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+---
+layout: model
+title: twitter-xlm-roberta-base-sentiment
+author: veerdhwaj
+name: twitter_xlm_roberta_base_sentiment
+date: 2023-07-28
+tags: [sentiment, roberta, en, open_source, tensorflow]
+task: Text Classification
+language: en
+edition: Spark NLP 5.0.0
+spark_version: 3.2
+supported: false
+engine: tensorflow
+annotator: XlmRoBertaForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages
+Huggingface : https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment
+
+## Predicted Entities
+
+`sentiment`
+
+{:.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/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690535217423.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://community.johnsnowlabs.com/veerdhwaj/twitter_xlm_roberta_base_sentiment_en_5.0.0_3.2_1690535217423.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 spark.implicits._
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+  .setInputCols("document")
+  .setOutputCol("token")
+
+val sequenceClassifier = XlmRoBertaForSequenceClassification.pretrained('twitter_xlm_roberta_base_sentiment')
+  .setInputCols("token", "document")
+  .setOutputCol("class")
+  .setCaseSensitive(true)
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  tokenizer,
+  sequenceClassifier
+))
+
+val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
+val result = pipeline.fit(data).transform(data)
+
+result.select("class.result").show(false)
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|twitter_xlm_roberta_base_sentiment|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Community|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|1.0 GB|
+|Case sensitive:|true|
+|Max sentence length:|512|
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