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Browse files- NMT_EN_RU-v2.1.0-doi.html +1565 -0
- NMT_EN_RU-v2.1.0.zip +3 -0
- NMT_Ru-En_v1-doi.html +1229 -0
- ru-en-nmt-v040426-1935.ipynb +1 -0
NMT_EN_RU-v2.1.0-doi.html
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Published April 4, 2026
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<span
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class="label text-muted"> | Version v2.1.0</span>
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<h1 id="record-title"
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class="wrap-overflowing-text">DominiqueLoyer/NMT_EN_RU: v2.1.0</h1>
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data-tooltip="Université du Québec à Montréal"
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href="/search?q=metadata.creators.person_or_org.name:%22Loyer,+Dominique%22"
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>
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<span class="creatibutor-name">Loyer, Dominique
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(Researcher)</span><sup class="font-tiny">1</sup></a>
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<a href="https://orcid.org/0009-0003-9713-7109"
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aria-label="Loyer, Dominique's ORCID profile"
|
| 274 |
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title="Loyer, Dominique's ORCID profile">
|
| 275 |
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<img class="ml-5 inline-id-icon"
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Show affiliations
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1.
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<a class="no-text-decoration"
|
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href="https://ror.org/002rjbv21"
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aria-label="Université du Québec à Montréal's ROR profile"
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title="Université du Québec à Montréal's ROR profile"
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>
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/>
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Université du Québec à Montréal
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|
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<div style="word-wrap: break-word;">
|
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<p>release date 040426</p>
|
| 359 |
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<p><strong>Full Changelog</strong>: https://github.com/DominiqueLoyer/NMT_EN_RU/compare/v1.0.0...v2.1.0
|
| 360 |
+
<a href="https://github.com/user-attachments/files/26485182/ru-en-nmt-v040426-1935.ipynb">ru-en-nmt-v040426-1935.ipynb</a></p>
|
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id="record-files" class="rel-mt-2 rel-mb-3"
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class="trigger"
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aria-label="File preview"
|
| 380 |
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>
|
| 381 |
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<span id="preview-file-title">DominiqueLoyer/NMT_EN_RU-v2.1.0.zip</span>
|
| 382 |
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<i class="angle right icon" aria-hidden="true"></i>
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</div>
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role="region"
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class="active content preview-container pt-0 open"
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title="Preview"
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class="preview-iframe"
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id="preview-iframe"
|
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name="preview-iframe"
|
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src="/records/19423705/preview/DominiqueLoyer/NMT_EN_RU-v2.1.0.zip?include_deleted=0"
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>
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Files
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<small class="text-muted"> (880.6 kB)</small>
|
| 410 |
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<i class="angle right icon" aria-hidden="true"></i>
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</h3>
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<div role="region" id="files-list-accordion-panel" aria-labelledby="files-list-accordion-trigger" class="active content pt-0">
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<th>Size</th>
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<th class>
|
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<a role="button" class="ui compact mini button right floated archive-link" href="https://zenodo.org/api/records/19423705/files-archive">
|
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<i class="file archive icon button" aria-hidden="true"></i> Download all
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<td class="ten wide">
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<div>
|
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<a href="/records/19423705/files/DominiqueLoyer/NMT_EN_RU-v2.1.0.zip?download=1">DominiqueLoyer/NMT_EN_RU-v2.1.0.zip</a>
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</div>
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<small class="ui text-muted font-tiny">md5:c38bffb8d838b927be1687c748a009bb
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<div class="ui icon inline-block" data-tooltip="This is the file fingerprint (checksum), which can be used to verify the file integrity.">
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<i class="question circle checksum icon"></i>
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</small>
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</td>
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<td>880.6 kB</td>
