2023-06-26-distilbert_embeddings_finetuned_sarcasm_classification_en

docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh.md

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+---
+layout: model
+title: Chinese Deberta Embeddings Cased model (from IDEA-CCNL)
+author: John Snow Labs
+name: deberta_embeddings_erlangshen_v2_chinese_sentencepiece
+date: 2023-06-26
+tags: [open_source, deberta, deberta_embeddings, debertav2formaskedlm, zh, onnx]
+task: Embeddings
+language: zh
+edition: Spark NLP 5.0.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: DeBertaEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained DebertaV2ForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece` is a Chinese model originally trained by `IDEA-CCNL`.
+
+## 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/deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh_5.0.0_3.0_1687781761029.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_embeddings_erlangshen_v2_chinese_sentencepiece_zh_5.0.0_3.0_1687781761029.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 = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings") \
+    .setCaseSensitive(True)
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document")
+    .setOutputCol("token")
+
+val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh")
+    .setInputCols(Array("document", "token"))
+    .setOutputCol("embeddings")
+    .setCaseSensitive(True)
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("I love Spark-NLP").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+
+embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings") \
+    .setCaseSensitive(True)
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["I love Spark-NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document")
+    .setOutputCol("token")
+
+val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_erlangshen_v2_chinese_sentencepiece","zh")
+    .setInputCols(Array("document", "token"))
+    .setOutputCol("embeddings")
+    .setCaseSensitive(True)
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("I love Spark-NLP").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|deberta_embeddings_erlangshen_v2_chinese_sentencepiece|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|zh|
+|Size:|443.8 MB|
+|Case sensitive:|false|

docs/_posts/ahmedlone127/2023-06-26-deberta_embeddings_mlm_test_en.md

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+---
+layout: model
+title: English Deberta Embeddings model (from domenicrosati)
+author: John Snow Labs
+name: deberta_embeddings_mlm_test
+date: 2023-06-26
+tags: [deberta, open_source, deberta_embeddings, debertav2formaskedlm, en, onnx]
+task: Embeddings
+language: en
+edition: Spark NLP 5.0.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: DeBertaEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained DebertaEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `deberta-mlm-test` is a English model originally trained by `domenicrosati`.
+
+## 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/deberta_embeddings_mlm_test_en_5.0.0_3.0_1687782209221.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_embeddings_mlm_test_en_5.0.0_3.0_1687782209221.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 = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings") \
+    .setCaseSensitive(True)
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document")
+    .setOutputCol("token")
+
+val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en")
+    .setInputCols(Array("document", "token"))
+    .setOutputCol("embeddings")
+    .setCaseSensitive(true)
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("I love Spark NLP").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+
+tokenizer = Tokenizer() \
+    .setInputCols("document") \
+    .setOutputCol("token")
+
+embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en") \
+    .setInputCols(["document", "token"]) \
+    .setOutputCol("embeddings") \
+    .setCaseSensitive(True)
+
+pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
+
+data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val documentAssembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document")
+    .setOutputCol("token")
+
+val embeddings = DeBertaEmbeddings.pretrained("deberta_embeddings_mlm_test","en")
+    .setInputCols(Array("document", "token"))
+    .setOutputCol("embeddings")
+    .setCaseSensitive(true)
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
+
+val data = Seq("I love Spark NLP").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|deberta_embeddings_mlm_test|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence, token]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|265.4 MB|
+|Case sensitive:|false|