Showing 47 open source projects for "corpora"

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  • 1
    Paul Graham GPT

    Paul Graham GPT

    RAG on Paul Graham's essays

    Paul Graham GPT is a specialized AI-powered search and chat app built on a corpus of essays from Paul Graham, giving users the ability to query and discuss his writings in a conversational way. The repo stores the full text of his essays (chunked), uses embeddings (e.g. via OpenAI embeddings) to allow semantic search over that corpus, and hosts a chat interface that combines retrieval results with LLM-based answering — enabling RAG (retrieval-augmented generation) over a fixed dataset. The...
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  • 2
    Open Speech Corpora

    Open Speech Corpora

    A list of accessible speech corpora for ASR, TTS

    Open Speech Corpora is a curated catalog of speech datasets intended to support research and development in automatic speech recognition, text-to-speech, and other speech technologies. The repository is organized as a set of tables that list corpora along with their languages, total hours, number of speakers, download links, and licenses, giving practitioners a quick way to find data that matches their needs.
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  • 3
    GiantMIDI-Piano

    GiantMIDI-Piano

    Classical piano MIDI dataset

    ...Because the dataset is machine-generated via an automated transcription pipeline, it offers consistency, scale, and accessibility that would be difficult to achieve manually — enabling researchers to work with large corpora of piano music without copyright restrictions on symbolic data.
    Downloads: 2 This Week
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  • 4
    DrQA

    DrQA

    Reading Wikipedia to Answer Open-Domain Questions

    DrQA is an open-domain question answering system that reads large text corpora—famously Wikipedia—to answer natural language questions with extractive spans. It follows a two-stage pipeline: a fast document retriever first narrows down candidate articles, and a neural machine reader then predicts the exact answer span from those passages. The retriever relies on classic IR features (like TF-IDF and n-gram statistics) to remain lightweight and scalable to millions of documents. ...
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  • 5
    XLM (Cross-lingual Language Model)

    XLM (Cross-lingual Language Model)

    PyTorch original implementation of Cross-lingual Language Model

    ...The repository provides preprocessing pipelines, training code, and fine-tuning scripts so you can reproduce benchmark results or adapt models to your own multilingual corpora. Pretrained checkpoints cover dozens of languages and multiple model sizes, balancing quality and compute needs.
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  • 6
    PyTorch SimCLR

    PyTorch SimCLR

    PyTorch implementation of SimCLR: A Simple Framework

    ...Nowadays, pre-trained Deep Convolution Neural Networks (DCNNs) are the first go-to pre-solutions to learn a new task. These large models are trained on huge supervised corpora, like the ImageNet. And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. In situations like this, we take the models’ pre-trained weights, append a new classifier layer on top of it, and retrain the network. This is called transfer learning, and is one of the most used techniques in CV. ...
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  • 7
    CC-Net

    CC-Net

    Tools to download and cleanup Common Crawl data

    cc_net provides tools to download, segment, clean, and filter Common Crawl to build large-scale text corpora, including monolingual datasets and the multilingual CC-100 collection introduced in the associated paper. It includes pipelines to fetch snapshots, extract text, de-duplicate, identify language, and apply quality filtering based on heuristics and language models. The outputs are intended for pretraining language models and for creating standardized corpora that can be reproduced or updated with new crawls. ...
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  • 8
    NLP Best Practices

    NLP Best Practices

    Natural Language Processing Best Practices & Examples

    ...Data scientists started moving from traditional methods to state-of-the-art (SOTA) deep neural network (DNN) algorithms which use language models pretrained on large text corpora. This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in NLP algorithms, neural architectures, and distributed machine learning systems.
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  • 9
    YouTokenToMe

    YouTokenToMe

    Unsupervised text tokenizer focused on computational efficiency

    YouTokenToMe is a fast and efficient unsupervised text tokenization library designed for training subword embeddings, particularly useful for NLP models.
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  • 10
    cocoNLP

    cocoNLP

    A Chinese information extraction tool

    cocoNLP is a lightweight natural-language processing toolkit geared toward practical information extraction from raw text, especially for Chinese and mixed Chinese–English content. Instead of requiring a heavy pipeline, it focuses on quick wins such as extracting names, places, organizations, emails, phone numbers, and dates directly from unstructured sentences. The project blends pattern-based methods with NLP heuristics, giving developers dependable results for real-world texts like chats,...
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  • 11

