Spark Nlp Versions Save

State of the Art Natural Language Processing

5.1.2

7 months ago

:loudspeaker: Overview

For the first time, Spark NLP 5.1.2 🚀 proudly presents a new image-to-text annotator designed for captioning images. Additionally, we've added over 3,000 state-of-the-art transformer models in ONNX format to ensure rapid inference in your RAG when you are using LLMs.

We're pleased to announce that our Models Hub now boasts 21,000+ free and truly open-source models & pipelines 🎉. Our deepest gratitude goes out to our community for their invaluable feedback, feature suggestions, and contributions.

:fire: New Features & Enhancements

  • NEW: We're excited to introduce the VisionEncoderDecoderForImageCaptioning annotator, designed specifically for image-to-text captioning. We used VisionEncoderDecoderModel to import models fine-tuned for auto image captioning

The VisionEncoderDecoder can be employed to set up an image-to-text model. The encoding part can utilize any pretrained Transformer-based vision model, such as ViT, BEiT, DeiT, or Swin. Meanwhile, for the decoding part, it can make use of any pretrained language model like RoBERTa, GPT2, BERT, or DistilBERT.

The efficacy of using pretrained checkpoints to initialize image-to-text-sequence models is evident in the study titled TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, and Furu Wei.

Image Captioning Using Hugging Face Vision Encoder Decoder — Step2Step Guide (Part 2)

  • NEW: We've added cutting-edge transformer models in ONNX format for seamless integration. Our annotators will automatically recognize and utilize these models, streamlining your LLM pipelines without any additional setup.

  • We have added all the missing features from our documentation and added examples to Python and Scala APIs:

    • E5Embeddings
    • InstructorEmbeddings
    • MPNetEmbeddings
    • OpenAICompletion
    • VisionEncoderDecoderForImageCaptioning
    • DocumentSimilarityRanker
    • BartForZeroShotClassification
    • XlmRoBertaForZeroShotClassification
    • CamemBertForQuestionAnswering
    • DeBertaForSequenceClassification
    • DeBertaForTokenClassification
    • Date2Chunk

🐛 Bug Fixes

  • We've made a minor adjustment to the beam search algorithm, enhancing the quality of the BART Transformer results.

:notebook: New Notebooks

Notebooks Colab
Vision Encoder Decoder: Image Captioning at Scale in Spark NLP Open In Colab
Import Whisper models (ONNX) Open In Colab

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==5.1.2

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):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.2

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.2

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.2

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.2

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>5.1.2</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>5.1.2</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>5.1.2</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>5.1.2</version>
</dependency>

FAT JARs

What's Changed

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/5.1.1...5.1.2

5.1.1

8 months ago

:loudspeaker: Overview

Spark NLP 5.1.1 🚀 comes with new ONNX support for MPNet, AlbertForTokenClassification, AlbertForSequenceClassification, and AlbertForQuestionAnswering annotators, a new getVectors feature in Word2VecModel, Doc2VecModel, and WordEmbeddingsModel annotators, 460+ new ONNX models for MPNet and BERT transformers, and bug fixes!

We want to thank our community for their valuable feedback, feature requests, and contributions. Our Models Hub now contains over 18,800+ free and truly open-source models & pipelines. 🎉

:fire: New Features & Enhancements

  • NEW: Introducing support for ONNX Runtime in MPNet embedding annotator
  • NEW: Introducing support for ONNX Runtime in AlbertForTokenClassification annotator
  • NEW: Introducing support for ONNX Runtime in AlbertForSequenceClassification annotator
  • NEW: Introducing support for ONNX Runtime in AlbertForQuestionAnswering annotator
  • Implement getVectors feature in Word2VecModel, Doc2VecModel, and WordEmbeddingsModel annotators. This new feature allows access to the entire tokens and their vectors from the loaded models.

🐛 Bug Fixes

  • Fix how to save and load Whisper models
  • Fix saving ONNX model on Windows operating system

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • JohnSnowLabs official Medium
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==5.1.1

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):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.1

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.1

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.1

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.1

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>5.1.1</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>5.1.1</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>5.1.1</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>5.1.1</version>
</dependency>

FAT JARs

What's Changed

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/5.1.0...5.1.1

5.1.0

8 months ago

:loudspeaker: And RAG whispered to Spark NLP, you complete me!

It's a well-established principle: any LLM, whether open-source or proprietary, isn't dependable without a RAG. And truly, there can't be an effective RAG without an NLP library that is production-ready, natively distributed, state-of-the-art, and user-friendly. This holds true in our 5.1.0 release!

Release Summary: We're excited to unveil Spark NLP 🚀 5.1.0 with:

  • New OpenAI Whisper, Embeddings and Completions!
  • Extended ONNX support for highly-rated E5 embeddings. Anticipate swifter inferences, seamless optimizations, and quantization for exporting LLM models.
  • MPNet, a cherished sentence-embedding LLM boasting 140+ ready-to-use models!
  • Cutting-edge BGE and GTE text embedding models lead the MTEB leaderboard, surpassing even the renowned OpenAI text-embedding-ada-002. We employ these models for text vectorization, pairing them with LLM models to ensure accuracy and prevent misinterpretations.
  • Unified Support for All Major Cloud Storage (Azure, GCP, and S3)
  • BART multi-lingual Zero-Shot multi-class/multi-label text classification
  • and more!

