Seo fixes (#1284)

docs/_posts/Mary-Sci/2023-01-27-legclf_absence_of_litigation_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `absence-of-litigation` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

docs/_posts/Mary-Sci/2023-01-27-legclf_application_of_trust_money_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `application-of-trust-money` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

docs/_posts/Mary-Sci/2023-01-27-legclf_benefits_of_indenture_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `benefits-of-indenture` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

docs/_posts/Mary-Sci/2023-01-27-legclf_capital_expenditures_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `capital-expenditures` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

docs/_posts/Mary-Sci/2023-01-27-legclf_deposit_of_redemption_price_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `deposit-of-redemption-price` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

docs/_posts/Mary-Sci/2023-01-27-legclf_disclosure_of_information_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `disclosure-of-information` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

docs/_posts/Mary-Sci/2023-01-27-legclf_indemnification_procedures_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `indemnification-procedures` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

docs/_posts/Mary-Sci/2023-01-27-legclf_method_of_exercise_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `method-of-exercise` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

docs/_posts/Mary-Sci/2023-01-27-legclf_satisfaction_and_discharge_of_indenture_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `satisfaction-and-discharge-of-indenture` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

docs/_posts/Mary-Sci/2023-01-27-legclf_termination_for_convenience_clause_en.md

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `termination-for-convenience` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

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

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `annual-bonus` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

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

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

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

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `board-of-directors` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

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

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `business-expenses` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

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

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 This model is a Binary Classifier (True, False) for the `conditions-of-underwriters-obligations` 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

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

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.

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

@@ -22,7 +22,7 @@ use_language_switcher: "Python-Scala-Java"
 
 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:
+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/legal-nlp/01.Page_Splitting.ipynb)), namely:
 - Paragraph splitting (by multiline);
 - Splitting by headers / subheaders;
 - etc.