11 datasets found
  1. Uncoated fine papers market will grow at a CAGR of 4.00% from 2024 to 2031.

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
    Updated Jan 1, 2023
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    Cognitive Market Research (2023). Uncoated fine papers market will grow at a CAGR of 4.00% from 2024 to 2031. [Dataset]. https://www.cognitivemarketresearch.com/uncoated-fine-papers-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Jan 1, 2023
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    According to Cognitive Market Research, the global uncoated fine papers market size is USD 20154.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 4.00% from 2024 to 2031.

    North America holds the major market of more than 40% of the global revenue with a market size of USD 8061.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.2% from 2024 to 2031.
    Europe accounts for a share of over 30% of the global market size of USD 6046.2 million.
    Asia Pacific holds the market of around 23% of the global revenue with a market size of USD 4635.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.0% from 2024 to 2031.
    Latin America's market has more than 5% of the global revenue, with a market size of USD 1007.71 million in 2024, and will grow at a compound annual growth rate (CAGR) of 3.4% from 2024 to 2031.
    Middle East and Africa holds the market of around 2% of the global revenue with a market size of USD 403.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
    The printing category holds the highest uncoated fine papers market revenue share in 2024.
    

    Market Dynamics of Uncoated Fine Papers Market

    Key Drivers for Uncoated Fine Papers Market

    Increasing Demand for Uncoated Fine Paper to Increase the Demand Globally

    Industry growth is being driven by the need for eco-friendly printing solutions and the rising need for recyclable paper resources. Global manufacturers of writing and printing papers favor recycling mechanical uncoated free sheet paper as a means of promoting sustainable development and environmental preservation. Additionally, the growing number of consumers who read magazines, journals, and newspapers is fueling demand in the global industry. Industry growth is being driven by the need for eco-friendly printing solutions and the rising need for recyclable paper resources. Due to rising concerns about environmental protection and sustainable development, manufacturers of writing and printing papers worldwide support the use of mechanically uncoated free sheet paper and its recycling.

    Growing Use of Uncoated Fine Paper in Packaging to Propel Market Growth

    One of the main industry trends is the growing use of uncoated fine paper in packaging. With the introduction of Industry 4.0, technological advancements have been made, and the use of sustainable packaging solutions is growing. Growing internet sales drive the packaging sector. The main cause of soil pollution is plastic packaging. Additionally, a lot of big markets use eco-friendly packaging options, such as biodegradable packaging, to lessen their negative environmental consequences. It is better for the environment than plastic and can be used to make packaging that decomposes naturally. Thus, throughout the forecast period, these variables will have an increasing effect on the growth of the uncoated fine paper market under consideration.

    Restraint Factor for the Uncoated Fine Papers Market

    Stringent Environmental Laws and Swift Digital Technology Adoption to Limit the Sales

    From conception to death, the production of paper requires a large amount of energy and natural resources. First, trees are cut down, and the wood is split into fragments. After that, the wood is divided into individual fibers with the help of heat, water, and occasionally chemicals. The fiber is mixed with a large amount of water (and often recycled fiber) and sprayed onto a large flat wire screen that is being fed through the paper machine quickly. As the water evaporates, the fibers join together. Furthermore, the shift towards digital technology has not been brought about by the use of paper. E-books and E-news are more popular these days than paper. In order to achieve their goals and make the most of each technology to promote innovation and enhance processes, individuals must first understand the potential of digital resources. This is known as digital adoption, which is a process of change and learning.

    Impact of Covid-19 on the Uncoated Fine Papers Market

    The COVID-19 pandemic has had a significant impact on the uncoated fine paper sector. The thriving e-commerce sector has increased demand for paper products, leading to more packaging and shipping of items; nevertheless, supply chain disruptions and lower production rates caused by the pandemic have hindered the uncoated fine paper...

