Spark NLP: State-of-the-Art Natural Language Processing & LLMs Library
Spark NLP is an advanced Natural Language Processing (NLP) library built on top of Apache Spark. It is designed to offer simple, high-performance, and accurate NLP annotations in machine learning pipelines, which can efficiently scale in distributed environments.
What Does Spark NLP Offer?
Spark NLP stands out with over 83,000 pretrained pipelines and models available in more than 200 languages. It caters to numerous NLP tasks such as:
- Tokenization and Word Segmentation
- Part-of-Speech Tagging
- Word and Sentence Embeddings
- Named Entity Recognition
- Dependency Parsing
- Spell Checking, Text Classification, and Sentiment Analysis
- Token Classification
- Machine Translation across 180 languages
- Summarization, Question Answering, and Text Generation
- Image Classification and Captioning
- Automatic Speech Recognition
- Zero-Shot Learning
State-of-the-Art Transformers
Spark NLP is unique in being the only open-source NLP library in production that offers cutting-edge transformers like BERT, CamemBERT, and RoBERTa, among many others. These transformers are accessible not only to Python and R but also in the JVM ecosystem (Java, Scala, and Kotlin), thanks to its integration with Apache Spark.
Model Importing Support
Spark NLP makes it easy to import models from popular machine learning frameworks, including:
- TensorFlow
- ONNX
- OpenVINO
- Llama.cpp (GGUF)
This flexibility allows users to incorporate models from diverse sources into their NLP workflows effectively.
Quick Start Guide
To get started with Spark NLP using Python and PySpark, ensure Java 8 or 11 is installed. You can create a Python environment using Conda and install Spark NLP and PySpark. Here is a brief code snippet to demonstrate usage:
from sparknlp.base import *
from sparknlp.annotator import *
from sparknlp.pretrained import PretrainedPipeline
import sparknlp
# Start Spark NLP Session
spark = sparknlp.start()
# Load a pre-trained pipeline
pipeline = PretrainedPipeline('explain_document_dl', lang='en')
# Your testing dataset
text = """
The Mona Lisa is a 16th-century oil painting created by Leonardo.
It's held at the Louvre in Paris.
"""
# Annotate your dataset
result = pipeline.annotate(text)
# View results
print(result['entities'])
Compatibility and Support
Spark NLP version 5.5.1 is compatible with Apache Spark versions 3.0 to 3.5, making it accessible across numerous platforms, including Databricks and Amazon EMR. It supports several Python and Scala versions, ensuring wide usability.
Installation Options
Whether you prefer command-line interfaces or specific programming languages like Scala or Python, Spark NLP offers installation flexibility. Detailed instructions are provided for various platforms, ensuring a smooth setup process.
Community and Contributions
Spark NLP thrives on community support and encouragement for contributions, be it ideas, documentation, or bug reports. Users can engage through GitHub, Slack, and other platforms for discussions and updates.
Final Note
Spark NLP not only facilitates the integration of state-of-the-art NLP capabilities into machine learning pipelines but also supports offline use. Visit the Spark NLP website for an exhaustive list of features, models, and support documentation.