2023-01-29-legclf_other_definitional_provisions_clause_en

docs/_posts/Mary-Sci/2023-01-29-legclf_assignment_and_subletting_clause_en.md

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+---
+layout: model
+title: Legal Assignment And Subletting Clause Binary Classifier
+author: John Snow Labs
+name: legclf_assignment_and_subletting_clause
+date: 2023-01-29
+tags: [en, legal, classification, assignment, subletting, clauses, assignment_and_subletting, licensed, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: LegalClassifierDLModel
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model is a Binary Classifier (True, False) for the `assignment-and-subletting` clause type. To use this model, make sure you provide enough context as an input. Adding Sentence Splitters to the pipeline will make the model see only sentences, not the whole text, so it's better to skip it, unless you want to do Binary Classification as sentence level.
+
+If you have big legal documents, and you want to look for clauses, we recommend you to split the documents using any of the techniques available in our Legal NLP Workshop Tokenization & Splitting Tutorial (link [here](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings_JSL/Legal/1.Tokenization_Splitting.ipynb)), namely:
+- Paragraph splitting (by multiline);
+- Splitting by headers / subheaders;
+- etc.
+
+Take into consideration the embeddings of this model allows up to 512 tokens. If you have more than that, consider splitting in smaller pieces (you can also check the same tutorial link provided above).
+
+This model can be combined with any of the other 200+ Legal Clauses Classifiers you will find in Models Hub, getting as an output a series of True/False values for each of the legal clause model you have added.
+
+## Predicted Entities
+
+`assignment-and-subletting`, `other`
+
+{:.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/legclf_assignment_and_subletting_clause_en_1.0.0_3.0_1674993574865.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legclf_assignment_and_subletting_clause_en_1.0.0_3.0_1674993574865.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")
+
+embeddings = nlp.BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "en")\
+    .setInputCols("document")\
+    .setOutputCol("sentence_embeddings")
+
+doc_classifier = legal.ClassifierDLModel.pretrained("legclf_assignment_and_subletting_clause", "en", "legal/models")\
+    .setInputCols(["sentence_embeddings"])\
+    .setOutputCol("category")
+
+nlpPipeline = nlp.Pipeline(stages=[
+    document_assembler, 
+    embeddings,
+    doc_classifier])
+
+df = spark.createDataFrame([["YOUR TEXT HERE"]]).toDF("text")
+
+model = nlpPipeline.fit(df)
+
+result = model.transform(df)
+
+```
+
+</div>
+
+## Results
+
+```bash
+
++-------+
+|result|
++-------+
+|[assignment-and-subletting]|
+|[other]|
+|[other]|
+|[assignment-and-subletting]|
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legclf_assignment_and_subletting_clause|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|22.7 MB|
+
+## References
+
+Legal documents, scrapped from the Internet, and classified in-house
+
+## Benchmarking
+
+```bash
+                    label  precision    recall  f1-score   support
+assignment-and-subletting       1.00      0.96      0.98        26
+                    other       0.97      1.00      0.99        38
+                 accuracy          -         -      0.98        64
+                macro-avg       0.99      0.98      0.98        64
+             weighted-avg       0.98      0.98      0.98        64
+```