  2. h

    fineweb

    • huggingface.co
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    FineData, fineweb [Dataset]. http://doi.org/10.57967/hf/2493
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset authored and provided by
    FineData
    License

    https://choosealicense.com/licenses/odc-by/https://choosealicense.com/licenses/odc-by/

    Description

    🍷 FineWeb

    15 trillion tokens of the finest data the 🌐 web has to offer

      What is it?
    

    The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. 🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb.

  3. h

    fineweb-edu

    • huggingface.co
    Updated Jan 3, 2025
    + more versions
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    FineData (2025). fineweb-edu [Dataset]. http://doi.org/10.57967/hf/2497
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 3, 2025
    Dataset authored and provided by
    FineData
    License

    https://choosealicense.com/licenses/odc-by/https://choosealicense.com/licenses/odc-by/

    Description

    📚 FineWeb-Edu

    1.3 trillion tokens of the finest educational data the 🌐 web has to offer

    Paper: https://arxiv.org/abs/2406.17557

      What is it?
    

    📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version. To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu.

  4. P

    FineWeb Dataset

    • paperswithcode.com
    Updated May 27, 2025
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    (2025). FineWeb Dataset [Dataset]. https://paperswithcode.com/dataset/fineweb
    Explore at:
    Dataset updated
    May 27, 2025
    Description

    The FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated English web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and runs on the datatrove library, our large-scale data processing library.

    FineWeb was originally meant to be a fully open replication of RefinedWeb, with a release of the full dataset under the ODC-By 1.0 license. However, by carefully adding additional filtering steps, we managed to push the performance of FineWeb well above that of the original RefinedWeb, and models trained on our dataset also outperform models trained on other commonly used high-quality web datasets (like C4, Dolma-v1.6, The Pile, SlimPajama, RedPajam2) on our aggregate group of benchmark tasks.

  5. h

    fineweb-2

    • huggingface.co
    Updated Jun 27, 2025
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    FineData (2025). fineweb-2 [Dataset]. http://doi.org/10.57967/hf/3744
    Explore at:
    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    FineData
    License

    https://choosealicense.com/licenses/odc-by/https://choosealicense.com/licenses/odc-by/

    Description

    🥂 FineWeb2

    A sparkling update with 1000s of languages

      What is it?
    

    This is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages. The 🥂 FineWeb2 dataset is fully reproducible, available under the permissive ODC-By 1.0 license and extensively validated through hundreds of ablation experiments. In particular, on the set of 9 diverse languages we used to guide our processing decisions, 🥂 FineWeb2… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-2.

  6. z

    Data from: PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large...

    • zenodo.org
    bin
    Updated May 25, 2024
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    Matt Murtagh-White; Matt Murtagh-White (2024). PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data [Dataset]. http://doi.org/10.5281/zenodo.10993684
    Explore at:
    binAvailable download formats
    Dataset updated
    May 25, 2024
    Dataset provided by
    Matt Murtagh White
    Authors
    Matt Murtagh-White; Matt Murtagh-White
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    May 25, 2024
    Description

    Describes the taxonomy used in the paper "PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data", presented at the Second Workshop on Semantic Technologies and Deep Learning Models for Scientific, Technical and Legal Data at the Extended Semantic Web Conference (ESWC) 2024.

  7. z

    Europe Pulp and Paper Automation Market 2024 To 2033

    • zenodo.org
    Updated Mar 12, 2025
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    Nitin Sirsat; Nitin Sirsat (2025). Europe Pulp and Paper Automation Market 2024 To 2033 [Dataset]. http://doi.org/10.5281/zenodo.15009878
    Explore at:
    Dataset updated
    Mar 12, 2025
    Dataset provided by
    Custom Market Insights
    Authors
    Nitin Sirsat; Nitin Sirsat
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Europe Pulp and Paper Automation Market Size, Trends and Insights By Type (Distributed control systems (DCS), Programmable logic controllers (PLCs), Supervisory control and data acquisition (SCADA), Sensors and transmitters, Flowmeters, Manufacturing execution systems (MES), Asset performance management (APM), Advanced process control (APC), Enterprise asset management (EAM), Valves, Vision systems), By Application (Pulp, Tissue, Board, Paper, Packaging Paper, Special papers, Magazines papers, Printing Papers, Fine Papers), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

    Reports Description

    As per the current market research conducted by the CMI Team, the Europe Pulp and Paper Automation Market is expected to record a CAGR of 9% from 2023 to 2032. In 2023, the market size is projected to reach a valuation of USD 1,163 Million. By 2032, the valuation is anticipated to reach USD 2,517.2 Million.