We want to thank our community for their valuable feedback, feature requests, and contributions. Our Models Hub now contains over 18,000+ free and truly open-source models & pipelines. 🎉

Don't miss our free Webinar: From GPT-4 to Llama-2: Supercharging State-of-the-Art Embeddings for Vector Databases

:fire: New Features

Spark NLP :heart: ONNX (toujours)

SPARK NLP

In Spark NLP 5.1.0, we're persisting with our commitment to ONNX Runtime support. Following our introduction of ONNX Runtime in Spark NLP 5.0.0—which has notably augmented the performance of models like BERT—we're further integrating features to bolster model efficiency. Our endeavors include optimizing existing models and expanding our ONNX-compatible offerings. For a detailed overview of ONNX compatibility in Spark NLP, refer to this issue.

NEW: In the 5.1.0 release, we've extended ONNX support to the E5 embedding annotator and introduced 15 new E5 models in ONNX format. This includes both optimized and quantized versions. Impressively, the enhanced ONNX support and these new models showcase a performance boost ranging from 2.3x to 3.4x when compared to the TensorFlow versions released in the 5.0.0 update.

image

OpenAI Whisper: Robust Speech Recognition via Large-Scale Weak Supervision

NEW: Introducing WhisperForCTC annotator in Spark NLP 🚀. WhisperForCTC can load all state-of-the-art Whisper models inherited from OpenAI Whisper for Robust Speech Recognition. Whisper was trained and open-sourced that approaches human level robustness and accuracy on English speech recognition.

image

We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zeroshot transfer setting without the need for any finetuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing. For more details, check out the official paper

audio_assembler = AudioAssembler() \
    .setInputCol("audio_content") \
    .setOutputCol("audio_assembler")

speech_to_text = WhisperForCTC \
    .pretrained()\
    .setInputCols("audio_assembler") \
    .setOutputCol("text")

pipeline = Pipeline(stages=[
  audio_assembler,
  speech_to_text,
])

MPNet: Masked and Permuted Pre-training for Language Understanding

NEW: Introducing MPNetEmbeddings annotator in Spark NLP 🚀. MPNetEmbeddings can load all state-of-the-art MPNet Models for Text Embeddings.

image

We propose MPNet, a novel pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g., BERT, XLNet, RoBERTa) under the same model setting. MPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu

Available new state-of-the-art BGE, TGE, E5, and INSTRUCTOR models for Text Embeddings are currently dominating the top of the MTEB leaderboard positioning themselves way above OpenAI text-embedding-ada-002 image

Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the MTEB GitHub repository 🤗

New OpenAI Embeddings and Completions

NEW: In Spark NLP 5.1.0, we're thrilled to introduce the integration of OpenAI Embeddings and Completions transformers. By merging the prowess of OpenAI's language model with the robust NLP processing capabilities of Spark NLP, we've created a powerful synergy. Specifically, with the newly introduced OpenAIEmbeddings and OpenAICompletion transformers, users can now make direct API calls to OpenAI's Embeddings and Completion endpoints right from an Apache Spark DataFrame. This enhancement promises to elevate the efficiency and versatility of data processing workflows within Spark NLP pipelines.

# to use OpenAI completions endpoint
document_assembler = DocumentAssembler() \
        .setInputCol("text") \
        .setOutputCol("document")

openai_completion = OpenAICompletion() \
       .setInputCols("document") \
       .setOutputCol("completion") \
       .setModel("text-davinci-003") \
       .setMaxTokens(50)

# to use OpenAI embeddings endpoint
document_assembler = DocumentAssembler() \
        .setInputCol("text") \
        .setOutputCol("document")

openai_embeddings = OpenAIEmbeddings() \
       .setInputCols("document") \
       .setOutputCol("embeddings") \
       .setModel("text-embedding-ada-002")

# Define the pipeline
pipeline = Pipeline(stages=[
    document_assembler, openai_embeddings
])

Unified Support for All Major Cloud Storage

In Spark NLP 5.1.0, we're thrilled to announce a holistic integration of all major cloud and distributed file storage systems. Building on our existing support for AWS, DBFS, and HDFS, we've now introduced seamless operations with Google Cloud Platform (GCP) and Azure. Here's a brief overview of what's been added and improved:

  • Comprehensive Integration: We've successfully unified all externally supported file systems and cloud access, ensuring a consistent experience across platforms.
  • Enhanced Cloud Access: Undergoing refactoring, the cache_pretrained property now offers unified cloud access, making it easier to cache models from any supported platform.
  • New Azure Storage Support: We've integrated Azure dependencies, allowing for Azure support in all cloud operations, ensuring users of Microsoft's cloud platform have a first-class experience.
  • New GCP Storage support: Users can now effortlessly export NER log files directly to GCP Storage. Additionally, importing HF models from GCP has been made straightforward.
  • Refinements and Fixes: We've relocated the Credentials component to the AWS package for better organization and addressed issues related to HDFS log and NER Graph loading.
  • Documentation: To help users get started and transition smoothly, comprehensive documentation has been added detailing the support for Azure, GCP, and S3 operations.