docs/_posts/Mary-Sci/2023-01-29-legclf_cusip_numbers_clause_en.md

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+---
+layout: model
+title: Legal Cusip Numbers Clause Binary Classifier
+author: John Snow Labs
+name: legclf_cusip_numbers_clause
+date: 2023-01-29
+tags: [en, legal, classification, cusip, numbers, clauses, cusip_numbers, licensed, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: LegalClassifierDLModel
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model is a Binary Classifier (True, False) for the `cusip-numbers` clause type. To use this model, make sure you provide enough context as an input. Adding Sentence Splitters to the pipeline will make the model see only sentences, not the whole text, so it's better to skip it, unless you want to do Binary Classification as sentence level.
+
+If you have big legal documents, and you want to look for clauses, we recommend you to split the documents using any of the techniques available in our Legal NLP Workshop Tokenization & Splitting Tutorial (link [here](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings_JSL/Legal/1.Tokenization_Splitting.ipynb)), namely:
+- Paragraph splitting (by multiline);
+- Splitting by headers / subheaders;
+- etc.
+
+Take into consideration the embeddings of this model allows up to 512 tokens. If you have more than that, consider splitting in smaller pieces (you can also check the same tutorial link provided above).
+
+This model can be combined with any of the other 200+ Legal Clauses Classifiers you will find in Models Hub, getting as an output a series of True/False values for each of the legal clause model you have added.
+
+## Predicted Entities
+
+`cusip-numbers`, `other`
+
+{:.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/legclf_cusip_numbers_clause_en_1.0.0_3.0_1674994284758.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legclf_cusip_numbers_clause_en_1.0.0_3.0_1674994284758.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")
+
+embeddings = nlp.BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "en")\
+    .setInputCols("document")\
+    .setOutputCol("sentence_embeddings")
+
+doc_classifier = legal.ClassifierDLModel.pretrained("legclf_cusip_numbers_clause", "en", "legal/models")\
+    .setInputCols(["sentence_embeddings"])\
+    .setOutputCol("category")
+
+nlpPipeline = nlp.Pipeline(stages=[
+    document_assembler, 
+    embeddings,
+    doc_classifier])
+
+df = spark.createDataFrame([["YOUR TEXT HERE"]]).toDF("text")
+
+model = nlpPipeline.fit(df)
+
+result = model.transform(df)
+
+```
+
+</div>
+
+## Results
+
+```bash
+
++-------+
+|result|
++-------+
+|[cusip-numbers]|
+|[other]|
+|[other]|
+|[cusip-numbers]|
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legclf_cusip_numbers_clause|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|22.7 MB|
+
+## References
+
+Legal documents, scrapped from the Internet, and classified in-house
+
+## Benchmarking
+
+```bash
+        label  precision    recall  f1-score   support
+cusip-numbers       0.93      0.96      0.95        28
+        other       0.97      0.95      0.96        37
+     accuracy          -         -      0.95        65
+    macro-avg       0.95      0.96      0.95        65
+ weighted-avg       0.95      0.95      0.95        65
+```

docs/_posts/Mary-Sci/2023-01-29-legclf_due_authorization_clause_en.md

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+---
+layout: model
+title: Legal Due Authorization Clause Binary Classifier
+author: John Snow Labs
+name: legclf_due_authorization_clause
+date: 2023-01-29
+tags: [en, legal, classification, tax, treatment, clauses, due_authorization, licensed, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: LegalClassifierDLModel
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model is a Binary Classifier (True, False) for the `due-authorization` clause type. To use this model, make sure you provide enough context as an input. Adding Sentence Splitters to the pipeline will make the model see only sentences, not the whole text, so it's better to skip it, unless you want to do Binary Classification as sentence level.
+
+If you have big legal documents, and you want to look for clauses, we recommend you to split the documents using any of the techniques available in our Legal NLP Workshop Tokenization & Splitting Tutorial (link [here](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings_JSL/Legal/1.Tokenization_Splitting.ipynb)), namely:
+- Paragraph splitting (by multiline);
+- Splitting by headers / subheaders;
+- etc.
+
+Take into consideration the embeddings of this model allows up to 512 tokens. If you have more than that, consider splitting in smaller pieces (you can also check the same tutorial link provided above).
+
+This model can be combined with any of the other 200+ Legal Clauses Classifiers you will find in Models Hub, getting as an output a series of True/False values for each of the legal clause model you have added.
+
+## Predicted Entities
+
+`due-authorization`, `other`
+
+{:.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/legclf_due_authorization_clause_en_1.0.0_3.0_1674993500619.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legclf_due_authorization_clause_en_1.0.0_3.0_1674993500619.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")
+
+embeddings = nlp.BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "en")\
+    .setInputCols("document")\
+    .setOutputCol("sentence_embeddings")
+
+doc_classifier = legal.ClassifierDLModel.pretrained("legclf_due_authorization_clause", "en", "legal/models")\
+    .setInputCols(["sentence_embeddings"])\
+    .setOutputCol("category")
+
+nlpPipeline = nlp.Pipeline(stages=[
+    document_assembler, 
+    embeddings,
+    doc_classifier])
+
+df = spark.createDataFrame([["YOUR TEXT HERE"]]).toDF("text")
+
+model = nlpPipeline.fit(df)
+
+result = model.transform(df)
+
+```
+
+</div>
+
+## Results
+
+```bash
+
++-------+
+|result|
++-------+
+|[due-authorization]|
+|[other]|
+|[other]|
+|[due-authorization]|
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legclf_due_authorization_clause|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|22.7 MB|
+
+## References
+
+Legal documents, scrapped from the Internet, and classified in-house
+
+## Benchmarking
+
+```bash
+            label  precision    recall  f1-score   support
+due-authorization       0.98      1.00      0.99        61
+            other       1.00      0.99      1.00       106
+         accuracy          -         -      0.99       167
+        macro-avg       0.99      1.00      0.99       167
+     weighted-avg       0.99      0.99      0.99       167
+```