2024-11-10-rubert_address_elements_ru

docs/_posts/ahmedlone127/2024-11-10-afriberta_v2_large_en.md

@@ -0,0 +1,94 @@
+---
+layout: model
+title: English afriberta_v2_large XlmRoBertaEmbeddings from castorini
+author: John Snow Labs
+name: afriberta_v2_large
+date: 2024-11-10
+tags: [en, open_source, onnx, embeddings, xlm_roberta]
+task: Embeddings
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: XlmRoBertaEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained XlmRoBertaEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`afriberta_v2_large` is a English model originally trained by castorini.
+
+{:.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/afriberta_v2_large_en_5.5.1_3.0_1731282953480.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/afriberta_v2_large_en_5.5.1_3.0_1731282953480.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 = XlmRoBertaEmbeddings.pretrained("afriberta_v2_large","en") \
+      .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 = XlmRoBertaEmbeddings.pretrained("afriberta_v2_large","en") 
+    .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:|afriberta_v2_large|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[xlm_roberta]|
+|Language:|en|
+|Size:|698.8 MB|
+
+## References
+
+https://huggingface.co/castorini/afriberta_v2_large

docs/_posts/ahmedlone127/2024-11-10-afriberta_v2_large_pipeline_en.md

@@ -0,0 +1,70 @@
+---
+layout: model
+title: English afriberta_v2_large_pipeline pipeline XlmRoBertaEmbeddings from castorini
+author: John Snow Labs
+name: afriberta_v2_large_pipeline
+date: 2024-11-10
+tags: [en, open_source, pipeline, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained XlmRoBertaEmbeddings, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`afriberta_v2_large_pipeline` is a English model originally trained by castorini.
+
+{:.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/afriberta_v2_large_pipeline_en_5.5.1_3.0_1731282989499.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/afriberta_v2_large_pipeline_en_5.5.1_3.0_1731282989499.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("afriberta_v2_large_pipeline", lang = "en")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("afriberta_v2_large_pipeline", lang = "en")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|afriberta_v2_large_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|698.8 MB|
+
+## References
+
+https://huggingface.co/castorini/afriberta_v2_large
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- XlmRoBertaEmbeddings

docs/_posts/ahmedlone127/2024-11-10-bert_base_chinese_finetuned_food_en.md

@@ -0,0 +1,94 @@
+---
+layout: model
+title: English bert_base_chinese_finetuned_food BertForTokenClassification from zhiguoxu
+author: John Snow Labs
+name: bert_base_chinese_finetuned_food
+date: 2024-11-10
+tags: [en, open_source, onnx, token_classification, bert, ner]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: BertForTokenClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_finetuned_food` is a English model originally trained by zhiguoxu.
+
+{:.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/bert_base_chinese_finetuned_food_en_5.5.1_3.0_1731279799981.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_food_en_5.5.1_3.0_1731279799981.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')
+
+tokenClassifier  = BertForTokenClassification.pretrained("bert_base_chinese_finetuned_food","en") \
+     .setInputCols(["documents","token"]) \
+     .setOutputCol("ner")
+
+pipeline = Pipeline().setStages([documentAssembler, tokenizer, tokenClassifier])
+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("document")
+    .setOutputCol("token")
+
+val tokenClassifier = BertForTokenClassification.pretrained("bert_base_chinese_finetuned_food", "en")
+    .setInputCols(Array("documents","token")) 
+    .setOutputCol("ner") 
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, tokenClassifier))
+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:|bert_base_chinese_finetuned_food|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|381.1 MB|
+
+## References
+
+https://huggingface.co/zhiguoxu/bert-base-chinese-finetuned-food

docs/_posts/ahmedlone127/2024-11-10-bert_base_chinese_finetuned_food_pipeline_en.md

@@ -0,0 +1,70 @@
+---
+layout: model
+title: English bert_base_chinese_finetuned_food_pipeline pipeline BertForTokenClassification from zhiguoxu
+author: John Snow Labs
+name: bert_base_chinese_finetuned_food_pipeline
+date: 2024-11-10
+tags: [en, open_source, pipeline, onnx]
+task: Named Entity Recognition
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained BertForTokenClassification, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_base_chinese_finetuned_food_pipeline` is a English model originally trained by zhiguoxu.
+
+{:.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/bert_base_chinese_finetuned_food_pipeline_en_5.5.1_3.0_1731279819532.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_base_chinese_finetuned_food_pipeline_en_5.5.1_3.0_1731279819532.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("bert_base_chinese_finetuned_food_pipeline", lang = "en")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("bert_base_chinese_finetuned_food_pipeline", lang = "en")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_base_chinese_finetuned_food_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|381.2 MB|
+
+## References
+
+https://huggingface.co/zhiguoxu/bert-base-chinese-finetuned-food
+
+## Included Models
+
+- DocumentAssembler
+- TokenizerModel
+- BertForTokenClassification