Dataset Card for TexPert

TeXpert is a benchmark dataset designed to evaluate the ability of language models to generate LaTeX code from natural language prompts. The dataset focuses on generating LaTeX for scientific documents and is structured around "atomic" LaTeX commands—minimal functional units typically used in academic writing. The prompts are divided into three difficulty classes (Simple, Average, Hard) based on the number of LaTeX commands, required packages, and… See the full description on the dataset page: https://huggingface.co/datasets/knowledge-verse-ai/TeXpert.

AI-Generated Personalized Poem Market Outlook

According to our latest research, the global AI-Generated Personalized Poem market size reached USD 1.18 billion in 2024, with a robust compound annual growth rate (CAGR) of 22.9% anticipated through the forecast period. By 2033, the market is forecasted to surge to around USD 9.5 billion, driven by increasing adoption across gifting, marketing, and educational sectors. This remarkable expansion is underpinned by the convergence of advanced natural language processing (NLP) algorithms, rising demand for unique and emotionally resonant content, and the proliferation of digital platforms enabling seamless user engagement.

One of the primary growth factors propelling the AI-Generated Personalized Poem market is the surging consumer appetite for hyper-personalized experiences. As individuals and organizations strive to create more meaningful connections, AI-powered poetry generators are gaining traction for their ability to craft bespoke poems tailored to specific occasions, personalities, or branding needs. Innovations in generative AI, particularly large language models, have enabled the creation of poems that capture nuanced emotions and context, making them highly appealing for gifting, marketing campaigns, and special events. This shift towards personalization is further catalyzed by the growing digital literacy and accessibility of AI tools, allowing even non-technical users to generate professional-quality poems effortlessly.

Another significant driver is the integration of AI-generated poetry into marketing and advertising strategies. Brands are increasingly leveraging AI-powered poems to differentiate their messaging, foster emotional engagement, and enhance customer loyalty. The ability to generate customized poetic content at scale offers a unique value proposition for campaigns, social media promotions, and branded storytelling. Additionally, educational institutions are incorporating AI-generated poems into curricula to foster creativity, language skills, and digital literacy among students. The versatility of AI-generated poetry, spanning from text-based to multimedia formats, is widening its applicability across diverse sectors and demographics.

Technological advancements are also playing a pivotal role in the market's expansion. The evolution of natural language generation (NLG) and deep learning techniques has significantly improved the quality, coherence, and creativity of AI-generated poems. These advancements enable real-time customization, multilingual support, and integration with voice and video technologies, broadening the appeal of AI-generated poetry to new user segments. Furthermore, the proliferation of mobile apps, online platforms, and social media channels is making it easier for users to access and share personalized poems, amplifying market reach and engagement. As AI technologies continue to mature, the market is poised for sustained innovation and growth.

Regionally, North America remains the dominant market for AI-Generated Personalized Poems, accounting for over 38% of the global market share in 2024. This leadership is attributed to the region's early adoption of AI technologies, high digital penetration, and a thriving creative economy. Europe and Asia Pacific are emerging as high-growth regions, with increasing investments in AI research, expanding e-commerce ecosystems, and rising consumer demand for personalized digital content. The Asia Pacific region, in particular, is expected to witness the fastest CAGR of 25.4% during the forecast period, driven by rapid digitalization and a burgeoning young population keen on novel forms of expression.

Product Type Analysis

The Product Type segment of the AI-Generated Personalized Poem market is categorized into Text-Based Poems, Audio Poems, and Video Poems, each addressing distinct consumer needs and engagement preferences. Text-Based Poems remain the most widely adopted format, owing to their simplicity, ease of customization, and versatility across applications such as gifting, education, and marketing. The accessibility of text-based AI poem generators via web and mobile platforms has democratized the creation and sharing of personalized poetry, enabling users to craft unique messages for birthdays, anniversaries, and other special occasions. The ability to instantly generate, edit, and distribu

AI-Generated Personalized Poem Market Outlook

As per our latest research, the AI-Generated Personalized Poem market size reached USD 412 million in 2024, with a robust year-over-year growth rate, reflecting the rising adoption of artificial intelligence in creative content generation. The market is experiencing significant momentum, backed by a CAGR of 22.9% projected from 2025 to 2033. By the end of 2033, the global AI-Generated Personalized Poem market is expected to attain a value of USD 2.97 billion, driven by technological advancements, increasing consumer demand for personalized digital experiences, and the proliferation of AI-powered applications across multiple industries.

