Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Size:
100K - 1M
License:
| pretty_name: TENDRIL-Train | |
| language: | |
| - de | |
| - fr | |
| - en | |
| license: other | |
| license_name: openrail-d | |
| license_link: LICENSE.md | |
| task_categories: | |
| - text-retrieval | |
| task_ids: | |
| - document-retrieval | |
| annotations_creators: | |
| - machine-generated | |
| source_datasets: | |
| - original | |
| size_categories: | |
| - 100K<n<1M | |
| tags: | |
| - public-procurement | |
| - sustainability | |
| - switzerland | |
| - llm-as-a-judge | |
| - retrieval | |
| extra_gated_prompt: TENDRIL-Train is released under the TENDRIL Open RAIL-D License (see LICENSE.md). Its use-based restrictions | |
| (Attachment A) include a ban on extracting personal data or re-identifying persons, and a duty to disclose training on the | |
| test collection. They must be passed on to anyone who receives the data or a derivative. | |
| extra_gated_fields: | |
| I agree to the TENDRIL Open RAIL-D License and its use-based restrictions: checkbox | |
| extra_gated_button_content: Agree and access the dataset | |
| configs: | |
| - config_name: corpus | |
| data_files: | |
| - split: train | |
| path: data/corpus.parquet | |
| - config_name: queries | |
| data_files: | |
| - split: train | |
| path: data/queries.parquet | |
| - config_name: qrels | |
| data_files: | |
| - split: train | |
| path: data/qrels.parquet | |
| default: true | |
| # TENDRIL-Train: LLM-Judged Tender Retrieval Data | |
| Training data for retrieving sustainability-relevant passages from Swiss public procurement tenders. It accompanies | |
| the human-annotated benchmark [IntelliProcure/TENDRIL](https://huggingface.co/datasets/IntelliProcure/TENDRIL). | |
| Queries are the areas of action (Handlungsfelder) and criteria of our sustainability catalogs, which are based on | |
| Swiss and EU recommendations for sustainable public procurement. Each query-passage pair was graded by a large | |
| language model. | |
| ## Dataset structure | |
| The configs are in MTEB retrieval format, all with the split `train`: | |
| | Config | Columns | Rows | | |
| |---|---|---| | |
| | `corpus` | `id`, `text`, `project`, `document`, `page` | 85,772 passages | | |
| | `queries` | `id`, `text`, `level`, `catalog`, `area` | 1,041 queries | | |
| | `qrels` (default) | `query-id`, `corpus-id`, `score` | 911,540 judgments | | |
| - **`score`**: 0 = not relevant, 1 = partially relevant, 2 = relevant. | |
| - **`level`**: `area_of_action` (413 queries) or `criterion` (628 queries). For criteria, `area` gives the parent | |
| area of action. | |
| - **`project` and `document`**: keyed hashes of the SIMAP project number and of the document file name. They let | |
| you group passages by project or document (631 projects, 2,892 documents) but cannot be reversed. A passage was | |
| only judged against queries selected for its own project. | |
| - **`page`**: the page of the source document the passage comes from. | |
| | Level | Score 0 | Score 1 | Score 2 | | |
| |---|---|---|---| | |
| | Area of action | 413,877 | 30,631 | 12,141 | | |
| | Criterion | 425,928 | 22,570 | 6,393 | | |
| | Total | 839,805 | 53,201 | 18,534 | | |
| Queries per catalog: Construction_Buildings 152, Construction_Area 127, Light_Elect_Appl 126, | |
| Construction_Civil_Engineering_Infrastructure 124, Info_Com_Tech 123, Road_Transport 114, Furniture 63, | |
| Product_Independent_Criteria 61, Print_Services 48, Green_Spaces 41, Food 26, Paper_and_Stationery 16, | |
| Electricity 12, IT_Software 8. | |
|  | |
| Each row covers the judgments against one catalog's queries, with the number of projects in parentheses. A project can | |
| be judged against several catalogs, so projects, documents, and passages repeat across rows, and the column headers | |
| give the unique totals. A positive is a judgment with score 1 or 2. | |
| ```python | |
| from datasets import load_dataset | |
| corpus = load_dataset("IntelliProcure/TENDRIL-Train", "corpus", split="train") | |
| queries = load_dataset("IntelliProcure/TENDRIL-Train", "queries", split="train") | |
| qrels = load_dataset("IntelliProcure/TENDRIL-Train", "qrels", split="train") | |
