Demos
Version 1.19.0 of the library comes with a topic classification multi-label 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.
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:
At one point, I saw this label on Coke cans and wondered: 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 must agree to 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 have 30-day free licenses for you with technical support from our legal team of experts and SME. This trial includes 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 (NLP for contracts) 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.
