Add model 2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en (#1…

…3757) Co-authored-by: Naveen-004 <chinna.nk4@gmail.com>
JohnSnowLabs · Apr 13, 2023 · afb700e · afb700e
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diff --git a/docs/_posts/Naveen-004/2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en.md b/docs/_posts/Naveen-004/2023-04-13-CyberbullyingDetection_ClassifierDL_tfhub_en.md
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+---
+layout: model
+title: Cyberbullying Detection
+author: Naveen-004
+name: CyberbullyingDetection_ClassifierDL_tfhub
+date: 2023-04-13
+tags: [en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 4.4.0
+spark_version: 3.0
+supported: false
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Identify cyberbullying using a multi-class classification framework that distinguishes six different types of cyberbullying. We have used a Twitter dataset from Kaggle and applied various techniques such as text cleaning, data augmentation, document assembling, universal sentence encoding and tensorflow classification model to process and analyze the data. We have also used snscrape to retrieve tweet data for validating our model’s performance. Our results show that our model achieved an accuracy of 85% for testing data and 89% for training data.
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+[Open in Colab](https://colab.research.google.com/drive/1xaIlDtpiGzf14EA1umhJoOXI0FZaYtRc?authuser=4#scrollTo=os2C1v2WW1Hi){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/community.johnsnowlabs.com/Naveen-004/CyberbullyingDetection_ClassifierDL_tfhub_en_4.4.0_3.0_1681363209630.zip){:.button.button-orange}
+[Copy S3 URI](s3://community.johnsnowlabs.com/Naveen-004/CyberbullyingDetection_ClassifierDL_tfhub_en_4.4.0_3.0_1681363209630.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("cleaned_text")\
+    .setOutputCol("document")
+
+use = UniversalSentenceEncoder.pretrained(name="tfhub_use_lg", lang="en")\
+ .setInputCols("document")\
+ .setOutputCol("sentence_embeddings")\
+ .setDimension(768)
+
+classifierdl = ClassifierDLApproach()\
+  .setInputCols(["sentence_embeddings"])\
+  .setOutputCol("class")\
+  .setLabelColumn("cyberbullying_type")\
+  .setBatchSize(16)\
+  .setMaxEpochs(42)\
+  .setDropout(0.4) \
+  .setEnableOutputLogs(True)\
+  .setLr(4e-3)
+use_clf_pipeline = Pipeline(
+    stages = [documentAssembler,
+        use,
+        classifierdl])
+```
+
+</div>
+
+## Results
+
+```bash
+           precision    recall  f1-score   support
+
+                age       0.94      0.96      0.95       796
+          ethnicity       0.94      0.94      0.94       810
+             gender       0.87      0.86      0.86       816
+  not_cyberbullying       0.74      0.67      0.70       766
+other_cyberbullying       0.67      0.71      0.69       775
+           religion       0.94      0.96      0.95       731
+
+           accuracy                           0.85      4694
+          macro avg       0.85      0.85      0.85      4694
+       weighted avg       0.85      0.85      0.85      4694
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|CyberbullyingDetection_ClassifierDL_tfhub|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.0+|
+|License:|Open Source|
+|Edition:|Community|
+|Language:|en|
+|Size:|811.9 MB|
+
+## Included Models
+
+- DocumentAssembler
+- UniversalSentenceEncoder
+- ClassifierDLModel