Datasets:

amergoel
/

SoftOR

+# SoftOR release terms
+Copyright (c) 2026 SoftOR authors.
+The authors' original SoftOR text, annotations, benchmark designs, documentation, and original LexiBench data are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0):
+https://creativecommons.org/licenses/by/4.0/
+The full legal terms are available at:
+https://creativecommons.org/licenses/by/4.0/legalcode
+You may share and adapt the original contributions, including commercially, subject to attribution, a link to the license, indication of changes, and the other license conditions. No warranties are provided.
+This grant applies only to rights held by the SoftOR authors. It does not replace or expand licenses on third-party source material. This is a mixed-source release, which is why the dataset metadata uses `license: other` rather than asserting one license for every file.
+- COMPASS source material: MIT notice in `licenses/COMPASS.txt`.
+- TravelPlanner dataset material: CC BY 4.0, as stated by its official Hugging Face dataset card. The separate source-code MIT notice is retained in `licenses/TravelPlanner-code.txt` and is not a substitute dataset license.
+- SchedulingBenchmarks Nurse Rostering source material: attributed to the original source. No explicit source-data license was found on the NRP landing page at preparation time. This release grants no additional rights in that underlying material. Original SoftOR adaptations remain covered by the original-contribution grant above to the extent of the authors' rights.
+See THIRD_PARTY_NOTICES.md for source links.

README.md ADDED Viewed

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+---
+language:
+- en
+license: other
+license_name: softor-mixed-source-terms
+license_link: LICENSE.md
+task_categories:
+- text-generation
+size_categories:
+- n<1K
+tags:
+- optimization
+- operations-research
+- mathematical-modeling
+- soft-constraints
+- lexicographic-optimization
+- benchmark
+pretty_name: SoftOR
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: test
+    path: data/*.parquet
+- config_name: compass
+  data_files:
+  - split: test
+    path: data/compass.parquet
+- config_name: travelplanner
+  data_files:
+  - split: test
+    path: data/travelplanner.parquet
+- config_name: lexibench
+  data_files:
+  - split: test
+    path: data/lexibench.parquet
+- config_name: nurse_rostering
+  data_files:
+  - split: test
+    path: data/nurse_rostering.parquet
+---
+# SoftOR
+SoftOR is a 355-instance benchmark for translating natural-language decision problems into optimization models that distinguish hard requirements from soft preferences. It accompanies ASTOR, a method for compiling grammar-guided abstract syntax trees into solver code.
+This release contains the exact instance IDs from the paper's main evaluation cohort, task text from saved experiment records, external instance data, and reference annotations or gold evaluation records. It does not include model-generated answers or experiment logs.
+## Components
+| Component | Instances | Evaluation |
+|---|---:|---|
+| COMPASS | 85 (78 hotel, 7 complex) | Hard feasibility and equal-priority preference satisfaction, using the source oracle |
+| TravelPlanner | 180 | Hard feasibility, ordered preference satisfaction where present, and cost |
+| LexiBench | 75 | Hard feasibility, lexicographic feature counts, and an optional final tie-break |
+| Nurse Rostering | 15 | Hard feasibility and published-weight staffing/request penalties |
+| **Total** | **355** | |
+All instances are in a single `test` split. This is an evaluation split, not a new random train/test partition. TravelPlanner instances come from its upstream validation set. Of the 180 TravelPlanner instances, 95 have soft-preference tiers and 85 are cost-minimization instances without such tiers. COMPASS here is the 85-instance cohort used in the paper, not the separate experimental `compass_lexicographic` or `compass_lexicompose` datasets.
+## Load the dataset
+```python
+from datasets import load_dataset
+ds = load_dataset("amergoel/SoftOR", split="test")
+lexibench = load_dataset("amergoel/SoftOR", "lexibench", split="test")
+print(ds[0]["problem_text"])
+```
+The table keeps large structured inputs and evaluation artifacts in separate compressed JSON files. Download a row's input like this:
+```python
+import gzip
+import json
+from huggingface_hub import hf_hub_download
+row = ds[0]
+path = hf_hub_download(
+    "amergoel/SoftOR", row["input_path"], repo_type="dataset"
+)
+with gzip.open(path, "rt", encoding="utf-8") as f:
+    instance = json.load(f)
+problem_text = instance["problem_text"]
+external_parameters = instance["parameters"]
+parameter_schema = instance["parameter_schema"]
+```
+For local evaluation, download `reference_path` in the same way. **Reference files contain gold answers and evaluator annotations. Do not include them in model prompts.**
+In the paper's blind formulation setup, the model receives the problem text and structural data contract. Numerical external parameters are bound after formulation. Direct-answer methods need access to the instance data to produce a decision. Supplying all numeric data to every formulation method is a different protocol. The `problem_text` field includes the recorded data contract, but not each method's full system prompt or benchmark adapter instructions.
+## Fields
+| Field | Meaning |
+|---|---|
+| `id` | Stable instance identifier |
+| `benchmark` | `compass`, `travelplanner`, `lexibench`, or `nurse_rostering` |
+| `subset` | Source stratum, distinguishing hotel and complex COMPASS |
+| `problem_text` | Recorded natural-language task plus structural data contract |
+| `objective_type` | Weighted penalty, lexicographic tiers, equal-priority feature count, or cost minimization |
+| `difficulty`, `problem_type`, `priority_signal` | Reporting metadata from the paper cohort. Not additional model instructions. |
+| `input_path` | Repository path to compressed JSON with the task and external parameter data |
+| `reference_path` | Repository path to compressed evaluator-only JSON |
+### Reference files
+- **COMPASS:** original structured task, exhaustive feasible-selection oracle with utility scores, and the mapping from candidate IDs to booking components. Higher oracle utility is better. Evaluate against the supplied oracle, not a newly inferred replacement objective.
+- **TravelPlanner:** original task record, saved gold record, lexicographic gold when available, and the constructed candidate bundle. Preference tiers are compared in order before the cost tie-break. Source constraints and feasibility checks still apply.
+- **LexiBench:** the original self-contained benchmark record, including `task`, `annotation`, and `evaluation`. Tiers count satisfied preferences, highest priority first. The gold record includes a witness and solver certificate.
+- **Nurse Rostering:** source row, canonical scheduling instance, and published gold objective. The main protocol hides published numerical request weights from modeling and uses them only for evaluation. Methods infer their own weights from the preference wording. A hard-infeasible roster is not a correct solution regardless of its penalty.
+This repository distributes evaluation data, not a new universal scoring function. Reproducing the paper requires the benchmark-specific feasibility and scoring routines and method prompts from the [ASTOR/SoftOR code artifact](https://anonymous.4open.science/r/astor-softor-iclr-E34C/README.md). In particular, matching an objective number alone does not establish feasibility.
+## Construction and provenance
+- **COMPASS:** natural-language adaptations of the publicly released [COMPASS benchmark](https://github.com/sunnytqin/compass). Source booking data and oracle semantics are retained.
+- **TravelPlanner:** tasks and reference information from [OSU NLP's TravelPlanner](https://huggingface.co/datasets/osunlp/TravelPlanner), with SoftOR preference interpretation and gold evaluation artifacts. See the [source paper](https://arxiv.org/abs/2402.01622).
+- **LexiBench:** 75 original instances covering three problem types, five preference patterns, and five domains per combination. Gold lexicographic values were certified during the original benchmark construction. This upload does not rerun those solver certifications.
+- **Nurse Rostering:** natural-language adaptations of [SchedulingBenchmarks Nurse Rostering](https://www.schedulingbenchmarks.org/nrp/), using its published scheduling data and reference objectives. See the [source citation](https://doi.org/10.1016/j.ejor.2014.01.039).
+The dataset was prepared using author-designed construction and annotation procedures, including generative-AI assistance for natural-language rendering as described in the paper. Scenarios concern travel planning and staff scheduling. This is a research benchmark, not a validated operational scheduling or booking service.
+`manifest.json` records the cohort and provenance. `SHA256SUMS` records release-file checksums. Task text is taken from saved main-run records. External parameter data is reconstructed using the existing blind-mode adapters, except Nurse Rostering inputs come directly from the saved public-input snapshots. Reference records are taken from the existing local benchmark artifacts. No new model experiments were run for this release.
+## License and attribution
+Original SoftOR contributions are released under **CC BY 4.0**. Upstream material retains its own terms. COMPASS's MIT notice and TravelPlanner's code MIT notice are preserved in `licenses/`. The TravelPlanner dataset is CC BY 4.0. The SchedulingBenchmarks NRP page did not state an explicit redistribution license when checked, so this release does not label that upstream material as CC BY or MIT. See [LICENSE.md](LICENSE.md) and [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md) for scope and attribution.
+Please credit SoftOR/ASTOR and the applicable source benchmarks when using the data. A final paper citation can be added when its public bibliographic identifier is available.

