搜索条件

清除筛选
筛选: 部门 Digital Scholarship Center (DSC) 删除限定条件 部门: Digital Scholarship Center (DSC)

搜索结果

  • Coherence_Evaluations - pma_environmental.csv

    Dataset thumbnail: Coherence_Evaluations - pma_environmental.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Coherence_Evaluations - pma_climate.csv

    Dataset thumbnail: Coherence_Evaluations - pma_climate.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Coherence_Evaluations - pma_earth.csv

    Dataset thumbnail: Coherence_Evaluations - pma_earth.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Coherence_Evaluations - cas_random5k.csv

    Dataset thumbnail: Coherence_Evaluations - cas_random5k.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Coherence_Evaluations - cas_environmental.csv

    Dataset thumbnail: Coherence_Evaluations - cas_environmental.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Coherence_Evaluations - cas_pollution.csv

    Dataset thumbnail: Coherence_Evaluations - cas_pollution.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Coherence_Evaluations - cas_climate.csv

    Dataset thumbnail: Coherence_Evaluations - cas_climate.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] / Chicago Novel Corpus [nvl] / Newspaper Corpus [nws] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    11/11/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Coherence_Evaluations - cas_earth.csv

    Dataset thumbnail: Coherence_Evaluations - cas_earth.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Poetry Foundation Metadata

    Dataset thumbnail: Poetry Foundation Metadata
    Type:
    Dataset
    摘抄:
    This file contains poet metadata for a dataset of poems collected from Poetry Foundation. This metadata relates to a poetry corpus collected from Kaggle available on that site:  https://www.kaggle.com/datasets/ultrajack/modern-renaissance-poetry Sean Ayres collected publication metadata and generated other relevant metadata fields based on his subject matter expertise. The original Kaggle poetry dataset contains approximately X lines of poetry. The metadata for this dataset references X poets
    作者:
    Ayres, Sean and McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    07/11/2022
    更改日期:
    07/11/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Project Gutenberg Poetry Metadata

    Dataset thumbnail: Project Gutenberg Poetry Metadata
    Type:
    Dataset
    摘抄:
    This file contains book metadata for a Project Gutenberg dataset of poetry metadata. This metadata relates to a poetry corpus collected by Allison Parrish available on GitHub:  https://github.com/aparrish/gutenberg-poetry-corpus Sean Ayres collected the publication metadata and generated other relevant metadata fields based on his subject matter expertise. The poetry dataset contains approximately three million lines of poetry The metadata for the dataset references X hundreds of books.
    作者:
    Ayres, Sean; McCabe, Erin E., and Parrish, Allison
    提交者:
    Erin E. McCabe
    上传日期:
    07/11/2022
    更改日期:
    07/11/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)