We're confident these updates will provide a smoother, more unified experience for users across all cloud platforms for the following features:

  • Define a custom path for cache_pretrained directory
  • Store logs during training
  • Load TF graphs for NerDL annotator
  • Importing any HF model into Spark NLP

BART: New multi-lingual Zero-Shot Text Classification

  • NEW: Introducing BartForZeroShotClassification annotator for Zero-Shot Text Classification in Spark NLP 🚀. You can use the BartForZeroShotClassification annotator for text classification with your labels! :100:

Zero-Shot Learning (ZSL): Traditionally, ZSL most often referred to a fairly specific type of task: learning a classifier on one set of labels and then evaluating on a different set of labels that the classifier has never seen before. Recently, especially in NLP, it's been used much more broadly to get a model to do something it wasn't explicitly trained to do. A well-known example of this is in the GPT-2 paper where the authors evaluate a language model on downstream tasks like machine translation without fine-tuning on these tasks directly.

Let's see how easy it is to just use any set of labels our trained model has never seen via the setCandidateLabels() param:

zero_shot_classifier = BartForZeroShotClassification \
    .pretrained() \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class") \
    .setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"])

For Zero-Short Multi-class Text Classification:

+----------------------------------------------------------------------------------------------------------------+--------+
|result                                                                                                          |result  |
+----------------------------------------------------------------------------------------------------------------+--------+
|[I have a problem with my iPhone that needs to be resolved asap!!]                                              |[mobile]|
|[Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.]|[mobile]|
|[I have a phone and I love it!]                                                                                 |[mobile]|
|[I want to visit Germany and I am planning to go there next year.]                                              |[travel]|
|[Let's watch some movies tonight! I am in the mood for a horror movie.]                                         |[movie] |
|[Have you watched the match yesterday? It was a great game!]                                                    |[sport] |
|[We need to hurry up and get to the airport. We are going to miss our flight!]                                  |[urgent]|
+----------------------------------------------------------------------------------------------------------------+--------+

For Zero-Short Multi-class Text Classification:

+----------------------------------------------------------------------------------------------------------------+-----------------------------------+
|result                                                                                                          |result                             |
+----------------------------------------------------------------------------------------------------------------+-----------------------------------+
|[I have a problem with my iPhone that needs to be resolved asap!!]                                              |[urgent, mobile, movie, technology]|
|[Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.]|[urgent, technology]               |
|[I have a phone and I love it!]                                                                                 |[mobile]                           |
|[I want to visit Germany and I am planning to go there next year.]                                              |[travel]                           |
|[Let's watch some movies tonight! I am in the mood for a horror movie.]                                         |[movie]                            |
|[Have you watched the match yesterday? It was a great game!]                                                    |[sport]                            |
|[We need to hurry up and get to the airport. We are going to miss our flight!]                                  |[urgent, travel]                   |
+----------------------------------------------------------------------------------------------------------------+-----------------------------------+
  • NEW: New BAAI general embedding (BGE) and General Text Embeddings (GTE) Models

:star::bug: Improvements & Bug Fixes

  • Fix max sentence length issue with E5Embeddings where it crashed with sequences larger than 512
  • Welcoming 3 new Databricks runtimes to our Spark NLP family:
    • Databricks 13.3 LTS
    • Databricks 13.3 LTS ML
    • Databricks 13.3 LTS ML GPU
  • Welcoming a new EMR 6.12.0 version with Apache Spark 3.4.0 support

:floppy_disk: Models

Spark NLP 5.1.0 comes with more than 200+ new state-of-the-art pre-trained transformer models in multi-languages.

Model Name Lang
WhisperForCTC asr_whisper_tiny xx
WhisperForCTC asr_whisper_tiny_opt xx
BertEmbeddings bge_small en
BertEmbeddings bge_base en
BertEmbeddings bge_large en
BertEmbeddings gte_small en
BertEmbeddings gte_base en
BertEmbeddings gte_large en

The complete list of all 18400+ models & pipelines in 230+ languages is available on Models Hub

:notebook: New Notebooks

Notebooks Colab
Whisper: Automatic Speech Recognition in Spark NLP Open In Colab
Import Whisper models (ONNX) Open In Colab
Import Whisper models (TF) Open In Colab
OpenAICompletion Example Open In Colab
OpenAIEmbeddings Example Open In Colab
Import Transformers from 🤗 into Spark NLP 🚀 with GCP Open In Colab
Import Transformers from 🤗 into Spark NLP 🚀 with Azure Open In Colab
Import Transformers from 🤗 into Spark NLP 🚀 with AWS Open In Colab

