Let's suppose I have taken a look at Hugging Face Models Hub (https://huggingface.co/models), and I have detected 7 models I want to import into Spark NLP, for BertForSequenceClassification: Since the steps are more or less the same as described in first example, I'm going to automatize in a loop all the steps from downloading, to importing into SparkNLP and inferring, to illustrate an end-to-end import. There is one extra step we need to carry out when importing Classifiers from Hugging Face: we need a labels.txt, That file can be created using the config.json However, we may find models without that field, what leads to using just numeric values for the labels, what is not very user friendly. To support both importing from config.json and creating our own labels, let's declare an array: If the value is None, then we will import the tags from the model.

Apache Spark is an open-source framework for fast and general-purpose data processing. It provides a unified engine that can run complex analytics, including Machine Learning, in a fast and distributed way. Spark NLP is an Apache Spark module that provides advanced Natural Language Processing (NLP) capabilities to Spark applications. It can be used to build complex text processing pipelines, including tokenization, sentence splitting, part of speech tagging, parsing, and named entity recognition. Although the documentation, which describes how to install Spark NLP is quite clear, sometimes you can get stuck, while installing it.

Now that we have a single-node cluster with a GPU we can continue our benchmarks to see how Hugging Face performs on this machine in Databricks.

We are excited to welcome the new 1.2.0 version of Legal NLP, including the following new capabilities. Legal NLP has been built on top of Spark NLP, which uses Spark MLLib pipelines. This means, You can have a common pipeline with any component of Spark NLP of Spark MLLib. Also, you combine it with the rest of our licensed libraries, such as Visual NLP, Healthcare NLP or Finance NLP. The library works on the top of Transformers and other Deep Learning architectures, providing state-of-the-art models which can be run on Spark Clusters.

I am one of the contributors to the Spark NLP open-source project and just recently this library started supporting end-to-end Vision Transformers (ViT) models. I use Spark NLP and other ML/DL open-source libraries for work daily and I have decided to deploy a ViT pipeline for a state-of-the-art image classification task and provide in-depth comparisons between Hugging Face and Spark NLP. The purpose of this article is to demonstrate how to scale out Vision Transformer (ViT) models from Hugging Face and deploy them in production-ready environments for accelerated and high-performance inference. By the end, we will scale a ViT model from Hugging Face by 25x times (2300%) by using Databricks, Nvidia, and Spark NLP. Back in 2017, a group of researchers at Google AI published a paper that introduced a transformer model architecture that changed all Natural Language Processing (NLP) standards.

John Snow Labs is an award-winning AI and NLP company, accelerating progress in data science by providing state-of-the-art software, data, and models. Founded in 2015, it helps healthcare and life science companies build, deploy, and operate AI products and services. John Snow Labs is the winner of the 2018 AI Solution Provider of the Year Award, the 2019 AI Platform of the Year Award, the 2019 International Data Science Foundation Technology award, and the 2020 AI Excellence Award. John Snow Labs is the developer of Spark NLP - the world's most widely used NLP library in the enterprise - and is the world's leading provider of state-of-the-art clinical NLP software, powering some of the world's largest healthcare & pharma companies. John Snow Labs is a global team of specialists, of which 33% hold a Ph.D. or M.D. and 75% hold at least a Master's degree in disciplines covering data science, medicine, software engineering, pharmacy, DevOps and SecOps.

The proliferation of healthcare data has contributed to the widespread usage of the PICO paradigm for creating specific clinical questions from RCT. PICO is a mnemonic that stands for: PICO is an essential tool that aids evidence-based practitioners in creating precise clinical questions and searchable keywords to address those issues. It calls for a high level of technical competence and medical domain knowledge, but it's also frequently very time-consuming. Automatically identifying PICO elements from this large sea of data can be made easier with the aid of machine learning (ML) and natural language processing (NLP). Empirical studies have shown that the use of PICO frames improves the specificity and conceptual clarity of clinical problems, elicits more information during pre-search reference interviews, leads to more complex search strategies, and yields more precise search results. Let's have a look at how this model can accurately identify medical texts using the PICO framework as an example.

We're excited to announce another excellent integration with Comet -- Spark NLP! This integration allows data scientists and teams to leverage Comet's experiment tracking and visualization tools with Spark NLP's powerful library for building production-grade, state-of-the-art NLP models. Spark NLP is an open-source text processing library (available in Python, Java, and Scala) from John Snow Labs that provides access to production-grade, scalable, and trainable versions of the latest research in natural language processing. Spark NLP offers an unmatched combination of speed, scalability, and accuracy that makes it the most widely-used NLP library in the enterprise. It includes out-of-the-box functionality for 8 different NLP tasks, more than 4,000 pre-trained models and pipelines, and support for more than 200 languages. Spark NLP now ships with a dedicated CometLogger.

With evermore personal data being produced and stored by organizations, data privacy is becoming an increasing priority. Businesses have access to a lot of sensitive information about their customers, service providers, and employees and are required to protect that data in order to minimize the risks of scams or fraud. De-identification is used to overcome data privacy challenges and keep information safe from unauthorized parties. This post explains what de-identification is, how it works and how natural language processing (NLP) is used to automate the process of removing sensitive data from datasets. De-identification is a technique used to remove any data that could identify a person from a dataset.

There are many Natural Language Processing (NLP) tasks that require text to be split in chunks of varying granularity: Making a task to extract names and addresses of a person is almost impossible under these conditions – just because the data preparation stage was not up for it. Subject specific technical terms are sometimes abbreviated in a way that is otherwise, generally not used: (German Legal References: "Putzo ZPO 39. So, lets take the German legal reference example from above and apply Spark NLPs extended capabilities on a sample project (with a series of CoLab notebooks) to see how this will help us splitting text correctly into sentences. And make the first 1000 rulings available as a separate JSON file (since handling a larger data collections is otherwise difficult with a normal CoLab license). I developed a command line tool called unsplit to parse the text from the German legal court rulings to split sentences at a period, except when the period character was at one of the known abbreviations in the previously curated list (the unsplit tool is a C#/.Net command line program which I can publish on GitHub if people are interested). But honestly, I use this as hint towards the quality of a model and tend to say "the truth is in the pudding" and trust the a real world test more than any KPIs: I'll be looking forward on comments about things that could be improved in the data preparation stage of this sentence detection modelling task or other items you might find worth giving me feedback about. Making a task to extract names and addresses of a person is almost impossible under these conditions – just because the data preparation stage was not up for it. Subject specific technical terms are sometimes abbreviated in a way that is otherwise, generally not used: (German Legal References: "Putzo ZPO 39.