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2022-10-19-legre_warranty_en #12951

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---
layout: model
title: Legal Relation Extraction (Warranty)
author: John Snow Labs
name: legre_warranty
date: 2022-10-19
tags: [legal, en, re, licensed, warranty]
task: Relation Extraction
language: en
edition: Spark NLP for Legal 1.0.0
spark_version: 3.0
supported: true
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

This is a Legal Relation Extraction Model to identify the Subject (who), Action (web), Object(the indemnification) and Indirect Object (to whom) from warranty clauses.

## Predicted Entities

`is_warranty_indobject`, `is_warranty_object`, `is_warranty_subject`

{:.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/legre_warranty_en_1.0.0_3.0_1666154293071.zip){:.button.button-orange.button-orange-trans.arr.button-icon}

## 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")

embeddings = nlp.RoBertaEmbeddings.pretrained("roberta_embeddings_legal_roberta_base","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")

ner_model = legal.NerModel.pretrained('legner_warranty', 'en', 'legal/models') \
.setInputCols(["document", "token", "embeddings"]) \
.setOutputCol("ner")

ner_converter = nlp.NerConverter() \
.setInputCols(["document","token","ner"]) \
.setOutputCol("ner_chunk")

reDL = legal.RelationExtractionDLModel.pretrained("legre_warranty", "en", "legal/models") \
.setPredictionThreshold(0.5) \
.setInputCols(["ner_chunk", "document"]) \
.setOutputCol("relations")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings, ner_model, ner_converter, reDL])

text = """ARTICLE XI - WARRANTIES 11.1 In addition to the warranties set forth in Article IX of the General Terms and Conditions of Transporter's FERC Gas Tariff, Shipper warrants the following: (a) Shipper warrants that all upstream and downstream transportation arrangements are in place, or will be in place as of the requested effective date of service, and that it has advised the upstream and downstream transporters of the receipt and delivery points under this Agreement and any quantity limitations for each point as specified on Exhibit "A" attached hereto."""

data = spark.createDataFrame([[text]]).toDF("text")
model = pipeline.fit(data)
res = model.transform(data)
```

</div>

## Results

```bash
|relation |entity1 |entity1_begin|entity1_end|chunk1 |entity2 |entity2_begin|entity2_end|chunk2 |confidence|
|-------------------|----------------|-------------|-----------|--------|---------------|-------------|-----------|---------------------------------------------------------------------------------------------------------------------------------------|----------|
|is_warranty_subject|WARRANTY_SUBJECT|158 |164 |Shipper |WARRANTY_ACTION|166 |173 |warrants |0.98402506|
|is_warranty_subject|WARRANTY_SUBJECT|196 |202 |Shipper |WARRANTY_ACTION|204 |211 |warrants |0.9707028 |
|is_warranty_object |WARRANTY_SUBJECT|196 |202 |Shipper |WARRANTY |218 |352 |all upstream and downstream transportation arrangements are in place, or will be in place as of the requested effective date of service|0.9917001 |
|is_warranty_object |WARRANTY_SUBJECT|196 |202 |Shipper |WARRANTY |367 |474 |has advised the upstream and downstream transporters of the receipt and delivery points under this Agreement |0.79867786|
|is_warranty_object |WARRANTY_ACTION |204 |211 |warrants|WARRANTY |218 |352 |all upstream and downstream transportation arrangements are in place, or will be in place as of the requested effective date of service|0.97821265|
|is_warranty_object |WARRANTY_ACTION |204 |211 |warrants|WARRANTY |367 |474 |has advised the upstream and downstream transporters of the receipt and delivery points under this Agreement |0.80337876|

```

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|legre_warranty|
|Compatibility:|Spark NLP for Legal 1.0.0+|
|License:|Licensed|
|Edition:|Official|
|Language:|en|
|Size:|409.9 MB|

## References

In-house annotated examples from CUAD legal dataset

## Benchmarking

```bash
Relation Recall Precision F1 Support
is_warranty_indobject 1.000 1.000 1.000 15
is_warranty_object 1.000 1.000 1.000 44
is_warranty_subject 1.000 1.000 1.000 29
Avg. 1.000 1.000 1.000
Weighted Avg. 1.000 1.000 1.000
```