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • JohnSnowLabs official Medium
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==5.1.0

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):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.0

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.0

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.0

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.0

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.0

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.0

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>5.1.0</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>5.1.0</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>5.1.0</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>5.1.0</version>
</dependency>

FAT JARs

What's Changed

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/5.0.2...5.1.0

5.0.2

9 months ago

:loudspeaker: Overview

Spark NLP 5.0.2 🚀 comes with new ONNX support for ALBERT, CmameBERT, and XLM-RoBERTa annotators, a new Zero-Short Classifier for XLM-RoBERTa transformer, 200+ new ONNX models, and bug fixes! We want to thank our community for their valuable feedback, feature requests, and contributions. Our Models Hub now contains over 18,000+ free and truly open-source models & pipelines. 🎉

:fire: New Features

  • NEW: Introducing support for ONNX Runtime in ALBERT, CamemBERT, and XLM-RoBERTa annotators. We have already converted 200+ models to ONNX format for these annotators for our community
  • NEW: Implement XlmRoBertaForZeroShotClassification annotator for Zero-Shot multi-class & multi-label text classification based on XLM-RoBERTa transformer

🐛 Bug Fixes & Enhancements

  • Fix MarianTransformers annotator breaking with java.lang.ClassCastException in Python
  • Fix out of 0.0/1.0 accuracy in SentenceDetectorDL and MultiClassifierDL annotators
  • Fix BART issue with a low-temperature value that only occurred when there are no non-infinite logits satisfying the low temperature and top_k values
  • Add missing E5Embeddings and InstructorEmbeddings annotators to annotators in Scala for easy all-in-one import

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • JohnSnowLabs official Medium
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==5.0.2

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):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.2

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.0.2

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.0.2

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.0.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.0.2

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>5.0.2</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>5.0.2</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>5.0.2</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>5.0.2</version>
</dependency>

FAT JARs

What's Changed

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/5.0.1...5.0.2

5.0.1

9 months ago

:loudspeaker: Overview

Spark NLP 5.0.1 🚀 is a patch release with bug fixes and other improvements. We want to thank our community for their valuable feedback, feature requests, and contributions. Our Models Hub now contains over 18,000+ free and truly open-source models & pipelines. 🎉

🐛 Bug Fixes & Enhancements

  • Fix multiLabel param issue in XXXForSequenceClassitication and XXXForZeroShotClassification annotators
  • Add the missing threshold param to all XXXForSequenceClassitication in Python
  • Fix issue with passing spark.driver.cores config as a param into start() function in Python and Scala
  • Fix 600+ models' cards on Models Hub with duplicated code snippets
  • Add new notebooks to export BERT, DistilBERT, RoBERTa, and DeBERTa models to ONNX format

:notebook: New Notebooks

Spark NLP Notebooks Colab
BertEmbeddings HuggingFace in Spark NLP - BERT BERT
DistilBertEmbeddings HuggingFace in Spark NLP - DistilBERT DistilBERT
RoBertaEmbeddings HuggingFace in Spark NLP - RoBERTa RoBERTa
DeBertaEmbeddings HuggingFace in Spark NLP - DeBERTa DeBERTa

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • JohnSnowLabs official Medium
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==5.0.1

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):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.1

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.0.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.0.1

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.0.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.0.1

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.0.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.0.1

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>5.0.1</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>5.0.1</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>5.0.1</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>5.0.1</version>
</dependency>

FAT JARs

What's Changed

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/5.0.0...5.0.1

5.0.0

10 months ago

:loudspeaker: It's All About That Search!

We are delighted to announce the release of Spark NLP 🚀 5.0.0, featuring the highly anticipated support for ONNX! From the start of 2023, we have been working tirelessly to ensure that the integration of ONNX is not just possible but also seamless for all our users. With this support, you can look forward to faster inference, automatic optimization, and quantization when exporting your LLM models. Additionally, we are also set to release an array of new LLM models fine-tuned specifically for chat and instruction, now that we have successfully integrated ONNX Runtime into Spark NLP 🚀.

We have introduced two state-of-the-art models for text embedding, INSTRUCTOR and E5 embeddings. Currently, these models are leading the way on the MTEB leaderboard, even outperforming the widely recognized OpenAI text-embedding-ada-002. These cutting-edge models are now being utilized in production environments to populate Vector Databases. In addition, they are being paired with LLM models like Falcon, serving to augment their existing knowledge base and reduce the chances of hallucinations.

We want to thank our community for their valuable feedback, feature requests, and contributions. Our Models Hub now contains over 18,000+ free and truly open-source models & pipelines. 🎉

:fire: New Features

Spark NLP :heart: ONNX

SPARK NLP

NEW: Introducing support for ONNX Runtime in Spark NLP🚀. Serving as a high-performance inference engine, ONNX Runtime can handle machine learning models in the ONNX format and has been proven to significantly boost inference performance across a multitude of models.

