sparknlp.annotator.embeddings.instructor_embeddings#

Classes#

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

Sentence embeddings using INSTRUCTOR.

Instructor👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) by simply providing the task instruction, without any finetuning. Instructor👨‍ achieves sota on 70 diverse embedding tasks!

Pretrained models can be loaded with pretrained() of the companion object:

>>> embeddings = InstructorEmbeddings.pretrained() \
...     .setInputCols(["document"]) \
...     .setInstruction("Represent the Medicine sentence for clustering: ") \
...     .setOutputCol("instructor_embeddings")

The default model is "instructor_base", 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

instruction

Set transformer instruction, e.g. ‘summarize:’

maxSentenceLength

Max sentence length to process, by default 128

configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

References

One Embedder, Any Task: Instruction-Finetuned Text Embeddings

HKUNLP/instructor-embedding

Paper abstract

We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets. Our model, code, and data are available at this https URL <https://instructor-embedding.github.io/>.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> embeddings = InstructorEmbeddings.pretrained() \
...     .setInputCols(["document"]) \
...     .setInstruction("Represent the Medicine sentence for clustering: ") \
...     .setOutputCol("instructor_embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \
...     .setInputCols(["instructor_embeddings"]) \
...     .setOutputCols("finished_embeddings") \
...     .setOutputAsVector(True)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     embeddings,
...     embeddingsFinisher
... ])
>>> data = spark.createDataFrame([["Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[-2.3497989177703857,0.480538547039032,-0.3238905668258667,-1.612930893898010...|
+--------------------------------------------------------------------------------+

setInstruction(value)[source]#

Sets transformer instruction, e.g. ‘summarize:’.

Parameters:

valuestr

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)[source]#

Loads a locally saved model.

Parameters:

folderstr

Folder of the saved model

spark_sessionpyspark.sql.SparkSession

The current SparkSession

Returns:

InstructorEmbeddings

The restored model

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

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “instructor_base”

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:

InstructorEmbeddings

The restored model