| ``` | |
| ## Dataset creation | |
| 1. **Project and document selection.** 800 tender projects published on [simap.ch](https://www.simap.ch) were | |
| sampled, with up to five documents each. Every project gets one criterion per selected area of action. Areas are | |
| drawn by quota from tiers of prior relevance evidence (validated relevant, unvalidated relevant, unsure, | |
| irrelevant), plus a coverage top-up so that every area appears in at least three projects. Projects of the | |
| TENDRIL test set are excluded. The random seed is 42. | |
| 2. **Passage filtering.** Passages from the IntelliProcure document pipeline are kept if they have 20–5,000 | |
| characters, are not dominated by digits or symbols, are not degenerate tables, and are not near-duplicates | |
| (5-gram Jaccard ≥ 0.7). | |
| 3. **Anonymization.** Person names, e-mail addresses, phone numbers, URLs, account numbers and credentials are | |
| replaced by placeholders such as `<PERSON_1>`, using [gheim](https://huggingface.co/joelbarmettler/gheim-ch-560m). | |
| 4. **Query wording.** Areas of action use a German summary of at most 100 words, generated from the catalog by | |
| `deepseek/deepseek-v4-flash` (temperature 0). Criteria use the criterion text followed by its ambition levels | |
| (Basis, Gute Praxis, Vorbild). The same wording is used in the test set. | |
| 5. **Relevance judgments.** Each query-passage pair of a project was graded by `z-ai/glm-5.3-flash` (low reasoning | |
| effort, temperature 0) via OpenRouter, using the shortest of three versions of the German annotation guidelines, | |
| in October 2026. Of 906,984 requests, 7 could not be parsed and 47 were truncated. | |
| 6. **Restricted documents.** Projects whose tender documents limit their use to the preparation of an offer, or | |
| prohibit publication or disclosure to third parties, were excluded (169 of the 800 projects). | |
| ## Label quality | |
| The judge was selected on a human-annotated calibration set of 1,425 query-passage pairs (three annotators, ten | |
| catalogs), out of eight LLMs at two reasoning efforts and three prompt versions. Agreement between the selected judge | |
| and the curated human labels: | |
| | Level | Cohen's κ (0/1/2) | Cohen's κ (relevant vs. not) | | |
| |---|---|---| | |
| | Area of action | 0.48 | 0.62 | | |
| | Criterion | 0.53 | 0.65 | | |
| For comparison, Krippendorff's α between the three human annotators is 0.57 (area) and 0.59 (criterion). The labels | |
| are noisy and should be treated as weak supervision. | |
| ## Limitations | |
| - **Missing test sectors.** No training queries come from the Textiles or Wastepaper_Cleaning_Refuse catalogs, which | |
| appear in the test set. | |
| - **Skewed labels.** Queries have very different numbers of judgments (median 349, maximum 22,170), and 92% of | |
| judgments are 0. | |
| - **Imperfect anonymization.** Automatic anonymization can miss entities; please report any personal data you find. | |
| ## Access and versions | |
| The dataset is gated: access is granted automatically after accepting the license terms. Passages are removed on | |
| request, for example when a person reports remaining personal data. Each release is marked with a git tag; pin a tag | |
| or commit with `revision=`. | |
| ## License | |
| The selection, queries, relevance judgments, anonymization and identifiers (the Licensed Contributions) are | |
| released under the [TENDRIL Open RAIL-D License](LICENSE.md), which permits commercial use but adds use-based | |
| restrictions specific to procurement. Models trained on this data are not Derivatives of the Data under that | |
| license. The tender documents themselves remain the property of their authors; see the license for details. | |
| ## Citation | |
| ```bibtex | |
| @misc{rolshoven2026tendril, | |
| title = {{TENDRIL}: Benchmarking Sustainability-Focused Retrieval in Swiss Public Procurement}, | |
| author = {Rolshoven, Luca and Probst, Sophie and Matoshi, Veton and Ellendorff, Tilia and Meili, Rahel and | |
| Hostettler, Sarah and Jann, Jeremy Austin and Jans, Meret and Tschumi, Pascal and St{\"u}rmer, Matthias}, | |
| year = {2026} | |
| } | |
| ``` | |