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8 results for “British Library”
Khotanese Manuscripts from Chinese Turkestan in the British Library (XML records)
<p>The file contains XML records matching the print edition of Skjaervo's catalogue, in TEI schema P4.</p> <p>The records in this file are a <strong>draft version</strong>. They have not yet been proofed and checked against physical holdings, which will be done with the next version release.</p> <p>The XML records have been produced as part of the work for the project <em>Beyond Boundaries: Religion, Region, Language and the State</em> (An ERC Synergy project from the European Research Council under the EU's 7th Framework Programme (FP7/2007-2013)/ERC grant agreement no.609823)</p>
Diachronic word embeddings from 19th-century newspapers digitised by the British Library (1800-1919)
<p>Word vectors related to the paper <em>Machines in the media: semantic change in the lexicon of mechanization in 19th-century British newspapers </em>by Nilo Pedrazzini and Barbara McGillivray (2022).</p> <p>The embeddings were trained on a 4.2-billion-word corpus of 19th-century British newspapers using Word2Vec and the following parameters:</p> <pre><code>sg = True min_count = 1 window = 3 vector_size = 200 epochs = 5</code></pre> <p>The embeddings are divided into periods of ten years each, with the vectors from each decade aligned to the ones from the most recent decade (1910s) using Orthogonal Procrustes.</p> <p>See related GitHub repository for the full documentation: <a href="https://github.com/Living-with-machines/DiachronicEmb-BigHistData">https://github.com/Living-with-machines/DiachronicEmb-BigHistData</a></p> <p>Project webpage (Living with Machines): <a href="https://livingwithmachines.ac.uk/">https://livingwithmachines.ac.uk/</a></p>
Decade-level Word2Vec models from automatically transcribed 19th-century newspapers digitised by the British Library (1800-1919)
<p>Word embeddings trained on a 4.2-billion-word corpus of 19th-century British newspapers using Word2Vec and the following parameters:</p> <pre><code>sg = True min_count = 5 window = 5 vector_size = 100 epochs = 5</code></pre> <p>The embeddings are divided into periods of ten years each. Unlike those in <a href="https://doi.org/10.5281/zenodo.7181682">this repository</a>, these were not aligned and OCR errors skimmed from the vocabulary. </p> <p>See related GitHub repository for the full documentation: <a href="https://github.com/Living-with-machines/DiachronicEmb-BigHistData">https://github.com/Living-with-machines/DiachronicEmb-BigHistData</a></p> <p>Project website (Living with Machines): <a href="https://livingwithmachines.ac.uk/">https://livingwithmachines.ac.uk/</a></p>
British Library Books genre detection model
<p><strong>Model description</strong></p> <p>This model is intended to predict, from the title of a book, whether it is 'fiction' or 'non-fiction'.</p> <p>This model was trained on data created from the <a href="https://www.bl.uk/collection-guides/digitised-printed-books">Digitised printed books (18th-19th Century)</a> book collection. The datasets in this collection are comprised and derived from 49,455 digitised books (65,227 volumes), mainly from the 19th Century. This dataset is dominated by English language books and includes books in several other languages in much smaller numbers. </p> <p>This model was originally developed for use as part of the <a href="https://livingwithmachines.ac.uk/">Living with Machines</a> project to be able to 'segment' this large dataset of books into different categories based on a 'crude' classification of genre i.e. whether the title was `fiction` or `non-fiction`.</p> <p>The model's training data (discussed more below) primarily consists of 19th Century book titles from the <a href="https://www.bl.uk/collection-guides/digitised-printed-books">British Library Digitised printed books (18th-19th century)</a> collection. These books have been catalogued according to British Library cataloguing practices. The model is likely to perform worse on any book titles from earlier or later periods. While the model is multilingual, it has training data in non-English book titles; these appear much less frequently.</p> <p><strong>How to use</strong></p> <p>To use this within fastai, first install version 2 of the fastai library. Following the documentation <a href="https://docs.fast.ai/#Installing">instructions</a>. Once you have fastai installed, you can use the model as follows:</p> <pre><code class="language-python">from fastai.text.all import load_learner learn = load_learner("20210928-model.pkl") learn.predict("Oliver Twist")</code></pre> <p><strong>Limitations and bias</strong></p> <p>The model was developed based on data from the <a href="https://www.bl.uk/collection-guides/digitised-printed-books">British Library's Digitised printed books (18th-19th Century)</a> collection. This dataset is not representative of books from the period covered with biases towards certain types (travel) and a likely absence of books that were difficult to digitise.