Search Results for "language processing" - Page 22
Sort By:
Showing 554 open source projects for "language processing"
View related business solutions-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
1
Cathnet is developing the infrastructure for the Catholic Semantic Web. Technologies involved include, but are not limited to, XML, RDF, NLP, Zope, Plone and Plone products.
-
2
Collection of Python scripts providing interactions between the sociological investigation platform and webservices (such as NLP, search engine, web database).
-
3
AutoSummary uses Natural Language Processing to generate a contextually-relevant synopsis of plain text. It uses statistical and rule-based methods for part-of-speech tagging, word sense disambiguation, sentence deconstruction and semantic analysis.
-
4
TaxonGrab is an NLP (Natural Language Processing) solution written in PHP, which leverages systematic nomenclature rules that are used for taxonomic nomenclature in scientific publications.
-
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
-
5
The Language Processing Agency provides agents and an agent-oriented infrastructure for developing language processors in .NET. Typical language processors are transformation tools, extraction tools, language interpreters, etc.
-
6
JFex is a feature extraction application for Natural Language Processing systems based on machine learning techniques.
-
7
CoPT, Corpus Processing Tools, is a set of java classes intended to assist field linguists, NLP researchers and developers, students and software developers in all corpus-related processing.
-
8
The nlpFarm is a Natural Language Processing (NLP) resource where early research prototypes (Java) can evolve into robust and useful open source. Our farmstead collaborates under the OpenNLP initiative, in order to make NLP software publically available.
-
9
uNLPBot is a chatter bot based on Natural Language Processing theory, able to parse small but representative subsets of english natural language and to produce english sentences compliant to english grammar and related to conversation threads.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
10
A library of Natural Language Processing (NLP) functions for use in REALbasic (RB) programs, all implemented in pure RB code.
-
11
The Aikernel is an intelligence server and cell runtime environment that uses natural language processing and other pattern matching with Activators, Contexts, Concepts to allow multi tasking between installed cells.
-
12
Grok is a library of natural language processing components, including support for parsing with categorial grammars and various preprocessing tasks such as part-of-speech tagging, sentence detection, and tokenization.
-
13
-
14
Nemotron 3 Nano
LL model providing reasoning and conversational capabilities
...This architecture allows the system to maintain strong reasoning capabilities while improving throughput and reducing the computational cost associated with large context processing. The model is designed as a general-purpose language system capable of handling tasks such as chat interaction, coding assistance, document analysis, and instruction following. -
15
Mistral Small 4
Model that fuses instruct, reasoning and agentic skills
The Mistral Small 4 collection is a set of open-weight large language models developed by Mistral AI that aim to unify multiple capabilities, including instruction following, reasoning, and coding, within a single efficient architecture. These models are part of the broader Mistral Small family, which is designed to deliver strong performance across a wide range of everyday AI tasks while maintaining relatively low latency and efficient deployment requirements. The collection reflects an... -
16
Leni is an experimental human language recognition and processing tool which is based on learning and therefore nearly independent on any particular spoken language.
-
17
A WebCrawler for Natural Language Processing. This WebCrawler searches for monolingual (in a specified language) and bilingual, parallel text.
-
18
BibleNLP
Natural Language Processing using The Holy Bible
This project attempts to develop natural language processing routines as applied to a Bible text domain. Many common technologies (e.g., tokenization, Brill POS tagger) are used in conjunction with theoretical paradigms (e.g., hierarchical word definition trees, phrasal concordance). -
19
Metafrastes is a natural language processing system that can take an input query from the user and translate that internally in order to communicate with a knowledge base in the form of an ontology.
-
20
NXTAI's main objective is to create a new generation of natural language processing program by using a unique neural networking model. To demonstrate the algorithm works, A chatbot will be developed as a classical example of how it can be used.
-
21
EORS
rational agent
This project aims at creating rationally thinking agents. The agent gather information through command line or network and stores it in its memory. It uses Stanford's NLP library to understand the language statements. -
22
The OBO-Annotator is a semantic NLP tool that is designed to give its end-users a great deal of flexibility to combine any number of OBO ontologies from the OBO foundry regardless of their format and use them to annotate text-bases.
-
23
Bio_ClinicalBERT
ClinicalBERT model trained on MIMIC notes for clinical NLP tasks
Bio_ClinicalBERT is a domain-specific language model tailored for clinical natural language processing (NLP), extending BioBERT with additional training on clinical notes. It was initialized from BioBERT-Base v1.0 and further pre-trained on all clinical notes from the MIMIC-III database (~880M words), which includes ICU patient records. The training focused on improving performance in tasks like named entity recognition and natural language inference within the healthcare domain. ... -
24
MiMo-V2.5
Omnimodal AI model for agents, coding, and long-context tasks
MiMo-V2.5 is a native omnimodal large language model developed by Xiaomi, designed for advanced agentic workflows, multimodal reasoning, and long-context processing. Built on a Mixture-of-Experts architecture with approximately 309B total parameters and around 15B activated per inference, it balances high capability with efficient execution. The model natively processes text, images, video, and audio within a unified system, enabling cross-modal understanding and complex task execution in a single pipeline. ... -
25
DeepSeek-V4-Flash
Efficient MoE model for million-token reasoning and coding
DeepSeek-V4-Flash is a preview Mixture-of-Experts language model built for efficient million-token context intelligence. It has 284B total parameters with 13B activated and supports a 1M-token context window, making it suitable for long-document reasoning, complex coding, agentic workflows, and large-scale information processing. The model uses a hybrid attention architecture that combines Compressed Sparse Attention and Heavily Compressed Attention to improve long-context efficiency, while Manifold-Constrained Hyper-Connections strengthen signal stability across layers. ...
