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92 changes: 92 additions & 0 deletions 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
114 changes: 114 additions & 0 deletions docs/_posts/gadde5300/2023-11-10-legembeddings_bge_base_en.md
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---
layout: model
title: Legal Embeddings BGE Base
author: John Snow Labs
name: legembeddings_bge_base
date: 2023-11-10
tags: [en, licensed, onnx, embeddings]
task: Embeddings
language: en
edition: Legal NLP 1.0.0
spark_version: 3.0
supported: true
engine: onnx
annotator: BertEmbeddings
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This model is a legal version of the BGE base model fine-tuned on in-house curated datasets. Reference: Xiao, S., Liu, Z., Zhang, P., & Muennighof, N. (2023). C-pack: Packaged resources to advance general chinese embedding. arXiv preprint arXiv:2309.07597.

## 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/legembeddings_bge_base_en_1.0.0_3.0_1699632504201.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legembeddings_bge_base_en_1.0.0_3.0_1699632504201.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 = nlp.DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")

tokenizer = nlp.Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")

BGE_loaded = nlp.BertEmbeddings.load("legembeddings_bge_base","en", "legal/models")\
.setInputCols(["document","token"])\
.setOutputCol("BGE")\

pipeline = nlp.Pipeline(
stages = [
documentAssembler,
tokenizer,
BGE_loaded
])

data = spark.createDataFrame([['''Receiving Party shall not use any Confidential Information for any purpose other than the purposes stated in Agreement.''']]).toDF("text")

model = pipeline.fit(data)
result = model.transform(data)
result.show(truncate=150)
```

</div>

## Results

```bash
+----------------------------------------------------------------------------------------------------+
| embeddings|
+----------------------------------------------------------------------------------------------------+
|[-0.060075462, -0.26741037, 0.32553613, 0.13449538, 0.22019976, -0.35624868, 1.1038424, 0.8212698...|
|[-0.10228735, -0.3738884, 0.27723783, 0.17312518, 0.26656383, -0.24942908, 1.1518378, 0.7217457, ...|
|[-0.38215938, -0.5851373, 0.35209915, -0.30132422, -0.9744857, 0.5976255, 0.86980593, 0.5825193, ...|
|[-0.8023102, -0.1705234, 0.4355616, -0.16370925, -0.99943596, -0.13651904, 1.0603938, 0.76027215,...|
|[0.17291568, -0.74328834, 0.43998405, -0.1694346, -0.7754292, -0.025751337, 1.1425712, 0.43741557...|
|[-0.27675575, -0.17631046, 0.09160468, -0.22860324, -0.6295841, -0.11335259, 1.0146872, 0.6610859...|
|[-0.11538671, -0.31234437, 0.21929267, 0.10618421, 0.2265009, -0.37587893, 1.1389759, 0.7971325, ...|
|[0.009457495, -0.33288023, 0.2432522, 0.12458266, 0.2707794, -0.36873063, 1.0906105, 0.70786965, ...|
|[-0.295701, -0.61499435, 0.07829141, -0.74933016, -0.531358, -0.18479005, 1.1679127, 0.5615579, 0...|
|[-0.67664135, 0.12311895, 0.08994642, -0.07882077, -0.6767479, -0.16962644, 1.0955209, 0.6912421,...|
|[-0.33884412, -0.26324403, -0.03943791, 0.12610006, -0.6458304, -0.3981361, 0.6717623, 0.5545144,...|
|[-0.84253764, -0.18777902, -0.0011436939, -0.29669517, -0.008230045, -0.19728595, 0.9491053, 0.67...|
|[-0.70816183, -0.22422114, -0.07173601, -0.18688664, -0.1930152, -0.30726036, 0.8886021, 0.789013...|
|[-0.18011564, 0.055544622, 0.061416026, -0.110076465, -0.028466597, -0.27377772, 0.98722064, 0.91...|
|[-0.4780874, -0.28484517, -0.105963364, 0.060177833, -0.75987476, -0.36107045, 0.6527582, 0.53413...|
|[-0.39539725, -0.6021485, -0.018175352, -0.12834826, -0.71462053, -0.17749298, 0.8468195, 0.59975...|
|[-0.095429584, -0.8838102, 0.5930538, -0.33268213, 0.010708451, 0.06336981, 1.2200518, 0.9934566,...|
|[0.06960945, -0.17862234, 0.36319345, 0.28421152, 0.22127056, -0.4145783, 1.0451053, 1.0578575, 0...|
|[-0.07706641, -0.09056446, 0.47557953, -0.14709732, 0.37253422, -0.39098266, 1.2081625, 1.2230319...|
+----------------------------------------------------------------------------------------------------+
```
{:.model-param}
## Model Information
{:.table-model}
|---|---|
|Model Name:|legembeddings_bge_base|
|Compatibility:|Legal NLP 1.0.0+|
|License:|Licensed|
|Edition:|Official|
|Input Labels:|[sentence, token]|
|Output Labels:|[bert]|
|Language:|en|
|Size:|1.2 GB|
|Case sensitive:|true|
## References
In-house curated legal datasets.

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