juhoinkinen (Juho Inkinen)

posted an update about 1 month ago

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**AI4LAM’s annual conference, AI Everywhere, All at Once
December 3 – 5, 2025, British Library, London**

See the conference programme: 👉 https://www.conftool.org/fantastic-futures-2025/sessions.php

Some program items related to NatLibFi/Annif:
• Workshop:
• Evaluating Automated Subject Indexing Methods, Maximilian Kähler
• Presentations:
• Autocat Cataloguing Assistant
• The usage of hardware resources for automatic subject cataloguing at the German National Library – an analysis and outlook for future challenges, Christoph Poley
• Posters:
• AI-Powered Subject Indexing in the Archives – Piloting Finto AI at the Finnish Literature Society, Milla Eräsaari and Teemu Hirvonen
• From Annotation to Insight: Human-in-the-Loop Machine Learning for Historical Archives in HAICu WP2, C.A. Romein and others

posted an update 7 months ago

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We investigated how the relevance of keyword suggestions produced by Annif has evolved in the publication repositories of

NatLibFi .

The plot shows the scores averaged over months (dots) and between Annif model updates (horizontal lines): the quality of subject suggestions has essentially remained constant since the third model update.

https://doi.org/10.25033/sig.162683
https://github.com/NatLibFi/Annif

posted an update 8 months ago

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We ( @osma , @MonaLehtinen & me, i.e. the Annif team at the National Library of Finland) recently took part in the LLMs4Subjects challenge at the SemEval-2025 workshop. The task was to use large language models (LLMs) to generate good quality subject indexing for bibliographic records, i.e. titles and abstracts.

We are glad to report that our system performed well; it was ranked

🥇 1st in the category where the full vocabulary was used
🥈 2nd in the smaller vocabulary category
🏅 4th in the qualitative evaluations.

14 participating teams developed their own solutions for generating subject headings and the output of each system was assessed using both quantitative and qualitative evaluations. Research papers about most of the systems are going to be published around the time of the workshop in late July, and many pre-prints are already available.

We applied Annif together with several LLMs that we used to preprocess the data sets: translated the GND vocabulary terms to English, translated bibliographic records into English and German as required, and generated additional synthetic training data. After the preprocessing, we used the traditional machine learning algorithms in Annif as well as the experimental XTransformer algorithm that is based on language models. We also combined the subject suggestions generated using English and German language records in a novel way.

More information can be found in our system description preprint: Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs (2504.19675)

See also the task description preprint: SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog (2504.07199)

The Annif models trained for this task are available here: NatLibFi/Annif-LLMs4Subjects-data

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replied to their post 10 months ago

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

replied to their post 10 months 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 10 months ago

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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.

6 replies

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posted an update over 1 year ago

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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 /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).

NatLibFi/Annif

reacted to Wauplin's post with ❤️ over 1 year ago

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🚀 I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is.

Why use the InferenceClient?
🔄 Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.
🤗 Direct integration: easily launch a model to run inference using our Inference Endpoint service.
🚀 Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.

More details in https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility

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Juho Inkinen

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Juho Inkinen

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