Add model 2023-11-05-legembedding_e5_base_en (#742)

Co-authored-by: gadde5300 <gadde5300@gmail.com>

docs/_posts/gadde5300/2023-11-05-legembedding_e5_base_en.md

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+---
+layout: model
+title: Legal E5 Embedding Base
+author: John Snow Labs
+name: legembedding_e5_base
+date: 2023-11-05
+tags: [legal, en, e5, sentence_embeddings, onnx, licensed]
+task: Embeddings
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: E5Embeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model is a legal version of the E5 base model fine-tuned on Edgar and legal question-answering datasets. Reference: Wang, Liang, et al. “Text embeddings by weakly-supervised contrastive pre-training.” arXiv preprint arXiv:2212.03533 (2022).
+
+## 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/legal/models/legembedding_e5_base_en_1.0.0_3.0_1699207424943.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legembedding_e5_base_en_1.0.0_3.0_1699207424943.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
+document_assembler = (
+    nlp.DocumentAssembler().setInputCol("text").setOutputCol("document")
+)
+
+E5_embedding = (
+    nlp.E5Embeddings.pretrained(
+        "legembedding_e5_base", "en", "legal/models"
+    )
+    .setInputCols(["document"])
+    .setOutputCol("E5")
+)
+pipeline = nlp.Pipeline(stages=[document_assembler, E5_embedding])
+
+data = spark.createDataFrame([[' What is the rate of shipment for crude oil from the Lincoln Parish Plant to the Mount Olive Plant and from the Mount Olive Plant to the DCP Black Lake in Ada, LA?']]).toDF("text")
+
+
+result = pipeline.fit(data).transform(data)
+result. Select("E5.result").show()
+```
+
+</div>
+
+## Results
+
+```bash
++----------------------------------------------------------------------------------------------------+
+|                                                                                          embeddings|
++----------------------------------------------------------------------------------------------------+
+|[-1.0422493, 0.008562431, -0.31533027, -0.39874774, 0.27517456, 0.6205345, -0.34923095, 0.2872358...|
++----------------------------------------------------------------------------------------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legembedding_e5_base|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[document]|
+|Output Labels:|[E5]|
+|Language:|en|
+|Size:|393.9 MB|
+
+## References
+
+For our Legal models, we will use publicly available datasets to fine-tune the model:
+
+- [EDGAR](https://huggingface.co/datasets/pile-of-law/pile-of-law)
+- In-house annotated Earning Calls Transcripts