Legal NLP Releases new Multilabel model on stack exchange topics

Version 1.19.0 of the library comes with a topic classification multilabel model using the INSTRUCTOR embedding

New Multilabel Model on Legal Stack Exchange topics with INSTRUCTOR

This model extends a previously released model to multilabel, trained on a more recent dataset for Legal stack exchange questions topics using the recently released INSTRUCTOR embedding model.

Source: Instructor Text Embedding (instructor-embedding.github.io)

The model can identify more than thirty topics, listed below:

`business`, `california`, `canada`, `civil-law`, `constitutional-law`, `consumer-protection`, `contract-law`, `copyright`, `corporate-law`, `criminal-law`, `employment`, `england-and-wales`, `european-union`, `fraud`, `gdpr`, `germany`, `intellectual-property`, `international`, `internet`, `landlord`, `legal-terms`, `liability`, `licensing`, `police`, `privacy`, `property`, `real-estate`, `rental-property`, `software`, `tax-law`, `terms-of-service`, `trademark`, `traffic`, `united-kingdom`, `united-states`, `us-constitution`

Please note that some categories from the original data were removed due to limited examples. The model depends on the INSTRUCTOR embedding, which can be used in a Spark NLP pipeline as follows:

embeddings = (
    nlp.InstructorEmbeddings.pretrained("instructor_large", "en")
    .setInstruction("Represent for multilabel classification:")
    .setInputCols(["document"])
    .setOutputCol("sentence_embeddings")
)

classifierdl = (
    nlp.MultiClassifierDLModel.pretrained(
        "legmulticlf_law_stack_exchange", "en", "legal/models"
    )
    .setInputCols(["sentence_embeddings"])
    .setOutputCol("class")
)

clf_pipeline = nlp.Pipeline(
    stages=[document_assembler, embeddings, classifierdl]
)

For the given example:

I’ve seen this label on Coke cans at one point, and I was wondering: is this legally enforceable? If it’s not, is it possible for a retailer in any way to disallow the resale of an item purchased? Something like, I don’t know, maybe a license you have to agree to in order to be allowed to purchase said item? This comes in the larger context of these new tech releases (GPUs, consoles) and how the producers/retailers could legally prevent scalpers.

We can extract the topics with the pipeline (text formatted for visualization purposes):

df = spark.createDataFrame(
    [
        [
            """I've seen this label on Coke cans at one point, and I was 
            wondering: is this legally enforceable? If it's not, is it possible
            for a retailer in any way to disallow the resale of an item
            purchased? Something like, I don't know, maybe a license you have
            to agree to in order to be allowed to purchase said item?
            This comes in the larger context of these new tech releases
            (GPUs, consoles) and how the producers/retailers could legally
            prevent scalpers."""
        ]
    ]
).toDF("text")

model = clf_pipeline.fit(df)
result = model.transform(df)

result.select("class.result").show(truncate=False)

Obtaining the following results:

+------------------------+
result                  |
+------------------------+
|[contract-law, business]|
+------------------------+

The topics obtained were contract-law and business.

Fancy trying?

We’ve got 30-day free licenses for you with technical support from our legal team of experts and SME. This trial includes complete access to more than 926 models, including Classification, NER, Relation Extraction, Similarity Search, Summarization, Sentiment Analysis, Question Answering, etc. and 120+ legal language models.

Just go to https://www.johnsnowlabs.com/install/ and follow the instructions!

Don’t forget to check our notebooks and demos.

How to run

Legal NLP is extremely easy to run on both clusters and driver-only environments using johnsnowlabs library:

pip install johnsnowlabs

from johnsnowlabs import nlp


nlp.install(force_browser=True)

# Start Spark Session
spark = nlp.start()

# Import the Legal NLP module
from johnsnowlabs import legal

For alternative installation methods of how to install in specific environments, please check the docs.