sparknlp.annotator.embeddings.nomic_embeddings#

Classes#

class NomicEmbeddings(classname='com.johnsnowlabs.nlp.embeddings.NomicEmbeddings', java_model=None)[source]#

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 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.

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

microsoft/unilm

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...|
+--------------------------------------------------------------------------------+

setConfigProtoBytes(b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters:

bList[int]

ConfigProto from tensorflow, serialized into byte array

static loadSavedModel(folder, spark_session, use_openvino=False)[source]#

Loads a locally saved model.

Parameters:

folderstr

Folder of the saved model

spark_sessionpyspark.sql.SparkSession

The current SparkSession

Returns:

NomicEmbeddings

The restored model

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

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “nomic_small”

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:

NomicEmbeddings

The restored model