    The Europe Pulp and Paper Automation Market pertains to the industry segment encompassing the adoption of automated technologies in pulp and paper manufacturing processes across the European region. This market involves the implementation of advanced control systems, Industrial Internet of Things (IIoT) solutions, and digital technologies to enhance operational efficiency, reduce costs, and meet sustainability goals.

    Key players in this market include ABB Ltd., Siemens AG, and Honeywell International. The sector has experienced growth driven by technological advancements, a focus on sustainability, and the integration of digital transformation strategies in recent years.

    For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=39421

  8. h

    BLIFT

    • huggingface.co
    Updated May 29, 2025
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    Behavior In The Wild (2025). BLIFT [Dataset]. https://huggingface.co/datasets/behavior-in-the-wild/BLIFT
    Explore at:
    Dataset updated
    May 29, 2025
    Authors
    Behavior In The Wild
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    BLIFT: Behavior-LLaVA Instruction Fine-Tuning Dataset

    Paper: Teaching Human Behavior Improves Content Understanding Abilities of VLMs Website: https://behavior-in-the-wild.github.io/behavior-llava.html

      Dataset Summary
    

    BLIFT (Behavior-LLaVA Instruction Fine-Tuning) is a large-scale multimodal instruction tuning dataset designed to teach Vision-Language Models (VLMs) human behavior. It contains over 730k images and videos collected from Reddit and YouTube, annotated with… See the full description on the dataset page: https://huggingface.co/datasets/behavior-in-the-wild/BLIFT.

  9. AceMath-Instruct-Training-Data

    • huggingface.co
    Updated Jan 17, 2025
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    NVIDIA (2025). AceMath-Instruct-Training-Data [Dataset]. https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 17, 2025
    Dataset provided by
    Nvidiahttp://nvidia.com/
    Authors
    NVIDIA
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    website | paper

      AceMath-Instruct Training Data Card
    

    We release all the datasets to train AceMath-1.5B/7B/72B-Instruct models. These models are built upon the Qwen2.5-Math-Base models through a multi-stage supervised fine-tuning (SFT) process. The fine-tuning begins with general-purpose SFT data (general_sft_stage1.parquet and general_sft_stage2.parquet) and is followed by math-specific SFT data (math_sft.parquet). In our experiments, fine-tuning the Qwen2.5-Math-Base models using… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data.

  10. h

    SafeEdit

    • huggingface.co
    Updated Mar 21, 2024
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    ZJUNLP (2024). SafeEdit [Dataset]. https://huggingface.co/datasets/zjunlp/SafeEdit
    Explore at:
    Dataset updated
    Mar 21, 2024
    Dataset authored and provided by
    ZJUNLP
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    Dataset for Detoxifying Large Language Models via Knowledge Editing

    Comparison • Usage • Citation • Paper • Website

      🌟 Comparison
    

    SafeEdit encompasses 4,050 training, 2,700 validation, and 1,350 test instances. SafeEdit can be utilized across a range of methods, from supervised fine-tuning to reinforcement learning that demands preference data for more secure responses, as well as knowledge editing methods that require a diversity of evaluation texts.… See the full description on the dataset page: https://huggingface.co/datasets/zjunlp/SafeEdit.

  11. hh-rlhf

    • huggingface.co
    Updated Dec 9, 2022
    + more versions
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    Anthropic (2022). hh-rlhf [Dataset]. https://huggingface.co/datasets/Anthropic/hh-rlhf
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 9, 2022
    Dataset authored and provided by
    Anthropichttps://anthropic.com/
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Dataset Card for HH-RLHF

      Dataset Summary
    

    This repository provides access to two different kinds of data:

    Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf.