Our integration of ONNX Runtime has already led to substantial improvements when serving our LLM models, including BERT. Furthermore, this integration empowers Spark NLP users to optimize their model performance. As users export their models to ONNX, they can utilize the built-in features provided by libraries such as onnx-runtime, transformers, optimum, and PyTorch. Notably, these libraries offer out-of-the-box capabilities for optimization and quantization, enhancing model efficiency and performance.

image

In the realm of Vector Databases, the quest for faster and more efficient Embeddings models has become an imperative pursuit. Models like BERT, DistilBERT, and DeBERTa have revolutionized natural language processing tasks by capturing intricate semantic relationships between words. However, their computational demands and slow inference times pose significant challenges in the game of Vector Databases.

In Vector Databases, the speed at which queries are processed and embeddings are retrieved directly impacts the overall performance and responsiveness of the system. As these databases store vast amounts of vectorized data, such as documents, sentences, or entities, swiftly retrieving relevant embeddings becomes paramount. It enables real-time applications like search engines, recommendation systems, sentiment analysis, and chat/instruct-like products similar to ChatGPT to deliver timely and accurate results, ensuring a seamless user experience.

Keeping this in mind, we've initiated ONNX support for the following annotators:

  • We've introduced ONNX support for the BertEmbeddings annotator. Approximately 180 models of the same name have already been converted to the ONNX format to automatically benefit from the associated performance enhancements.
  • We've added ONNX support for the RoBertaEmbeddings annotator. Roughly 55 models of the same name have been imported in the ONNX format, thus allowing for automatic speed improvements.
  • ONNX support has been initiated for the DistilBertEmbeddings annotator. Around 25 models with the same name have been converted to the ONNX format, facilitating automatic speed enhancements.
  • We've incorporated ONNX support into the DeBertaEmbeddings annotator. About 12 models bearing the same name have been imported in the ONNX format, enabling them to automatically reap the benefits of speed improvements.

We have successfully identified all existing models for these annotators on our Models Hub, converted them into the ONNX format, and re-uploaded them under the same names. This process was carried out to ensure a seamless transition for our community starting with Spark NLP 5.0.0. We will continue to import additional models in the ONNX format in the days ahead. To keep track of the ONNX compatibility with Spark NLP, follow this issue: https://github.com/JohnSnowLabs/spark-nlp/issues/13866.

INSTRUCTOR: Instruction-Finetuned Text Embeddings

NEW: Introducing InstructorEmbeddings annotator in Spark NLP 🚀. InstructorEmbeddings can load new state-of-the-art INSTRUCTOR Models inherited from Google T5 for Text embedding.

This annotator is compatible with all the models trained/fine-tuned by using T5EncoderModel for PyTorch or TFT5EncoderModel for TensorFlow models in HuggingFace 🤗

image

Instructor👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) by simply providing the task instruction, without any finetuning. Instructor👨‍ achieves sota on 70 diverse embedding tasks! For more details, check out the official paper and the project page!

NOTEBOOK: https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/sentence-embeddings/InstructorEmbeddings.ipynb

E5: Text Embeddings by Weakly-Supervised Contrastive Pre-training

NEW: Introducing E5Embeddings annotator in Spark NLP 🚀. E5Embeddings can load new state-of-the-art E5 Models based on BERT for Text Embeddings.

image

Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022

NOTEBOOK: https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/sentence-embeddings/E5Embeddings.ipynb

Our new state-of-the-art annotators for Text Embeddings are currently dominating the top of the MTEB leaderboard positioning themselves above OpenAI text-embedding-ada-002 image

Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the MTEB GitHub repository 🤗

Document Similarity Ranker by LSH techniques

NEW: Introducing DocumentSimilarityRanker annotator in Spark NLP 🚀. DocumentSimilarityRanker is a new annotator that uses LSH techniques present in Spark ML lib to execute approximate nearest neighbors search on top of sentence embeddings, It aims to capture the semantic meaning of a document in a dense, continuous vector space and return it to the ranker search.

NOTEBOOK: https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/text/english/text-similarity/doc-sim-ranker/test_doc_sim_ranker.ipynb

  • Welcoming 6 new Databricks runtimes to our Spark NLP family:
    • Databricks 13.1 LTS
    • Databricks 13.1 LTS ML
    • Databricks 13.1 LTS ML GPU
    • Databricks 13.2 LTS
    • Databricks 13.2 LTS ML
    • Databricks 13.2 LTS ML GPU
  • Welcome AWS EMR 6.11 version to our Spark NLP family
  • Fix BART issue with input longer than the maxInputLength

:floppy_disk: Models

Spark NLP 5.0.0 comes with more than 400+ new Large Language Models (LLMs) in ONNX format. We are also providing optimized and quantized versions of popular models that can be used immediately in any Spark NLP pipelines:

