The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 missing columns ({'meta'})
This happened while the json dataset builder was generating data using
hf://datasets/recursal/Devopedia/data/dev_index.json (at revision b7c3b8cc33cd387698951d3e4b14f4356566bb68)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
text: string
to
{'text': Value(dtype='string', id=None), 'meta': {'title': Value(dtype='string', id=None), 'href': Value(dtype='string', id=None)}}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1323, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 938, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 missing columns ({'meta'})
This happened while the json dataset builder was generating data using
hf://datasets/recursal/Devopedia/data/dev_index.json (at revision b7c3b8cc33cd387698951d3e4b14f4356566bb68)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
text string | meta dict |
|---|---|
# Hypothesis Testing and Types of Errors
## Summary
Suppose we want to study income of a population. We study a sample from the population and draw conclusions. The sample should represent the population for our study to be a reliable one.
**Null hypothesis** \((H\_0)\) is that sample represents population. Hypothe... | {
"title": "Hypothesis Testing and Types of Errors",
"href": "hypothesis-testing-and-types-of-errors"
} |
# Polygonal Modelling
## Summary
Polygonal modelling is a 3D modelling approach that utilizes edges, vertices and faces to form models. Modellers start with simple shapes and add details to build on them. They alter the shapes by adjusting the coordinates of one or more vertices. A polygonal model is called faceted ... | {
"title": "Polygonal Modelling",
"href": "polygonal-modelling"
} |
# Relation Extraction
## Summary
Consider the phrase "President Clinton was in Washington today". This describes a *Located* relation between Clinton and Washington. Another example is "Steve Balmer, CEO of Microsoft, said…", which describes a *Role* relation of Steve Balmer within Microsoft.
The task of extractin... | {
"title": "Relation Extraction",
"href": "relation-extraction"
} |
# React Native
## Summary
Traditionally, *native mobile apps* have been developed in specific languages that call platform-specific APIs. For example, Objective-C and Swift for iOS app development; Java and Kotlin for Android app development. This means that developers who wish to release their app on multiple platf... | {
"title": "React Native",
"href": "react-native"
} |
# Web of Things
## Summary
Web of Things (WoT) is a set of building blocks that seeks to make the Internet of Things (IoT) more interoperable and usable. It simplifies application development (including cross-domain applications) by adopting the web paradigm. Web developers will have a low barrier to entry when prog... | {
"title": "Web of Things",
"href": "web-of-things"
} |
# TensorFlow
## Summary
TensorFlow is an open source software library for numerical computation using **data flow graphs**. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This dataflow paradigm enables paralleli... | {
"title": "TensorFlow",
"href": "tensorflow"
} |
# Wi-Fi Calling
## Summary
Wi-Fi Calling is a technology that allows users to make or receive voice calls via a local Wi-Fi hotspot rather than via their mobile network operator's cellular radio connection. Voice calls are thus carried over the Internet, implying that Wi-Fi Calling relies on VoIP. However, unlike ot... | {
"title": "Wi-Fi Calling",
"href": "wi-fi-calling"
} |
# Design Thinking
## Summary
Design thinking is a problem-solving method used to create practical and creative solutions while addressing the needs of users. The process is extremely user centric as it focuses on understanding the needs of users and ensuring that the solutions created solve users' needs.
It's an i... | {
"title": "Design Thinking",
"href": "design-thinking"
} |
# Single Page Application
## Summary
A web application broadly consists of two things: data (content) and control (structure, styling, behaviour). In traditional applications, these are spread across multiple pages of HTML, CSS and JS files. Each page is served in HTML with links to suitable CSS/JS files. A Single P... | {
"title": "Single Page Application",
"href": "single-page-application"
} |
# Document Object Model
## Summary
Document Object Model (DOM) is the object-oriented representation of an HTML or XML document. It defines a platform-neutral programming interface for accessing various components of a webpage, so that JavaScript programs can change document structure, style, and content programmati... | {
"title": "Document Object Model",
"href": "document-object-model"
} |
# Open Data
## Summary
The idea of open data is to share data freely with others. Openness also allows others to modify, reuse or redistribute the data. Openness has two facets: legal and technical. **Legal openness** is about applying a suitable open license to the data. **Technical openness** is about removing tec... | {
"title": "Open Data",
"href": "open-data"
} |
# Remote Pair Programming
## Summary
Pair programming is a practice in which developers work in pairs. When the pair is not sitting next to each other, we call it **Remote Pair Programming (RPP)**. Remote pair programming has the same benefits as pair programming: higher quality software, fewer defects, knowledge sh... | {
"title": "Remote Pair Programming",
"href": "remote-pair-programming"
} |
# Continuous Integration
## Summary
Continuous Integration (CI) is the practice of routinely integrating code changes into the main branch of a repository, and testing the changes, as early and often as possible. Ideally, developers will integrate their code daily, if not multiple times a day.
