Text Classification
Showing 36 of 45 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 TensorFlow implementation of a convolutional neural network for sentence classification based on Yoon Kim's paper.
Snips Python library to extract meaning from text
Seamlessly integrate large language models like ChatGPT into scikit-learn for enhanced text analysis tasks.
A full-featured full-text search engine written entirely in PHP with fuzzy search, geo-search, and dynamic index updates.
A BERT language model pre-trained on a large corpus of scientific papers for natural language processing tasks in scientific domains.
A Go machine learning library with online learning capabilities and a variety of implemented models.
A collection of TensorFlow tutorials and examples covering image classification, GANs, text classification, and model deployment.
A Rust library for natural language detection using trigram models, focusing on simplicity and performance.
Generate datasets for AI chatbots, NLP tasks, NER, and text classification using a simple domain-specific language.
A model-agnostic method for generating high-precision rule-based explanations for black-box classifier predictions.
A Go library for naive Bayesian classification and TF-IDF calculations on string sets.
A Ruby library for text classification with Bayesian, LSI, logistic regression, k-NN, and TF-IDF algorithms.
A modern C++ toolkit for text retrieval and analysis, featuring indexing, ranking, topic modeling, classification, and language models.
A BERT model pre-trained on PubMed abstracts and clinical notes for biomedical natural language processing tasks.
A Ruby gem for simple sentiment analysis that classifies text as positive, negative, or neutral based on configurable thresholds.
A Neo4j extension for document and text classification using graph-based hierarchical pattern recognition.
A Naive Bayes machine learning implementation in Elixir with multiple models and storage options.
A Julia package providing standard tools and models for text analysis and natural language processing.
A real-time online machine learning library built on Apache Storm for scalable stream processing with incremental algorithms.
A Python library for interpretable text classification using the SS3 model, with built-in visualization tools for explainable AI.
A pure Go package for running inference with pre-trained Transformer models from Hugging Face, enabling NLP tasks without external languages.
A TensorFlow implementation of fastText for embedding-based text classification with support for character ngrams and distributed training.
A curated collection of datasets, corpora, and resources for Indonesian natural language processing tasks.
A Python library that simplifies using, finetuning, and deploying state-of-the-art machine learning models for various AI tasks.
Bayesian text classifier for Go with flexible tokenizers and storage backends.
A deprecated Node.js sample application demonstrating IBM Watson Natural Language Classifier service features.
A full-featured Ruby implementation of Naive Bayes for probabilistic classification with customizable features.
An open-source starter solution for the Kaggle Toxic Comment Classification Challenge, providing ready-to-use machine learning pipelines for detecting online harassment.
Archived R package for accessing the Monkeylearn API for text classification and extraction.
TensorFlow implementation of hierarchical attention networks for document classification using GRU cells and attention mechanisms.
An open-source prompt guard model that detects prompt injection attacks while mitigating over-defense against benign inputs.
A BERT-based model that detects six types of toxicity in text comments, deployable as a Docker container.
A pre-trained BERT-based model for detecting positive or negative sentiment in short text fragments.
Machine learning model that predicts whether Stack Overflow questions will be closed based on their content and metadata.
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