2024-10-21-bge_medembed_small_v0_1_en

docs/_posts/Cabir40/2024-10-21-bge_medembed_base_v0_1_en.md

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+---
+layout: model
+title: English bge_medembed_base_v0_1 BGEEmbeddings from abhinand
+author: John Snow Labs
+name: bge_medembed_base_v0_1
+date: 2024-10-21
+tags: [embedding, en, open_source, bge, medical, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BGEEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BGEEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. 
+`bge_medembed_base_v0_1` is a English model originally trained by abhinand
+
+{:.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/bge_medembed_base_v0_1_en_5.5.0_3.0_1729515433167.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bge_medembed_base_v0_1_en_5.5.0_3.0_1729515433167.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
+
+document_assembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("document")
+
+embeddings = BGEEmbeddings.pretrained("bge_medembed_base_v0_1","en")\
+      .setInputCols(["document"])\
+      .setOutputCol("embeddings")       
+
+pipeline = Pipeline(
+    stages = [
+        document_assembler, 
+        embeddings
+])
+
+data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+
+```
+```scala
+
+val document_assembler = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val embeddings = BGEEmbeddings.pretrained("bge_medembed_base_v0_1","en") 
+    .setInputCols(Array("document")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings))
+
+val data = Seq("I love spark-nlp").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+
+```
+</div>
+
+## Results
+
+```bash
+
++----------------------------------------------------------------------------------------------------+
+|                                                                                       bge_embedding|
++----------------------------------------------------------------------------------------------------+
+|[{sentence_embeddings, 0, 15, I love spark-nlp, {sentence -> 0}, [-0.018065551, -0.032784615, 0.0...|
++----------------------------------------------------------------------------------------------------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bge_medembed_base_v0_1|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document]|
+|Output Labels:|[bge]|
+|Language:|en|
+|Size:|389.7 MB|

docs/_posts/Cabir40/2024-10-21-bge_medembed_large_v0_1_en.md

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+---
+layout: model
+title: English bge_medembed_large_v0_1 BGEEmbeddings from abhinand
+author: John Snow Labs
+name: bge_medembed_large_v0_1
+date: 2024-10-21
+tags: [embedding, en, open_source, bge, medical, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BGEEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BGEEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. 
+`bge_medembed_large_v0_1` is a English model originally trained by abhinand
+
+{:.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/bge_medembed_large_v0_1_en_5.5.0_3.0_1729515260623.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bge_medembed_large_v0_1_en_5.5.0_3.0_1729515260623.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
+
+document_assembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("document")
+
+embeddings = BGEEmbeddings.pretrained("bge_medembed_large_v0_1","en")\
+      .setInputCols(["document"])\
+      .setOutputCol("embeddings")       
+
+pipeline = Pipeline(
+    stages = [
+        document_assembler, 
+        embeddings
+])
+
+data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+
+```
+```scala
+
+val document_assembler = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val embeddings = BGEEmbeddings.pretrained("bge_medembed_large_v0_1","en") 
+    .setInputCols(Array("document")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings))
+
+val data = Seq("I love spark-nlp").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+
+```
+</div>
+
+## Results
+
+```bash
+
++----------------------------------------------------------------------------------------------------+
+|                                                                                       bge_embedding|
++----------------------------------------------------------------------------------------------------+
+|[{sentence_embeddings, 0, 15, I love spark-nlp, {sentence -> 0}, [-0.018065551, -0.032784615, 0.0...|
++----------------------------------------------------------------------------------------------------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bge_medembed_large_v0_1|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document]|
+|Output Labels:|[bge]|
+|Language:|en|
+|Size:|1.2 GB|

docs/_posts/Cabir40/2024-10-21-bge_medembed_small_v0_1_en.md

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+---
+layout: model
+title: English bge_medembed_small_v0_1 BGEEmbeddings from abhinand
+author: John Snow Labs
+name: bge_medembed_small_v0_1
+date: 2024-10-21
+tags: [embedding, en, open_source, bge, medical, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BGEEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BGEEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. 
+`bge_medembed_small_v0_1` is a English model originally trained by abhinand
+
+{:.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/bge_medembed_small_v0_1_en_5.5.0_3.0_1729513920928.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bge_medembed_small_v0_1_en_5.5.0_3.0_1729513920928.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
+
+document_assembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("document")
+
+embeddings = BGEEmbeddings.pretrained("bge_medembed_small_v0_1","en")\
+      .setInputCols(["document"])\
+      .setOutputCol("embeddings")       
+
+pipeline = Pipeline(
+    stages = [
+        document_assembler, 
+        embeddings
+])
+
+data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+
+```
+```scala
+
+val document_assembler = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val embeddings = BGEEmbeddings.pretrained("bge_medembed_small_v0_1","en") 
+    .setInputCols(Array("document")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings))
+
+val data = Seq("I love spark-nlp").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+
+```
+</div>
+
+## Results
+
+```bash
+
++----------------------------------------------------------------------------------------------------+
+|                                                                                       bge_embedding|
++----------------------------------------------------------------------------------------------------+
+|[{sentence_embeddings, 0, 15, I love spark-nlp, {sentence -> 0}, [-0.07673764, -0.04207312, 0.026...|
++----------------------------------------------------------------------------------------------------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bge_medembed_small_v0_1|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document]|
+|Output Labels:|[bge]|
+|Language:|en|
+|Size:|116.4 MB|