Model Name Lang
BertEmbeddings bert_base_cased en
BertEmbeddings bert_base_cased_opt en
BertEmbeddings bert_base_cased_quantized en
BertEmbeddings small_bert_L2_768 en
BertEmbeddings small_bert_L2_768_opt en
BertEmbeddings small_bert_L2_768_quantized en
DeBertaEmbeddings roberta_base en
DeBertaEmbeddings roberta_base_opt en
DeBertaEmbeddings roberta_base_quantized en
DistilBertEmbeddings distilbert_base_cased en
DistilBertEmbeddings distilbert_base_cased_opt en
DistilBertEmbeddings distilbert_base_cased_quantized en
DistilBertEmbeddings distilbert_base_uncased en
DistilBertEmbeddings distilbert_base_uncased_opt en
DistilBertEmbeddings distilbert_base_uncased_quantized en
DistilBertEmbeddings distilbert_base_multilingual_cased en
DistilBertEmbeddings distilbert_base_multilingual_cased_opt en
DistilBertEmbeddings distilbert_base_multilingual_cased_quantized en
RoBertaBertEmbeddings roberta_base en
RoBertaBertEmbeddings roberta_base_opt en
RoBertaBertEmbeddings roberta_base_quantized en
InstructorEmbeddings instructor_base en
InstructorEmbeddings instructor_large en
E5Embeddings e5_small en
E5Embeddings e5_small_v2 en
E5Embeddings e5_base en
E5Embeddings e5_base_v2 en
E5Embeddings e5_large en
E5Embeddings e5_large_v2 en

The complete list of all 18000+ models & pipelines in 230+ languages is available on Models Hub

:notebook: New Notebooks

Notebooks Colab
Instructor Embeddings Open In Colab
E5 Embeddings Open In Colab
Document Similarity Ranker Open In Colab

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • JohnSnowLabs official Medium
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==5.0.0

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):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.0

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.0

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.0.0

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.0.0

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.0.0

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.0.0

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.0.0

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.0.0

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>5.0.0</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>5.0.0</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>5.0.0</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>5.0.0</version>
</dependency>

FAT JARs

What's Changed

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/4.4.4...5.0.0

4.4.4

11 months ago

:loudspeaker: Overview

Spark NLP 4.4.4 🚀 is a patch release with bug fixes and other improvements. We want to thank our community for their valuable feedback, feature requests, and contributions. Our Models Hub now contains over 17,000+ free and truly open-source models & pipelines. 🎉

Spark NLP has a new home! https://sparknlp.org is where you can find all the documentation, models, and demos for Spark NLP. It aims to provide valuable resources to anyone interested in 100% open-source NLP solutions by using Spark NLP 🚀.

:star: New Features & Enhancements

🐛 Bug Fixes

:notebook: New Notebooks

Notebooks Colab Colab
BertForZeroShotClassification HuggingFace in Spark NLP - BertForZeroShotClassification Open In Colab
DistilBertForZeroShotClassification HuggingFace in Spark NLP - DistilBertForZeroShotClassification Open In Colab
RoBertaForZeroShotClassification HuggingFace in Spark NLP - RoBertaForZeroShotClassification Open In Colab

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • JohnSnowLabs official Medium
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==4.4.4

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):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.4

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.4

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.4

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.4

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.4

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.4

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.4

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.4

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>4.4.4</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>4.4.4</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>4.4.4</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>4.4.4</version>
</dependency>

FAT JARs

What's Changed

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/4.4.3...4.4.4

4.4.3

11 months ago

:loudspeaker: Overview

Spark NLP 4.4.3 🚀 comes with a new param to switch from multi-class to multi-label in all of our classifiers including ZeroShot, extending support to download models directly with an S3 path in ResourceDownloader, bug fixes, and improvements!

We want to thank our community for their valuable feedback, feature requests, and contributions. Our Models Hub now contains over 18,000+ free and truly open-source models & pipelines. 🎉

Spark NLP has a new home! https://sparknlp.org is where you can find all the documentation, models, and demos for Spark NLP. It aims to provide valuable resources to anyone interested in 100% open-source NLP solutions by using Spark NLP 🚀

:star: New Features & Enhancements

  • New multilabel parameter to switch from multi-class to multi-label on all Classifiers in Spark NLP: AlbertForSequenceClassification, BertForSequenceClassification, DeBertaForSequenceClassification, DistilBertForSequenceClassification, LongformerForSequenceClassification, RoBertaForSequenceClassification, XlmRoBertaForSequenceClassification, XlnetForSequenceClassification, BertForZeroShotClassification, DistilBertForZeroShotClassification, and RobertaForZeroShotClassification
  • Refactor protected Params and Features to avoid unwanted exceptions during runtime https://github.com/JohnSnowLabs/spark-nlp/pull/13797
  • Add proper documentation and instructions for ZeroShot classifiers: BertForZeroShotClassification, DistilBertForZeroShotClassification, and RobertaForZeroShotClassification https://github.com/JohnSnowLabs/spark-nlp/pull/13798
  • Extend support for downloading models/pipelines directly by given name or S3 path in ResourceDownloader https://github.com/JohnSnowLabs/spark-nlp/pull/13796 
from sparknlp.pretrained import ResourceDownloader

# partial S3 path
ResourceDownloader.downloadModelDirectly("public/models/albert_base_sequence_classifier_ag_news_en_3.4.0_3.0_1639648298937.zip", remote_loc = "public/models")

# full S3 path
ResourceDownloader.downloadModelDirectly("s3://auxdata.johnsnowlabs.com/public/models/albert_base_sequence_classifier_ag_news_en_3.4.0_3.0_1639648298937.zip", remote_loc = "public/models", unzip = False)

🐛 Bug Fixes

Known issue: Current pre-trained pipelines don't work on PySpark 3.2 and 3.3. They will all be fixed in the next few days.

