#spaCy

Explore spaCy's robust NLP platform supporting over 70 languages using state-of-the-art neural networks. Access pretrained pipelines for essential tasks like tokenization, named entity recognition, and text classification. Leverage multi-task learning with BERT transformers, ensuring easy deployment and production-readiness. Enhance projects with custom models in frameworks like PyTorch or TensorFlow, and utilize powerful visualizers for syntax and NER. This open-source software, under the MIT license, offers high accuracy and extensibility for all your NLP needs.

Discover PyTextRank, a Python library enhancing spaCy with graph-based algorithms like TextRank for tasks such as phrase extraction and summarization. It efficiently converts unstructured data into structured insights. Installation is easy via PyPi or Conda, and the integration with spaCy models is seamless. Access tutorials and documentation for effective application in research or software development.

TextDescriptives is a Python library designed for calculating text metrics using spaCy v.3 components, providing a new API for enhanced analysis with metrics like quality, readability, and coherence. It features a code-free web application, ensuring seamless integration with spaCy pipelines for detailed analysis. Comprehensive documentation and tutorials support efficient use of the library.

This package integrates Hugging Face transformers like BERT, GPT-2, and XLNet into spaCy, providing a seamless blend into NLP workflows. Designed for spaCy v3, it features multi-task learning, automated token alignment, and customization options for transformer outputs. Installation is user-friendly via pip, compatible with both CPU and GPU. Though direct task-specific heads are unsupported, prediction outputs for text classification are accessible through wrappers.

The project efficiently demonstrates zero- and few-shot learning using OpenAI models and Prodigy for creating high-quality datasets with minimal annotation. It details the setup of Prodigy for named-entity recognition and text categorization, utilizing OpenAI predictions to build a gold-standard dataset. Task-specific prompt configuration enables precise classification and model training, while addressing imbalanced data and exporting annotations to spaCy or HuggingFace transformers.

Discover innovative clinical NLP and text processing tools designed to integrate with spaCy. This library offers specialized modules for sentence segmentation, contextual analysis, and clinical data visualization. Its modular design provides the flexibility to use specific features independently. The latest update introduces multi-language support, broadening accessibility. Suitable for healthcare professionals and researchers aiming to improve clinical data processing. Compatible with spaCy v3 and integrates with QuickUMLS for effective concept extraction. Extensive documentation and community contributions enhance learning opportunities.

This repository provides step-by-step tutorials on implementing sequence-to-sequence models with PyTorch for translating German to English text. It covers Python 3.9 dependency installation, spaCy tokenization, and seq2seq model workflows. The tutorials enhance translation outcomes by exploring encoder-decoder models from LSTMs to attention mechanisms, ideal for developers and researchers interested in neural network-based statistical machine translation.

The spacy-stanza package combines Stanza's, formerly StanfordNLP, models with spaCy, allowing integration of high-accuracy models for tasks like tokenization, POS tagging, and lemmatization across 68 languages. It supports advanced NLP tasks including named entity recognition using Stanza's sophisticated algorithms. Ideal for developers looking to leverage the strengths of both SpaCy and Stanza, it provides customizable options within SpaCy's pipeline and supports user-defined components. Compatible with SpaCy v3.0 and above for optimal performance.

SceneGraphParser is a Python-based toolkit that transforms sentences into scene graphs using dependency parsing. Unlike the Stanford Scene Graph Parser, it offers an intuitive interface and simple configuration. It identifies nouns and their relational connections, supporting visual-semantic embeddings and bridging vision with language. The tool uses spaCy as its backend, facilitating integration into Python projects without complex data handling. Participation in identifying failure cases is welcomed to advance development.

Access a range of spaCy NLP models for various language processing tasks, available as `.whl` and `.tar.gz` files for efficient downloads. Installation commands ensure compatibility across spaCy versions. Models are classified by capabilities, training data types, and sizes, offering flexibility for different applications. Consult the documentation for detailed installation guidance and usage instructions. This repository is suitable for developers looking for customizable NLP solutions.

This guide provides engineers with practical insights into natural language processing using Python, grounded in both classic and contemporary academic works. It offers code-first methods for tasks such as text classification, cleaning, and spell correction across seven thematic areas. Tools for named entity recognition, question and answer generation, and vector representations using word2vec and gensim are included. Discover text classification and ensemble techniques, and delve into deep learning methodologies with PyTorch, alongside a quick-start guide for chatbot creation.

floret offers an optimized solution for generating compact word vectors by merging fastText's subword approach with Bloom embeddings. It ensures efficient and accurate word representations across diverse languages, especially those with complex morphologies. The integration with spaCy further enhances vector handling, making it suitable for scalable NLP tasks.