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JohnSnowLabs · C-K-Loan · Oct 6, 2022 · Oct 5, 2022 · Oct 5, 2022 · Oct 6, 2022
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diff --git a/docs/en/NLU_under_the_hood.md b/docs/en/NLU_under_the_hood.md
@@ -8,9 +8,7 @@ permalink: /docs/en/under_the_hood
 modify_date: "2019-05-16"
 ---
 
-<div class="main-docs" markdown="1">
-
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+<div class="main-docs" markdown="1"><div class="h3-box" markdown="1">
 
 This page acts as reference on the internal working and implementation of NLU.
 It acts as a reference for internal development and open source contributers.

diff --git a/docs/en/concepts.md b/docs/en/concepts.md
@@ -8,9 +8,7 @@ key: docs-concepts
 modify_date: "2020-05-08"
 ---
 
-<div class="main-docs" markdown="1">
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 The NLU library provides 2 simple methods with which most NLU tasks can be solved while achieving state of the art results.   
 The **load** and **predict** method.    

diff --git a/docs/en/examples.md b/docs/en/examples.md
@@ -8,9 +8,7 @@ permalink: /docs/en/examples
 modify_date: "2019-05-16"
 ---
 
-<div class="main-docs" markdown="1">
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 ## Usage examples of NLU.load()
 The following examples demonstrate how to use nlu's load api accompanied by the outputs generated by it.
@@ -1043,7 +1041,7 @@ nlu.load('sentence_detector').predict('NLU can detect things. Like beginning and
 |Like beginning and endings of sentences.  |   [[0.4970400035381317, -0.013454999774694443, 0...]|    [NNP, MD, VB, NNS, ., IN, VBG, CC, NNS, IN, NN...]|    [O, O, O, O, O, B-sent, O, O, O, O, O, O, B-se...] |
 |It can also do much more! | [[0.4970400035381317, -0.013454999774694443, 0...]|   [NNP, MD, VB, NNS, ., IN, VBG, CC, NNS, IN, NN...]|    [O, O, O, O, O, B-sent, O, O, O, O, O, O, B-se...] |
 
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@@ -1064,6 +1062,8 @@ df
 |------|-------------|
 | `<!DOCTYPE html> <html> <head> <title>Example</title> </head> <body> <p>This is an example of a simple HTML page with one paragraph.</p> </body> </html>`       |Example This is an example of a simple HTML page with one paragraph.|
 
+</div><div class="h3-box" markdown="1">
+
 ## Word Segmenter
 [Word Segmenter Example](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/multilingual/japanese_ner_pos_and_tokenization.ipynb)     
 The WordSegmenter segments languages without any rule-based tokenization such as Chinese, Japanese, or Korean
@@ -1098,7 +1098,7 @@ df
 |	ませ|
 |	ん|
 
-
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 ## Translation 
 [Translation example](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/translation_demo.ipynb)
@@ -1129,17 +1129,13 @@ df
 |-----------|--------------|
 |Billy likes to go to the mall every sunday	| Billy geht gerne jeden Sonntag ins Einkaufszentrum|
 
-
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 ## T5
 [Example of every T5 task](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_tasks_summarize_question_answering_and_more.ipynb)
 ### Overview of every task available with T5
 [The T5 model](https://arxiv.org/pdf/1910.10683.pdf) is trained on various datasets for 17 different tasks which fall into 8 categories.
 
-
 1. Text summarization
 2. Question answering
 3. Translation
@@ -1149,6 +1145,8 @@ df
 7. Sentence Completion
 8. Word sense disambiguation
 
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 ### Every T5 Task with explanation:
 
 
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 - [Every T5 Task example notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_tasks_summarize_question_answering_and_more.ipynb) to see how to use every T5 Task.
 - [T5 Open and Closed Book question answering  notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_question_answering.ipynb)
 
-
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 ## Text Summarization 
 [Summarization example](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_tasks_summarize_question_answering_and_more.ipynb)
@@ -1208,6 +1205,7 @@ pipe.predict(data)
 |------------------|-------|
 | manchester united face newcastle in the premier league on wednesday . louis van gaal's side currently sit two points clear of liverpool in fourth . the belgian duo took to the dance floor on monday night with some friends .            | the belgian duo took to the dance floor on monday night with some friends . manchester united face newcastle in the premier league on wednesday . red devils will be looking for just their second league away win in seven . louis van gaal’s side currently sit two points clear of liverpool in fourth . | 
 
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 ## Binary Sentence similarity/ Paraphrasing 
 [Binary sentence similarity example](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_tasks_summarize_question_answering_and_more.ipynb)
@@ -1242,6 +1240,7 @@ t5.predict(data)
 |We acted because we saw the existing evidence in a new light , through the prism of our experience on 11 September , " Rumsfeld said .| Rather , the US acted because the administration saw " existing evidence in a new light , through the prism of our experience on September 11 " . | equivalent | 
 | I like to eat peanutbutter for breakfast| I like to play football | not_equivalent | 
 
