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  • 1
    DataDreamer

    DataDreamer

    DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models

    DataDreamer is a tool designed to assist in the generation and manipulation of synthetic data for various applications, including testing and machine learning.
    Downloads: 0 This Week
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  • 2
    LightAutoML

    LightAutoML

    Fast and customizable framework for automatic ML model creation

    LightAutoML is an automated machine learning (AutoML) framework optimized for efficient model training and hyperparameter tuning, focusing on both tabular and text data.
    Downloads: 0 This Week
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  • 3
    Weaviate

    Weaviate

    Weaviate is a cloud-native, modular, real-time vector search engine

    ...With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance of a cloud-native database, all accessible through GraphQL, REST, and various language clients.
    Downloads: 8 This Week
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  • 4
    DataProfiler

    DataProfiler

    Extract schema, statistics and entities from datasets

    DataProfiler is an AI-powered tool for automatic data analysis and profiling, designed to detect patterns, anomalies, and schema inconsistencies in structured and unstructured datasets. The DataProfiler is a Python library designed to make data analysis, monitoring, and sensitive data detection easy. Loading Data with a single command, the library automatically formats & loads files into a DataFrame. Profiling the Data, the library identifies the schema, statistics, entities (PII / NPI), and...
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    Train ML Models With SQL You Already Know

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  • 5
    Diffgram

    Diffgram

    Training data (data labeling, annotation, workflow) for all data types

    From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. Training Data is the art of supervising machines through data. This...
    Downloads: 0 This Week
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  • 6
    find-similar

    find-similar

    User-friendly library to find similar objects

    The mission of the FindSimilar project is to provide a powerful and versatile open source library that empowers developers to efficiently find similar objects and perform comparisons across a variety of data types. Whether dealing with texts, images, audio, or more, our project aims to simplify the process of identifying similarities and enhancing decision-making. https://github.com/findsimilar/find-similar - GitHub repo http://demo.findsimilar.org/ - Demo project and tutorial https://docs.findsimilar.org/ - Documentation
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  • 7
    OpenDelta

    OpenDelta

    A plug-and-play library for parameter-efficient-tuning

    ...OpenDelta is a toolkit for parameter-efficient tuning methods (we dub it as delta tuning), by which users could flexibly assign (or add) a small amount parameters to update while keeping the most parameters frozen. By using OpenDelta, users could easily implement prefix-tuning, adapters, Lora, or any other types of delta tuning with preferred PTMs.
    Downloads: 0 This Week
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  • 8

    MITRE Annotation Toolkit

    A toolkit for managing and manipulating text annotations

    The MITRE Annotation Toolkit (MAT) is a suite of tools which can be used for automated and human tagging of annotations. Annotation is a process, used mostly by researchers in natural language processing, of enhancing documents with information about the various phrase types the documents contain. MAT supports both UI interaction and command-line interaction, and provides various levels of control over the overall annotation process. It can be customized for specific tasks (e.g., named entity identification, de-identification of medical records). The goal of MAT is not to help you configure your training engine (in the default case, the Carafe CRF system) to achieve the best possible performance on your data. ...
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  • 9
    The Bracket Based Arabic Annotation (B2A2) scheme provides users with the ability to manually tag Arabic text with Part-of-Speech (POS) markers. B2A2 introduces a new approach that enables tagging Arabic text using morphology aware tag markers. Different types of tag markers can be incorporated e.g. grammatical, functional, semantic, linguistic markers.Tag-sets can be configured (modified/extended) by accessing the related table in the supporting database, The user can upload text files where sentences are normalized and inserted into the supporting database. Multiple narratives can be listed in the text file, where narratives are separated using a # symbol. ...
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    Go from Code to Production URL in Seconds

    Cloud Run deploys apps in any language instantly. Scales to zero. Pay only when code runs.

    Skip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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  • 10

    Medical Treebank

    Community-based linguistic annotation work on clinical documents.

    This project hosts linguistic annotations and guidelines for clinical text. We plan to include several types of annotation (Token, POS and Parse) in WordFreak format on clinical notes originally from the i2b2/VA NLP challenges. The guidelines are copyrighted, but free for the community to use. Annotation in WordFreak format contains only linguistic labels and character offsets, and can be distributed independently from the note text. Instruction is provided on setting up WordFreak for aligning/visualizing the annotations with the source text, which should be obtained through the official i2b2 data host https://www.i2b2.org/NLP/DataSets/Main.php.
    Downloads: 0 This Week
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