spark-nlp | State of the Art Natural Language Processing | Natural Language Processing library
kandi X-RAY | spark-nlp Summary
kandi X-RAY | spark-nlp Summary
Spark NLP is a state-of-the-art Natural Language Processing library built on top of Apache Spark. It provides simple, performant & accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment. Spark NLP comes with 4000+ pretrained pipelines and models in more than 200+ languages. It also offers tasks such as Tokenization, Word Segmentation, Part-of-Speech Tagging, Word and Sentence Embeddings, Named Entity Recognition, Dependency Parsing, Spell Checking, Text Classification, Sentiment Analysis, Token Classification, Machine Translation (+180 languages), Summarization & Question Answering, Text Generation, and many more NLP tasks. Spark NLP is the only open-source NLP library in production that offers state-of-the-art transformers such as BERT, ALBERT, ELECTRA, XLNet, DistilBERT, RoBERTa, XLM-RoBERTa, Longformer, ELMO, Universal Sentence Encoder, Google T5, MarianMT, and GPT2 not only to Python and R, but also to JVM ecosystem (Java, Scala, and Kotlin) at scale by extending Apache Spark natively.
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spark-nlp Key Features
spark-nlp Examples and Code Snippets
Lines of Code : 4License : Strong Copyleft (CC BY-SA 4.0)
CopyCOPY core ${LAMBDA_TASK_ROOT}
COPY core ${LAMBDA_TASK_ROOT}/core
from mlflow.tracking import MlflowClient
# Create an experiment with a name that is unique and case sensitive.
client = MlflowClient()
experiment_id = client.create_experiment("Social NLP Experiments")
client.set_experiment_tag(experiment"(?U)[^\w -]|_|-(?!\w)|(?from sparknlp.pretrained import PretrainedPipeline
df = spark.sql('select year, month, u_id, p_id, comment from MY_DF where rating_score = 1 and isnull(comment) = false')
df1 = df.withColumnRenamed('comment', 'text')
pipeline_dl = PretrCommunity Discussions
Trending Discussions on spark-nlp
QUESTION
i am using the below code to read the spark dataframe from hdfs:
...ANSWER
Answered 2022-Apr-03 at 08:47The configure_spark_with_delta_pip is just a shortcut to setup correct parameters of the SparkSession... If you look into its source code you'll see following code, you'll see that everything it's doing is configuring the spark.jars.packages. But because you're using it separately for SparkNLP, you're overwriting Delta's value. To handle such situations, configure_spark_with_delta_pip has an additional parameter extra_packages to specify additional packages to be configured. So in your case the code should look as following:
QUESTION
I am deploying a lambda function as a container image. Here's my project structure :
- core
- plugins
- lambda_handler.py
All three are at the same level - /var/task
Inside lambda_handler.py I am importing the core package, but when I test it locally it says :
...ANSWER
Answered 2022-Mar-14 at 08:57If you just use
QUESTION
I am trying to run "onto_electra_base_uncased" model on some data stored in hive table, I ran count() on df before saving the data into hive table and got this exception.
Spark Shell launch configurations:
...ANSWER
Answered 2021-Nov-18 at 09:23The solution to this issue is use kryo serialization, the default spark-shell or spark-submit invocation is using java serialization, the Annotate class in spark-nlp is implemented to use Kryo Serialization hence same should be used for running any spark-nlp jobs
QUESTION
I am trying to train a SparkNLP NerCrfApproach model with a dataset in CoNLL format that has custom labels for product entities (like I-Prod, B-Prod etc.). However, when using the trained model to make predictions, I get only "O" as the assigned label for all tokens. When using the same model trained on the CoNLL data from the SparkNLP workshop example, the classification works fine.
(cf. https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/jupyter/training/english/crf-ner)
So, the question is: Does NerCrfApproach rely on the standard tag set for NER labels used by the CoNLL data? Or can I use it for any custom labels and, if yes, do I need to specify these somehow? My assumption was that the labels are inferred from the training data.
Cheers, Martin
Update: The issue might not be related to the labels after all. I tried to replace my custom labels with CoNLL standard labels and I am still not getting the expected classification results.
...ANSWER
Answered 2021-Oct-14 at 06:26As it turns out, this issue was not caused by the labels, but rather by the size of the dataset. I was using a rather small dataset for development purposes. Not only was this dataset quite small, but also heavily imbalanced, with a lot more "O" labels than the other labels. Fixing this by using a dataset of 10x the original size (in terms of sentences), I am able to get meaningful results, even for my custom labels.