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+# Source attribution
+## COMPASS
+Source: https://github.com/sunnytqin/compass
+Paper: https://arxiv.org/abs/2510.07043
+License: MIT. The upstream notice is reproduced in licenses/COMPASS.txt.
+SoftOR changes include natural-language task rendering and preparation for optimization autoformulation evaluation. The release selects 85 tasks, not the full upstream dataset.
+## TravelPlanner
+Dataset: https://huggingface.co/datasets/osunlp/TravelPlanner
+Code: https://github.com/OSU-NLP-Group/TravelPlanner
+Paper: https://arxiv.org/abs/2402.01622
+Dataset license: CC BY 4.0, https://creativecommons.org/licenses/by/4.0/
+The separate code license is MIT and its notice is reproduced in licenses/TravelPlanner-code.txt.
+SoftOR changes include preference-tier interpretation, candidate-data adaptation, and gold optimization/evaluation artifacts. This release uses the 180 upstream validation queries.
+## SchedulingBenchmarks Nurse Rostering
+Source and original instance downloads: https://www.schedulingbenchmarks.org/nrp/
+Source creator identified in the site's dataset metadata: Tim Curtois.
+Source citation: https://doi.org/10.1016/j.ejor.2014.01.039
+No explicit license was stated on the source NRP landing page when checked on 2026-09-27. The CC BY grant for original SoftOR contributions does not relicense upstream scheduling data or published solutions.
+SoftOR changes include deterministic natural-language task rendering, qualitative preference phrases, external-data contracts, and evaluation preparation. Only the 15 instances in the paper's main cohort are included.
+## LexiBench
+Original SoftOR component. CC BY 4.0 under LICENSE.md.

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