And if you want have the models we us at Finto AI, you can get them with annif download "*-fi" NatLibFi/FintoAI-data-YSO from here: https://huggingface.co/NatLibFi/FintoAI-data-YSO
Juho Inkinen
juhoinkinen
AI & ML interests
NLP
Recent Activity
replied to
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12 days ago
Annif is a subject indexing toolkit developed by the National Library of Finland: https://github.com/NatLibFi/Annif
Last November we organized a survey for Annif users, and now the results have been published: https://www.doria.fi/bitstream/handle/10024/190930/Annif%20Users%20Survey.pdf
The report includes an overview of:
• The vocabularies and datasets that are used with Annif
• The workflows that Annif is integrated with
• The problems Annif users are facing
The average ratings for various aspects and features of Annif given by users are shown. In short, in a scale from 1 to 5, the ratings are:
• Overall: 4.4
• Features and functions: 4.1
• Documentation: 4.5
• Smoothness of initial setup: 4.2
• Usability: 4.4
• Achieved quality of subject suggestions: 3.6
The survey also gathered user views on the improvements and new features, which are briefly discussed in the report.
replied to
their
post
12 days ago
Annif is a subject indexing toolkit developed by the National Library of Finland: https://github.com/NatLibFi/Annif
Last November we organized a survey for Annif users, and now the results have been published: https://www.doria.fi/bitstream/handle/10024/190930/Annif%20Users%20Survey.pdf
The report includes an overview of:
• The vocabularies and datasets that are used with Annif
• The workflows that Annif is integrated with
• The problems Annif users are facing
The average ratings for various aspects and features of Annif given by users are shown. In short, in a scale from 1 to 5, the ratings are:
• Overall: 4.4
• Features and functions: 4.1
• Documentation: 4.5
• Smoothness of initial setup: 4.2
• Usability: 4.4
• Achieved quality of subject suggestions: 3.6
The survey also gathered user views on the improvements and new features, which are briefly discussed in the report.
posted
an
update
13 days ago
Annif is a subject indexing toolkit developed by the National Library of Finland: https://github.com/NatLibFi/Annif
Last November we organized a survey for Annif users, and now the results have been published: https://www.doria.fi/bitstream/handle/10024/190930/Annif%20Users%20Survey.pdf
The report includes an overview of:
• The vocabularies and datasets that are used with Annif
• The workflows that Annif is integrated with
• The problems Annif users are facing
The average ratings for various aspects and features of Annif given by users are shown. In short, in a scale from 1 to 5, the ratings are:
• Overall: 4.4
• Features and functions: 4.1
• Documentation: 4.5
• Smoothness of initial setup: 4.2
• Usability: 4.4
• Achieved quality of subject suggestions: 3.6
The survey also gathered user views on the improvements and new features, which are briefly discussed in the report.
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juhoinkinen's activity
replied to
their
post
12 days ago
replied to
their
post
12 days ago
Annif is just a tool. The intention is that users have their own vocabulary and corpora which they use to train their own Annif models. But the (public) corpora and vocabularies we use are here: https://github.com/NatLibFi/Annif-corpora
In the repo there are Finnish, Swedish and English documents indexed with the General Finnish Ontology and some other vocabularies; some txt documents are directly included and some are obtainable with Makefiles.
posted
an
update
13 days ago
Post
625
Annif is a subject indexing toolkit developed by the National Library of Finland: https://github.com/NatLibFi/Annif
Last November we organized a survey for Annif users, and now the results have been published: https://www.doria.fi/bitstream/handle/10024/190930/Annif%20Users%20Survey.pdf
The report includes an overview of:
• The vocabularies and datasets that are used with Annif
• The workflows that Annif is integrated with
• The problems Annif users are facing
The average ratings for various aspects and features of Annif given by users are shown. In short, in a scale from 1 to 5, the ratings are:
• Overall: 4.4
• Features and functions: 4.1
• Documentation: 4.5
• Smoothness of initial setup: 4.2
• Usability: 4.4
• Achieved quality of subject suggestions: 3.6
The survey also gathered user views on the improvements and new features, which are briefly discussed in the report.
Last November we organized a survey for Annif users, and now the results have been published: https://www.doria.fi/bitstream/handle/10024/190930/Annif%20Users%20Survey.pdf
The report includes an overview of:
• The vocabularies and datasets that are used with Annif
• The workflows that Annif is integrated with
• The problems Annif users are facing
The average ratings for various aspects and features of Annif given by users are shown. In short, in a scale from 1 to 5, the ratings are:
• Overall: 4.4
• Features and functions: 4.1
• Documentation: 4.5
• Smoothness of initial setup: 4.2
• Usability: 4.4
• Achieved quality of subject suggestions: 3.6
The survey also gathered user views on the improvements and new features, which are briefly discussed in the report.
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Annif 1.2 has been released!
https://github.com/NatLibFi/Annif/releases/tag/v1.2.0
This release introduces language detection capabilities in the REST API and CLI, improves 🤗 Hugging Face Hub integration, and also includes the usual maintenance work and minor bug fixes.
The new REST API endpoint
The
This release also includes automation of downloading the NLTK datapackage used for tokenization to simplify Annif installation. Maintenance tasks include upgrading dependencies, including a new version of Simplemma that allows better control over memory usage. The bug fixes include restoring the
Python 3.12 is now fully supported (previously NN-ensemble and STWFSA backends were not supported on Python 3.12).
NatLibFi/Annif
https://github.com/NatLibFi/Annif/releases/tag/v1.2.0
This release introduces language detection capabilities in the REST API and CLI, improves 🤗 Hugging Face Hub integration, and also includes the usual maintenance work and minor bug fixes.
The new REST API endpoint
/v1/detect-language expects POST requests that contain a JSON object with the text whose language is to be analyzed and a list of candidate languages. Similarly, the CLI has a new command annif detect-language. Annif projects are typically language specific, so a text of a given language needs to be processed with a project intended for that language; the language detection feature can help in this. For details see this [Wiki page](https://github.com/NatLibFi/Annif/wiki/Language-detection). The language detection is performed with the Simplemma library by [@adbar](https://github.com/adbar) et al.The
annif download command has a new --trust-repo option, which needs to be used if the repository to download from has not been used previously (that is if the repository does not appear in the local Hugging Face Hub cache). This option is introduced to raise awareness of the risks of downloading projects from the internet; the project downloads should only be done from trusted sources. For more information see the [Hugging Face Hub documentation](https://huggingface.co/docs/hub/en/security-pickle).This release also includes automation of downloading the NLTK datapackage used for tokenization to simplify Annif installation. Maintenance tasks include upgrading dependencies, including a new version of Simplemma that allows better control over memory usage. The bug fixes include restoring the
--host option of the annif run command.Python 3.12 is now fully supported (previously NN-ensemble and STWFSA backends were not supported on Python 3.12).
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