-base
**Paper abstract**
*We introduce ModernBERT, a modernized bidirectional encoder model that is 8x faster,
uses 5x less memory, and achieves better downstream performance than traditional BERT models.
ModernBERT incorporates modern improvements including Flash Attention, unpadding, and
GeGLU activation functions. The model supports sequence lengths up to 8192 tokens while
maintaining competitive performance on tasks requiring long context understanding.*
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")
>>> embeddings = ModernBertEmbeddings.pretrained() \\
... .setInputCols(["token", "document"]) \\
... .setOutputCol("embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \\
... .setInputCols(["embeddings"]) \\
... .setOutputCols("finished_embeddings") \\
... .setOutputAsVector(True) \\
... .setCleanAnnotations(False)
>>> pipeline = Pipeline() \\
... .setStages([
... documentAssembler,
... tokenizer,
... embeddings,
... embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["This is a sentence."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[-0.8951656818389893,0.13753339648246765,0.11818419396877289,-0.6969502568244...|
|[-0.9860016107559204,-0.6775270700454712,-0.046373113244771957,-1.5230885744094...|
|[-0.9671071767807007,-0.17220760881900787,-0.09954319149255753,-1.1178797483444...|
|[-0.9847850799560547,-0.6675535440444946,-0.06431620568037033,-1.4423584938049...|
|[-0.8978064060211182,0.16901421546936035,0.1306578516960144,-0.6813133358955383...|
+--------------------------------------------------------------------------------+
"""
[docs] name = "ModernBertEmbeddings"
[docs] outputAnnotatorType = AnnotatorType.WORD_EMBEDDINGS
[docs] maxSentenceLength = Param(Params._dummy(),
"maxSentenceLength",
"Max sentence length to process",
typeConverter=TypeConverters.toInt)
[docs] configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
typeConverter=TypeConverters.toListInt)
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def setMaxSentenceLength(self, value):
"""Sets max sentence length to process.
Parameters
----------
value : int
Max sentence length to process
"""
if value > 8192:
raise ValueError(
"ModernBERT models do not support sequences longer than 8192 because of trainable positional embeddings.")
if value < 1:
raise ValueError("The maxSentenceLength must be at least 1")
return self._set(maxSentenceLength=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.embeddings.ModernBertEmbeddings", java_model=None):
super(ModernBertEmbeddings, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
dimension=768,
batchSize=8,
maxSentenceLength=8192,
caseSensitive=False
)
@staticmethod
[docs] def loadSavedModel(folder, spark_session, use_openvino=False):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
use_openvino : bool
Use OpenVINO backend
Returns
-------
ModernBertEmbeddings
The restored model
"""
from sparknlp.internal import _ModernBertEmbeddingsLoader
jModel = _ModernBertEmbeddingsLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return ModernBertEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="modernbert-base", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "modernbert-base"
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 NLP repositories otherwise.
Returns
-------
ModernBertEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(ModernBertEmbeddings, name, lang, remote_loc)