Add model 2023-04-26-legmulticlf_online_terms_of_service_english_en (#…

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Co-authored-by: Mary-Sci <meryemyildiz366@gmail.com>

docs/_posts/Mary-Sci/2023-04-26-legmulticlf_online_terms_of_service_english_en.md

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+---
+layout: model
+title: Legal Multilabel Classifier on Online Terms of Service
+author: John Snow Labs
+name: legmulticlf_online_terms_of_service_english
+date: 2023-04-26
+tags: [en, licensed, multilabel, classification, legal, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: MultiClassifierDLModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This is the Multi-Label Text Classification model that can be used to identify potentially unfair clauses in online Terms of Service. The classes are as follows:
+
+     - Arbitration 
+     - Choice_of_law
+     - Content_removal
+     - Jurisdiction
+     - Limitation_of_liability
+     - Other
+     - Unilateral_change
+     - Unilateral_termination
+
+## Predicted Entities
+
+`Arbitration`, `Choice_of_law`, `Content_removal`, `Jurisdiction`, `Limitation_of_liability`, `Other`, `Unilateral_change`, `Unilateral_termination`
+
+{:.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/legal/models/legmulticlf_online_terms_of_service_english_en_1.0.0_3.0_1682519205970.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legmulticlf_online_terms_of_service_english_en_1.0.0_3.0_1682519205970.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
+document_assembler = nlp.DocumentAssembler() \
+        .setInputCol('text')\
+        .setOutputCol('document')
+
+tokenizer = nlp.Tokenizer() \
+        .setInputCols(['document'])\
+        .setOutputCol('token')
+
+embeddings = nlp.BertEmbeddings.pretrained("bert_embeddings_sec_bert_base", "en") \
+        .setInputCols(['document', 'token'])\
+        .setOutputCol("embeddings")
+
+embeddingsSentence = nlp.SentenceEmbeddings() \
+        .setInputCols(['document', 'embeddings'])\
+        .setOutputCol('sentence_embeddings')\
+        .setPoolingStrategy('AVERAGE')
+
+classifierdl = nlp.MultiClassifierDLModel.pretrained('legmulticlf_online_terms_of_service_english', 'en', 'legal/models')
+         .setInputCols(["sentence_embeddings"])\
+         .setOutputCol("class")
+
+clf_pipeline = nlp.Pipeline(stages=[document_assembler, 
+                                    tokenizer, 
+                                    embeddings, 
+                                    embeddingsSentence, 
+                                    classifierdl])
+
+df = spark.createDataFrame([["We are not responsible or liable for (and have no obligation to verify) any wrong or misspelled email address or inaccurate or wrong (mobile) phone number or credit card number."]]).toDF("text")
+
+model = clf_pipeline.fit(df)
+result = model.transform(df)
+
+result.select("text", "class.result").show(truncate=False)
+```
+
+</div>
+
+## Results
+
+```bash
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------+
+|sentence                                                                                                                                                                         |result                   |
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------+
+|We are not responsible or liable for (and have no obligation to verify) any wrong or misspelled email address or inaccurate or wrong (mobile) phone number or credit card number.|[Limitation_of_liability]|
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legmulticlf_online_terms_of_service_english|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|13.9 MB|
+
+## References
+
+Train dataset available [here](https://huggingface.co/datasets/joelito/online_terms_of_service)
+
+## Benchmarking
+
+```bash
+label                    precision  recall  f1-score  support 
+Arbitration              1.00       0.50    0.67      4       
+Choice_of_law            0.67       0.67    0.67      3       
+Content_removal          1.00       0.67    0.80      3       
+Jurisdiction             0.80       1.00    0.89      4       
+Limitation_of_liability  0.73       0.73    0.73      15      
+Other                    0.86       0.89    0.88      28      
+Unilateral_change        0.86       1.00    0.92      6       
+Unilateral_termination   1.00       0.80    0.89      5       
+micro-avg                0.84       0.82    0.83      68      
+macro-avg                0.86       0.78    0.81      68      
+weighted-avg             0.85       0.82    0.83      68      
+samples-avg              0.80       0.82    0.81      68      
+```