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Chalk is a natural language processing library.
The main features of scalanlp/chalk are: Machine Learning Frameworks, Natural Language Processing.
Projects with overlapping indexed features include: google/trax — Trax is a deep learning framework and hardware-agnostic tensor engine designed for designing and training neural… huggingface/tokenizers — This project is a high-performance library for converting raw text into tokens and IDs for machine learning models. It… facebookresearch/starspace — Starspace is a vector embedding framework designed for training high-dimensional representations of text and images.… factorie/factorie — FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides… guillaume-be/rust-bert — Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...). johnsnowlabs/spark-nlp — Spark NLP is a toolkit for scalable text analysis and machine learning built on the Apache Spark distributed computing…
FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.
Trax is a deep learning framework and hardware-agnostic tensor engine designed for designing and training neural networks. It serves as a research tool providing high-level combinators for composing complex architectures, alongside a dedicated library for building transformer models and a toolkit for reinforcement learning. The framework is distinguished by its support for reversible and sparse transformer architectures, which reduce memory and computational overhead. It enables a single set of model instructions to execute across different hardware backends without changing the underlying co
Starspace is a vector embedding framework designed for training high-dimensional representations of text and images. It functions as a machine learning system for neural ranking, text classification, and knowledge graph embedding, mapping different object types into a shared numerical space to facilitate retrieval and prediction tasks. The system includes specialized tools for knowledge graph completion and link prediction by representing entities and their relationships within a multi-relational vector space. It further provides capabilities for semantic content recommendation and large-scal
Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)