6.0.2 #14590
DevinTDHa
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6.0.2
#14590
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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
InternVLForMultiModal
model, 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.Partition
andPartitionTransformer
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:
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)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
Partition
andPartitionTransformer
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)PartitionTransformer
annotator allows you to use thePartition
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)🐛 Bug Fixes
AutoGGUFModel
(How does set Grammar works in AutoGGUFModel? #14576)❤️ Community Support
⚙️ Installation
Python
#PyPI pip install spark-nlp==6.0.2
Spark 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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