Add model 2023-05-27-explain_document_sm_sv

docs/_posts/ahmedlone127/2023-05-27-explain_document_sm_sv.md

@@ -0,0 +1,123 @@
+---
+layout: model
+title: Explain Document Pipeline for Swedish
+author: John Snow Labs
+name: explain_document_sm
+date: 2023-05-27
+tags: [open_source, swedish, explain_document_sm, pipeline, sv]
+task: Named Entity Recognition
+language: sv
+edition: Spark NLP 4.4.2
+spark_version: 3.2
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The explain_document_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps.
+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/explain_document_sm_sv_4.4.2_3.2_1685198339255.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/explain_document_sm_sv_4.4.2_3.2_1685198339255.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 PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+
+
+```
+
+{:.nlu-block}
+```python
+
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.explain').predict(text)
+result_df
+
+```
+</div>
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('explain_document_sm', lang = 'sv')
+annotations =  pipeline.fullAnnotate(""Hej från John Snow Labs! "")[0]
+annotations.keys()
+```
+```scala
+val pipeline = new PretrainedPipeline("explain_document_sm", lang = "sv")
+val result = pipeline.fullAnnotate("Hej från John Snow Labs! ")(0)
+```
+
+{:.nlu-block}
+```python
+import nlu
+text = [""Hej från John Snow Labs! ""]
+result_df = nlu.load('sv.explain').predict(text)
+result_df
+```
+</div>
+
+## Results
+
+```bash
+Results
+
+
+|    | document                      | sentence                     | token                                    | lemma                                    | pos                                        | embeddings                   | ner                                   | entities            |
+|---:|:------------------------------|:-----------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------|:--------------------------------------|:--------------------|
+|  0 | ['Hej från John Snow Labs! '] | ['Hej från John Snow Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['Hej', 'från', 'John', 'Snow', 'Labs!'] | ['NOUN', 'ADP', 'PROPN', 'PROPN', 'PROPN'] | [[0.0306969992816448,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
+
+
+{:.model-param}
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|explain_document_sm|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 4.4.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|sv|
+|Size:|176.0 MB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- LemmatizerModel
+- PerceptronModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter