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"""Contains classes concerning AlbertEmbeddings."""
from sparknlp.common import *
[docs]class AlbertEmbeddings(AnnotatorModel,
HasEmbeddingsProperties,
HasCaseSensitiveProperties,
HasStorageRef,
HasBatchedAnnotate,
HasEngine,
HasMaxSentenceLengthLimit):
"""ALBERT: A Lite Bert For Self-Supervised Learning Of Language
Representations - Google Research, Toyota Technological Institute at Chicago
These word embeddings represent the outputs generated by the Albert model.
All official Albert releases by google in TF-HUB are supported with this
Albert Wrapper:
**Ported TF-Hub Models:**
============================ ============================================================== =====================================================
Model Name TF-Hub Model Model Properties
============================ ============================================================== =====================================================
``"albert_base_uncased"`` `albert_base <https://tfhub.dev/google/albert_base/3>`__ 768-embed-dim, 12-layer, 12-heads, 12M parameters
``"albert_large_uncased"`` `albert_large <https://tfhub.dev/google/albert_large/3>`__ 1024-embed-dim, 24-layer, 16-heads, 18M parameters
``"albert_xlarge_uncased"`` `albert_xlarge <https://tfhub.dev/google/albert_xlarge/3>`__ 2048-embed-dim, 24-layer, 32-heads, 60M parameters
``"albert_xxlarge_uncased"`` `albert_xxlarge <https://tfhub.dev/google/albert_xxlarge/3>`__ 4096-embed-dim, 12-layer, 64-heads, 235M parameters
============================ ============================================================== =====================================================
This model requires input tokenization with SentencePiece model, which is
provided by Spark-NLP (See tokenizers package).
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> embeddings = AlbertEmbeddings.pretrained() \\
... .setInputCols(["sentence", "token"]) \\
... .setOutputCol("embeddings")
The default model is ``"albert_base_uncased"``, if no name is provided.
For extended examples of usage, see the `Examples
<https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_albert.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
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
maxSentenceLength
Max sentence length to process, by default 128
Notes
-----
ALBERT uses repeating layers which results in a small memory footprint,
however the computational cost remains similar to a BERT-like architecture
with the same number of hidden layers as it has to iterate through the same
number of (repeating) layers.
References
----------
`ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS <https://arxiv.org/pdf/1909.11942.pdf>`__
https://github.com/google-research/ALBERT
https://tfhub.dev/s?q=albert
**Paper abstract:**
*Increasing model size when pretraining natural language representations
often results in improved performance on downstream tasks. However, at some
point further model increases become harder due to GPU/TPU memory
limitations and longer training times. To address these problems, we present
two parameter reduction techniques to lower memory consumption and increase
the training speed of BERT (Devlin et al., 2019). Comprehensive empirical
evidence shows that our proposed methods lead to models that scale much
better compared to the original BERT. We also use a self-supervised loss
that focuses on modeling inter-sentence coherence, and show it consistently
helps downstream tasks with multi-sentence inputs. As a result, our best
model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD
benchmarks while having fewer parameters compared to BERT-large.*
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"]) \\
>>> embeddings = AlbertEmbeddings.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|
+--------------------------------------------------------------------------------+
|[1.1342473030090332,-1.3855540752410889,0.9818322062492371,-0.784737348556518...|
|[0.847029983997345,-1.047153353691101,-0.1520637571811676,-0.6245765686035156...|
|[-0.009860038757324219,-0.13450059294700623,2.707749128341675,1.2916892766952...|
|[-0.04192575812339783,-0.5764210224151611,-0.3196685314178467,-0.527840495109...|
|[0.15583214163780212,-0.1614152491092682,-0.28423872590065,-0.135491415858268...|
+--------------------------------------------------------------------------------+
See Also
--------
AlbertForTokenClassification : for AlbertEmbeddings with a token classification layer on top
"""
name = "AlbertEmbeddings"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT, AnnotatorType.TOKEN]
outputAnnotatorType = AnnotatorType.WORD_EMBEDDINGS
configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
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)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.embeddings.AlbertEmbeddings", java_model=None):
super(AlbertEmbeddings, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
batchSize=8,
dimension=768,
maxSentenceLength=128,
caseSensitive=False
)
@staticmethod
[docs] def 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
-------
AlbertEmbeddings
The restored model
"""
from sparknlp.internal import _AlbertLoader
jModel = _AlbertLoader(folder, spark_session._jsparkSession)._java_obj
return AlbertEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="albert_base_uncased", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "albert_base_uncased"
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
-------
AlbertEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(AlbertEmbeddings, name, lang, remote_loc)