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+---
+layout: model
+title: MPNet Sequence Classification - UKR Message Classifier
+author: John Snow Labs
+name: mpnet_sequence_classifier_ukr_message
+date: 2024-01-10
+tags: [en, mpnet, sequence, classification, open_source, onnx]
+task: Text Classification
+language: en
+edition: Spark NLP 5.2.3
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: MPNetForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+MPNet Sequence Classification imported from huggingface. 
+
+Originally a SetFit model, reference: https://huggingface.co/rodekruis/sml-ukr-message-classifier
+
+## Predicted Entities
+
+`ANOMALY`, `ARMY`, `CHILDREN`, `CONNECTIVITY`, `CONNECTWITHREDCROSS`, `EDUCATION`, `FOOD`, `GOODSSERVICES`, `HEALTH`, `INCLUSIONCVA`, `LEGAL`, `MONEY/BANKING`, `NFINONFOODITEMS`, `OTHERPROGRAMSOTHERNGOS`, `PARCEL`, `PAYMENTCVA`, `PETS`, `PMER/NEWPROGRAMOPERTUNITIES`, `PROGRAMINFO`, `PROGRAMINFORMATION`, `PSSRFL`, `REGISTRATIONCVA`, `SENTIMENT/FEEDBACK`, `SHELTER`, `TRANSLATION/LANGUAGE`, `TRANSPORT/CAR`, `TRANSPORT/MOVEMENT`, `WASH`, `WORK/JOBS`
+
+{:.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/mpnet_sequence_classifier_ukr_message_en_5.2.3_3.0_1704907644396.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_sequence_classifier_ukr_message_en_5.2.3_3.0_1704907644396.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
+document = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+tokenizer = Tokenizer() \
+    .setInputCols(["document"]) \
+    .setOutputCol("token")
+sequenceClassifier = MPNetForSequenceClassification \
+    .pretrained() \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("label")
+data = spark.createDataFrame([
+    ["I love driving my car."],
+    ["The next bus will arrive in 20 minutes."],
+    ["pineapple on pizza is the worst 🤮"],
+]).toDF("text")
+pipeline = Pipeline().setStages([document, tokenizer, sequenceClassifier])
+pipelineModel = pipeline.fit(data)
+results = pipelineModel.transform(data)
+results.select("label.result").show()
+```
+```scala
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.ml.Pipeline
+import spark.implicits._
+
+val document = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+  .setInputCols(Array("document"))
+  .setOutputCol("token")
+
+val modelPath = "onnx_exported/rodekruis/sml-ukr-message-classifier"
+
+val sequenceClassifier = MPNetForSequenceClassification
+  .loadSavedModel(modelPath, spark)
+//        .pretrained()
+  .setInputCols(Array("document", "token"))
+  .setOutputCol("label")
+
+val texts: Seq[String] = Seq(
+  "I love driving my car.",
+  "The next bus will arrive in 20 minutes.",
+  "pineapple on pizza is the worst 🤮")
+val data = texts.toDF("text")
+
+val pipeline = new Pipeline().setStages(Array(document, tokenizer, sequenceClassifier))
+val pipelineModel = pipeline.fit(data)
+val results = pipelineModel.transform(data)
+
+results.select("label.result").show()
+```
+</div>
+
+## Results
+
+```bash
++--------------------+
+|              result|
++--------------------+
+|     [TRANSPORT/CAR]|
+|[TRANSPORT/MOVEMENT]|
+|              [FOOD]|
++--------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|mpnet_sequence_classifier_ukr_message|
+|Compatibility:|Spark NLP 5.2.3+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[label]|
+|Language:|en|
+|Size:|403.5 MB|
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