|
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<td class="right aligned">
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<span>
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<a role="button" class="ui compact mini button preview-link" href="/records/19423705/preview/DominiqueLoyer/NMT_EN_RU-v2.1.0.zip?include_deleted=0" target="preview-iframe" data-file-key="DominiqueLoyer/NMT_EN_RU-v2.1.0.zip">
|
| 452 |
+
<i class="eye icon" aria-hidden="true"></i>Preview
|
| 453 |
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</a>
|
| 454 |
+
|
| 455 |
+
<a role="button" class="ui compact mini button" href="/records/19423705/files/DominiqueLoyer/NMT_EN_RU-v2.1.0.zip?download=1">
|
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<i class="download icon" aria-hidden="true"></i>Download
|
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</a>
|
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</span>
|
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</td>
|
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</tr>
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</section>
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<section id="additional-details" class="rel-mt-2" aria-label="Additional record details">
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<h2 id="record-details-heading">Additional details</h2>
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<div class="ui divider"></div>
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<h3 class="ui header">Related works</h3>
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</div>
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<div class="sixteen wide mobile twelve wide tablet thirteen wide computer column">
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<dt class="ui tiny header">Is supplement to</dt>
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<dd>
|
| 505 |
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|
| 506 |
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Software:
|
| 507 |
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|
| 508 |
+
|
| 509 |
+
|
| 510 |
+
|
| 511 |
+
|
| 512 |
+
<a href="https://github.com/DominiqueLoyer/NMT_EN_RU/tree/v2.1.0" target="_blank" title="Opens in new tab">
|
| 513 |
+
https://github.com/DominiqueLoyer/NMT_EN_RU/tree/v2.1.0
|
| 514 |
+
</a>
|
| 515 |
+
|
| 516 |
+
|
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|
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(URL)
|
| 519 |
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|
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</dd>
|
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|
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</dl>
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<div class="ui fluid accordion padded grid rel-mb-1">
|
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<div class="active title sixteen wide mobile four wide tablet three wide computer column">
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<h3 class="ui header">
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<div
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id="custom-fields-accordion-trigger"
|
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role="button"
|
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tabindex="0"
|
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+
aria-expanded="true"
|
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aria-controls="custom-fields-panel"
|
| 559 |
+
class="trigger"
|
| 560 |
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>
|
| 561 |
+
<i class="caret right icon" aria-hidden="true"></i>Software
|
| 562 |
+
</div>
|
| 563 |
+
</h3>
|
| 564 |
+
</div>
|
| 565 |
+
<div
|
| 566 |
+
id="custom-fields-panel"
|
| 567 |
+
role="region"
|
| 568 |
+
aria-labelledby="custom-fields-accordion-trigger"
|
| 569 |
+
class="active content sixteen wide mobile twelve wide tablet thirteen wide computer column"
|
| 570 |
+
>
|
| 571 |
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|
| 572 |
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<dl class="details-list">
|
| 573 |
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| 574 |
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|
| 575 |
+
|
| 576 |
+
|
| 577 |
+
|
| 578 |
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<dt class="ui tiny header">
|
| 579 |
+
Repository URL
|
| 580 |
+
|
| 581 |
+
|
| 582 |
+
<a href="https://codemeta.github.io/terms/#codeRepository" aria-label="https://codemeta.github.io/terms/#codeRepository">
|
| 583 |
+
<i class="icon external" aria-hidden="true"></i>
|
| 584 |
+
</a>
|
| 585 |
+
|
| 586 |
+
</dt>
|
| 587 |
+
|
| 588 |
+
<dd>
|
| 589 |
+
|
| 590 |
+
|
| 591 |
+
<a href="https://github.com/DominiqueLoyer/NMT_EN_RU">https://github.com/DominiqueLoyer/NMT_EN_RU</a>
|
| 592 |
+
|
| 593 |
+
</dd>
|
| 594 |
+
|
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</dl>
|
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</div>
|
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</div>
|
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<div class="ui divider"></div>
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|
| 792 |
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</section>
|
| 793 |
+
|
| 794 |
+
<section
|
| 795 |
+
id="citations-search"
|
| 796 |
+
data-record-pids='{"doi": {"client": "datacite", "identifier": "10.5281/zenodo.19423705", "provider": "datacite"}, "oai": {"identifier": "oai:zenodo.org:19423705", "provider": "oai"}}'
|
| 797 |
+
data-record-parent-pids='{"doi": {"client": "datacite", "identifier": "10.5281/zenodo.18210211", "provider": "datacite"}}'
|
| 798 |
+
data-citations-endpoint="https://zenodo-broker.web.cern.ch/api/relationships"
|
| 799 |
+
aria-label="Record citations"
|
| 800 |
+
class="rel-mb-1"
|
| 801 |
+
>
|
| 802 |
+
</section>
|
| 803 |
+
|
| 804 |
+
</article>
|
| 805 |
+
|
| 806 |
+
|
| 807 |
+
