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+---
+layout: model
+title: BLIP Question Answering
+author: John Snow Labs
+name: blip_vqa_base
+date: 2024-10-03
+tags: [en, open_source, tensorflow]
+task: Question Answering
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.4
+supported: true
+engine: tensorflow
+annotator: BLIPForQuestionAnswering
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+BLIP Model for visual question answering. The model consists of a vision encoder, a text encoder as well as a text decoder. The vision encoder will encode the input image, the text encoder will encode the input question together with the encoding of the image, and the text decoder will output the answer to the question.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+[Open in Colab](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace_in_Spark_NLP_BLIPForQuestionAnswering.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/blip_vqa_base_en_5.5.0_3.4_1727997969354.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/blip_vqa_base_en_5.5.0_3.4_1727997969354.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+To proceed, please create a DataFrame with two columns:
+
+- An image column that contains the file path for each image in the directory.
+- A text column where you can input the specific question you would like to ask about each image.
+
+For example:
+
+```python
+from pyspark.sql.functions import lit
+
+images_path = "./images/"
+image_df = spark.read.format("image").load(path=images_path)
+
+test_df = image_df.withColumn("text", lit("What's this picture about?"))
+test_df.show()
+```
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+imageAssembler = ImageAssembler() \
+  .setInputCol("image") \
+  .setOutputCol("image_assembler") \
+
+imageClassifier = BLIPForQuestionAnswering.load("./{}_spark_nlp".format(MODEL_NAME)) \
+  .setInputCols("image_assembler") \
+  .setOutputCol("answer") \
+  .setSize(384)
+
+pipeline = Pipeline(
+    stages=[
+        imageAssembler,
+        imageClassifier,
+    ]
+)
+
+model = pipeline.fit(test_df)
+result = model.transform(test_df)
+result.select("image_assembler.origin", "answer.result").show(truncate = False)
+```
+```scala
+val imageAssembler: ImageAssembler = new ImageAssembler()
+      .setInputCol("image")
+      .setOutputCol("image_assembler")
+
+val loadModel = BLIPForQuestionAnswering
+  .pretrained()
+  .setInputCols("image_assembler")
+  .setOutputCol("answer")
+  .setSize(384)
+
+val newPipeline: Pipeline =
+  new Pipeline().setStages(Array(imageAssembler, loadModel))
+
+newPipeline.fit(testDF)
+val result = model.transform(testDF)
+
+result.select("image_assembler.origin", "answer.result").show(truncate = false)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|blip_vqa_base|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.4 GB|
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