One of the primary growth factors fueling the expansion of the AI-Generated Personalized Poem market is the surging demand for hyper-personalized content in both consumer and enterprise applications. As individuals seek unique, emotionally resonant experiences for occasions such as birthdays, anniversaries, and corporate events, AI-powered platforms offer scalable, cost-effective solutions that can generate bespoke poems tailored to specific recipients and contexts. This trend is further amplified by the growing influence of social media, where users share personalized content to enhance their digital presence and foster deeper connections. The ability of AI algorithms to analyze user data, preferences, and emotional cues enables the creation of poems that resonate on a personal level, thereby driving engagement and repeat usage.

Another significant driver is the integration of AI-generated personalized poems into diverse sectors such as education, marketing, advertising, and entertainment. In educational settings, these AI tools are being adopted to foster creativity and linguistic skills among students, providing them with customized poetic content and interactive learning experiences. In marketing and advertising, brands leverage AI-generated poems to craft compelling, targeted campaigns that evoke emotional responses and enhance brand loyalty. The entertainment industry, too, is capitalizing on this technology to deliver immersive storytelling experiences, interactive performances, and audience-specific content. The versatility of AI-generated poems across these varied applications underscores their growing relevance and adoption, contributing substantially to market growth.

The rapid evolution of AI and natural language processing (NLP) technologies is also instrumental in driving the market forward. Advanced generative models, such as large language models and multimodal AI, are capable of producing high-quality, contextually relevant poems in multiple formats, including text, audio, and video. These technological advancements have significantly reduced the barriers to entry for both consumers and businesses, enabling seamless integration with mobile apps, online platforms, and social media channels. Furthermore, the continuous improvement in AI’s ability to mimic human creativity and emotional intelligence enhances the authenticity and appeal of personalized poems, positioning the market for sustained long-term growth.

From a regional perspective, North America currently dominates the AI-Generated Personalized Poem market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of leading AI technology providers, high digital adoption rates, and a culture of innovation contribute to North America’s leadership position. Meanwhile, Asia Pacific is witnessing the fastest growth, propelled by rising internet penetration, increasing disposable incomes, and a burgeoning youth population keen on digital personalization. Europe, with its rich literary heritage and strong emphasis on cultural content, is also emerging as a significant market. Latin America and the Middle East & Africa, although at earlier stages of adoption, are expected to present lucrative opportunities as digital infrastructure and AI awareness improve over the forecast period.

Product Type Analysis

This dataset estimates the duration of Malayalam Poem syllables written in three Vruthas, Kakali, Manjari, and Keka.

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  Dataset Card for Tarteel AI's EveryAyah Dataset





  Dataset Summary

This dataset is a collection of Quranic verses and their transcriptions, with diacritization, by different reciters.

  How to download

!pip install -q datasets

from datasets import load_dataset dataset =load_dataset("Salama1429/tarteel-ai-everyayah-Quran", verification_mode="no_checks")

  Supported Tasks and Leaderboards

[Needs More Information]

  Languages

The audio is in… See the full description on the dataset page: https://huggingface.co/datasets/Salama1429/tarteel-ai-everyayah-Quran.

The absence of publicly available reusable test collections for Arabic question answering on the Holy Qur’an has impeded the possibility of fairly comparing the performance of systems in that domain. In this article, we introduce AyaTEC, a reusable test collection for verse-based question answering on the Holy Qur’an, which serves as a common experimental testbed for this task. AyaTEC includes 207 questions (with their corresponding 1,762 answers) covering 11 topic categories of the Holy Qur’an that target the information needs of both curious and skeptical users. To the best of our effort, the answers to the questions (each represented as a sequence of verses) in AyaTEC were exhaustive—that is, all qur’anic verses that directly answered the questions were exhaustively extracted and annotated. To facilitate the use of AyaTEC in evaluating the systems designed for that task, we propose several evaluation measures to support the different types of questions and the nature of verse-based answers while integrating the concept of partial matching of answers in the evaluation.

