Search Results for "sentence" - Page 2
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Showing 167 open source projects for "sentence"
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Sepia
De-AI writing skill for any Agent Skills-compatible agent
...Rather than focusing only on vocabulary substitutions, it targets higher-level narrative structure and document-specific writing habits. Fiction processing emphasizes narrative architecture before sentence-level polishing. Professional modes apply different rules to release notes, technical articles, pull request replies, postmortems, tickets, and other workplace formats. Four workflows are provided: writing from scratch, diagnostic review, minimal refactoring, and complete recreation. The skill follows the standard Agent Skills format and can therefore be installed across many compatible assistants. ... -
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cs-paper-checklist
A final sanity checklist to help your CS paper get accepted
...The checklist reviews titles, abstracts, introductions, related work, methods, experiments, references, figures, tables, and appendices. It emphasizes reproducibility, meaningful baselines, ablation studies, statistical reporting, and clear definitions. Dedicated writing checks address sentence length, paragraph size, passive voice, vague language, and undefined abbreviations. Citation safeguards encourage manual verification of every reference, especially material suggested by language models. Final checks cover anonymity, page limits, compilation errors, file naming, coauthor review, and cross-device rendering. -
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Stop Slop
A skill file for removing AI tells from prose
...It gives an assistant concrete editing rules for making text sound more direct, human, specific, and trustworthy. The project targets common AI habits such as filler openings, overused contrasts, unnecessary adverbs, vague language, passive phrasing, and metronomic sentence rhythm. It also includes a scoring rubric that rates drafts across dimensions such as directness, rhythm, trust, authenticity, and density. The skill is useful for drafting, editing, polishing, and quality-checking prose before publication. Its main value is giving writers and AI assistants a practical checklist for making text feel less synthetic and more intentional. -
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model2Vec
Fast State-of-the-Art Static Embeddings
model2vec is an innovative embedding framework that converts large sentence transformer models into compact, high-speed static embedding models while preserving much of their semantic performance. The project focuses on dramatically reducing the computational cost of generating embeddings, achieving significant improvements in speed and model size without requiring large datasets for retraining. By using a distillation-based approach, it can produce lightweight models that run efficiently on CPUs, making it suitable for edge applications and large-scale processing pipelines. ... -
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Tokenizers
Fast State-of-the-Art Tokenizers optimized for Research and Production
...Easy to use, but also extremely versatile. Designed for both research and production. Full alignment tracking. Even with destructive normalization, it’s always possible to get the part of the original sentence that corresponds to any token. Does all the pre-processing: Truncation, Padding, add the special tokens your model needs. -
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Venom
Venom is the most complete javascript library for Whatsapp
Venom is a high-performance system developed with JavaScript to create a bot for WhatsApp, support for creating any interaction, such as customer service, media sending, sentence recognition based on artificial intelligence and all types of design architecture for WhatsApp. It's a high-performance alternative API to whatzapp, you can send, text messages, files, images, videos and more. Remember, the API was developed on a platform called RESTful Web services, providing interoperability between computer systems on the Internet. ... -
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AutoTrain Advanced
Faster and easier training and deployments
...The project provides a no-code and low-code interface that allows users to train models using custom datasets without needing extensive expertise in machine learning engineering. It supports a wide range of tasks including text classification, sequence-to-sequence modeling, token classification, sentence embedding training, and large language model fine-tuning. The system integrates closely with the Hugging Face ecosystem and allows developers to train models using datasets hosted on the Hugging Face Hub. AutoTrain Advanced can run locally or in cloud environments, making it adaptable to different computational setups. By automating tasks such as model configuration, hyperparameter selection, and training pipelines, the project significantly reduces the technical barrier to building AI systems. -
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Infinity
Low-latency REST API for serving text-embeddings
Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. Infinity is developed under MIT License. Infinity powers inference behind Gradient.ai and other Embedding API providers. -
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Large Concept Model
Language modeling in a sentence representation space
Large Concept Model is a research codebase centered on concept-centric representation learning at scale, aiming to capture shared structure across many categories and modalities. It organizes training around concepts (rather than just raw labels), encouraging models to understand attributes, relations, and compositional structure that transfer across tasks. The repository provides training loops, data tooling, and evaluation routines to learn and probe these concept embeddings, typically... -
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RealtimeTTS
Converts text to speech in realtime
RealtimeTTS is a low-latency text-to-speech library built for real-time applications such as voice chat with LLMs, assistants, and interactive tools. It is designed around a streaming model: you can feed it text incrementally (for example, as an LLM responds) and get audio output almost immediately, which keeps end-to-end latency very low. The library is engine-agnostic and plugs into a wide range of cloud and local TTS systems, including OpenAI, ElevenLabs, Azure, Coqui, Piper, StyleTTS2,... -
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TextSight AI Detector API Client
AI detector and AI humanizer API client for Python and JavaScript
Free, open-source Python and JavaScript client for the TextSight AI detector and AI humanizer API. Detect if text was written by ChatGPT, Claude or Gemini with a sentence-by-sentence AI score, and rewrite AI-sounding text so it reads naturally, in 3 lines of code. A developer-friendly alternative to the GPTZero, Originality.ai and ZeroGPT APIs. Zero dependencies, TypeScript types, automatic retries and a command line tool. Requires a TextSight API key. -
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Stop the Slop
Browser extension to detect AI-generated YouTube scripts.
...KEY FEATURES - Instant On-Screen Thumbnail Badges: See AI probability ratings directly on video thumbnails before you click across your Home feed, Subscriptions, Search results, and sidebar recommendations. - Probability Gauge: Get an instant, visual probability score from 0% to 100% in the extension popup. - Sentence-Level Analysis: Identifies robotic or repetitive sentences with itemized confidence scores. HOW TO USE IT - Browse YouTube as normal — videos analyzed by the community will display an AI badge directly on their thumbnail. - Click the extension icon on any YouTube video to run a full in-depth transcript detection scan. - Review the AI probability score, verdict, and sentence breakdown. -
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nlg
Natural language generator.
