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* Add model 2024-09-12-italian_emotion_analyzer_it

* Add model 2024-09-17-distilbert_base_uncased_finetuned_squad_d5716d28_serhii_korobchenko_pipeline_en

* Add model 2024-09-17-distilbert_base_cased_distilled_squad_full_lora_merged_en

* Add model 2024-09-11-multi_qa_mpnet_base_dot_v1_covidqa_search_75_25_2epoch_full_en

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* Add model 2024-09-09-quberta_qu

* Add model 2024-09-17-whisper_tiny_chinese_zhihcheng_pipeline_zh

* Add model 2024-09-11-distilbert_base_uncased_finetuned_squad_test2_en

---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>

docs/_posts/ahmedlone127/2024-09-02-bulbert_chitanka_model_bg.md

@@ -0,0 +1,94 @@
+---
+layout: model
+title: Bulgarian bulbert_chitanka_model BertEmbeddings from mor40
+author: John Snow Labs
+name: bulbert_chitanka_model
+date: 2024-09-02
+tags: [bg, open_source, onnx, embeddings, bert]
+task: Embeddings
+language: bg
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BertEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BertEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bulbert_chitanka_model` is a Bulgarian model originally trained by mor40.
+
+{:.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/bulbert_chitanka_model_bg_5.5.0_3.0_1725318518639.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bulbert_chitanka_model_bg_5.5.0_3.0_1725318518639.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
+
+documentAssembler = DocumentAssembler() \
+      .setInputCol("text") \
+      .setOutputCol("document")
+
+tokenizer = Tokenizer() \ 
+      .setInputCols("document") \ 
+      .setOutputCol("token")
+
+embeddings = BertEmbeddings.pretrained("bulbert_chitanka_model","bg") \
+      .setInputCols(["document", "token"]) \
+      .setOutputCol("embeddings")       
+
+pipeline = Pipeline().setStages([documentAssembler, tokenizer, embeddings])
+data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
+pipelineModel = pipeline.fit(data)
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+val documentAssembler = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer() 
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val embeddings = BertEmbeddings.pretrained("bulbert_chitanka_model","bg") 
+    .setInputCols(Array("document", "token")) 
+    .setOutputCol("embeddings")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+val data = Seq("I love spark-nlp").toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bulbert_chitanka_model|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[bert]|
+|Language:|bg|
+|Size:|306.1 MB|
+
+## References
+
+https://huggingface.co/mor40/BulBERT-chitanka-model

docs/_posts/ahmedlone127/2024-09-03-oorito_en.md

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+---
+layout: model
+title: English oorito MarianTransformer from LRJ1981
+author: John Snow Labs
+name: oorito
+date: 2024-09-03
+tags: [en, open_source, onnx, translation, marian]
+task: Translation
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: MarianTransformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained MarianTransformer model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`oorito` is a English model originally trained by LRJ1981.
+
+{:.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/oorito_en_5.5.0_3.0_1725404166090.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/oorito_en_5.5.0_3.0_1725404166090.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
+
+documentAssembler = DocumentAssembler() \
+      .setInputCol("text") \
+      .setOutputCol("document")
+
+sentenceDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx") \
+      .setInputCols(["document"]) \
+      .setOutputCol("translation")
+
+marian = MarianTransformer.pretrained("oorito","en") \
+      .setInputCols(["sentence"]) \
+      .setOutputCol("embeddings")       
+
+pipeline = Pipeline().setStages([documentAssembler, sentenceDL, marian])
+data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
+pipelineModel = pipeline.fit(data)
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+val documentAssembler = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val marian = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
+	.setInputCols(Array("document"))
+	.setOutputCol("sentence")
+
+val embeddings = MarianTransformer.pretrained("oorito","en") 
+    .setInputCols(Array("sentence")) 
+    .setOutputCol("translation")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDL, marian))
+val data = Seq("I love spark-nlp").toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|oorito|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentences]|
+|Output Labels:|[translation]|
+|Language:|en|
+|Size:|504.7 MB|
+
+## References
+
+https://huggingface.co/LRJ1981/OORito

docs/_posts/ahmedlone127/2024-09-04-deberta_v3_large_survey_nepal_bhasa_fact_main_passage_rater_gpt4_en.md

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+---
+layout: model
+title: English deberta_v3_large_survey_nepal_bhasa_fact_main_passage_rater_gpt4 DeBertaForSequenceClassification from domenicrosati
+author: John Snow Labs
+name: deberta_v3_large_survey_nepal_bhasa_fact_main_passage_rater_gpt4
+date: 2024-09-04
+tags: [en, open_source, onnx, sequence_classification, deberta]
+task: Text Classification
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: DeBertaForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained DeBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`deberta_v3_large_survey_nepal_bhasa_fact_main_passage_rater_gpt4` is a English model originally trained by domenicrosati.
+
+{:.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/deberta_v3_large_survey_nepal_bhasa_fact_main_passage_rater_gpt4_en_5.5.0_3.0_1725440063827.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_large_survey_nepal_bhasa_fact_main_passage_rater_gpt4_en_5.5.0_3.0_1725440063827.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
+
+documentAssembler = DocumentAssembler() \
+    .setInputCol('text') \
+    .setOutputCol('document')
+
+tokenizer = Tokenizer() \
+    .setInputCols(['document']) \
+    .setOutputCol('token')
+
+sequenceClassifier  = DeBertaForSequenceClassification.pretrained("deberta_v3_large_survey_nepal_bhasa_fact_main_passage_rater_gpt4","en") \
+     .setInputCols(["documents","token"]) \
+     .setOutputCol("class")
+
+pipeline = Pipeline().setStages([documentAssembler, tokenizer, sequenceClassifier])
+data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
+pipelineModel = pipeline.fit(data)
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+val documentAssembler = new DocumentAssembler()
+    .setInputCols("text")
+    .setOutputCols("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val sequenceClassifier = DeBertaForSequenceClassification.pretrained("deberta_v3_large_survey_nepal_bhasa_fact_main_passage_rater_gpt4", "en")
+    .setInputCols(Array("documents","token")) 
+    .setOutputCol("class") 
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
+val data = Seq("I love spark-nlp").toDS.toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|deberta_v3_large_survey_nepal_bhasa_fact_main_passage_rater_gpt4|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|1.5 GB|
+
+## References
+
+https://huggingface.co/domenicrosati/deberta-v3-large-survey-new_fact_main_passage-rater-gpt4

docs/_posts/ahmedlone127/2024-09-04-dummy_model_umalakshmi07_pipeline_en.md

@@ -0,0 +1,70 @@
+---
+layout: model
+title: English dummy_model_umalakshmi07_pipeline pipeline CamemBertEmbeddings from Umalakshmi07
+author: John Snow Labs
+name: dummy_model_umalakshmi07_pipeline
+date: 2024-09-04
+tags: [en, open_source, pipeline, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained CamemBertEmbeddings, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`dummy_model_umalakshmi07_pipeline` is a English model originally trained by Umalakshmi07.
+
+{:.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/dummy_model_umalakshmi07_pipeline_en_5.5.0_3.0_1725409109729.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/dummy_model_umalakshmi07_pipeline_en_5.5.0_3.0_1725409109729.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
+
+pipeline = PretrainedPipeline("dummy_model_umalakshmi07_pipeline", lang = "en")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("dummy_model_umalakshmi07_pipeline", lang = "en")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|dummy_model_umalakshmi07_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|264.0 MB|
+
+## References
+
+https://huggingface.co/Umalakshmi07/dummy-model
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- CamemBertEmbeddings