    OLiA

    OWL/DL ontologies for linguistic annotations

    ...The OLiA Reference Model itself is linked to community-maintained repositories such as GOLD (http://linguistics-ontology.org/) and ISOcat (http://www.isocat.org) The OLiA ontologies were originally developed as part of an infrastructure for the sustainable maintenance of linguistic resources (http://www.sfb441.uni-tuebingen.de/c2/index-engl.html), their fields of application include the formalization of annotation schemes, concept-based querying over heterogeneously annotated corpora, and the development of interoperable NLP pipelines.
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  • 12

    Arabic Corpus

    Text categorization, arabic language processing, language modeling

    ...More useful references to check: ------------------------------------------- https://sites.google.com/site/mouradabbas9/corpora
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    Downloads: 4 This Week
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  • 13
    Tacotron-2

    Tacotron-2

    DeepMind's Tacotron-2 Tensorflow implementation

    ...The repository is structured as a full training pipeline: dataset preparation, preprocessing into spectrograms, Tacotron training, WaveNet (or Griffin-Lim) vocoder training, and final waveform synthesis. It includes directory layouts and logging directories for multiple datasets such as LJSpeech and M-AILABS en_US/en_UK, making it easier to adapt to new English corpora. Separate log trees track mel-spectrograms, attention plots, evaluation audio, and vocoder outputs, so you can inspect how alignment and audio quality evolve over time.
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  • 14
    Scattertext 0.2.1

    Scattertext 0.2.1

    Beautiful visualizations of how language differs among document types

    A tool for finding distinguishing terms in corpora and displaying them in an interactive HTML scatter plot. Points corresponding to terms are selectively labeled so that they don't overlap with other labels or points.
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  • 15

    BioC

    We describe a simple XML format to share text documents and annotation

    ...Allows a large number of different annotations to be represented. Project files contain: - simple code to hold/read/write data and perform sample processing. - BioC-formatted corpora - BioC tools that work with BioC corpora BioC goals - simplicity - interoperability - broad use - reuse There should be little investment required to learn to use a format or a software module to process that format. We are interested in reuse, and we focus on common NLP tasks that are broadly useful for textmining.
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    Downloads: 7 This Week
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  • 16

    mwetoolkit

    THIS PROJECT MIGRATED TO https://gitlab.com/mwetoolkit/mwetoolkit3/

    THIS PROJECT MIGRATED TO https://gitlab.com/mwetoolkit/mwetoolkit3/ The Multiword Expressions toolkit aids in the automatic identification and extraction of multiword units in running text. These include idioms (kick the bucket), noun compounds (cable car), phrasal verbs (take off, give up), etc. Even though it focuses on multiword expresisons, the framework is quite complete and can also be useful in any corpus-based study in computational linguistics. The mwetoolkit can be...
    Downloads: 1 This Week
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  • 17
    DeSR is a multilingual statistical dependency parser. It produces dependency parse trees for natural language sentences using a parsing model learned from annotated corpora.
    Downloads: 0 This Week
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  • 18
    TextBlob

    TextBlob

    TextBlob is a Python library for processing textual data

    ...Also, it comes with a WordNet integration. If you only intend to use TextBlob’s default models (no model overrides), you can pass the lite argument. This downloads only those corpora needed for basic functionality. TextBlob is also available as a conda package.
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  • 19
    Knowtator is a general-purpose text annotation tool that is integrated with the Protégé knowledge representation system. Knowtator facilitates the manual creation of training and evaluation corpora for a variety of biomedical language processing tasks.
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  • 20

    Hermes Natural Language Processing

    A repository of software, documentation and data for NLP

    Hermes is a repository of software, documentation and data for NLP. I am currently adding corpora extracted from Wikipedia (mostrly in Romance languages).
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  • 21
    The Infomap NLP software performs automatic indexing of words and documents from free-text corpora, using a variant of LSA to enable information retrieval and other applications. It was developed by the Infomap Project at Stanford University's CSLI.
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  • 22
    GigaChat 3 Ultra

    GigaChat 3 Ultra

    High-performance MoE model with MLA, MTP, and multilingual reasoning

    GigaChat 3 Ultra is a flagship instruct-model built on a custom Mixture-of-Experts architecture with 702B total and 36B active parameters. It leverages Multi-head Latent Attention to compress the KV cache into latent vectors, dramatically reducing memory demand and improving inference speed at scale. The model also employs Multi-Token Prediction, enabling multi-step token generation in a single pass for up to 40% faster output through speculative and parallel decoding techniques. Its...
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