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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}
+
+
+[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
+
+
+
+
+{% 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)
+```
+
+
+
+## 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
+```
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