A nutritional label for food for thought.

Overview

Lexiscore

As a first effort in tackling the theme of information overload in content consumption, I've been working on the lexiscore: a nutritional label for food for thought designed to help you narrow in on resources which personally bring you the most value. The open source companion software can automatically label raw text originating from RSS feeds, bookmarked pages, PDFs, EPUBs, and more. In the scope of this project, I'm considering valuable resources to be those from which you learn a lot, those which are packed with ideas you find surprising.

Read more...

Installation

Note: This tool requires a running instance of the conceptarium as a proxy for your knowledge.

The lexiscore labeler can either be deployed from source or using Docker.

Docker

To deploy the lexiscore labeler using Docker, first make sure to have Docker installed, then simply run the following.

docker run -p 8501:8501 paulbricman/lexiscore 

The tool should be available at localhost:8501.

From Source

To set up the lexiscore labeler, clone the repository and run the following:

python3 -m pip install -r requirements.txt
streamlit run main.py

The tool should be available at localhost:8501.

Screenshots

You might also like...
MPLP: Metapath-Based Label Propagation for Heterogenous Graphs

MPLP: Metapath-Based Label Propagation for Heterogenous Graphs Results on MAG240M Here, we demonstrate the following performance on the MAG240M datase

Official Pytorch Implementation of:
Official Pytorch Implementation of: "Semantic Diversity Learning for Zero-Shot Multi-label Classification"(2021) paper

Semantic Diversity Learning for Zero-Shot Multi-label Classification Paper Official PyTorch Implementation Avi Ben-Cohen, Nadav Zamir, Emanuel Ben Bar

 Shared Attention for Multi-label Zero-shot Learning
Shared Attention for Multi-label Zero-shot Learning

Shared Attention for Multi-label Zero-shot Learning Overview This repository contains the implementation of Shared Attention for Multi-label Zero-shot

Official PyTorch Implementation of Embedding Transfer with Label Relaxation for Improved Metric Learning, CVPR 2021
Official PyTorch Implementation of Embedding Transfer with Label Relaxation for Improved Metric Learning, CVPR 2021

Embedding Transfer with Label Relaxation for Improved Metric Learning Official PyTorch implementation of CVPR 2021 paper Embedding Transfer with Label

Code for Two-stage Identifier:
Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition"

Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition", accepted at ACL 2021. For details of the model and experiments, please see our paper.

General Multi-label Image Classification with Transformers

General Multi-label Image Classification with Transformers Jack Lanchantin, Tianlu Wang, Vicente Ordóñez Román, Yanjun Qi Conference on Computer Visio

Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021.

UniRE Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021. Requirements python: 3.7.6 pytorch: 1.8.1 transformers:

Implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
Implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

SemCo The official pytorch implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

Official implementation of paper
Official implementation of paper "Query2Label: A Simple Transformer Way to Multi-Label Classification".

Introdunction This is the official implementation of the paper "Query2Label: A Simple Transformer Way to Multi-Label Classification". Abstract This pa

Comments
  • RSS OPML only pulls last article

    RSS OPML only pulls last article

    Summary

    Adding RSS from OPML only pulls the last article.

    Details

    After setting up lexiscore, I take my blog's RSS (https://ivans.io/rss/) and convert it to OPML using https://opml-gen.ovh/. This yields the following file:

    <opml version="2.0">
    	<body>
    		<outline text="Subscriptions" title="Subscriptions">
    			<outline xmlUrl='https://ivans.io/rss/' />
    	
    		</outline>
    	</body>
    </opml>
    

    After adding this to lexiscore, only the most recent article appears in the reading list. I've checked the RSS feed, and all articles are fully present.

    Desired Behavior

    RSS feeds should pull all articles.

    opened by issmirnov 4
  • NLTK downloader problem when deploying with docker.

    NLTK downloader problem when deploying with docker.

    I have deployed the docker image to my personal server. After importing the RSS from my blog (https://ivans.io/rss/) as an OPML file, I click on "start labelling". This causes a stack trace:

    LookupError: ********************************************************************** 
    Resource punkt not found. Please use the NLTK Downloader to obtain the resource: 
    [31m>>> import nltk >>> nltk.download('punkt') 
    [0m For more information see: https://www.nltk.org/data.html Attempted to load tokenizers/punkt/PY3/english.pickle
    [0m Searched in: - '/root/nltk_data' - '/usr/local/nltk_data' - '/usr/local/share/nltk_data' - '/usr/local/lib/nltk_data' - '/usr/share/nltk_data' - '/usr/local/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/local/lib/nltk_data' - '' **********************************************************************
    Traceback:
    File "/usr/local/lib/python3.8/site-packages/streamlit/script_runner.py", line 354, in _run_script
        exec(code, module.__dict__)
    File "/app/main.py", line 30, in <module>
        cart_section(col2)
    File "/app/components.py", line 110, in cart_section
        content_paragraphs = get_paragraphs(row['text'])
    File "/app/processing.py", line 19, in get_paragraphs
        sents = sent_tokenize(line)
    File "/usr/local/lib/python3.8/site-packages/nltk/tokenize/__init__.py", line 107, in sent_tokenize
        tokenizer = load("tokenizers/punkt/{0}.pickle".format(language))
    File "/usr/local/lib/python3.8/site-packages/nltk/data.py", line 750, in load
        opened_resource = _open(resource_url)
    File "/usr/local/lib/python3.8/site-packages/nltk/data.py", line 875, in _open
        return find(path_, path + [""]).open()
    File "/usr/local/lib/python3.8/site-packages/nltk/data.py", line 583, in find
        raise LookupError(resource_not_found)
    
    opened by issmirnov 3
  • Add aggregator page as input source

    Add aggregator page as input source

    Imagine adding this as input sources of type "Aggregator": https://metaphor.so/search?q=The%20coolest%20essay%20on%20human-machine%20collaboration%2C%20cognitive%20augmentation%2C%20and%20tools%20for%20thought%20is