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==4.4.3

Spark Packages

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x (Scala 2.12):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.3

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.3

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.3

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.3

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.3

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.3

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.3

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.3

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>4.4.3</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>4.4.3</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>4.4.3</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>4.4.3</version>
</dependency>

FAT JARs

What's Changed

New Contributors

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/4.4.2...4.4.3

4.4.2

1 year ago

:loudspeaker: Overview

Spark NLP 4.4.2 🚀 comes with a new RoBertaForZeroShotClassification annotator for Zero-Shot tex classification (both multi-class and multi-label), full support for Apache Spark 3.4, faster and more memory-efficient BART models, a new cache feature for BartTransformer, new Databricks runtimes, and many more!

We want to thank our community for their valuable feedback, feature requests, and contributions. Our Models Hub now contains over 17,000+ free and truly open-source models & pipelines. 🎉

Spark NLP has a new home! https://sparknlp.org is where you can find all the documentation, models, and demos for Spark NLP. It aims to provide valuable resources to anyone interested in 100% open-source NLP solutions by using Spark NLP 🚀

:star: New Features & Enhancements

  • NEW: Introducing ** RoBertaForZeroShotClassification** annotator for Zero-Shot Text Classification in Spark NLP 🚀. You can use the RoBertaForZeroShotClassification annotator for text classification with your labels! :100:

Zero-Shot Learning (ZSL): Traditionally, ZSL most often referred to a fairly specific type of task: learning a classifier on one set of labels and then evaluating on a different set of labels that the classifier has never seen before. Recently, especially in NLP, it's been used much more broadly to get a model to do something it wasn't explicitly trained to do. A well-known example of this is in the GPT-2 paper where the authors evaluate a language model on downstream tasks like machine translation without fine-tuning on these tasks directly.

Let's see how easy it is to just use any set of labels our trained model has never seen via the setCandidateLabels() param:

zero_shot_classifier = RoBertaForZeroShotClassification \
    .pretrained() \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class") \
    .setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"])

For Zero-Short Multi-class Text Classification:

+----------------------------------------------------------------------------------------------------------------+--------+
|result                                                                                                          |result  |
+----------------------------------------------------------------------------------------------------------------+--------+
|[I have a problem with my iPhone that needs to be resolved asap!!]                                              |[mobile]|
|[Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.]|[mobile]|
|[I have a phone and I love it!]                                                                                 |[mobile]|
|[I want to visit Germany and I am planning to go there next year.]                                              |[travel]|
|[Let's watch some movies tonight! I am in the mood for a horror movie.]                                         |[movie] |
|[Have you watched the match yesterday? It was a great game!]                                                    |[sport] |
|[We need to hurry up and get to the airport. We are going to miss our flight!]                                  |[urgent]|
+----------------------------------------------------------------------------------------------------------------+--------+

For Zero-Short Multi-class Text Classification:

+----------------------------------------------------------------------------------------------------------------+-----------------------------------+
|result                                                                                                          |result                             |
+----------------------------------------------------------------------------------------------------------------+-----------------------------------+
|[I have a problem with my iPhone that needs to be resolved asap!!]                                              |[urgent, mobile, movie, technology]|
|[Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.]|[urgent, technology]               |
|[I have a phone and I love it!]                                                                                 |[mobile]                           |
|[I want to visit Germany and I am planning to go there next year.]                                              |[travel]                           |
|[Let's watch some movies tonight! I am in the mood for a horror movie.]                                         |[movie]                            |
|[Have you watched the match yesterday? It was a great game!]                                                    |[sport]                            |
|[We need to hurry up and get to the airport. We are going to miss our flight!]                                  |[urgent, travel]                   |
+----------------------------------------------------------------------------------------------------------------+-----------------------------------+

🐛 Bug Fixes

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==4.4.2

Spark Packages

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x (Scala 2.12):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.2

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.2

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.2

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.2

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.2

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>4.4.2</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>4.4.2</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>4.4.2</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>4.4.2</version>
</dependency>

FAT JARs

What's Changed

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/4.4.1...4.4.2

4.4.1

1 year ago

:loudspeaker: Overview

Spark NLP 4.4.1 🚀 comes with a new DistilBertForZeroShotClassification annotator for Zero-Shot tex classification (both multi-class and multi-label), a new threshold parameter in all XXXForSequenceClassification annotators to filter out classes based on their scores, and new notebooks to import models for Image Classification with Swin and ConvNext architectures.