This dataset is from a study on human social learning biases conducted by C.Easter (University of Leeds) as part of her PhD thesis.This data was collected using a novel research tool, "Virtual Environments for Research into Social Evolution" (VERSE), which uses gaming technology (Unity3D) to study human social learning behaviour within realistic, open world environments. VERSE aims to tackle some of the limitations of previous lab-based experiments, which are restricted by the use of abstract tasks, unrealistic social information sources and extremely localised spatial scales.In this study, 143 undergraduate students from the University of Leeds were asked to solve a series of novel tasks within a set of virtual environments. Participants were divided into two groups:-- "Same Rewards": Rewards equal in the environment.-- "Different Rewards": Rewards vary in the environment. One demonstrator in each demonstrator condition always displays a more profitable option than the alternative demonstrator.The tasks were as follows: "Container" task, deposit a token into one of two containers over ten rounds. "Route Choice" task, find the shortest route to a fixed end point. "Foraging" task, navigate a large, open environment to collect food items. For each task, participants were subjected to 6 demonstrator conditions:-- "Asocial": No demonstrators present, participant plays alone.-- "SocVsAsoc": One demonstrator present, all other options are undemonstrated.-- "Dominance": Two demonstrators present, a dominant AI and a subordinate AI, distinguished by physical appearance and behavioural differences. Dominant AI displays one option (in the 'Different Rewards group, always the more profitable option) while one AI displays an alternative.-- "Frequency": Four demonstrators present, three AIs display one option (in the 'Different Rewards group, always the more profitable option) while one AI displays an alternative.-- "Gender": Two demonstrators present, a male and a female. The male AI displays one option (in the 'Different Rewards group, always the more profitable option) while the female AI displays an alternative.-- "Size": Two demonstrators present, a large AI and a small AI. The large AI displays one option (in the 'Different Rewards group, always the more profitable option) while the small AI displays an alternative.The data is arranged as follows.In the root of the "HumanLearningVERSE" folder:Three R code files:-- "_Dataset_Generation_Code": Generates the 'Diff_' and 'Same_' datasets in the root folder.-- "_GLM_Analysis_Code": Conducts the glm analyses in the main paper.-- "_Graphs_Additional_Analyses_Code": Creates the graphs in the main manuscript and in the supplementary material. Also conducts some additional analyses, e.g. correlations in social information usage.Datasets:-- A dataset called "ParticipantData", which gives each participant's answers to a series of questions asked after the study. These answers are used as individual variables for each participant during the analysis. These include: gender, age, a series of answers to Bryant and Smith’s (2001), how often they play video games and how easy they found it to follow the instructions given / play the game during the experiment.-- A series of datasets beginning with "Same_" and "Diff_". These datasets give the proportion of times each demonstrator or no demonstrator were copied by each participant, during each demonstrator condition, for each task. Files are labelled with the task type (Container, Route, Foraging) and the reward group (Different Rewards, Same Rewards) the participant was placed in. Files ending in "ILV" are the main datasets, giving a summary of all the choices made by each participant. Files ending in "InitialChoice" give only the initial choices made by each participant, at the beginning of each demonstrator condition.The HumanLearningVERSE folder contains two additional folders, "DiffRewards" and "SameRewards", which contain the raw data collected from VERSE during the experiment. "DiffRewards" contains data for participants in the Different Rewards group and "SameRewards" for the Same Rewards group. In these folders are a series of folders, named with the participant's reference number (these numbers match the data in the ParticipantData csv file). In each participant's folders are the data for each of the three tasks, again placed into their individual folders. The name of each data file is descriptive and gives details of the replicate in question like so: "Ref_participantReferenceNumber_DataTaskName_NumberOfGameLevel/Replicate_SceneName(IncludingRewardGroupAndDemonstratorCondition)_DataType.csv"For the Container task, there are two types of data per participant:-- "InteractionsData": All interactions with 'interactable objects' including which character interacted (participant = "player", demonstrators are labelled by their names, e.g. "AI (Large)", which object they interacted with, and when it occurred. 'ContainerY' and 'ContainerB' refer to the yellow and blue containers.-- "FoodCollectionScore": The final value for the for the player's food collection score and the potential amount they could have collected.For the Foraging task, four data types are collected:-- "FoodPatchVisits": Reports which character visited which food patch and when.-- "PlayerFoodEatenData": Reports which food items were collected by the player and when, plus the nutritional value of each food item and a cumulative nutrition score. -- "FoodCollected": The final value for the for the player's food collection score and the potential amount they could have collected.-- A "PositionData" dataset for each character: The x,y,z coordinates for a particular character each timestep, for . The character is stated in the filename.For the Route Choice task, two data types were collected:-- A "PositionData" dataset for each character: The x,y,z coordinates for a particular character each timestep, for . The character is stated in the filename.-- "RemainingEnergy": The final energy value of the player at the end of the 'level'/replicate.