+</div><div class="h3-box" markdown="1">
 
 ### How to configure T5 task for MRPC and pre-process text
 `.setTask('mrpc sentence1:)` and prefix second sentence with `sentence2:`
@@ -1254,7 +1253,7 @@ sentence1: We acted because we saw the existing evidence in a new light , throug
 sentence2: Rather , the US acted because the administration saw " existing evidence in a new light , through the prism of our experience on September 11",
 ```
 
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 ## Regressive Sentence similarity/ Paraphrasing
 
@@ -1299,12 +1298,12 @@ t5.predict(data)
 |What was it like in Ancient rome?  | What was Ancient rome like?| 5.0 | 
 |What was live like as a King in Ancient Rome??       | What is it like to live in Rome? | 3.2 | 
 
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 ### How to configure T5 task for stsb and pre-process text
 `.setTask('stsb sentence1:)` and prefix second sentence with `sentence2:`
 
-
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 ### Example pre-processed input for T5 STSB - Regressive semantic sentence similarity
 
@@ -1314,9 +1313,7 @@ sentence1: What attributes would have made you highly desirable in ancient Rome?
 sentence2: How I GET OPPERTINUTY TO JOIN IT COMPANY AS A FRESHER?',
 ```
 
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 ## Grammar Checking
 [Grammar checking with T5 example](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_tasks_summarize_question_answering_and_more.ipynb))
@@ -1339,10 +1336,7 @@ pipe.predict(data)
 | Anna and Mike is going skiing and they is liked is | unacceptable |      
 | Anna and Mike like to dance | acceptable | 
 
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 ## Open book question answering
 [T5 Open and Closed Book question answering tutorial](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_question_answering.ipynb)
@@ -1420,7 +1414,7 @@ surged 5%	| How did Alibaba stocks react? |
 100 rural teachers	| Whom did Jack Ma meet? | 
 Chinese regulators	|Who did Jack Ma hide from?|
 