<aside class="sixteen wide tablet five wide computer column sidebar"
|
| 808 |
+
aria-label="Record details">
|
| 809 |
+
|
| 810 |
+
|
| 811 |
+
|
| 812 |
+
|
| 813 |
+
|
| 814 |
+
<section id="metrics" aria-label="Metrics" class="ui segment rdm-sidebar sidebar-container">
|
| 815 |
+
|
| 816 |
+
|
| 817 |
+
<div class="ui tiny two statistics rel-mt-1">
|
| 818 |
+
|
| 819 |
+
|
| 820 |
+
|
| 821 |
+
<div class="ui statistic">
|
| 822 |
+
<div class="value">8</div>
|
| 823 |
+
<div class="label">
|
| 824 |
+
<i aria-hidden="true" class="eye icon"></i>
|
| 825 |
+
Views
|
| 826 |
+
</div>
|
| 827 |
+
</div>
|
| 828 |
+
|
| 829 |
+
<div class="ui statistic">
|
| 830 |
+
<div class="value">1</div>
|
| 831 |
+
<div class="label">
|
| 832 |
+
<i aria-hidden="true" class="download icon"></i>
|
| 833 |
+
Downloads
|
| 834 |
+
</div>
|
| 835 |
+
</div>
|
| 836 |
+
</div>
|
| 837 |
+
|
| 838 |
+
<div class="ui accordion rel-mt-1 centered">
|
| 839 |
+
<div class="title trigger" tabindex="0">
|
| 840 |
+
<i class="caret right icon" aria-hidden="true"></i>
|
| 841 |
+
<span
|
| 842 |
+
data-open-text="Show more details"
|
| 843 |
+
data-close-text="Show less details"
|
| 844 |
+
>
|
| 845 |
+
Show more details
|
| 846 |
+
</span>
|
| 847 |
+
</div>
|
| 848 |
+
|
| 849 |
+
<div class="content">
|
| 850 |
+
<table id="record-statistics" class="ui definition table fluid">
|
| 851 |
+
<thead>
|
| 852 |
+
<tr>
|
| 853 |
+
<th></th>
|
| 854 |
+
<th class="right aligned">All versions</th>
|
| 855 |
+
<th class="right aligned">This version</th>
|
| 856 |
+
</tr>
|
| 857 |
+
</thead>
|
| 858 |
+
<tbody>
|
| 859 |
+
<tr>
|
| 860 |
+
<td>
|
| 861 |
+
Views
|
| 862 |
+
<i
|
| 863 |
+
tabindex="0"
|
| 864 |
+
role="button"
|
| 865 |
+
style="position:relative"
|
| 866 |
+
class="popup-trigger question circle small icon"
|
| 867 |
+
aria-expanded="false"
|
| 868 |
+
aria-label="More info"
|
| 869 |
+
data-variation="mini inverted"
|
| 870 |
+
>
|
| 871 |
+
</i>
|
| 872 |
+
<p role="tooltip" class="popup-content ui flowing popup transition hidden">
|
| 873 |
+
Total views
|
| 874 |
+
</p>
|
| 875 |
+
</td>
|
| 876 |
+
<td data-label="All versions" class="right aligned">
|
| 877 |
+
|
| 878 |
+
8
|
| 879 |
+
|
| 880 |
+
</td>
|
| 881 |
+
<td data-label="This version" class="right aligned">
|
| 882 |
+
|
| 883 |
+
0
|
| 884 |
+
|
| 885 |
+
</td>
|
| 886 |
+
</tr>
|
| 887 |
+
<tr>
|
| 888 |
+
<td>
|
| 889 |
+
Downloads
|
| 890 |
+
<i
|
| 891 |
+
tabindex="0"
|
| 892 |
+
role="button"
|
| 893 |
+
style="position:relative"
|
| 894 |
+
class="popup-trigger question circle small icon"
|
| 895 |
+
aria-expanded="false"
|
| 896 |
+
aria-label="More info"
|
| 897 |
+
data-variation="mini inverted"
|
| 898 |
+
>
|
| 899 |
+
</i>
|
| 900 |
+
<p role="tooltip" class="popup-content ui flowing popup transition hidden">
|
| 901 |
+
Total downloads
|
| 902 |
+
</p>
|
| 903 |
+
</td>
|
| 904 |
+
<td data-label="All versions" class="right aligned">
|
| 905 |
+
|
| 906 |
+
1
|
| 907 |
+
|
| 908 |
+
</td>
|
| 909 |
+
<td data-label="This version" class="right aligned">
|
| 910 |
+
|
| 911 |
+
0
|
| 912 |
+
|
| 913 |
+
</td>
|
| 914 |
+
</tr>
|
| 915 |
+
<tr>
|
| 916 |
+
<td>
|
| 917 |
+
Data volume
|
| 918 |
+
<i
|
| 919 |
+
tabindex="0"
|
| 920 |
+
role="button"
|
| 921 |
+
style="position:relative"
|
| 922 |
+
class="popup-trigger question circle small icon"
|
| 923 |
+
aria-expanded="false"
|
| 924 |
+
aria-label="More info"
|
| 925 |
+
data-variation="mini inverted"
|
| 926 |
+
>
|
| 927 |
+
</i>
|
| 928 |
+
<p role="tooltip" class="popup-content ui flowing popup transition hidden">
|
| 929 |
+
Total data volume
|
| 930 |
+
</p>
|
| 931 |
+
</td>
|
| 932 |
+
|
| 933 |
+
<td data-label="All versions" class="right aligned">391.5 kB</td>
|
| 934 |
+
<td data-label="This version" class="right aligned">0 Bytes</td>
|
| 935 |
+
</tr>
|
| 936 |
+
</tbody>
|
| 937 |
+
</table>
|
| 938 |
+
<p class="text-align-center rel-mt-1">
|
| 939 |
+
<small>
|
| 940 |
+
<a href="/help/statistics">More info on how stats are collected....</a>
|
| 941 |
+
</small>
|
| 942 |
+
</p>
|
| 943 |
+
</div>
|
| 944 |
+
|
| 945 |
+
</div>
|
| 946 |
+
|
| 947 |
+
</section>
|
| 948 |
+
|
| 949 |
+
<div class="sidebar-container">
|
| 950 |
+
<h2 class="ui medium top attached header mt-0">Versions</h2>
|
| 951 |
+
<div id="record-versions" class="ui segment rdm-sidebar bottom attached pl-0 pr-0 pt-0">
|
| 952 |
+
<div class="versions">
|
| 953 |
+
<div id="recordVersions" data-record='{"access": {"embargo": {"active": false, "reason": null}, "files": "public", "record": "public", "status": "open"}, "created": "2026-04-04T23:44:42.942563+00:00", "custom_fields": {"code:codeRepository": "https://github.com/DominiqueLoyer/NMT_EN_RU"}, "deletion_status": {"is_deleted": false, "status": "P"}, "expanded": {"parent": {"access": {"owned_by": {"active": null, "blocked_at": null, "confirmed_at": null, "email": "", "id": "1490455", "is_current_user": false, "links": {"avatar": "https://zenodo.org/api/users/1490455/avatar.svg", "records_html": "https://zenodo.org/search/records?q=parent.access.owned_by.user:1490455", "self": "https://zenodo.org/api/users/1490455"}, "profile": {"affiliations": "Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al", "full_name": "Loyer Dominique"}, "username": "DominiqueLoyer", "verified_at": null}}}}, "files": {"count": 1, "default_preview": "DominiqueLoyer/NMT_EN_RU-v2.1.0.zip", "enabled": true, "entries": 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Published January 7, 2026
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<span class="creatibutor-name">Loyer, Dominique
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(Researcher)</span><sup class="font-tiny">1</sup></a>
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Show affiliations
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<section class="ui sixteen wide column content" id="creators-affiliations" aria-label="Affiliations for creators">
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1.