    The labeling software would sift through and add a nutritional value filter on top of a cool "search" approach

    enhancement 
    opened by paulbricman 0
  • Save concptarium URL with local cookie

    Save concptarium URL with local cookie

    Currently, I have this deployed via docker on a personal server. On every page refresh, I am asked for the url of the conceptarium. It would be useful to have this URL saved in local cookie storage.

    enhancement 
    opened by issmirnov 1
Releases(v1.0.0)
Owner
Paul Bricman
Building tools which augment the mind.
Paul Bricman
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective

Unofficial pytorch implementation of the paper "Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective"

16 Nov 21, 2022
Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving

Visual 3D Detection Package: This repo aims to provide flexible and reproducible visual 3D detection on KITTI dataset. We expect scripts starting from

Yuxuan Liu 305 Dec 19, 2022
Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch

Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch

Kim Seonghyeon 2.2k Jan 01, 2023
Sequential GCN for Active Learning

Sequential GCN for Active Learning Please cite if using the code: Link to paper. Requirements: python 3.6+ torch 1.0+ pip libraries: tqdm, sklearn, sc

45 Dec 26, 2022
⚓ Eurybia monitor model drift over time and securize model deployment with data validation

View Demo · Documentation · Medium article 🔍 Overview Eurybia is a Python library which aims to help in : Detecting data drift and model drift Valida

MAIF 172 Dec 27, 2022
Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

GANInversion_with_ConsecutiveImgs Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images" https://a

QingyangXu 38 Dec 07, 2022
Official code for the paper "Self-Supervised Prototypical Transfer Learning for Few-Shot Classification"

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification This repository contains the reference source code and pre-trained models (

EPFL INDY 44 Nov 04, 2022
Safe Policy Optimization with Local Features

Safe Policy Optimization with Local Feature (SPO-LF) This is the source-code for implementing the algorithms in the paper "Safe Policy Optimization wi

Akifumi Wachi 6 Jun 05, 2022
Code for the ICML 2021 paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"

ViLT Code for the paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision" Install pip install -r requirements.txt pip

Wonjae Kim 922 Jan 01, 2023
Search and filter videos based on objects that appear in them using convolutional neural networks

Thingscoop: Utility for searching and filtering videos based on their content Description Thingscoop is a command-line utility for analyzing videos se

Anastasis Germanidis 354 Dec 04, 2022
ICCV2021 - A New Journey from SDRTV to HDRTV.

ICCV2021 - A New Journey from SDRTV to HDRTV.

XyChen 82 Dec 27, 2022
Categorical Depth Distribution Network for Monocular 3D Object Detection

CaDDN CaDDN is a monocular-based 3D object detection method. This repository is based off of [OpenPCDet]. Categorical Depth Distribution Network for M

Toronto Robotics and AI Laboratory 289 Jan 05, 2023
Inteligência artificial criada para realizar interação social com idosos.

IA SONIA 4.0 A SONIA foi inspirada no assistente mais famoso do mundo e muito bem conhecido JARVIS. Todo mundo algum dia ja sonhou em ter o seu própri

Vinícius Azevedo 2 Oct 21, 2021
In this project, we create and implement a deep learning library from scratch.

ARA In this project, we create and implement a deep learning library from scratch. Table of Contents Deep Leaning Library Table of Contents About The

22 Aug 23, 2022
Deep Learning for Morphological Profiling

Deep Learning for Morphological Profiling An end-to-end implementation of a ML System for morphological profiling using self-supervised learning to di

Danielh Carranza 0 Jan 20, 2022
PyContinual (An Easy and Extendible Framework for Continual Learning)

PyContinual (An Easy and Extendible Framework for Continual Learning) Easy to Use You can sumply change the baseline, backbone and task, and then read

176 Jan 05, 2023
Keras Image Embeddings using Contrastive Loss

Keras-Image-Embeddings-using-Contrastive-Loss Image to Embedding projection in vector space. Implementation in keras and tensorflow for custom data. B

Shravan Anand K 5 Mar 21, 2022
NEG loss implemented in pytorch

Pytorch Negative Sampling Loss Negative Sampling Loss implemented in PyTorch. Usage neg_loss = NEG_loss(num_classes, embedding_size) optimizer =

Daniil Gavrilov 123 Sep 13, 2022
Image Data Augmentation in Keras

Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset.

Grace Ugochi Nneji 3 Feb 15, 2022
Powerful and efficient Computer Vision Annotation Tool (CVAT)

Computer Vision Annotation Tool (CVAT) CVAT is free, online, interactive video and image annotation tool for computer vision. It is being used by our

OpenVINO Toolkit 8.6k Jan 01, 2023