Merge Models Hub into Master

docs/_posts/ahmedlone127/2025-05-17-internvl3_8b_int4_en.md

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+---
+layout: model
+title: InternVL 3 8B int4
+author: John Snow Labs
+name: internvl3_8b_int4
+date: 2025-05-17
+tags: [en, open_source, openvino]
+task: Image Captioning
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: InternVLForMultiModal
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Visual Question Answering using InternVL.
+
+InternVLForMultiModal can load InternVL Vision models for visual question answering.
+The model consists of a vision encoder, a text encoder, a text decoder and a model merger.
+The vision encoder will encode the input image, the text encoder will encode the input text,
+the model merger will merge the image and text embeddings, and the text decoder will output the answer.
+
+InternVL 2.5 is an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0,
+maintaining its core model architecture while introducing significant enhancements in training and testing
+strategies as well as data quality. Key features include:
+- Large context window support
+- Multilingual support
+- Multimodal capabilities handling both text and image inputs
+- Optimized for deployment with int4 quantization
+
+{:.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/internvl3_8b_int4_en_5.5.1_3.0_1747457622170.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/internvl3_8b_int4_en_5.5.1_3.0_1747457622170.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
+
+python
+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").load(path=images_path) # Replace with your image path
+test_df = image_df.withColumn("text", lit("<|im_start|><image>
+Describe this image in detail.<|im_end|><|im_start|>assistant
+"))
+
+imageAssembler = ImageAssembler()   
+          .setInputCol("image")   
+          .setOutputCol("image_assembler")
+
+visualQAClassifier = InternVLForMultiModal.pretrained()   
+          .setInputCols("image_assembler")   
+          .setOutputCol("answer")
+
+pipeline = Pipeline().setStages([
+          imageAssembler,
+          visualQAClassifier
+])
+
+result = pipeline.fit(test_df).transform(test_df)
+result.select("image_assembler.origin", "answer.result").show(False)
+
+```
+```scala
+
+import spark.implicits._
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions.lit
+
+val imageFolder = "path/to/your/images" // Replace with your image path
+
+val imageDF: DataFrame = spark.read
+     .format("image")
+     .option("dropInvalid", value = true)
+     .load(imageFolder)
+
+val testDF: DataFrame = imageDF.withColumn("text", lit("<|im_start|><image>
+Describe this image in detail.<|im_end|><|im_start|>assistant
+"))
+
+val imageAssembler: ImageAssembler = new ImageAssembler()
+     .setInputCol("image")
+     .setOutputCol("image_assembler")
+
+val visualQAClassifier = InternVLForMultiModal.pretrained()
+     .setInputCols("image_assembler")
+     .setOutputCol("answer")
+
+val pipeline = new Pipeline().setStages(Array(
+     imageAssembler,
+     visualQAClassifier
+))
+
+val result = pipeline.fit(testDF).transform(testDF)
+
+result.select("image_assembler.origin", "answer.result").show(false)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|internvl3_8b_int4|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[image_assembler]|
+|Output Labels:|[answer]|
+|Language:|en|
+|Size:|7.9 GB|

docs/_posts/ahmedlone127/2025-05-20-bce_reranker_base_v1_maidalun1020_en.md

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+---
+layout: model
+title: English bce_reranker_base_v1_maidalun1020 XlmRoBertaForSequenceClassification from maidalun1020
+author: John Snow Labs
+name: bce_reranker_base_v1_maidalun1020
+date: 2025-05-20
+tags: [en, open_source, onnx, sequence_classification, xlm_roberta, openvino]
+task: Text Classification
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: XlmRoBertaForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained XlmRoBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bce_reranker_base_v1_maidalun1020` is a English model originally trained by maidalun1020.
+
+## 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/bce_reranker_base_v1_maidalun1020_en_5.5.1_3.0_1747745225117.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bce_reranker_base_v1_maidalun1020_en_5.5.1_3.0_1747745225117.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
+documentAssembler = DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+tokenizer = Tokenizer() \
+    .setInputCols(['document']) \
+    .setOutputCol('token')
+
+sequenceClassifier  = XlmRoBertaForSequenceClassification.pretrained("bce_reranker_base_v1_maidalun1020","en") \
+     .setInputCols(["documents","token"]) \
+     .setOutputCol("class")
+
+pipeline = Pipeline().setStages([documentAssembler, tokenizer, sequenceClassifier])
+data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
+pipelineModel = pipeline.fit(data)
+pipelineDF = pipelineModel.transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler()
+    .setInputCols("text")
+    .setOutputCols("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained("bce_reranker_base_v1_maidalun1020", "en")
+    .setInputCols(Array("documents","token")) 
+    .setOutputCol("class") 
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
+val data = Seq("I love spark-nlp").toDS.toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bce_reranker_base_v1_maidalun1020|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|981.6 MB|
+
+## References
+
+References
+
+https://huggingface.co/maidalun1020/bce-reranker-base_v1

docs/_posts/ahmedlone127/2025-05-20-bce_reranker_base_v1_maidalun1020_pipeline_en.md

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+---
+layout: model
+title: English bce_reranker_base_v1_maidalun1020_pipeline pipeline XlmRoBertaForSequenceClassification from maidalun1020
+author: John Snow Labs
+name: bce_reranker_base_v1_maidalun1020_pipeline
+date: 2025-05-20
+tags: [en, open_source, pipeline, onnx]
+task: Text Classification
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained XlmRoBertaForSequenceClassification, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bce_reranker_base_v1_maidalun1020_pipeline` is a English model originally trained by maidalun1020.
+
+## 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/bce_reranker_base_v1_maidalun1020_pipeline_en_5.5.1_3.0_1747745288657.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bce_reranker_base_v1_maidalun1020_pipeline_en_5.5.1_3.0_1747745288657.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
+pipeline = PretrainedPipeline("bce_reranker_base_v1_maidalun1020_pipeline", lang = "en")
+annotations =  pipeline.transform(df)
+```
+```scala
+val pipeline = new PretrainedPipeline("bce_reranker_base_v1_maidalun1020_pipeline", lang = "en")
+val annotations = pipeline.transform(df)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bce_reranker_base_v1_maidalun1020_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|981.6 MB|
+
+## References
+
+References
+
+https://huggingface.co/maidalun1020/bce-reranker-base_v1
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- XlmRoBertaForSequenceClassification