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<a class="no-text-decoration"
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href="https://ror.org/002rjbv21"
|
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aria-label="Université du Québec à Montréal's ROR profile"
|
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title="Université du Québec à Montréal's ROR profile"
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>
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<img class="ml-5 inline-id-icon"
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src="/static/images/ror-icon.svg"
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alt="ROR icon"
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/>
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</a>
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Université du Québec à Montréal
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</li>
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<div style="word-wrap: break-word;">
|
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<p>Russian to English Neural Translation model based on Marian HF </p>
|
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<p>v1.0 https://doi.org/10.5281/zenodo.18167090</p>
|
| 360 |
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<p>v2.0 https://doi.org/10.5281/zenodo.18210212</p>
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</div>
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</section>
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id="record-files" class="rel-mt-2 rel-mb-3"
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aria-label="Files"
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<div class="ui accordion panel mb-10 open" href="#files-preview-accordion-panel">
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>
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<span id="preview-file-title">taln-enru-17avril25.ipynb</span>
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<i class="angle right icon" aria-hidden="true"></i>
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|
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|
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role="region"
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id="files-preview-accordion-panel"
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aria-labelledby="files-preview-accordion-trigger"
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class="active content preview-container pt-0 open"
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<div>
|
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|
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|
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<iframe
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src="/records/18167090/preview/taln-enru-17avril25.ipynb?include_deleted=0"
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|
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<div class="ui accordion panel mb-10 open" href="#files-list-accordion-panel">
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<h3 class="active title panel-heading open m-0">
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<div role="button" id="files-list-accordion-trigger" aria-controls="files-list-accordion-panel" aria-expanded="true" tabindex="0" class="trigger">
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Files
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<i class="angle right icon" aria-hidden="true"></i>
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<div role="region" id="files-list-accordion-panel" aria-labelledby="files-list-accordion-trigger" class="active content pt-0">
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|
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<thead>
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<tr>
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<th>Name</th>
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<th>Size</th>
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<th class>
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<a role="button" class="ui compact mini button right floated archive-link" href="https://zenodo.org/api/records/18167090/files-archive">
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<i class="file archive icon button" aria-hidden="true"></i> Download all
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<td class="ten wide">
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<div>
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<a href="/records/18167090/files/taln-enru-17avril25.ipynb?download=1">taln-enru-17avril25.ipynb</a>
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<small class="ui text-muted font-tiny">md5:9f230b53e226e59dd402f31a231c7a0f
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<i class="question circle checksum icon"></i>
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<td>236.3 kB</td>
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<span>
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<a role="button" class="ui compact mini button preview-link" href="/records/18167090/preview/taln-enru-17avril25.ipynb?include_deleted=0" target="preview-iframe" data-file-key="taln-enru-17avril25.ipynb">
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<i class="eye icon" aria-hidden="true"></i>Preview
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<a role="button" class="ui compact mini button" href="/records/18167090/files/taln-enru-17avril25.ipynb?download=1">
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<i class="download icon" aria-hidden="true"></i>Download
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</a>
|
| 458 |
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| 460 |
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|
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|
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|
