Description
UAE is a novel angle-optimized text embedding model, designed to improve semantic textual similarity tasks, which are crucial for Large Language Model (LLM) applications. By introducing angle optimization in a complex space, AnglE effectively mitigates saturation of the cosine similarity function.
This model is based on UAE-Large-V1 and was orignally exported from https://huggingface.co/WhereIsAI/UAE-Large-V1. Several embedding pooling strategies can be set. Please refer to the class for more information.
Predicted Entities
How to use
import sparknlp
from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
embeddings = UAEEmbeddings.pretrained() \
.setInputCols(["document"]) \
.setOutputCol("embeddings")
embeddingsFinisher = EmbeddingsFinisher() \
.setInputCols("embeddings") \
.setOutputCols("finished_embeddings") \
.setOutputAsVector(True)
pipeline = Pipeline().setStages([
documentAssembler,
embeddings,
embeddingsFinisher
])
data = spark.createDataFrame([["hello world", "hello moon"]]).toDF("text")
result = pipeline.fit(data).transform(data)
result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.embeddings.UAEEmbeddings
import com.johnsnowlabs.nlp.EmbeddingsFinisher
import org.apache.spark.ml.Pipeline
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val embeddings = UAEEmbeddings.pretrained()
.setInputCols("document")
.setOutputCol("UAE_embeddings")
val embeddingsFinisher = new EmbeddingsFinisher()
.setInputCols("UAE_embeddings")
.setOutputCols("finished_embeddings")
.setOutputAsVector(true)
val pipeline = new Pipeline().setStages(Array(
documentAssembler,
embeddings,
embeddingsFinisher
))
val data = Seq("hello world", "hello moon").toDF("text")
val result = pipeline.fit(data).transform(data)
result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
Results
+--------------------------------------------------------------------------------+
| result|
+--------------------------------------------------------------------------------+
|[0.50387806, 0.5861606, 0.35129607, -0.76046336, -0.32446072, -0.117674336, 0...|
|[0.6660665, 0.961762, 0.24854276, -0.1018044, -0.6569202, 0.027635604, 0.1915...|
+--------------------------------------------------------------------------------+
Model Information
| Model Name: | uae_large_v1 |
| Compatibility: | Spark NLP 5.3.3+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document] |
| Output Labels: | [embeddings] |
| Language: | en |
| Size: | 1.2 GB |