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We are thrilled to announce the release of Spark NLP 6.0.2! This version introduces powerful new multimodal models and significantly enhances document processing workflows. Upgrade to 6.0.2 to leverage these cutting-edge features and expand your NLP and vision task capabilities at scale.
Stay updated with our latest examples and tutorials by visiting our Medium - Spark NLP blog!
🔥 Highlights
Introducing InternVL: Support for the state-of-the-art InternVLForMultiModal model, enabling advanced visual question answering with InternVL 2, 2.5, and 3 series models.
Introducing Florence-2: Integration of Florence-2 in Florance2Transformer, a sophisticated vision foundation model for diverse prompt-based vision and vision-language tasks like captioning, object detection, and segmentation.
New Document Partitioning Feature: Added the Partition and PartitionTransformer annotator for a unified and configurable interface with Spark NLP readers, simplifying unstructured data loading.
🚀 New Features & Enhancements
Advanced Multimodal Model Integrations
This release significantly boosts Spark NLP's multimodal processing power with the integration of two new visual language models:
InternVL:InternVLForMultiModal is a powerful multimodal large language model is specifically designed for visual question answering. This annotator is versatile, supporting the InternVL 2, 2.5, and 3 families of models, allowing users to tackle complex visual-linguistic tasks. (Link to notebook)
Florence-2: Introducing Florance2Transformer, an advanced vision foundation model. Florence-2 utilizes a prompt-based approach, enabling it to perform a wide array of vision and vision-language tasks. Users can leverage simple text prompts to execute tasks such as image captioning, object detection, and image segmentation with high accuracy. (Link to notebook)
Enhanced Unstructured Document Processing
Partitioning Documents: This release introduces the new Partition and PartitionTransformer annotator.
Partition provides a unified interface for extracting structured content from various document formats into Spark DataFrames. It supports input from files, URLs, in-memory strings, or byte arrays and handles formats such as text, HTML, Word, Excel, PowerPoint, emails, and PDFs. It automatically selects the appropriate reader based on file extension or MIME type and allows customization via parameters. (Link to notebook)
The PartitionTransformer annotator allows you to use the Partition feature more smoothly within existing Spark NLP workflows, enabling seamless reuse of your pipelines. PartitionTransformer can be used for extracting structured content from various document types using Spark NLP readers. It supports reading from files, URLs, in-memory strings, or byte arrays, and returns parsed output as a structured Spark DataFrame. (Link to notebook)
Key Improvements:
Simplifies integration with Spark NLP readers through a unified interface.
Adds flexibility by enabling more reader-specific configurations.
Enhances the maintainability and scalability of data loading workflows.
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📢 Spark NLP 6.0.2: Advancing Multimodal Capabilities and Streamlining Document Processing
We are thrilled to announce the release of Spark NLP 6.0.2! This version introduces powerful new multimodal models and significantly enhances document processing workflows. Upgrade to 6.0.2 to leverage these cutting-edge features and expand your NLP and vision task capabilities at scale.
Stay updated with our latest examples and tutorials by visiting our Medium - Spark NLP blog!
🔥 Highlights
InternVLForMultiModalmodel, enabling advanced visual question answering with InternVL 2, 2.5, and 3 series models.Florance2Transformer, a sophisticated vision foundation model for diverse prompt-based vision and vision-language tasks like captioning, object detection, and segmentation.PartitionandPartitionTransformerannotator for a unified and configurable interface with Spark NLP readers, simplifying unstructured data loading.🚀 New Features & Enhancements
Advanced Multimodal Model Integrations
This release significantly boosts Spark NLP's multimodal processing power with the integration of two new visual language models:
InternVLForMultiModalis a powerful multimodal large language model is specifically designed for visual question answering. This annotator is versatile, supporting the InternVL 2, 2.5, and 3 families of models, allowing users to tackle complex visual-linguistic tasks. (Link to notebook)Florance2Transformer, an advanced vision foundation model. Florence-2 utilizes a prompt-based approach, enabling it to perform a wide array of vision and vision-language tasks. Users can leverage simple text prompts to execute tasks such as image captioning, object detection, and image segmentation with high accuracy. (Link to notebook)Enhanced Unstructured Document Processing
PartitionandPartitionTransformerannotator.Partitionprovides a unified interface for extracting structured content from various document formats into Spark DataFrames. It supports input from files, URLs, in-memory strings, or byte arrays and handles formats such as text, HTML, Word, Excel, PowerPoint, emails, and PDFs. It automatically selects the appropriate reader based on file extension or MIME type and allows customization via parameters. (Link to notebook)PartitionTransformerannotator allows you to use thePartitionfeature more smoothly within existing Spark NLP workflows, enabling seamless reuse of your pipelines.PartitionTransformercan be used for extracting structured content from various document types using Spark NLP readers. It supports reading from files, URLs, in-memory strings, or byte arrays, and returns parsed output as a structured Spark DataFrame. (Link to notebook)🐛 Bug Fixes
AutoGGUFModel(How does set Grammar works in AutoGGUFModel? #14576)❤️ Community Support
⚙️ Installation
Python
#PyPI pip install spark-nlp==6.0.2Spark Packages
spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.x (Scala 2.12):
GPU
Apple Silicon
AArch64
Maven
spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.x:
spark-nlp-gpu:
spark-nlp-silicon:
spark-nlp-aarch64:
FAT JARs
What's Changed
Full Changelog: 6.0.1...6.0.2
This discussion was created from the release 6.0.2.
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