How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.
The main features of life4/textdistance are: Natural Language Processing, NLP, Text Processing.
Projects with overlapping indexed features include: jdkato/prose — :book: A Golang library for text processing, including tokenization, part-of-speech tagging, and named-entity… mozillazg/go-pinyin — 汉字转拼音. facebookresearch/fasttext — fastText is a library and framework for word embedding generation, text vectorization, and supervised text… facebookresearch/pytext — PyText is an extensible PyTorch-based framework for building, training, and deploying custom natural language… abadojack/whatlanggo — Natural language detection library for Go. mozillazg/python-pinyin — python-pinyin is a Python library for transliterating simplified and traditional Chinese characters into phonetic…
fastText is a library and framework for word embedding generation, text vectorization, and supervised text classification. It provides tools to transform raw text into fixed-length vector representations and to train models that assign category labels to sentences or documents. The system utilizes subword-based vectorization and character n-gram embeddings, allowing it to generate meaningful vectors for words that were not present during training. To manage resource usage, it includes a quantized language model implementation that employs product quantization and dimensionality reduction to d
PyText is an extensible PyTorch-based framework for building, training, and deploying custom natural language processing models, including text classifiers, sequence taggers, and intent-slot predictors. It provides a modular toolkit that allows developers to assemble these models using pluggable registries for model architectures, data formats, and tensorizers, all configurable through YAML files without requiring code changes. The framework distinguishes itself through its comprehensive support for the full NLP model lifecycle, from training to production inference. It includes pre-built neu
:book: A Golang library for text processing, including tokenization, part-of-speech tagging, and named-entity extraction.
