2023-06-28-roberta_base_en

docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_en.md

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+---
+layout: model
+title: DeBERTa base model
+author: John Snow Labs
+name: deberta_v3_base
+date: 2023-06-28
+tags: [en, english, open_source, embeddings, deberta, v3, base, 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
+
+The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: Decoding-enhanced BERT with Disentangled Attention]] by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%).
+
+## 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_v3_base_en_5.0.0_3.0_1687957496351.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_base_en_5.0.0_3.0_1687957496351.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
+embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""")
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|deberta_v3_base|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|435.2 MB|
+|Case sensitive:|true|
+|Max sentence length:|128|
+
+## Benchmarking
+
+```bash
+Benchmarking
+```

docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_opt_en.md

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+---
+layout: model
+title: DeBERTa base model
+author: John Snow Labs
+name: deberta_v3_base_opt
+date: 2023-06-28
+tags: [en, english, open_source, embeddings, deberta, v3, base, 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
+
+The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: Decoding-enhanced BERT with Disentangled Attention]] by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%).
+
+## 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_v3_base_opt_en_5.0.0_3.0_1687958380723.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_base_opt_en_5.0.0_3.0_1687958380723.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
+embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""")
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|deberta_v3_base_opt|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|469.3 MB|
+|Case sensitive:|true|
+|Max sentence length:|128|
+
+## Benchmarking
+
+```bash
+Benchmarking
+```

docs/_posts/ahmedlone127/2023-06-28-deberta_v3_base_quantized_en.md

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+---
+layout: model
+title: DeBERTa base model
+author: John Snow Labs
+name: deberta_v3_base_quantized
+date: 2023-06-28
+tags: [en, english, open_source, embeddings, deberta, v3, base, 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
+
+The DeBERTa model was proposed in [[https://arxiv.org/abs/2006.03654 DeBERTa: Decoding-enhanced BERT with Disentangled Attention]] by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%).
+
+## 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_v3_base_quantized_en_5.0.0_3.0_1687958846162.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_base_quantized_en_5.0.0_3.0_1687958846162.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
+embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""")
+```
+
+</div>
+
+{:.model-param}
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en") \
+.setInputCols("sentence", "token") \
+.setOutputCol("embeddings")
+```
+```scala
+val embeddings = DeBertaEmbeddings.pretrained("deberta_v3_base", "en")
+.setInputCols("sentence", "token")
+.setOutputCol("embeddings")
+```
+
+{:.nlu-block}
+```python
+import nlu
+nlu.load("en.embed.deberta_v3_base").predict("""Put your text here.""")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|deberta_v3_base_quantized|
+|Compatibility:|Spark NLP 5.0.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[embeddings]|
+|Language:|en|
+|Size:|310.7 MB|
+|Case sensitive:|true|
+|Max sentence length:|128|
+
+## Benchmarking
+
+```bash
+Benchmarking
+```