-
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 ## Closed book question answering
 [T5 Open and Closed Book question answering tutorial](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_question_answering.ipynb)
@@ -1454,5 +1448,4 @@ nlu.load('en.t5').predict('What is the capital of Germany?')
 >>> Berlin
 ```
 
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diff --git a/docs/en/examples_healthcare.md b/docs/en/examples_healthcare.md
@@ -7,9 +7,7 @@ key: docs-examples-hc
 permalink: /docs/en/examples_hc
 modify_date: "2019-05-16"
 ---
-<div class="main-docs" markdown="1">
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+<div class="main-docs" markdown="1"><div class="h3-box" markdown="1">
 
 ## Usage examples of NLU.load()
 The following examples demonstrate how to use nlu's load api accompanied by the outputs generated by it.
@@ -18,9 +16,7 @@ You need to pass one NLU reference to the load method.
 You can also pass multiple whitespace separated references.      
 [You can find all NLU references here](https://nlu.johnsnowlabs.com/docs/en/spellbook)
 
-
-
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 ## Medical Named Entity Recognition (NER) 
 [Medical NER tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/medical_named_entity_recognition/overview_medical_entity_recognizers.ipynb)    
@@ -48,6 +44,8 @@ df = nlu.load('med_ner.jsl.wip.clinical en.resolve_chunk.cpt_clinical').predict(
 
 See the [Models Hub for all avaiable Entity Resolution Models](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition)
 
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+
 ## Entity Resolution (for sentences)
 [Entity Resolution tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/entity_resolution/entity_resolvers_overview.ipynb)
 
@@ -67,8 +65,7 @@ data = ["""He has a starvation ketosis but nothing found for significant for dry
 
 See the [Models Hub for all avaiable Entity Resolution Models](https://nlp.johnsnowlabs.com/models?task=Entity+Resolution)
 
-
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 ## Entity Resolution (for chunks)
 [Entity Resolution tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/entity_resolution/entity_resolvers_overview.ipynb)
@@ -95,10 +92,9 @@ df = nlu.load('med_ner.jsl.wip.clinical en.resolve_chunk.cpt_clinical').predict(
 | for 2 days                  | Duration                        |                              0.5479 |                      35390 |                                           for 2 days                          |                           2.3929 |                                  1 |                                 35390 |                                  2.3929 |                                         0.22   |
 
 
-
-
 See the [Models Hub for all avaiable Entity Resolution Models](https://nlp.johnsnowlabs.com/models?task=Entity+Resolution)
 
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 ## Relation Extraction
 [Relation Extraction tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/relation_extraction/overview_relation.ipynb)
@@ -150,12 +146,7 @@ df = nlu.load('en.med_ner.jsl.wip.clinical.greedy en.relation').predict(data)
 | MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia" |                  0 | Internal_organ_or_component | Internal_organ_or_component | cerebellum             | basil ganglia          |                   0.975779 | ['MRI', 'infarction', 'upper', 'brain stem', 'left', 'cerebellum', 'right', 'basil ganglia'] | ['Test', 'Disease_Syndrome_Disorder', 'Direction', 'Internal_organ_or_component', 'Direction', 'Internal_organ_or_component', 'Direction', 'Internal_organ_or_component'] | ['0.9979', '0.5062', '0.2152', '0.2636', '0.4775', '0.8135', '0.5086', '0.3236'] |
 | MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia" |                  1 | Direction                   | Internal_organ_or_component | right                  | basil ganglia          |                   0.999613 | ['MRI', 'infarction', 'upper', 'brain stem', 'left', 'cerebellum', 'right', 'basil ganglia'] | ['Test', 'Disease_Syndrome_Disorder', 'Direction', 'Internal_organ_or_component', 'Direction', 'Internal_organ_or_component', 'Direction', 'Internal_organ_or_component'] | ['0.9979', '0.5062', '0.2152', '0.2636', '0.4775', '0.8135', '0.5086', '0.3236'] |
 
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 ## Assertion
 [Assertion tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/assertion/assertion_overview.ipynb)
@@ -174,6 +165,7 @@ assert_df = nlu.load('en.med_ner.clinical en.assert ').predict(data)
 
 See the [Models Hub for all avaiable Assertion Models](https://nlp.johnsnowlabs.com/models?task=Assertion+Status)
 
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 ## De-Identification
 [De-Identification tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/de_identification/DeIdentification_model_overview.ipynb)
@@ -192,6 +184,7 @@ df = nlu.load('de_identify').predict(data)
 
 See the [Models Hub for all avaiable De-Identification Models](https://nlp.johnsnowlabs.com/models?task=De-identification)
 
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 ## Drug Normalizer
 [Drug Normalizer tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/drug_normalization/drug_norm.ipynb)
@@ -217,6 +210,7 @@ nlu.load('norm_drugs').predict(data)
 | interferon alfa - 2b 10000000 unt ( 1 ml ) injection | interferon alfa-2b 10 million unit ( 1 ml ) injec |
 | Sodium Chloride / Potassium Chloride 13 bag          | Sodium Chloride/Potassium Chloride 13bag          |
 
+</div><div class="h3-box" markdown="1">
 
 ## Rule based NER with Context Matcher
 [Rule based NER with context matching tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/rule_based_named_entity_recognition_and_resolution/rule_based_NER_and_resolution_with_context_matching.ipynb)
@@ -270,6 +264,8 @@ gender_NER_pipe.predict(sample_text)
 | girl            |                       0.13 |
 | girl            |                       0.13 |
 
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 ### Context Matcher Parameters
 You can define the following parameters in your rules.json file to define the entities to be matched
 
@@ -290,27 +286,28 @@ You can define the following parameters in your rules.json file to define the en
 | completeMatchRegex | `Optional[str]`| Wether to use complete or partial matching, either `"true"` or `"false"` |
 | ruleScope | `str` | currently only `sentence` supported |
 
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 ## Authorize access to licensed features and install healthcare dependencies
 You need a set of **credentials** to access the licensed healthcare features.   
 [You can grab one here](https://www.johnsnowlabs.com/spark-nlp-try-free/)
 
+</div><div class="h3-box" markdown="1">
 
 ### Automatically Authorize Google Colab via JSON file
 By default, nlu checks `/content/spark_nlp_for_healthcare.json` on google colabe enviroments for a `spark_nlp_for_healthcare.json` file that you recieve via E-mail from us.
 If you upload the `spark_nlp_for_healthcare.json` file to the standard colab directory, `nlu.load()` will automatically find it and authorize your enviroment.
 
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 ### Authorize anywhere via providing via JSON file 
 You can specify the location of your `spark_nlp_for_healthcare.json` like this :
 ```python
 path = '/path/to/spark_nlp_for_healthcare.json'
 nlu.auth(path).load('licensed_model').predict(data)
 ```
 
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 ### Authorize via providing String parameters
 ```python
@@ -321,12 +318,6 @@ AWS_SECRET_ACCESS_KEY       = 'YOUR_SECRETS'
 JSL_SECRET                  = 'YOUR_SECRETS'
 
 nlu.auth(SPARK_NLP_LICENSE,AWS_ACCESS_KEY_ID,AWS_SECRET_ACCESS_KEY,JSL_SECRET)
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