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<section id="additional-details" class="rel-mt-2" aria-label="Additional record details">
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<section
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id="citations-search"
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|
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<aside class="sixteen wide tablet five wide computer column sidebar"
|
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aria-label="Record details">
|
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|
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<section id="metrics" aria-label="Metrics" class="ui segment rdm-sidebar sidebar-container">
|
| 511 |
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|
| 513 |
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<div class="ui tiny two statistics rel-mt-1">
|
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|
| 515 |
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|
| 516 |
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|
| 517 |
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<div class="ui statistic">
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<i aria-hidden="true" class="eye icon"></i>
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Views
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<div class="value">0</div>
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| 527 |
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<div class="label">
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| 528 |
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<i aria-hidden="true" class="download icon"></i>
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Downloads
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| 530 |
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| 531 |
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| 532 |
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| 533 |
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|
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<div class="ui accordion rel-mt-1 centered">
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<div class="title trigger" tabindex="0">
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<i class="caret right icon" aria-hidden="true"></i>
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<span
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data-open-text="Show more details"
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Show more details
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| 542 |
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| 543 |
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| 544 |
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<div class="content">
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<table id="record-statistics" class="ui definition table fluid">
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<thead>
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<tr>
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<th></th>
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<th class="right aligned">All versions</th>
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| 551 |
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<th class="right aligned">This version</th>
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| 552 |
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|
| 553 |
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| 554 |
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<tbody>
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| 556 |
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Views
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| 558 |
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</i>
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| 568 |
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<p role="tooltip" class="popup-content ui flowing popup transition hidden">
|
| 569 |
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Total views
|
| 570 |
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</p>
|
| 571 |
+
</td>
|
| 572 |
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<td data-label="All versions" class="right aligned">
|
| 573 |
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|
| 574 |
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1
|
| 575 |
+
|
| 576 |
+
</td>
|
| 577 |
+
<td data-label="This version" class="right aligned">
|
| 578 |
+
|
| 579 |
+
1
|
| 580 |
+
|
| 581 |
+
</td>
|
| 582 |
+
</tr>
|
| 583 |
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<tr>
|
| 584 |
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<td>
|
| 585 |
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Downloads
|
| 586 |
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<i
|
| 587 |
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tabindex="0"
|
| 588 |
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role="button"
|
| 589 |
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style="position:relative"
|
| 590 |
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class="popup-trigger question circle small icon"
|
| 591 |
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aria-expanded="false"
|
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aria-label="More info"
|
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data-variation="mini inverted"
|
| 594 |
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>
|
| 595 |
+
</i>
|
| 596 |
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<p role="tooltip" class="popup-content ui flowing popup transition hidden">
|
| 597 |
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Total downloads
|
| 598 |
+
</p>
|
| 599 |
+
</td>
|
| 600 |
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<td data-label="All versions" class="right aligned">
|
| 601 |
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|
| 602 |
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0