Martin Fowler, Chief... | {
"title": "Continuous Integration",
"href": "continuous-integration"
} |
# Grammar and Spell Checker
## Summary
A well-written article with correct grammar, punctuation and spelling along with an appropriate tone and style to match the needs of the intended reader or community is always important. Software tools offer algorithm-based solutions for grammar and spell checking and correctio... | {
"title": "Grammar and Spell Checker",
"href": "grammar-and-spell-checker"
} |
# Question Answering
## Summary
Search engines, and information retrieval systems in general, help us obtain relevant documents to any search query. In reality, people want answers. Question Answering (QA) is about giving a direct answer in the form of a grammatically correct sentence.
QA is a subfield of Computer... | {
"title": "Question Answering",
"href": "question-answering"
} |
# Inter-Service Communication for Microservices
## Summary
In a monolithic application, all parts of the app access a shared database. Each part can easily invoke the functionality of another part. In a microservices architecture, an app is composed of many microservices, each potentially managing its own database. ... | {
"title": "Inter-Service Communication for Microservices",
"href": "inter-service-communication-for-microservices"
} |
Dataset Card for Devopedia
Waifu Husbando to catch your attention.
Dataset Description
Devopedia is a ~1.15 M Tokens (llama-2-7b-chat-tokenizer) / ~999.32K Tokens (RWKV Tokenizer) scrape of Devopedia. It serves as a training resource for large language models and other NLP tasks. This card details the dataset's origin, content, and limitations.
- Curated by: KaraKaraWitch
- Funded by: Recursal.ai (I work there lol)
- Shared by: KaraKaraWitch
- Language(s) (NLP): English
- License: cc-by-sa-4.0
Devopedia was created under time constraints for the release of EagleX v1, and may contain biases in selection.
Supported Tasks and Leaderboards
Primarily used for language modeling.
Languages
While the dataset is focused on English. Keep in mind there are other languages as well.
Processing and Filtering
We scraped the Devopedia for a list of articles. Writing them to a compiled file dev_index.json. Before scraping individual article for its page contents.
The article contents are then selected by sections. Each section is converted to Markdown. Including the appropriate title. No filtering was done over the dataset.
Data Instances
Refer to the following sample:
{
"text": "# Hypothesis Testing and Types of Errors\n\n## Summary\n\n\nSuppose we want to study income of a population. We study a sample from the population and draw conclusions. The sample should represent the population for our study to be a reliable one.\n\n**Null hypothesis** is that sample represents population. Hypothesis testing provides us with framework to conclude if we have sufficient evidence to either accept or reject null hypothesis. \n\nPopulation characteristics are either assumed or drawn from third-party sources or judgements by subject matter experts. Population data and sample data are characterised by moments of its distribution (mean, variance, skewness and kurtosis). We test null hypothesis for equality of moments where population characteristic is available and conclude if sample represents population.\n\nFor example, given only mean income of population, <TRUNCATED...>"
}
Data Keys
Each json line is a dictionary with a text str.
Recursal's Vision
To make AI accessible to everyone, regardless of language, or economical status
This is the collective goal of the RWKV Open Source foundation and Recursal AI, the commercial entity who backs it.
We believe that AI should not be controlled by a select few individual organization. And that it should be made accessible regardless if you are rich or poor, or a native speaker of english.
About RWKV
RWKV is an Open Source, non profit group, under the linux foundation. Focused on developing the RWKV AI architecture, in accordence to our vision.
The RWKV architecture scales efficiently and economically. As an RNN & Transformer hybrid, it is able to provide the performance similar to leading transformer models, while having the compute and energy efficiency of an RNN based architecture.
You can find out more about the project, and latest models, at the following
About Recursal AI
Recursal AI, is the commercial entity built to provide support for RWKV model development and users, while providing commercial services via its public cloud, or private-cloud / on-premise offerings.
As part of our vision. Our commitment, is to ensure open source development and access to the best foundational AI models and datasets.
The following dataset/models provided here, is part of that commitment.
You can find out more about recursal AI here
Dataset Curators
KaraKaraWitch. (I typically hang out in PygmalionAI discord, sometimes EleutherAI. If something is wrong, @karakarawitch on discord.)
I'd be happy if you could spread the word and recommend this dataset.
Licensing Information
Devopedia lists their content as under CC-BY-SA.
Recursal Waifus [Husbandos] (The banner image) are licensed under CC-BY-SA. They do not represent the related websites in any official capacity unless otherwise or announced by the website. You may use them as a banner image. However, you must always link back to the dataset.
Citation Information
@misc{Devopedia,
title = {Devopedia},
author = {KaraKaraWitch, recursal.ai},
year = {2024},
howpublished = {\url{https://huggingface.co/datasets/recursal/Devopedia}},
}
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