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+---
+layout: model
+title: UAE-Large-V1 for Sentence Embeddings
+author: John Snow Labs
+name: uae_large_v1
+date: 2024-04-05
+tags: [uae, en, sentence, embeddings, open_source, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.3.3
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: UAEEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## 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
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/uae_large_v1_en_5.3.3_3.0_1712335736995.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/uae_large_v1_en_5.3.3_3.0_1712335736995.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+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)
+```
+```scala
+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)
+```
+</div>
+
+## Results
+
+```bash
++--------------------------------------------------------------------------------+
+|                                                                          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-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|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|
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