JohnSnowLabs
diff --git a/‎docs/_posts/ahmedlone127/2023-05-19-dependency_parse_en.md
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diff --git a/‎docs/_posts/ahmedlone127/2023-05-19-match_pattern_en.md
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diff --git a/‎docs/_posts/ahmedlone127/2023-05-20-analyze_sentiment_en.md
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+---
+layout: model
+title: Typed Dependency Parsing pipeline for English
+author: John Snow Labs
+name: dependency_parse
+date: 2023-05-19
+tags: [pipeline, dependency_parsing, untyped_dependency_parsing, typed_dependency_parsing, laballed_depdency_parsing, unlaballed_depdency_parsing, en, open_source]
+task: Dependency Parser
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Typed Dependency parser, trained on the on the CONLL dataset. 
+
+Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads.
+
+## Predicted Entities
+
+
+
+{:.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/public/models/dependency_parse_en_4.4.2_3.0_1684522392175.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dependency_parse_en_4.4.2_3.0_1684522392175.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
+
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('dependency_parse', lang = 'en')
+annotations =  pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("dependency_parse", lang = "en")
+val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0)
+
+```
+
+{:.nlu-block}
+```python
+
+nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence")
+
+    
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline('dependency_parse', lang = 'en')
+annotations =  pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("dependency_parse", lang = "en")
+val result = pipeline.fullAnnotate("Dependencies represents relationships betweens words in a Sentence")(0)
+```
+
+{:.nlu-block}
+```python
+nlu.load("dep.typed").predict("Dependencies represents relationships betweens words in a Sentence")
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
++---------------------------------------------------------------------------------+--------------------------------------------------------+
+|result                                                                           |result                                                  |
++---------------------------------------------------------------------------------+--------------------------------------------------------+
+|[ROOT, Dependencies, represents, words, relationships, Sentence, Sentence, words]|[root, parataxis, nsubj, amod, nsubj, case, nsubj, flat]|
++---------------------------------------------------------------------------------+--------------------------------------------------------+
+
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|dependency_parse|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|23.8 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- PerceptronModel
+- DependencyParserModel
+- TypedDependencyParserModel
@@ -0,0 +1,77 @@
+---
+layout: model
+title: Match Pattern
+author: John Snow Labs
+name: match_pattern
+date: 2023-05-19
+tags: [en, open_source]
+task: Text Classification
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The match_pattern is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps and matches pattrens  .
+	It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.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/public/models/match_pattern_en_4.4.2_3.0_1684521353408.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/match_pattern_en_4.4.2_3.0_1684521353408.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
+
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models")
+	result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+pipeline = PretrainedPipeline("match_pattern", "en", "clinical/models")
+	result = pipeline.annotate("""I love johnsnowlabs!  """)
+```
+
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|match_pattern|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|29.1 KB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- RegexMatcherModel
@@ -0,0 +1,130 @@
+---
+layout: model
+title: Sentiment Analysis pipeline for English
+author: John Snow Labs
+name: analyze_sentiment
+date: 2023-05-20
+tags: [open_source, english, analyze_sentiment, pipeline, en]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 4.4.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The analyze_sentiment is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps 
+and recognizes entities .
+It performs most of the common text processing tasks on your dataframe
+
+## Predicted Entities
+
+
+
+{:.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/public/models/analyze_sentiment_en_4.4.2_3.0_1684625826708.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/analyze_sentiment_en_4.4.2_3.0_1684625826708.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
+
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en')
+
+result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en")
+
+val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""]
+result_df = nlu.load('en.classify').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline('analyze_sentiment', lang = 'en')
+
+result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("analyze_sentiment", lang = "en")
+
+val result = pipeline.fullAnnotate("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = ["""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!"""]
+result_df = nlu.load('en.classify').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | text                                                                                                                                                                                                                                                                      | sentiment   |
+|---:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------|
+|  0 | Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now! | positive    |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|analyze_sentiment|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|5.1 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- NorvigSweetingModel
+- ViveknSentimentModel