We want to thank our community for their valuable feedback, feature requests, and contributions. Our Models Hub now contains over 17,000+ free and truly open-source models & pipelines. 🎉

Spark NLP has a new home! https://sparknlp.org is where you can find all the documentation, models, and demos for Spark NLP. It aims to provide valuable resources to anyone interested in 100% open-source NLP solutions by using Spark NLP 🚀.

:star: New Features & Enhancements

  • NEW: Introducing DistilBertForZeroShotClassification annotator for Zero-Shot Text Classification in Spark NLP 🚀. You can use the DistilBertForZeroShotClassification annotator for text classification with your labels! :100:

Zero-Shot Learning (ZSL): Traditionally, ZSL most often referred to a fairly specific type of task: learning a classifier on one set of labels and then evaluating on a different set of labels that the classifier has never seen before. Recently, especially in NLP, it's been used much more broadly to get a model to do something it wasn't explicitly trained to do. A well-known example of this is in the GPT-2 paper where the authors evaluate a language model on downstream tasks like machine translation without fine-tuning on these tasks directly.

Let's see how easy it is to just use any set of labels our trained model has never seen via the setCandidateLabels() param:

zero_shot_classifier = DistilBertForZeroShotClassification \
    .pretrained() \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class") \
    .setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"])

For Zero-Short Multi-class Text Classification:

+----------------------------------------------------------------------------------------------------------------+--------+
|result                                                                                                          |result  |
+----------------------------------------------------------------------------------------------------------------+--------+
|[I have a problem with my iPhone that needs to be resolved asap!!]                                              |[mobile]|
|[Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.]|[mobile]|
|[I have a phone and I love it!]                                                                                 |[mobile]|
|[I want to visit Germany and I am planning to go there next year.]                                              |[travel]|
|[Let's watch some movies tonight! I am in the mood for a horror movie.]                                         |[movie] |
|[Have you watched the match yesterday? It was a great game!]                                                    |[sport] |
|[We need to hurry up and get to the airport. We are going to miss our flight!]                                  |[urgent]|
+----------------------------------------------------------------------------------------------------------------+--------+

For Zero-Short Multi-class Text Classification:

+----------------------------------------------------------------------------------------------------------------+-----------------------------------+
|result                                                                                                          |result                             |
+----------------------------------------------------------------------------------------------------------------+-----------------------------------+
|[I have a problem with my iPhone that needs to be resolved asap!!]                                              |[urgent, mobile, movie, technology]|
|[Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.]|[urgent, technology]               |
|[I have a phone and I love it!]                                                                                 |[mobile]                           |
|[I want to visit Germany and I am planning to go there next year.]                                              |[travel]                           |
|[Let's watch some movies tonight! I am in the mood for a horror movie.]                                         |[movie]                            |
|[Have you watched the match yesterday? It was a great game!]                                                    |[sport]                            |
|[We need to hurry up and get to the airport. We are going to miss our flight!]                                  |[urgent, travel]                   |
+----------------------------------------------------------------------------------------------------------------+-----------------------------------+
  • Adding threshold param to AlbertForSequenceClassification, BertForSequenceClassification, BertForZeroShotClassification, DistilBertForSequenceClassification, CamemBertForSequenceClassification, DeBertaForSequenceClassification, LongformerForSequenceClassification, RoBertaForQuestionAnswering, XlmRoBertaForSequenceClassification, and XlnetForSequenceClassification annotators
  • Add new notebooks to import models for SwinForImageClassification and ConvNextForImageClassification annotators for Image Classification

:notebook: New Notebooks

Notebooks Colab
Zero-Shot Text Classification Open In Colab
ConvNextForImageClassification Open In Colab
SwinForImageClassification Open In Colab

:book: Documentation

❤️ Community support

  • Slack For live discussion with the Spark NLP community and the team
  • GitHub Bug reports, feature requests, and contributions
  • Discussions Engage with other community members, share ideas, and show off how you use Spark NLP!
  • Medium Spark NLP articles
  • YouTube Spark NLP video tutorials

Installation

Python

#PyPI

pip install spark-nlp==4.4.1

Spark Packages

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x (Scala 2.12):

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1

GPU

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.1

Apple Silicon (M1 & M2)

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.1

AArch64

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.1

pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.1

Maven

spark-nlp on Apache Spark 3.0.x, 3.1.x, 3.2.x, and 3.3.x:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.12</artifactId>
    <version>4.4.1</version>
</dependency>

spark-nlp-gpu:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.12</artifactId>
    <version>4.4.1</version>
</dependency>

spark-nlp-silicon:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-silicon_2.12</artifactId>
    <version>4.4.1</version>
</dependency>

spark-nlp-aarch64:

<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-aarch64_2.12</artifactId>
    <version>4.4.1</version>
</dependency>

FAT JARs

What's Changed

Full Changelog: https://github.com/JohnSnowLabs/spark-nlp/compare/4.4.0...4.4.1