2023-12-22-Affiliation_Classifier_Roberta_en

docs/_posts/alex2awesome/2023-12-22-Affiliation_Classifier_Roberta_en.md

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+---
+layout: model
+title: Affiliation Classifier
+author: alex2awesome
+name: Affiliation_Classifier_Roberta
+date: 2023-12-22
+tags: [en, open_source, tensorflow]
+task: Text Classification
+language: en
+edition: Spark NLP 5.2.0
+spark_version: 3.2
+supported: false
+engine: tensorflow
+annotator: RoBertaForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Predicts the affiliation, if any, of the information in a paragraph.
+
+## 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/community.johnsnowlabs.com/alex2awesome/Affiliation_Classifier_Roberta_en_5.2.0_3.2_1703264189300.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://community.johnsnowlabs.com/alex2awesome/Affiliation_Classifier_Roberta_en_5.2.0_3.2_1703264189300.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
+from sparknlp.annotator import *
+from sparknlp.base import *
+
+document_assembler = DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+tokenizer = Tokenizer() \
+    .setInputCols(['document']) \
+    .setOutputCol('token')
+
+sequence_classifier = RoBertaForSequenceClassification.load(MODEL_NAME)
+  .setInputCols(["document",'token'])\
+  .setOutputCol("class")  
+
+pipeline = Pipeline(stages=[
+    document_assembler, 
+    tokenizer,
+    sequence_classifier
+])
+
+# couple of simple examples
+example = spark.createDataFrame([["I love you!"], ['I feel lucky to be here.']]).toDF("text")
+
+result = pipeline.fit(example).transform(example)
+
+# result is a DataFrame
+result.select("text", "class.result").show()
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|Affiliation_Classifier_Roberta|
+|Compatibility:|Spark NLP 5.2.0+|
+|License:|Open Source|
+|Edition:|Community|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|441.4 MB|
+|Case sensitive:|true|
+|Max sentence length:|128|
+|Dependencies:|None|