![]() ![]() These models can be used out of the box or fine-tuned on your own data. Spark NLP also offeres a large number of state-of-the-art Large Language Models (LLMs) like BERT, RoBERTa, ALBERT, T5, OpenAI Whisper, and many more for Text Embeddings (useful for RAG), Named Entity Recognition, Text Classification, Answering, Automatic Speech Recognition, and more. It also makes it easy to import your custom models from Hugging Face in TensorFlow and ONNX formats. Spark NLP: Offers over 18,000 diverse pre-trained models and pipelines for over 235 languages, making it easy to get started on various NLP tasks.spaCy: Designed for processing data on a single machine and it’s not natively built for distributed computing.Ģ.This makes it especially suitable for big data processing tasks that need to run on a cluster. Spark NLP: Built on top of Apache Spark, it’s designed for distributed processing and handling large datasets at scale.Here are some key differences between the two: Further Reading: Dive deeper into the official documentation for more detailed examples, a complete list of annotators and models, and best practices for building NLP pipelines.īoth spaCy and Spark NLP are popular libraries for Natural Language Processing, but Spark NLP shines when it comes to scalability and distributed processing. You can easily plug these into your pipeline and customize as needed.Ħ. Explore and Utilize Models: Spark NLP offers pre-trained models for tasks like Named Entity Recognition (NER), sentiment analysis, and more. Result = pipeline.fit(data).transform(data)ĥ. Transform Data: Once you have a pipeline, you can transform your data. Tokenizer = Tokenizer().setInputCols().setOutputCol("token") To use them, first create the appropriate pipeline.įrom sparknlp.base import DocumentAssemblerĭocumentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document") Use Annotators: Spark NLP offers a variety of annotators (e.g., Tokenizer, SentenceDetector, Lemmatizer). ![]() Initialize SparkSession with Spark NLP:ģ. If you don’t have PySpark you should also install the following dependencies:Ģ. To use Spark NLP in Python, follow these steps:
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