Takes a grammar tree as a specification and generates an English language phrase or sentence with correct tense, person, etc. -
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Text Line Duplicate Remover
Remove duplicate lines from your text
...It also includes the source code too. I made this when I was working with long lists of entries and needed something to automatically clean them up. As a bonus you can also change the Sentence Case of the text, make it lowercase, UPPERCASE or Sentence case. -
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Vendor Neutral AI SDK for EDA Agents
RTL, Verification, Lint, CDC, FECAD, Synthesis, DFT, SoC Integration
...One planner across RTL → CDC → DFT → DV → Signoff A modern sign-off chains ten or more tools, each with its own flags, logs and failure modes — and the engineer is the integration layer. EDAUtils turns a sentence into a dependency-checked plan, runs the real tools, and hands back the evidence plus a replay script for every step. -
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asterisk-audiosocket
Dependency-free Asterisk AudioSocket library for Node.js
...It also handles the part that catches people out. Asterisk forwards every frame to the channel the moment it arrives and does not buffer for you, so writing a whole sentence at once means the far end keeps a few frames and discards the rest. The playback helper paces one frame per 20 ms against a deadline and re-clamps rather than bursting to catch up. MIT licensed, published on npm. -
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Tibetan Unicode Keyboard
Screen keyboards in MS Excel and in Java for typing Tibetan characters
A Tibetan character is formed by stacking the middle (core) character with lower character stack(s) and upper stack. The tools in this package has been tested to work well in Windows 10 and above. The Java version is in the project titled Tibetan Java Keyboard. The MS Excel version does not work on Excel operating in MacOS. The Excel version uses Excel macros and VBA codes. One needs to Enable Macro to allow it to work. The motivation for this project is due to old proprietary Tibetan... -
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TEXminer
Text Mining Classification for Texts in ASCII, Unicode and PDF Format.
TEXminer uses generic Text Mining Methods to analyze Unicode Files as plain Text or PDF. The Text Database can be saved in XML where the orginal Text, the Sentence and Word Lists and additional Parameters (e.g. Abbreviations) are stored. TEXminer allows Language Detection by Letter Frequency Analysis, finding important Words by Cooccurrence Analysis, Determination of Central Expressions, Thematic Text Classification (also Semantic Groups) Fingerprint Comparison and Word Frequency. Because TEXminer is not disigned to have a Reference Corpus, Thematic Model Statistics uses Language Models (lexicons) to have Background Knowledge about certain Languages (English, German, French, Spanish, Italian, Russian), which are derived from Decaleon Project. ... -
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asterisk-ai-voice-agent
Self-hosted AI voice agent for Asterisk over AudioSocket
...The hard parts in telephony are where the work went. Audio is written one 320 byte frame every 20 ms against a deadline that re-clamps when synthesis falls behind, because sending a whole sentence at once means the far end keeps a few frames and discards the rest. Barge-in drops queued audio, so the agent stops when the caller starts rather than talking over them. Piper synthesis runs locally, so call audio never has to leave your infrastructure. MIT licensed, with a Docker image and a PyPI package. -
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Then
Tame async code with battle-tested promises
...Because async code is hard to write, hard to read, hard to reason about. A pain to maintain. By using a then keyword that enables you to write aSync code that reads like an English sentence. Async code is now concise, flexible and maintainable. -
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CLIP-as-service
Embed images and sentences into fixed-length vectors
...Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing. Easy-to-use. No learning curve, minimalist design on client and server. Intuitive and consistent API for image and sentence embedding. Async client support. Easily switch between gRPC, HTTP, WebSocket protocols with TLS and compression. Smooth integration with neural search ecosystem including Jina and DocArray. Build cross-modal and multi-modal solutions in no time. -
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Text2vec
Text to Vector, Get Sentence Embeddings
...It supports words, sentences, and paragraphs, making it useful for semantic search, similarity matching, clustering, retrieval, and ranking workflows. The project implements models and methods such as Word2Vec, RankBM25, BERT, Sentence-BERT, and CoSENT. It also compares model behavior on semantic matching and similarity calculation tasks. Developers can use it as an applied NLP library for embedding generation or as a study resource for text representation methods. It is especially useful for Chinese and multilingual NLP projects that need practical sentence embeddings and similarity scoring. -
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NLG-Eval
Evaluation code for various unsupervised automated metrics
NLG-Eval is a toolkit for evaluating the quality of natural language generation (NLG) outputs using multiple automated metrics such as BLEU, METEOR, and ROUGE. -
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TextGen
textgen, Text Generation models
Implementation of Text Generation models. textgen implements a variety of text generation models, including UDA, GPT2, Seq2Seq, BART, T5, SongNet and other models, out of the box. UDA, non-core word replacement. EDA, simple data augmentation technique: similar words, synonym replacement, random word insertion, deletion, replacement. This project refers to Google's UDA (non-core word replacement) algorithm and EDA algorithm, based on TF-IDF to replace some unimportant words in sentences with... -
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CSAw - NLP for low-resource languages
CSAw is an NLP framework for low-resource languages
CSAw is an NLP framework for low-resource languages with a focus on machine translation. The primary goal is to build language models automatically from bilingual text (e.g., front and back translations) using a deep transfer rule-based approach. The core of this strategy is the Concept Specification and Abstraction semantic representation which is specially designed with machine translation in mind. See the preprint article here: https://arxiv.org/abs/1807.02226 The current...
