Datasets:

alvations
/

stasis

pretty_name: STASIS — Semantic Textual Similarity datasets, unified
license: other
license_name: per-subset
task_categories:
  - sentence-similarity
  - text-classification
language:
  - en
  - ar
  - es
  - tr
tags:
  - semantic-textual-similarity
  - sts
  - sick
  - clss
  - semeval
  - paraphrase
  - entailment
size_categories:
  - 10K<n<100K
configs:
  - config_name: sts2012
    data_files:
      - split: train
        path: data/sts2012/train.parquet
      - split: test
        path: data/sts2012/test.parquet
  - config_name: sts2013
    data_files:
      - split: test
        path: data/sts2013/test.parquet
  - config_name: sts2014
    data_files:
      - split: test
        path: data/sts2014/test.parquet
  - config_name: sts2015
    data_files:
      - split: test
        path: data/sts2015/test.parquet
  - config_name: sts2016
    data_files:
      - split: test
        path: data/sts2016/test.parquet
  - config_name: sts2017
    data_files:
      - split: test
        path: data/sts2017/test.parquet
  - config_name: sick
    data_files:
      - split: train
        path: data/sick/train.parquet
      - split: validation
        path: data/sick/validation.parquet
      - split: test
        path: data/sick/test.parquet
  - config_name: clss-paragraph2sentence
    data_files:
      - split: train
        path: data/clss-paragraph2sentence/train.parquet
      - split: validation
        path: data/clss-paragraph2sentence/validation.parquet
      - split: test
        path: data/clss-paragraph2sentence/test.parquet
  - config_name: clss-sentence2phrase
    data_files:
      - split: train
        path: data/clss-sentence2phrase/train.parquet
      - split: validation
        path: data/clss-sentence2phrase/validation.parquet
      - split: test
        path: data/clss-sentence2phrase/test.parquet
  - config_name: clss-phrase2word
    data_files:
      - split: train
        path: data/clss-phrase2word/train.parquet
      - split: validation
        path: data/clss-phrase2word/validation.parquet
      - split: test
        path: data/clss-phrase2word/test.parquet
  - config_name: clss-word2sense
    data_files:
      - split: train
        path: data/clss-word2sense/train.parquet
      - split: validation
        path: data/clss-word2sense/validation.parquet
      - split: test
        path: data/clss-word2sense/test.parquet
  - config_name: all-sts-en
    data_files:
      - split: train
        path: data/all-sts-en/train.parquet

STASIS — Semantic Textual Similarity datasets, unified

Modernised release of the alvations/stasis collection of Semantic Textual Similarity (STS) shared-task data, repackaged into parquet under one unified schema so every subset can be loaded the same way.

Replaces the original graphlab-based wrappers (graphlab is defunct) with stdlib + pyarrow only.

Configs

Config	Source	Rows	Splits	Score scale
`sts2012`	SemEval-2012 STS	5,276	train (2,212), test (3,064)	0–5
`sts2013`	SemEval-2013 STS	1,505	test	0–5
`sts2014`	SemEval-2014 STS	3,747	test	0–5
`sts2015`	SemEval-2015 STS	2,984	test	0–5
`sts2016`	SemEval-2016 STS	1,169	test (gold-scored subset)	0–5
`sts2017`	SemEval-2017 STS (multilingual)	2,000	test	— (scores not released into this repo)
`sick`	SICK relatedness	9,927	train (4,500), validation (500), test (4,927)	1–5
`clss-paragraph2sentence`	SemEval-2014 CLSS	1,034	train (500), validation (34), test (500)	0–4
`clss-sentence2phrase`	SemEval-2014 CLSS	1,036	train (500), validation (36), test (500)	0–4
`clss-phrase2word`	SemEval-2014 CLSS	1,038	train (500), validation (38), test (500)	0–4
`clss-word2sense`	SemEval-2014 CLSS	1,048	train (500), validation (48), test (500)	0–4
`all-sts-en`	concat of `sts2012`–`sts2016`	14,681	train	0–5

Usage

from datasets import load_dataset

# any STS year — same call shape
ds = load_dataset("alvations/stasis", "sts2014", split="test")
print(ds[0])
# {'sent1': '...', 'sent2': '...', 'score': 4.4, 'year': 2014, ...}

# all English STS in one go
ds_all = load_dataset("alvations/stasis", "all-sts-en", split="train")
print(len(ds_all))   # 14681

# SICK with entailment labels
sick = load_dataset("alvations/stasis", "sick", split="test")
print(sick[0]["entailment"])    # 'NEUTRAL' / 'ENTAILMENT' / 'CONTRADICTION'

# multilingual STS 2017 (Arabic, Spanish, English, Turkish pairs)
sts17 = load_dataset("alvations/stasis", "sts2017", split="test")
print(sts17[0]["lang1"], sts17[0]["lang2"])    # e.g. 'ar' 'en'

Unified schema

All configs share one schema — makes cross-subset comparison trivial:

Column	Type	Description
`sent1`	string	First segment (sentence, phrase, or word — depends on subset).
`sent2`	string	Second segment.
`score`	float32	Gold similarity score. Scale depends on subset (STS 0–5, SICK 1–5, CLSS 0–4). Null where unreleased (e.g. STS2017 in this repo).
`year`	int32	Dataset year (2012–2017). SICK = 2014, CLSS = 2014.
`subset`	string	Sub-source within the year (`MSRpar`, `headlines`, `deft-news`, `track1.ar-ar`, `paragraph2sentence`, …).
`split`	string	`"train"` / `"validation"` / `"test"`.
`lang1`	string	ISO-2 language code of `sent1`. Always `en` except STS2017.
`lang2`	string	ISO-2 language code of `sent2`.
`pair_id`	string	Upstream pair identifier (SICK pair_ID; CLSS pair id). Null elsewhere.
`entailment`	string	SICK only: `NEUTRAL` / `ENTAILMENT` / `CONTRADICTION`. Null elsewhere.

