John Snow Labs Spark-NLP is a natural language processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment.
Take a look at our official spark-nlp page: http://nlp.johnsnowlabs.com/ for user documentation and examples
Questions? Feedback? Request access sending an email to nlp@johnsnowlabs.com
This library has been uploaded to the spark-packages repository https://spark-packages.org/package/JohnSnowLabs/spark-nlp .
To use the most recent version just add the --packages JohnSnowLabs:spark-nlp:1.7.2
to you spark command
spark-shell --packages JohnSnowLabs:spark-nlp:1.7.2
pyspark --packages JohnSnowLabs:spark-nlp:1.7.2
spark-submit --packages JohnSnowLabs:spark-nlp:1.7.2
export SPARK_HOME=/path/to/your/spark/folder
export PYSPARK_DRIVER_PYTHON=jupyter
export PYSPARK_DRIVER_PYTHON_OPTS=notebook
pyspark --packages JohnSnowLabs:spark-nlp:1.7.2
This way will work for both Scala and Python
export SPARK_SUBMIT_OPTIONS="--packages JohnSnowLabs:spark-nlp:1.7.2"
Alternatively, add the following Maven Coordinates to the interpreter's library list
com.johnsnowlabs.nlp:spark-nlp_2.11:1.7.2
If you installed pyspark through pip, you can now install sparknlp through pip
pip install spark-nlp==1.7.2
Then you'll have to create a SparkSession manually, for example:
spark = SparkSession.builder \
.appName("ner")\
.master("local[4]")\
.config("spark.driver.memory","4G")\
.config("spark.driver.maxResultSize", "2G") \
.config("spark.driver.extraClassPath", "lib/sparknlp.jar")\
.config("spark.kryoserializer.buffer.max", "500m")\
.getOrCreate()
If your distributed storage is S3 and you don't have a standard hadoop configuration (i.e. fs.defaultFS) You need to specify where in the cluster distributed storage you want to store Spark-NLP's tmp files. First, decide where you want to put your application.conf file
import com.johnsnowlabs.uti.ConfigLoader
ConfigLoader.setConfigPath("/somewhere/to/put/application.conf")
And then we need to put in such application.conf the following content
sparknlp {
settings {
cluster_tmp_dir = "somewhere in s3n:// path to some folder"
}
}
You may download fat-jar from here: Spark-NLP 1.7.2 FAT-JAR or non-fat from here Spark-NLP 1.7.2 PKG JAR Spark-NLP-OCR Module (Requires native Tesseract 4.x+ for image based OCR. Does not require Spark-NLP to work but highly suggested) Spark-NLP-OCR 1.7.2 FAT-JAR
Our package is deployed to maven central. In order to add this package as a dependency in your application:
<dependency>
<groupId>com.johnsnowlabs.nlp</groupId>
<artifactId>spark-nlp_2.11</artifactId>
<version>1.7.2</version>
</dependency>
libraryDependencies += "com.johnsnowlabs.nlp" % "spark-nlp_2.11" % "1.7.2"
If you are using scala 2.11
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "1.7.2"
If for some reason you need to use the jar, you can download the jar from the project's website: http://nlp.johnsnowlabs.com/
From there you can use it in your project setting the --classpath
To add jars to spark programs use the --jars
option
spark-shell --jars spark-nlp.jar
The preferred way to use the library when running spark programs is using the --packages
option as specified in the spark-packages
section.
If you have troubles using pretrained() models in your environment, here a list to various models (only valid for latest versions). If there is any older than current version of a model, it means they still work for current versions.
- PerceptronModel (POS)
- ViveknSentimentModel (Sentiment)
- SymmetricDeleteModel (Spell Checker)
- NorvigSweetingModel (Spell Checker)
- AssertionDLModel (Assertion Status)
- NerCRFModel (NER)
- NerDLModel (NER)
- LemmatizerModel (Lemmatizer)
- AssertionDLModel (Assertion)
Thanks in general to the community who have been lately reporting important issues and pull request with bugfixes. Community has been key in the last releases with feedback in various Spark based environments.
Here a few specific mentions for recurring feedback and slack participation
- @maziyarpanahi (https://github.com/maziyarpanahi) - For contributing with testing and valuable feedback
- @easimadi (https://github.com/easimadi) - For contributing with documentation and valuable feedback
We appreciate any sort of contributions:
- ideas
- feedback
- documentation
- bug reports
- nlp training and testing corpora
- development and testing
Clone the repo and submit your pull-requests! Or directly create issues in this repo.