textm2

TextM2 is an open-source software library designed for scalable text mining and natural language processing. It provides a framework to process large text corpora, extract features, and apply machine learning models to textual data. The project emphasizes modularity, language-agnostic tooling, and efficient performance on big datasets.

TextM2 originated in 2021 as a collaborative effort by researchers and developers seeking to unify preprocessing, representation learning, and evaluation under a single toolkit. It is maintained by a distributed community and hosted on public version control repositories. The name reflects its focus on text analysis and model-to-model workflows.

Core capabilities include language detection, tokenization, normalization, stemming and lemmatization, and robust preprocessing pipelines. It supports multiple vectorization techniques (TF-IDF, word embeddings, and contextual representations), topic modeling, text classification, and information retrieval utilities. The toolkit can operate in streaming or batch modes and aims to minimize memory usage on large-scale data.

Architecturally, TextM2 employs a modular core with pluggable components for tokenizers, analyzers, and models. It provides pipeline abstractions, data format adapters (CSV, JSON, Parquet), and sinks such as search engines or databases. The project offers a Python API with optional Rust bindings for performance-critical paths and can integrate with distributed processing frameworks.

Typical use cases include academic research, enterprise data analytics, and digital humanities projects. Common workflows involve data ingestion, preprocessing, feature extraction, model training and evaluation, and deployment in data science pipelines or data warehouses.

TextM2 is distributed under a permissive open-source license and governed by an inclusive community process. Contributions are welcomed, and the project maintains documentation, tutorials, and benchmarks to aid adoption. It has influenced related NLP tooling and benchmarks in several research groups.