|
| 603 |
+
|
| 604 |
+
</td>
|
| 605 |
+
<td data-label="This version" class="right aligned">
|
| 606 |
+
|
| 607 |
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0
|
| 608 |
+
|
| 609 |
+
</td>
|
| 610 |
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</tr>
|
| 611 |
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<tr>
|
| 612 |
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<td>
|
| 613 |
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Data volume
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| 614 |
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<i
|
| 615 |
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tabindex="0"
|
| 616 |
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role="button"
|
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style="position:relative"
|
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class="popup-trigger question circle small icon"
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aria-expanded="false"
|
| 620 |
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aria-label="More info"
|
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data-variation="mini inverted"
|
| 622 |
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>
|
| 623 |
+
</i>
|
| 624 |
+
<p role="tooltip" class="popup-content ui flowing popup transition hidden">
|
| 625 |
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Total data volume
|
| 626 |
+
</p>
|
| 627 |
+
</td>
|
| 628 |
+
|
| 629 |
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<td data-label="All versions" class="right aligned">0 Bytes</td>
|
| 630 |
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<td data-label="This version" class="right aligned">0 Bytes</td>
|
| 631 |
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</tr>
|
| 632 |
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</tbody>
|
| 633 |
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</table>
|
| 634 |
+
<p class="text-align-center rel-mt-1">
|
| 635 |
+
<small>
|
| 636 |
+
<a href="/help/statistics">More info on how stats are collected....</a>
|
| 637 |
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</small>
|
| 638 |
+
</p>
|
| 639 |
+
</div>
|
| 640 |
+
|
| 641 |
+
</div>
|
| 642 |
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|
| 643 |
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</section>
|
| 644 |
+
|
| 645 |
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<div class="sidebar-container">
|
| 646 |
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<h2 class="ui medium top attached header mt-0">Versions</h2>
|
| 647 |
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<div id="record-versions" class="ui segment rdm-sidebar bottom attached pl-0 pr-0 pt-0">
|
| 648 |
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<div class="versions">
|
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<h2 class="ui small header text-muted p-0 mb-5"><small>Technical metadata</small></h2>
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<dl class="m-0">
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<dt class="inline"><small>Created</small></dt>
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<small>April 4, 2026</small>
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<dt class="rel-mt-1 inline"><small>Modified</small></dt>
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<small>April 4, 2026</small>
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{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[],"dockerImageVersionId":31329,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-04-04T23:21:05.198394Z","iopub.execute_input":"2026-04-04T23:21:05.198720Z","iopub.status.idle":"2026-04-04T23:21:06.219509Z","shell.execute_reply.started":"2026-04-04T23:21:05.198693Z","shell.execute_reply":"2026-04-04T23:21:06.218934Z"}},"outputs":[],"execution_count":1},{"cell_type":"code","source":"# -*- coding: utf-8 -*-\n\"\"\"\nDIC-9345 - Projet 2: Traduction Automatique Neuronale (TAN) - EN->RU\nCe script est adapté pour tourner sur Kaggle avec un accélérateur GPU.\nCorrection des erreurs WandB, chrF++ et mise à jour de l'API Tokenizer (text_target).\n\"\"\"\n\n# @title 1. Installation des bibliothèques nécessaires\n# Installe les bibliothèques Hugging Face (transformers, datasets), SacreBLEU et Accelerate.\n# /!\\ ATTENTION SUR KAGGLE : Exécutez la ligne ci-dessous dans une CELLULE SÉPARÉE tout en haut du notebook /!\\\n# !pip install transformers[torch] datasets sacrebleu accelerate evaluate -q\n\nimport sys\nimport subprocess\nimport site\nfrom importlib import reload\n\ntry:\n import evaluate\n import datasets\n import sacrebleu\n print(\"Dépendances vérifiées et présentes.\")\nexcept ImportError:\n print(\"Installation automatique des dépendances en cours (veuillez patienter)...\")\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"transformers[torch]\", \"datasets\", \"sacrebleu\", \"accelerate\", \"evaluate\", \"-q\"])\n reload(site) # Force l'environnement Kaggle à reconnaître les nouveaux modules instantanément\n print(\"Installation terminée !\")\n\n# @title 2. Importations et Configuration Initiale\nimport os\nimport torch\nimport numpy as np\nfrom datasets import load_dataset\nimport evaluate \nfrom transformers import (\n AutoTokenizer,\n AutoModelForSeq2SeqLM,\n DataCollatorForSeq2Seq,\n Seq2SeqTrainingArguments,\n Seq2SeqTrainer\n)\n\n# Configuration pour Anglais -> Russe\nMODEL_CHECKPOINT = \"Helsinki-NLP/opus-mt-en-ru\" \nSOURCE_LANG = \"en\"\nTARGET_LANG = \"ru\"\nDATASET_NAME = \"opus_books\"\nDATASET_CONFIG = \"en-ru\"\n\n# Limites pour l'exemple (TEST TRÈS RAPIDE)\nMAX_TRAIN_SAMPLES = 1000 \nMAX_VAL_SAMPLES = 100 \nMAX_TEST_SAMPLES = 100 \nMAX_INPUT_LENGTH = 128\nMAX_TARGET_LENGTH = 128\n\n# Vérification explicite du device GPU\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Utilisation du périphérique : {device}\")\nif torch.cuda.is_available():\n print(f\"Nom du GPU: {torch.cuda.get_device_name(0)}\")\nelse:\n print(\"Aucun GPU détecté, utilisation du CPU.