Named Entity Recognition
Showing 34 of 34 projects
TensorFlow implementation and pre-trained models for BERT, a bidirectional Transformer for language understanding.
Industrial-strength Natural Language Processing library for Python, featuring pretrained pipelines for 70+ languages and production-ready training.
A community-driven repository tracking datasets and state-of-the-art results for common NLP tasks across multiple languages.
A simple Python framework for state-of-the-art natural language processing (NLP) tasks like named entity recognition and sentiment analysis.
A simple Python framework for state-of-the-art natural language processing (NLP) tasks like named entity recognition and sentiment analysis.
A lightweight JavaScript library for natural language processing that transforms text into structured data with a modest, pragmatic approach.
A Python NLP library from Stanford for tokenization, sentence segmentation, NER, and dependency parsing across 60+ languages.
An open-source NLP framework for building and deploying deep learning dialog systems and chatbots with PyTorch and transformers.
A state-of-the-art Natural Language Processing library built on Apache Spark, offering 100,000+ pretrained models and pipelines in 200+ languages.
A free, state-of-the-art library and toolkit for named entity extraction and binary relation detection from text.
A web/desktop application for collaborative labeling and annotation of images, text, audio, documents, and other data types.
A spaCy pipeline and models specifically designed for processing scientific and biomedical documents.
An easy-to-use, state-of-the-art named-entity recognition (NER) tool based on neural networks.
A BERT language model pre-trained on a large corpus of scientific papers for natural language processing tasks in scientific domains.
A comprehensive natural language processing framework for Ruby with support for text extraction, parsing, and machine learning.
Generate datasets for AI chatbots, NLP tasks, NER, and text classification using a simple domain-specific language.
An open-source suite featuring financial large language models (FinMA), instruction datasets (FIT), and evaluation benchmarks (FinBen) for financial AI.
Catalyst is a high-performance C# NLP library inspired by spaCy, offering pre-trained models, entity recognition, and embedding training.
A tool for automatically annotating mentions of DBpedia resources in text, linking entities to their global identifiers.
Pre-trained biomedical language representation model for biomedical text mining tasks like named entity recognition and relation extraction.
A Python wrapper and JSON-RPC server for Stanford CoreNLP, providing NLP tools like parsing, tagging, and coreference resolution.
A .NET wrapper for Stanford CoreNLP providing natural language processing capabilities including tokenization, parsing, and named entity recognition.
A BERT model pre-trained on PubMed abstracts and clinical notes for biomedical natural language processing tasks.
A comprehensive suite of Java NLP libraries and tools for text annotation, feature extraction, and language processing tasks.
A medical text mining and information extraction framework built on spaCy for rapid prototyping and training of predictive NLP models.
A curated collection of linguistic resources, tools, and datasets for Natural Language Processing and Computational Linguistics on Spanish.
A pure Go package for running inference with pre-trained Transformer models from Hugging Face, enabling NLP tasks without external languages.
A curated collection of open-source libraries, models, datasets, and tools for Natural Language Processing (NLP) in Norwegian.
A modular NLP framework for extracting information from French clinical notes, compatible with spaCy and PyTorch.
A natural language processing framework for JVM languages with comprehensive linguistic analysis tools.
Ruby bindings for Stanford NLP tools providing part-of-speech tagging and named entity recognition capabilities.
Ruby bindings to the OpenNLP Java toolkit for natural language processing tasks like tokenization, POS tagging, and named entity recognition.
A tagger, lemmatizer, morphological analyzer, and dependency parser for Dutch using memory-based NLP modules.
A Ruby wrapper for the spaCy NLP library via PyCall, enabling tokenization, POS tagging, NER, and OpenAI integration.
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