2024-07-05-phi2_7b_en

docs/_posts/ahmedlone127/2024-07-05-phi2_7b_en.md

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+---
+layout: model
+title: Phi2 text-to-text model 7b int8
+author: John Snow Labs
+name: phi2
+date: 2024-07-05
+tags: [phi2, en, llm, open_source, openvino]
+task: Text Generation
+language: en
+edition: Spark NLP 5.4.0
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: Phi2Transformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained phi2 model , adapted and imported into Spark NLP.
+
+{:.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/phi2_en_5.4.0_3.0_1720187078320.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/phi2_en_5.4.0_3.0_1720187078320.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')
+
+phi2 = Phi2Transformer \
+        .pretrained() \
+        .setMaxOutputLength(50) \
+        .setDoSample(False) \
+        .setInputCols(["document"]) \
+        .setOutputCol("phi2_generation")
+
+pipeline = Pipeline().setStages([documentAssembler, phi2])
+data = spark.createDataFrame([["Who is the founder of Spark-NLP?"]]).toDF("text")
+pipelineModel = pipeline.fit(data)
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+val documentAssembler = new DocumentAssembler()
+	.setInputCols("text")
+	.setOutputCols("document")
+
+val phi2 = Phi2Transformer         .pretrained()         .setMaxOutputLength(50)         .setDoSample(False)         .setInputCols(["document"])         .setOutputCol("phi2_generation")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, phi2))
+val data = Seq("Who is the founder of Spark-NLP?").toDS.toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|phi2|
+|Compatibility:|Spark NLP 5.4.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|en|
+|Size:|9.1 GB|

docs/_posts/ahmedlone127/2024-07-12-bart_large_cnn_en.md

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+---
+layout: model
+title: BART (large-sized model), fine-tuned on CNN Daily Mail
+author: John Snow Labs
+name: bart_large_cnn
+date: 2024-07-12
+tags: [bart, bartsummarization, cnn, text_to_text, en, open_source, tensorflow]
+task: Summarization
+language: en
+edition: Spark NLP 5.4.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: BartTransformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+BART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart).
+
+Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+Model description
+BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
+
+BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs
+
+{:.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/bart_large_cnn_en_5.4.0_3.0_1720754758442.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bart_large_cnn_en_5.4.0_3.0_1720754758442.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
+
+bart = BartTransformer.pretrained("bart_large_cnn")             .setTask("summarize:")             .setMaxOutputLength(200)             .setInputCols(["documents"])             .setOutputCol("summaries")
+
+```
+```scala
+
+val bart = BartTransformer.pretrained("bart_large_cnn")
+            .setTask("summarize:")
+            .setMaxOutputLength(200)
+            .setInputCols("documents")
+            .setOutputCol("summaries")
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bart_large_cnn|
+|Compatibility:|Spark NLP 5.4.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[generation]|
+|Language:|en|
+|Size:|974.9 MB|