QUESTION
I'm using AWS Glue to run some pyspark python code, sometimes it succeeded but sometimes failed with a dependency error: Resource Setup Error: Exception in thread "main" java.lang.RuntimeException: [unresolved dependency: JohnSnowLabs#spark-nlp;2.5.4: not found], here is the error logs:
ANSWER
Answered 2021-May-07 at 20:28spark-packages moved on May 1 2021. In my scala project I had to add a different resolver like so. It's got to be similar in java.
QUESTION
I saved a pre-trained model from spark-nlp, then I'm trying to run a Python script in Pycharm with anaconda env:
...ANSWER
Answered 2021-Mar-17 at 15:29some context first. The spark-nlp library depends on a jar file that needs to be present in the Spark classpath. There are three ways to provide this jar according to how you start the context in PySpark. a) When you start your Python app throught interpreter, you call sparknlp.start() and the jar will be automatically downloaded.
b) You pass the jar to pyspark command using the --jars switch. In this case you took the jar from the releases page and download it manually.
c) You start pyspark and pass --packages, here you need to pass a maven coordinate, example,
QUESTION
I am using jupyter lab to run spark-nlp text analysis. At the moment I am just running the sample code:
...ANSWER
Answered 2020-Dec-24 at 10:18Remove Spark 3.0.1, leave just PySpark 2.4.x. as Spark NLP still doesn't support Spark 3.x. Use Java 8 instead of Java 11 because it's not supported in Spark 2.4.
QUESTION
How can I install offline Spark NLP packages without internet connection.
I've downloaded the package (recognizee_entities_dl) and uploaded it to the cluster.
I've installed Spark NLP using pip install spark-nlp==2.5.5.
I'm using PySpark and from the cluster I'm unable to download the packages.
Already tried;
...ANSWER
Answered 2020-Aug-26 at 14:41Looking at your error:
QUESTION
I am very new to Zeppelin/spark and couldn't get an accurate description of steps to configure new dependencies like that of NLP libraries. Found similar issue here.
I was trying to use Johnsnowlabs NLP library in Zeppelin notebook (spark version2.2.1). Setup included :
- In Zeppelin's Interpreters configurations for Spark, include the following artifact: com.johnsnowlabs.nlp:spark-nlp_2.11:2.5.4
- Then, in conf/zeppelin-env.sh, setup SPARK_SUBMIT_OPTIONS. export SPARK_SUBMIT_OPTIONS=” — packages JohnSnowLabs:spark-nlp:2.2.2". Then restarted Zeppelin.
But the below program gives the error :
...ANSWER
Answered 2020-Jul-28 at 12:40you don't need to edit the conf/zeppelin-env.sh (anyway you're using it incorrectly, as you're specifying completely different version), you can make all changes via Zeppelin UI. Go to the Spark interpreter configuration, and put com.johnsnowlabs.nlp:spark-nlp_2.11:2.5.4 into spark.jars.packages configuration property (or add it if it doesn't exist), and into the Dependencies at the end of configuration (for some reason, it isn't automatically pulled into driver classpath).
QUESTION
I'm getting an exception when executing spark2-submit on my hadoop cluster, when reading a directory of .jsons in hdfs I have no idea how to resolve it.
I have found some question on several board about this, but none of them popular or with an answer.
I tried explicit importing org.apache.spark.sql.execution.datasources.json.JsonFileFormat, but it seems redundant, to importing SparkSession, so it's not getting recognised.
I can however confirm that both of these classes are available.
...ANSWER
Answered 2020-Jul-05 at 18:31It seems you have both Spark 2.x and 3.x jars in classpath. According to the sbt file, Spark 2.x should be used, however, JsonFileFormat was added in Spark 3.x with this issue
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Install spark-nlp
FAT-JAR for CPU on Apache Spark 3.0.x and 3.1.x
FAT-JAR for GPU on Apache Spark 3.0.x and 3.1.x
FAT-JAR for CPU on Apache Spark 3.2.x
FAT-JAR for GPU on Apache Spark 3.2.x
FAT-JAR for CPU on Apache Spark 2.4.x
FAT-JAR for GPU on Apache Spark 2.4.x
FAT-JAR for CPU on Apache Spark 2.3.x
FAT-JAR for GPU on Apache Spark 2.3.x
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