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universal-data-tool | easy web interface or desktop app | Data Labeling library

 by   UniversalDataTool JavaScript Version: v0.14.26 License: MIT

 by   UniversalDataTool JavaScript Version: v0.14.26 License: MIT

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kandi X-RAY | universal-data-tool Summary

universal-data-tool is a JavaScript library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Data Labeling, Deep Learning applications. universal-data-tool has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.
Try it out at udt.dev, download the desktop app or run on-premise. The Universal Data Tool is a web/desktop app for editing and annotating images, text, audio, documents and to view and edit any data defined in the extensible .udt.json and .udt.csv standard.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • universal-data-tool has a medium active ecosystem.
  • It has 1429 star(s) with 151 fork(s). There are 31 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 148 open issues and 101 have been closed. On average issues are closed in 58 days. There are 9 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of universal-data-tool is v0.14.26
universal-data-tool Support
Best in #Data Labeling
Average in #Data Labeling
universal-data-tool Support
Best in #Data Labeling
Average in #Data Labeling

quality kandi Quality

  • universal-data-tool has 0 bugs and 0 code smells.
universal-data-tool Quality
Best in #Data Labeling
Average in #Data Labeling
universal-data-tool Quality
Best in #Data Labeling
Average in #Data Labeling

securitySecurity

  • universal-data-tool has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • universal-data-tool code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
universal-data-tool Security
Best in #Data Labeling
Average in #Data Labeling
universal-data-tool Security
Best in #Data Labeling
Average in #Data Labeling

license License

  • universal-data-tool is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
universal-data-tool License
Best in #Data Labeling
Average in #Data Labeling
universal-data-tool License
Best in #Data Labeling
Average in #Data Labeling

buildReuse

  • universal-data-tool releases are available to install and integrate.
universal-data-tool Reuse
Best in #Data Labeling
Average in #Data Labeling
universal-data-tool Reuse
Best in #Data Labeling
Average in #Data Labeling
Top functions reviewed by kandi - BETA

kandi has reviewed universal-data-tool and discovered the below as its top functions. This is intended to give you an instant insight into universal-data-tool implemented functionality, and help decide if they suit your requirements.

  • Create a new browser window
  • Load plugins .
  • Loads the S3 bucket
  • Load a sample .
  • Asynchronously write a file in the directory
  • Download a file to a remote directory
  • Read data from filesystem
  • Get latest README
  • Prompts a directory to another directory .
  • validate url

universal-data-tool Key Features

Collaborate with others in real time, no sign up!

Usable on web or as Windows,Mac or Linux desktop application

Configure your project with an easy-to-use GUI

Easily create courses to train your labelers

Download/upload as easy-to-use CSV (sample.udt.csv) or JSON (sample.udt.json)

Support for Images, Videos, PDFs, Text, Audio Transcription and many other formats

Can be easily integrated into a React application

Annotate images or videos with classifications, tags, bounding boxes, polygons and points

Fast Automatic Smart Pixel Segmentation using WebWorkers and WebAssembly

Import data from Google Drive, Youtube, CSV, Clipboard and more

Annotate NLP datasets with Named Entity Recognition (NER), classification and Part of Speech (PoS) tagging.

Easily load into pandas or use with fast.ai

Runs with docker docker run -p 3000:3000 universaldatatool/universaldatatool

Runs with singularity singularity run universaldatatool/universaldatatool

Community Discussions

Trending Discussions on Data Labeling
  • How can I do this split process in Python?
  • Replacing a character with a space and dividing the string into two words in R
  • Azure ML FileDataset registers, but cannot be accessed for Data Labeling project
Trending Discussions on Data Labeling

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

import pandas as pd
from enum import Enum


df = pd.DataFrame({'first': ['product and other', 'product2 and other', 'price'], 'second':['product and prices', 'price2', 'product3 and price']})
df

class Tipos(Enum):
    B = 1
    I = 2
    L = 3

for index, row in df.iterrows():
    sentencas = row.values
    for sentenca in sentencas:
        for pos, palavra in enumerate(sentenca.split()):
            print(f"{palavra} {Tipos(pos+1).name}")

Results:

                first              second
0   product and other  product and prices
1  product2 and other              price2
2               price  product3 and price

product B
and I
other L
product B
and I
prices L
product2 B
and I
other L
price2 B
price B
product3 B
and I
price L

Desired Results:

        Word Ent
0    product B_first
1        and I_first
2      other L_first
3    product B_second
4        and I_second
5     prices L_second
6   product2 B_first
7        and I_first
8      other L_first
9     price2 B_second
10     price B_first
11  product3 B_second
12       and I_second
13     price L_second

# In that case, the sequence is like that: (B_first, I_first, L_first, L_first...) and if changes the column gets B_second, I_second, L_second...

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

out = df.unstack().str.split().explode().sort_index(level=1).to_frame('Word')
out['Ent'] = out.groupby(level=[0, 1]).cumcount().map(Tipos) \
                 + '_' + out.index.get_level_values(0)
out = out.reset_index(drop=True)

Output:

>>> out
        Word       Ent
0    product   B_first
1        and   I_first
2      other   L_first
3    product  B_second
4        and  I_second
5     prices  L_second
6   product2   B_first
7        and   I_first
8      other   L_first
9     price2  B_second
10     price   B_first
11  product3  B_second
12       and  I_second
13     price  L_second

Source https://stackoverflow.com/questions/70532286

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install universal-data-tool

You can download it from GitHub.

Support

Image Segmentation • Image Classification • Text Classification • Named Entity Recognition • Named Entity Relations / Part of Speech Tagging • Audio Transcription • Data Entry • Video Segmentation • Landmark / Pose Annotation.

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