</p> <p>The formatting of the British Library books corpus titles may differ from other collections, resulting in worse performance on other collections. It is recommended to evaluate the performance of the model before applying it to your own data. Likely, this model won't perform well for contemporary book titles without further fine-tuning.</p> <p><strong>Training data</strong></p> <p>The training data for this model will be available from the British Libary Research Repository shortly.</p> <p>The training data was created using the Zooniverse platform. British Library cataloguers carried out the majority of the annotations used as training data. More information on the process of creating the training data will be available soon. </p> <p><strong>Training procedure</strong></p> <p>Model training was carried out using the fastai library version 2.5.2. </p> <p>The notebook using for training the model will be available at: https://github.com/Living-with-machines/bl-books-genre-prediction</p> <p><strong>Eval result</strong></p> <p>The model was evaluated on a held out test set:</p> <pre> precision recall f1-score support Fiction 0.91 0.88 0.90 296 Non-fiction 0.94 0.95 0.95 554 accuracy 0.93 850 macro avg 0.93 0.92 0.92 850 weighted avg 0.93 0.93 0.93 850</pre>
Diachronic and diatopic word embeddings from newspapers digitised by the British Library (1830-1889): North and South England
<p>Diachronic word embeddings (decade-level) trained with Word2Vec (via Gensim) on different geographic subcorpora of the Heritage Made Digital British and the Living with Machines historical newspaper collections:</p> <p>- North England (north.zip)</p> <p>- South England (south.zip)</p> <p>At the moment, for each subcorpus, Word2Vec models are available for each decade in the period 1830-1889. More models are on the way for the following:</p> <p>- each decade in the periods 1780-1829 and 1890-1920 for both North and South England.</p> <p>- diachronic models for the following regions: Scotland, Wales, and Midlands.</p> <p>The models were trained using the following parameters:</p> <pre><code>sg = True min_count = 1 window = 5 vector_size = 200 epochs = 5</code></pre> <p>Like the embeddings in <a href="https://doi.org/10.5281/zenodo.7181682">this repository</a>, the model for each decade was aligned to the most recent one with Orthogonal Procrustes.</p> <p>See related GitHub repository for the full documentation: <a href="https://github.com/Living-with-machines/DiachronicEmb-BigHistData">https://github.com/Living-with-machines/DiachronicEmb-BigHistData</a>.</p> <p>Project website (Living with Machines): <a href="https://livingwithmachines.ac.uk/">https://livingwithmachines.ac.uk/</a></p> <p>Data related to: Nilo Pedrazzini & Barbara McGillivray, <em>Diachronic and diatopic word embeddings from British historical newspapers</em>, presented at AIUCD (Convegno dell’Associazione per l’Informatica Umanistica e la Cultura Digitale) in Siena (Italy), June 2023.</p>
British Library fragment Or.8210/S.9498 collated with དབའབཞེད་ (version 1.1)
<p>Comparison of BL S.9498+S.13683 with the DBA' BZHED MS (=DBA' 2000 in our referencing system). The purpose of the comparison is three fold: a) to tentatively reconstruct the disposition of lines and content across the folio from which the fragment came, b) to determine if there is enough space in the reconstructed folio to accommodate the names of the three ministers sent to investigate Śāntarakṣita, and c) to determine the likely position of ན་ visible as a tail in the top missing line.</p>
Example computer vision classification training data derived from British Library 19th Century Books Image collection
<p>Example computer vision classification training data derived from British Library 19th Century Books Image collection</p> <p>This dataset provides training data for image classification for use in a computer vision workshop. The images are derived from '<a href="https://doi.org/10.21250/db17">Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900. JPG'</a> from the year '1839'.</p> <p>Currently, included are four folders containing a variety of images derived from the BL books corpus.</p> <ul> <li>'cv_workshop_exercise_data' include images of: 'building', 'people', 'coat of arms'</li> <li>'humancats' contains images of humans and images of cats</li> </ul> <p>The 'fashion' and 'portraits' folders both contain images of people organised into 'female' and 'male'. These labels were annotated by a single annotator and these categories may themselves not be meaningful. They are included in the workshop data as a point of discussion about how we should label data both in general and when working with historical data. </p> <p>This data is intended primarily as an educational resource.</p>
Tuesday 5 May: The UK Web Archive, The British Library, Jason Webber, The British Library
<p>Tuesday 5 May: The UK Web Archive, The British Library, Jason Webber, The British Library</p>
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