# Copyright 2017-2022 John Snow Labs
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains classes for E5Embeddings."""
from sparknlp.common import *
[docs]class NomicEmbeddings(AnnotatorModel, HasEmbeddingsProperties, HasCaseSensitiveProperties, HasStorageRef,
HasBatchedAnnotate, HasMaxSentenceLengthLimit):
"""Sentence embeddings using NomicEmbeddings.
nomic-embed-text-v1 is 8192 context length text encoder that surpasses OpenAI
text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> embeddings = NomicEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("nomic_embeddings")
The default model is ``"nomic_small"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?q=Nomic>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT`` ``SENTENCE_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 512
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
References
----------
`Text Embeddings by Weakly-Supervised Contrastive Pre-training <https://arxiv.org/pdf/2212.03533>`__
https://github.com/microsoft/unilm/tree/master/nomic
**Paper abstract**
*This technical report describes the training
of nomic-embed-text-v1, the first fully reproducible,
open-source, open-weights, opendata, 8192 context length
English text embedding model that outperforms both OpenAI
Ada-002 and OpenAI text-embedding-3-small
on short and long-context tasks. We release
the training code and model weights under
an Apache 2 license. In contrast with other
open-source models, we release a training data
loader with 235 million curated text pairs that
allows for the full replication of nomic-embedtext-v1.
You can find code and data to replicate the
model at https://github.com/nomicai/contrastors.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> embeddings = NomicEmbeddings.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("nomic_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \\
... .setInputCols(["nomic_embeddings"]) \\
... .setOutputCols("finished_embeddings") \\
... .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... embeddings,
... embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["query: how much protein should a female eat",
... "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day." + \
... "But, as you can see from this chart, you'll need to increase that if you're expecting or training for a" + \
... "marathon. Check out the chart below to see how much protein you should be eating each day.",
... ]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[[8.0190285E-4, -0.005974853, -0.072875895, 0.007944068, 0.026059335, -0.0080...|
|[[0.050514214, 0.010061974, -0.04340176, -0.020937217, 0.05170225, 0.01157857...|
+--------------------------------------------------------------------------------+
"""
name = "NomicEmbeddings"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.SENTENCE_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.NomicEmbeddings", java_model=None):
super(NomicEmbeddings, self).__init__(classname=classname, java_model=java_model)
self._setDefault(dimension=768, batchSize=8, maxSentenceLength=512, 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
Returns
-------
NomicEmbeddings
The restored model
"""
from sparknlp.internal import _NomicLoader
jModel = _NomicLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return NomicEmbeddings(java_model=jModel)
@staticmethod
[docs] def pretrained(name="nomic_small", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "nomic_small"
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
-------
NomicEmbeddings
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(NomicEmbeddings, name, lang, remote_loc)