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Cognitive Market Research (2023). Uncoated fine papers market will grow at a CAGR of 4.00% from 2024 to 2031. [Dataset]. https://www.cognitivemarketresearch.com/uncoated-fine-papers-market-report
Organization logo

Uncoated fine papers market will grow at a CAGR of 4.00% from 2024 to 2031.

Explore at:
pdf,excel,csv,pptAvailable download formats
Dataset updated
Jan 1, 2023
Dataset authored and provided by
Cognitive Market Research
License

https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

Time period covered
2021 - 2033
Area covered
Global
Description

According to Cognitive Market Research, the global uncoated fine papers market size is USD 20154.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 4.00% from 2024 to 2031.

North America holds the major market of more than 40% of the global revenue with a market size of USD 8061.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.2% from 2024 to 2031.
Europe accounts for a share of over 30% of the global market size of USD 6046.2 million.
Asia Pacific holds the market of around 23% of the global revenue with a market size of USD 4635.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.0% from 2024 to 2031.
Latin America's market has more than 5% of the global revenue, with a market size of USD 1007.71 million in 2024, and will grow at a compound annual growth rate (CAGR) of 3.4% from 2024 to 2031.
Middle East and Africa holds the market of around 2% of the global revenue with a market size of USD 403.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
The printing category holds the highest uncoated fine papers market revenue share in 2024.

Market Dynamics of Uncoated Fine Papers Market

Key Drivers for Uncoated Fine Papers Market

Increasing Demand for Uncoated Fine Paper to Increase the Demand Globally

Industry growth is being driven by the need for eco-friendly printing solutions and the rising need for recyclable paper resources. Global manufacturers of writing and printing papers favor recycling mechanical uncoated free sheet paper as a means of promoting sustainable development and environmental preservation. Additionally, the growing number of consumers who read magazines, journals, and newspapers is fueling demand in the global industry. Industry growth is being driven by the need for eco-friendly printing solutions and the rising need for recyclable paper resources. Due to rising concerns about environmental protection and sustainable development, manufacturers of writing and printing papers worldwide support the use of mechanically uncoated free sheet paper and its recycling.

Growing Use of Uncoated Fine Paper in Packaging to Propel Market Growth

One of the main industry trends is the growing use of uncoated fine paper in packaging. With the introduction of Industry 4.0, technological advancements have been made, and the use of sustainable packaging solutions is growing. Growing internet sales drive the packaging sector. The main cause of soil pollution is plastic packaging. Additionally, a lot of big markets use eco-friendly packaging options, such as biodegradable packaging, to lessen their negative environmental consequences. It is better for the environment than plastic and can be used to make packaging that decomposes naturally. Thus, throughout the forecast period, these variables will have an increasing effect on the growth of the uncoated fine paper market under consideration.

Restraint Factor for the Uncoated Fine Papers Market

Stringent Environmental Laws and Swift Digital Technology Adoption to Limit the Sales

From conception to death, the production of paper requires a large amount of energy and natural resources. First, trees are cut down, and the wood is split into fragments. After that, the wood is divided into individual fibers with the help of heat, water, and occasionally chemicals. The fiber is mixed with a large amount of water (and often recycled fiber) and sprayed onto a large flat wire screen that is being fed through the paper machine quickly. As the water evaporates, the fibers join together. Furthermore, the shift towards digital technology has not been brought about by the use of paper. E-books and E-news are more popular these days than paper. In order to achieve their goals and make the most of each technology to promote innovation and enhance processes, individuals must first understand the potential of digital resources. This is known as digital adoption, which is a process of change and learning.

Impact of Covid-19 on the Uncoated Fine Papers Market

The COVID-19 pandemic has had a significant impact on the uncoated fine paper sector. The thriving e-commerce sector has increased demand for paper products, leading to more packaging and shipping of items; nevertheless, supply chain disruptions and lower production rates caused by the pandemic have hindered the uncoated fine paper...

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