Notes on individual configs

STS 2012: includes the official train split (MSRpar, MSRvid, SMTeuroparl) plus the gold test set with the surprise domains (OnWN, SMTnews).
STS 2013–2015: test/gold splits only — those years didn't release new training data.
STS 2016: the upstream STS2016 input file has 9,183 sentence pairs but only 1,169 carry gold scores (the rest were unannotated test material). The unannotated rows are dropped here.
STS 2017: multilingual tracks (ar-ar, ar-en, es-es, es-en ×2, en-en, tr-en). Each row stores the original-language sentences in sent1/sent2; English translations from the upstream sts2017.csv are not surfaced as a separate column (open issue if you'd like them).
SICK: standard 4,500 / 500 / 4,927 split; entailment column carries the entailment judgment alongside the relatedness score.
CLSS: 4 cross-lexical-level configs from SemEval-2014 Task 3. For clss-word2sense, sent1 is word#pos and sent2 is the WordNet sense's human-readable form (the raw sense_id is in the upstream input but elided here — file a request if you need it).

Modernisation notes (vs upstream)

This release rebuilds the data with:

pure stdlib + pyarrow (no graphlab / turicreate — both defunct);
single unified parquet schema across every subset;
explicit lang1/lang2/split/subset columns instead of relying on tabular conventions;
empty-score rows from the upstream sts.csv (STS2016 unannotated subset) dropped, with the count tracked above;
one HF config per source year + a default-style all-sts-en aggregate.

The source files were not modified — the parquet shards are a faithful flatten of the upstream sts.csv and per-subset folders.

Citations

Cite the original tasks alongside this repackaging. The most important ones:

@inproceedings{agirre-etal-2012-semeval,
  title = {{S}em{E}val-2012 Task 6: A Pilot on Semantic Textual Similarity},
  author = {Agirre, Eneko and Cer, Daniel and Diab, Mona and Gonzalez-Agirre, Aitor},
  booktitle = {SemEval-2012},
  year = {2012},
}

@inproceedings{agirre-etal-2013-sem,
  title = {*{SEM} 2013 shared task: Semantic Textual Similarity},
  author = {Agirre, Eneko and Cer, Daniel and Diab, Mona and Gonzalez-Agirre, Aitor and Guo, Weiwei},
  booktitle = {*SEM 2013},
  year = {2013},
}

@inproceedings{agirre-etal-2014-semeval,
  title = {{S}em{E}val-2014 Task 10: Multilingual Semantic Textual Similarity},
  author = {Agirre, Eneko and Banea, Carmen and Cardie, Claire and Cer, Daniel and Diab, Mona and Gonzalez-Agirre, Aitor and Guo, Weiwei and Mihalcea, Rada and Rigau, German and Wiebe, Janyce},
  booktitle = {SemEval-2014},
  year = {2014},
}

@inproceedings{agirre-etal-2015-semeval,
  title = {{S}em{E}val-2015 Task 2: Semantic Textual Similarity, {E}nglish, {S}panish and Pilot on Interpretability},
  author = {Agirre, Eneko and others},
  booktitle = {SemEval-2015},
  year = {2015},
}

@inproceedings{agirre-etal-2016-semeval,
  title = {{S}em{E}val-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation},
  author = {Agirre, Eneko and Banea, Carmen and Cer, Daniel and Diab, Mona and Gonzalez-Agirre, Aitor and Mihalcea, Rada and Rigau, German and Wiebe, Janyce},
  booktitle = {SemEval-2016},
  year = {2016},
}

@inproceedings{cer-etal-2017-semeval,
  title = {{S}em{E}val-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation},
  author = {Cer, Daniel and Diab, Mona and Agirre, Eneko and Lopez-Gazpio, I{\~n}igo and Specia, Lucia},
  booktitle = {SemEval-2017},
  year = {2017},
}

@inproceedings{marelli-etal-2014-sick,
  title = {A {SICK} cure for the evaluation of compositional distributional semantic models},
  author = {Marelli, Marco and Menini, Stefano and Baroni, Marco and Bentivogli, Luisa and Bernardi, Raffaella and Zamparelli, Roberto},
  booktitle = {LREC 2014},
  year = {2014},
}

@inproceedings{jurgens-etal-2014-semeval,
  title = {{S}em{E}val-2014 Task 3: Cross-Level Semantic Similarity},
  author = {Jurgens, David and Pilehvar, Mohammad Taher and Navigli, Roberto},
  booktitle = {SemEval-2014},
  year = {2014},
}

If you want to cite the wrapper itself, the STASIS repo suggests citing:

@inproceedings{han-etal-2015-samsung,
  title = {Samsung: Align-and-Differentiate Approach to Semantic Textual Similarity},
  author = {Han, Lushan and Kashyap, Abhay L. and Finin, Tim and Mayfield, James and Weese, Jonathan and others},
  booktitle = {*SEM 2015},
  url = {http://www.aclweb.org/anthology/S15-2015},
  year = {2015},
}

License

Each subset inherits its upstream license:

STS 2012–2017: research use as released by the SemEval organisers.
SICK: CC BY-NC-SA 3.0.
CLSS: research use as released by the SemEval-2014 Task 3 organisers.

This repackaging adds no additional restrictions but downstream users must respect the per-subset terms.