Swiss Federal Supreme Court rulings already carry an official label. Apertus 1.5 8B reads the facts in German, French, or Italian. A small sheaf head on those hidden states returns a closed label. It does not write a judgment.

The facts of a case go in. The head returns one of two decisions. The first is the legal sub-area, one of thirteen official names. The second is the outcome, dismissal or approval. The label is the court's own. The reasoning section stays out of the text. Training uses the public sets rcds/swiss_judgment_prediction and rcds/swiss_law_area_prediction, both under CC BY-SA 4.0. Apertus stays frozen. Only the head is trained, for one pass over 63,328 original-language rulings.

On the official test file the legal-area head is right on 88.3 percent of 7,577 cases. Always choosing the most common sub-area is right on 26.0 percent. When the head is at least 90 percent confident it answers 72.5 percent of cases and is right on 98.0 percent of that slice. Rental and lease, employment, substantive criminal law, and urban planning are the strong classes. Public administration is weak, and some tax cases are read as criminal procedure. The validation file shows the same pattern: 93.4 percent accuracy against a 30.6 percent majority baseline.

The outcome task does not beat the obvious baseline. Test accuracy is 80.9 percent, the same as always saying dismissal. The head recalls 1.5 percent of approvals. A confidence of 90 percent or higher is always the label dismissal, including cases that were approved. That score is not a decision.

The useful piece is a triage aid for the legal area, shown only when the head is confident, with a person on everything else. It is not a system that decides a case.

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posted an update —

First training pass

Apertus 8B stayed frozen. A sheaf head trained for one pass on 63,328 original-language rulings. The input is the facts section. The label is the court's own.

On the official test file, legal area is correct for 88.3% of 7,577 cases, against 26.0% for the most common sub-area. At a confidence of 0.90 the head answers 72.5% of cases and is right on 98.0% of those. The validation file agrees: 93.4% against a 30.6% baseline.

Rental and lease, employment, substantive criminal law, and urban planning are the strong classes. Public administration is weak, and some tax cases are read as criminal procedure.

Dismissal or approval does not beat always saying dismissal. Test accuracy is 80.9% on both. Approval recall is 1.5%. A confidence of 0.90 or higher is always the label dismissal.

The head is a triage aid for the legal area when it is confident. It does not decide the case.

posted an update —

Swiss court files are curated

Legal area and judgment are both filtered and written to their own files. The audit passed.

Original languages, train / validation / test:

  • German judgments: 32,036 / 4,077 / 8,270
  • French judgments: 18,595 / 2,597 / 5,569
  • Italian judgments: 2,843 / 367 / 724
  • Legal area, 13 sub-areas, all three languages together: 9,854 / 2,872 / 7,577

The English machine translation is a separate file: 32,363 train rows. It has no official validation or test split. It fits the 2,048-token window. The original-language file does not go into that English pack.

Together the original-language train file has 63,328 rows. The other curated pack stays separate and is still much larger.

posted an update —

Swiss rulings, same rules, own files

The two Federal Supreme Court sets are being curated now, beside the other pack. They are written to their own files, not mixed into that pack.

Legal area is through this pass. The note is the facts. The court's considerations are not stored. The label is the dataset's own sub-area, 13 of them. Train kept 9,854 of 10,475. Rows that did not fit were dropped, not cut. Validation kept 2,872 and test kept 7,577.

Judgment, dismissed or approved, is downloading now. German, French, and Italian are the original rulings. The English file is only the machine translation, and only that file has to fit the 2,048-token window used by the other English notes. Original-language facts may run to 24,000 characters, because that file is for Apertus.

A row is dropped when the facts are empty or when a court-reasoning heading is in the text. The English translation is capped at 40,000 train rows. Both sets are CC BY-SA 4.0.

posted an update —

Other labeled notes, same rules

Beside the two Federal Supreme Court sets, a second pack is being gathered now. Every row is a short note plus a closed label that the source already published. The pack is not finished. Consumer complaint narratives are still being scanned, and WANLI is next.