\")\n\n\n# @title 3. Chargement et Prétraitement des Données\ntry:\n raw_datasets_full = load_dataset(DATASET_NAME, DATASET_CONFIG)\n print(f\"Dataset {DATASET_NAME} ({DATASET_CONFIG}) chargé.\")\n \n train_test_split = raw_datasets_full['train'].train_test_split(test_size=0.05, seed=42) \n train_val_split = train_test_split['train'].train_test_split(test_size=0.05, seed=42) \n\n raw_datasets = {\n 'train': train_val_split['train'],\n 'validation': train_val_split['test'],\n 'test': train_test_split['test']\n }\n print(\"Dataset divisé en train/validation/test.\")\n\nexcept Exception as e:\n print(f\"Erreur lors du chargement ou de la division du dataset : {e}\")\n raise e\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_CHECKPOINT)\nprint(f\"Tokenizer chargé pour {MODEL_CHECKPOINT}\")\n\ndef preprocess_function(examples):\n if \"translation\" in examples: \n inputs = [ex[SOURCE_LANG] for ex in examples[\"translation\"]]\n targets = [ex[TARGET_LANG] for ex in examples[\"translation\"]]\n else: \n inputs = [ex[SOURCE_LANG] for ex in examples[\"translation\"]]\n targets = [ex[TARGET_LANG] for ex in examples[\"translation\"]]\n\n model_inputs = tokenizer(inputs, max_length=MAX_INPUT_LENGTH, truncation=True, padding=False) \n \n # <-- CORRECTION ICI : Utilisation de text_target au lieu de as_target_tokenizer()\n labels = tokenizer(text_target=targets, max_length=MAX_TARGET_LENGTH, truncation=True, padding=False)\n\n model_inputs[\"labels\"] = labels[\"input_ids\"]\n return model_inputs\n\ntokenized_datasets = {}\nfor split, dataset in raw_datasets.items():\n cols_to_remove = ['id', 'translation']\n tokenized_datasets[split] = dataset.map(\n preprocess_function,\n batched=True,\n remove_columns=cols_to_remove,\n desc=f\"Tokenizing {split} split...\"\n )\n\nprint(\"Prétraitement terminé.\")\n\n# Sélectionner un sous-ensemble\ntrain_dataset = tokenized_datasets[\"train\"]\neval_dataset = tokenized_datasets[\"validation\"]\ntest_dataset_tokenized = tokenized_datasets[\"test\"]\ntest_dataset_raw = raw_datasets[\"test\"] \n\nif MAX_TRAIN_SAMPLES and MAX_TRAIN_SAMPLES < len(train_dataset):\n train_dataset = train_dataset.shuffle(seed=42).select(range(MAX_TRAIN_SAMPLES))\nif MAX_VAL_SAMPLES and MAX_VAL_SAMPLES < len(eval_dataset):\n eval_dataset = eval_dataset.shuffle(seed=42).select(range(MAX_VAL_SAMPLES))\nif MAX_TEST_SAMPLES and MAX_TEST_SAMPLES < len(test_dataset_tokenized):\n indices = list(range(len(test_dataset_tokenized)))\n np.random.seed(42)\n np.random.shuffle(indices)\n selected_indices = indices[:MAX_TEST_SAMPLES]\n\n test_dataset_tokenized = test_dataset_tokenized.select(selected_indices)\n test_dataset_raw = test_dataset_raw.select(selected_indices)\n\n\n# @title 4. Chargement du Modèle et Configuration de l'Entraînement\nmodel = AutoModelForSeq2SeqLM.from_pretrained(MODEL_CHECKPOINT)\nmodel.to(device)\nprint(f\"Modèle {MODEL_CHECKPOINT} chargé et envoyé sur {device}.\")\n\nrun_name = f\"{MODEL_CHECKPOINT.split('/')[-1]}-finetuned-{SOURCE_LANG}-to-{TARGET_LANG}-quick-test-b4\"\n\n# Arguments d'entraînement \ntraining_args = Seq2SeqTrainingArguments(\n output_dir=run_name,\n eval_strategy=\"epoch\", \n learning_rate=2e-5,\n per_device_train_batch_size=4, \n per_device_eval_batch_size=4, \n weight_decay=0.01,\n save_total_limit=1, \n num_train_epochs=1, \n predict_with_generate=True,\n fp16=torch.cuda.is_available(), \n push_to_hub=False,\n logging_strategy=\"steps\",\n logging_steps=10, \n save_strategy=\"epoch\", \n load_best_model_at_end=True, \n metric_for_best_model=\"bleu\",\n greater_is_better=True,\n generation_max_length=MAX_TARGET_LENGTH,\n report_to=\"none\", \n)\n\ndata_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n\nsacrebleu_metric = evaluate.load(\"sacrebleu\")\nchrf_metric = evaluate.load(\"chrf\")\n\ndef postprocess_text(preds, labels):\n preds = [pred.strip() for pred in preds]\n labels = [[label.strip()] for label in labels]\n return preds, labels\n\ndef compute_metrics(eval_preds):\n preds, labels = eval_preds\n if isinstance(preds, tuple):\n preds = preds[0]\n\n preds = np.where(preds != -100, preds, tokenizer.pad_token_id)\n decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n\n labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n\n decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n\n bleu_result = sacrebleu_metric.compute(predictions=decoded_preds, references=decoded_labels)\n \n chrf_result = chrf_metric.compute(predictions=decoded_preds, references=decoded_labels, word_order=2) \n\n result = {\"bleu\": bleu_result[\"score\"], \"chrf++\": chrf_result[\"score\"]}\n\n prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n result[\"gen_len\"] = np.mean(prediction_lens)\n result = {k: round(v, 4) for k, v in result.items()}\n return result\n\ntrainer = Seq2SeqTrainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n eval_dataset=eval_dataset,\n processing_class=tokenizer,\n data_collator=data_collator,\n compute_metrics=compute_metrics,\n)\n\n\n# @title 5. Lancement de l'Entraînement\nprint(\"Début de l'entraînement TEST RAPIDE (BATCH 4) sur GPU...\")\ntry:\n train_result = trainer.train()\n trainer.save_model()\n metrics = train_result.metrics\n metrics[\"train_samples\"] = len(train_dataset) \n trainer.log_metrics(\"train\", metrics)\n trainer.save_metrics(\"train\", metrics)\n trainer.save_state()\n print(\"Entraînement TEST RAPIDE terminé et modèle sauvegardé.\")\nexcept Exception as e:\n print(f\"Une erreur est survenue pendant l'entraînement : {e}\")\n\n\n# @title 6. Évaluation sur le Jeu de Test\nprint(\"Début de l'évaluation sur le jeu de test (TEST RAPIDE)...