Add model 2025-06-10-e5v_int4_en (#14599)

Co-authored-by: prabod <prabod@rathnayaka.me>

Author: jsl-models

docs/_posts/prabod/2025-06-10-e5v_int4_en.md

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+---
+layout: model
+title: E5V Embeddings
+author: John Snow Labs
+name: e5v_int4
+date: 2025-06-10
+tags: [en, open_source, openvino]
+task: Embeddings
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: E5VEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Universal multimodal embeddings using E5-V.
+
+E5-V is a multimodal embedding model that bridges the modality gap between text and images, enabling strong performance in cross-modal retrieval, classification, clustering, and more. It supports both image+text and text-only embedding scenarios, and is fine-tuned from lmms-lab/llama3-llava-next-8b. The default model is `"e5v_int4"`.
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+
+## 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/e5v_int4_en_5.5.1_3.0_1749533458977.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/e5v_int4_en_5.5.1_3.0_1749533458977.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
+# Image + Text Embedding
+import sparknlp
+from sparknlp.base import *
+from sparknlp.annotator import *
+from pyspark.ml import Pipeline
+from pyspark.sql.functions import lit
+
+image_df = spark.read.format("image").option("dropInvalid", True).load(imageFolder)
+imagePrompt = "<|start_header_id|>user<|end_header_id|>\n\n<image>\\nSummary above image in one word: <|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n"
+test_df = image_df.withColumn("text", lit(imagePrompt))
+imageAssembler = ImageAssembler() \
+    .setInputCol("image") \
+    .setOutputCol("image_assembler")
+e5vEmbeddings = E5VEmbeddings.pretrained() \
+    .setInputCols(["image_assembler"]) \
+    .setOutputCol("e5v")
+pipeline = Pipeline().setStages([
+    imageAssembler,
+    e5vEmbeddings
+])
+result = pipeline.fit(test_df).transform(test_df)
+result.select("e5v.embeddings").show(truncate=False)
+
+# Text-Only Embedding
+from sparknlp.util import EmbeddingsDataFrameUtils
+textPrompt = "<|start_header_id|>user<|end_header_id|>\n\n<sent>\\nSummary above sentence in one word: <|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n"
+textDesc = "A cat sitting in a box."
+nullImageDF = spark.createDataFrame(
+    spark.sparkContext.parallelize([EmbeddingsDataFrameUtils.emptyImageRow]),
+    EmbeddingsDataFrameUtils.imageSchema)
+textDF = nullImageDF.withColumn("text", lit(textPrompt.replace("<sent>", textDesc)))
+e5vEmbeddings = E5VEmbeddings.pretrained() \
+    .setInputCols(["image"]) \
+    .setOutputCol("e5v")
+result = e5vEmbeddings.transform(textDF)
+result.select("e5v.embeddings").show(truncate=False)
+```
+```scala
+// Image + Text Embedding
+import org.apache.spark.sql.functions.lit
+import com.johnsnowlabs.nlp.base.ImageAssembler
+import com.johnsnowlabs.nlp.embeddings.E5VEmbeddings
+import org.apache.spark.ml.Pipeline
+
+val imageDF = spark.read.format("image").option("dropInvalid", value = true).load(imageFolder)
+val imagePrompt = "<|start_header_id|>user<|end_header_id|>\n\n<image>\\nSummary above image in one word: <|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n"
+val testDF = imageDF.withColumn("text", lit(imagePrompt))
+val imageAssembler = new ImageAssembler().setInputCol("image").setOutputCol("image_assembler")
+val e5vEmbeddings = E5VEmbeddings.pretrained()
+  .setInputCols("image_assembler")
+  .setOutputCol("e5v")
+val pipeline = new Pipeline().setStages(Array(imageAssembler, e5vEmbeddings))
+val result = pipeline.fit(testDF).transform(testDF)
+result.select("e5v.embeddings").show(truncate = false)
+
+// Text-Only Embedding
+import com.johnsnowlabs.nlp.util.EmbeddingsDataFrameUtils.{emptyImageRow, imageSchema}
+val textPrompt = "<|start_header_id|>user<|end_header_id|>\n\n<sent>\\nSummary above sentence in one word: <|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n"
+val textDesc = "A cat sitting in a box."
+val nullImageDF = spark.createDataFrame(spark.sparkContext.parallelize(Seq(emptyImageRow)), imageSchema)
+val textDF = nullImageDF.withColumn("text", lit(textPrompt.replace("<sent>", textDesc)))
+val e5vEmbeddings = E5VEmbeddings.pretrained()
+  .setInputCols("image")
+  .setOutputCol("e5v")
+val result2 = e5vEmbeddings.transform(textDF)
+result2.select("e5v.embeddings").show(truncate = false)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|e5v_int4|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[image_assembler]|
+|Output Labels:|[answer]|
+|Language:|en|
+|Size:|4.9 GB|