Train and test, capped per source:

  • BoolQ, SST-5, TREC, Banking77, IMDB, ContractNLI, CLINC, Aegis, GoEmotions, AG News
  • MultiNLI, SNLI, VitaminC
  • Amazon reviews, Yelp reviews, DBpedia, Yahoo Answers

Held out of training, so they only score later:

  • Emotion, TweetEval offensive, QNLI, PAWS, SciQ

Kept in a separate non-commercial file:

  • ANLI, SciFact

A row stays only when the source's own human label still matches the text that is stored.

  • The label is the dataset's label. Teacher labels and model-written labels are dropped. Aegis keeps human prompt labels and drops redacted prompts and jury labels. VitaminC keeps real revisions only. GoEmotions keeps a comment only when it has exactly one emotion.
  • For a claim or a question paired with a passage, both sides have to fit in full. If the passage would have to be cut, the row is dropped, so the label still refers to the whole source text.
  • SciQ stays only when the correct answer is written in the support. The correct option is rotated, so it is not always the first choice.
  • Empty labels, unlabeled test splits, and IMDB's unlabeled reviews are dropped.
  • Exact copies are removed. The official eval split is preferred over the train split.
  • One source can contribute at most 40,000 train rows and 5,000 test rows, stratified by label. Yahoo, DBpedia, and Yelp cannot crowd out the smaller sets.
  • The note has to fit the reader window. Non-commercial licenses stay out of the commercial train file.
  • Complaint text does not include the product name. The product is the label. Yahoo keeps the question, not the best answer.

Left out on purpose: FEVER and HoVer, because the hub file does not contain the evidence sentence. MMLU, because there is no supporting note. Open-Jev, because those rows are marked synthetic. Kev's packed suites, because they repeat the same documents. Generation sets such as commit messages, because they have no closed label.

That is the curation. A kept row is a decision the source already made, on text that is still complete, with no one source allowed to dominate the file.

posted an update —

8B as the reader

Apertus 1.5 8B is the reader. A small decision head sits on its hidden states and has to return a closed label, not an open answer.

The 70B does not fit a 24 GB GPU, so it is out. A full fine-tune of the 8B does not fit either. The base stays frozen, or takes a small adapter.

The sheaf model stays a separate line of work. It is not this prototype.

posted an update —

Own project

This entry is Apertus Adoption, Own project. The prototype starts during this hackathon and will be open source.

Swiss Voices is the track for dialects, Swiss knowledge, and values on the raw model. Academia only fits a published partner brief, and none of those briefs is this task.

Submissions close on 16 October 2026 at 12:00 CEST.

posted an update —

Court facts, official labels

Two public sets, both CC BY-SA 4.0:

  • rcds/swiss_judgment_prediction for approved or dismissed
  • rcds/swiss_law_area_prediction for the legal area

The note is the facts of the case. The court's reasoning stays out, so the outcome is not already written in the text. The label is the court's own label.

German, French, and Italian are the original rulings. Apertus reads those languages. The judgment set also has an English machine translation.

posted an update —

8B as the reader

Apertus 1.5 8B is the reader. A small decision head sits on its hidden states and has to return a closed label, not an open answer.

The 70B does not fit a 24 GB GPU, so it is out. A full fine-tune of the 8B does not fit either. The base stays frozen, or takes a small adapter.

The sheaf model stays a separate line of work. It is not this prototype.

posted an update —

Own project

This entry is Apertus Adoption, Own project. The prototype starts during this hackathon and will be open source.

Swiss Voices is the track for dialects, Swiss knowledge, and values on the raw model. Academia only fits a published partner brief, and none of those briefs is this task.

Submissions close on 16 October 2026 at 12:00 CEST.

posted an update —

Court facts, official labels

Two public sets, both CC BY-SA 4.0:

  • rcds/swiss_judgment_prediction for approved or dismissed
  • rcds/swiss_law_area_prediction for the legal area

The note is the facts of the case. The court's reasoning stays out, so the outcome is not already written in the text. The label is the court's own label.

German, French, and Italian are the original rulings. Apertus reads those languages. The judgment set also has an English machine translation.

posted an update —

8B as the reader

Apertus 1.5 8B is the reader. A small decision head sits on its hidden states and has to return a closed label, not an open answer.

The 70B does not fit a 24 GB GPU, so it is out. A full fine-tune of the 8B does not fit either. The base stays frozen, or takes a small adapter.

The sheaf model stays a separate line of work. It is not this prototype.

Submission history