\")\nmodel.eval() \n\npredict_results = trainer.predict(test_dataset_tokenized, metric_key_prefix=\"test\")\n\nmetrics = predict_results.metrics\nmetrics[\"test_samples\"] = len(test_dataset_raw) \n\nprint(\"----- Résultats de l'évaluation sur le jeu de test (TEST RAPIDE) -----\")\nprint(f\"Score SacreBLEU: {metrics.get('test_bleu', 'N/A'):.4f}\")\nprint(f\"Score chrF++: {metrics.get('test_chrf++', 'N/A'):.4f}\")\n\nif predict_results.predictions is not None:\n preds = predict_results.predictions\n preds = np.where(preds != -100, preds, tokenizer.pad_token_id)\n decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n references = [ex['translation'][TARGET_LANG] for ex in test_dataset_raw]\n cleaned_preds, cleaned_labels = postprocess_text(decoded_preds, references)\n\n final_bleu_metric = evaluate.load(\"sacrebleu\")\n test_bleu_results_detailed = final_bleu_metric.compute(predictions=cleaned_preds, references=cleaned_labels)\n\n output_prediction_file = os.path.join(run_name, \"test_predictions_ru.txt\")\n output_reference_file = os.path.join(run_name, \"test_references_ru.txt\")\n with open(output_prediction_file, \"w\", encoding=\"utf-8\") as writer:\n writer.write(\"\\n\".join(cleaned_preds))\n with open(output_reference_file, \"w\", encoding=\"utf-8\") as writer:\n writer.write(\"\\n\".join([ref[0] for ref in cleaned_labels]))\n\ntrainer.log_metrics(\"test\", metrics)\ntrainer.save_metrics(\"test\", metrics)\n\n\n# @title 7. Exemple d'Inférence\nprint(\"\\nExemple d'inférence avec le modèle final...\")\nsentence_en = \"Machine translation is fascinating.\"\ninputs = tokenizer(sentence_en, return_tensors=\"pt\").to(device) \nwith torch.no_grad():\n outputs = model.generate(**inputs, max_length=MAX_TARGET_LENGTH, num_beams=4, early_stopping=True)\n\ntranslation_ru = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(f\"Source ({SOURCE_LANG}): {sentence_en}\")\nprint(f\"Traduction ({TARGET_LANG}): {translation_ru}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-04T23:34:32.070566Z","iopub.execute_input":"2026-04-04T23:34:32.070944Z","iopub.status.idle":"2026-04-04T23:35:31.180860Z","shell.execute_reply.started":"2026-04-04T23:34:32.070915Z","shell.execute_reply":"2026-04-04T23:35:31.180031Z"}},"outputs":[{"name":"stdout","text":"Dépendances vérifiées et présentes.\nUtilisation du périphérique : cuda\nNom du GPU: Tesla T4\nDataset opus_books (en-ru) chargé.\nDataset divisé en train/validation/test.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/transformers/models/marian/tokenization_marian.py:176: UserWarning: Recommended: pip install sacremoses.\n warnings.warn(\"Recommended: pip install sacremoses.\")\n","output_type":"stream"},{"name":"stdout","text":"Tokenizer chargé pour Helsinki-NLP/opus-mt-en-ru\nPrétraitement terminé.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Loading weights: 0%| | 0/258 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d4803a94435b4fa380a3267306b66196"}},"metadata":{}},{"name":"stderr","text":"The tied weights mapping and config for this model specifies to tie model.shared.weight to model.decoder.embed_tokens.weight, but both are present in the checkpoints, so we will NOT tie them. You should update the config with `tie_word_embeddings=False` to silence this warning\nThe tied weights mapping and config for this model specifies to tie model.shared.weight to model.encoder.embed_tokens.weight, but both are present in the checkpoints, so we will NOT tie them. You should update the config with `tie_word_embeddings=False` to silence this warning\n","output_type":"stream"},{"name":"stdout","text":"Modèle Helsinki-NLP/opus-mt-en-ru chargé et envoyé sur cuda.\n","output_type":"stream"},{"name":"stderr","text":"The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None}.\n","output_type":"stream"},{"name":"stdout","text":"Début de l'entraînement TEST RAPIDE (BATCH 4) sur GPU...\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/torch/autograd/function.py:583: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n return super().apply(*args, **kwargs) # type: ignore[misc]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n <div>\n \n <progress value='125' max='125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n [125/125 00:36, Epoch 1/1]\n </div>\n <table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: left;\">\n <th>Epoch</th>\n <th>Training Loss</th>\n <th>Validation Loss</th>\n <th>Bleu</th>\n <th>Chrf++</th>\n <th>Gen Len</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>1</td>\n <td>4.251038</td>\n <td>1.930836</td>\n <td>24.321300</td>\n <td>47.007700</td>\n <td>26.990000</td>\n </tr>\n </tbody>\n</table><p>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Writing model shards: 0%| | 0/1 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"96166e759222466a891373ff8c123f90"}},"metadata":{}},{"name":"stderr","text":"There were missing keys in the checkpoint model loaded: ['model.encoder.embed_positions.weight', 'model.decoder.embed_positions.weight', 'lm_head.weight'].\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Writing model shards: 0%| | 0/1 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b96ba25f44634b7dac6327cbd8d076a4"}},"metadata":{}},{"name":"stdout","text":"***** train metrics *****\n epoch = 1.0\n total_flos = 14869GF\n train_loss = 4.2141\n train_runtime = 0:00:38.77\n train_samples = 1000\n train_samples_per_second = 25.791\n train_steps_per_second = 3.224\nEntraînement TEST RAPIDE terminé et modèle sauvegardé.\nDébut de l'évaluation sur le jeu de test (TEST RAPIDE)...\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/torch/autograd/function.py:583: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n return super().apply(*args, **kwargs) # type: ignore[misc]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":""},"metadata":{}},{"name":"stdout","text":"----- Résultats de l'évaluation sur le jeu de test (TEST RAPIDE) -----\nScore SacreBLEU: 21.3427\nScore chrF++: 44.8758\n***** test metrics *****\n test_bleu = 21.3427\n test_chrf++ = 44.8758\n test_gen_len = 31.22\n test_loss = 1.8129\n test_runtime = 0:00:11.90\n test_samples = 100\n test_samples_per_second = 8.398\n test_steps_per_second = 1.092\n\nExemple d'inférence avec le modèle final...\nSource (en): Machine translation is fascinating.\nTraduction (ru): Машинный перевод очарователен.\n","output_type":"stream"}],"execution_count":12}]}
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