---
language:
- tr
license: cc-by-sa-4.0
task_categories:
- sentence-similarity
tags:
- turkish
- semantic-textual-similarity
- sts
- dataset-construction
size_categories:
- n<1K
pretty_name: Turkish STS Dataset
configs:
- config_name: default
  data_files: sts_tr_dataset.parquet
---

# Turkish STS Dataset

**81 Turkish sentence pairs, scored for semantic similarity on a 0–5 scale by
two annotators**, together with the 527-sentence raw pool part of it was drawn
from.

```python
from datasets import load_dataset

ds = load_dataset("Erenyanic/sts-tr-dataset")
```

## Contributors and method

| Contributor | Pairs created | Sentence source |
|---|---|---|
| [Erenyanic](https://huggingface.co/Erenyanic) | 43 | News RSS feeds and Wikipedia, via the collector in this repo |
| [nursimakgul](https://huggingface.co/nursimakgul) | 38 | Web sources on film, cooking and general science |

Pair creation was split, but **scoring was not**. Both contributors scored all
81 pairs independently, and the published `score` is the mean of the two
ratings. Every row therefore reflects two people's judgement rather than one.

## The scored dataset (`sts_tr_dataset.parquet`)

| Column | Type | Description |
|---|---|---|
| `sentence1` | string | First Turkish sentence |
| `sentence2` | string | Second Turkish sentence |
| `score` | double | Semantic similarity, 0–5 |

Score distribution, rounded to the nearest whole score:

| Band | 0 | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|---|
| Half one — news / Wikipedia (43) | 7 | 10 | 5 | 5 | 6 | 10 |
| Half two — film / cooking / science (38) | 3 | 2 | 10 | 5 | 7 | 11 |
| **Total** | **10** | **12** | **15** | **10** | **13** | **21** |

81 pairs, mean 2.67, spanning the full range.

### What the scores mean

| | |
|---|---|
| **5** | Tamamen aynı anlam. Sözcükler farklı olabilir, aktarılan bilgi aynıdır. |
| **4** | Aynı olayı/olguyu anlatır, önemsiz bir ayrıntıda ayrışır. |
| **3** | Aynı konu ve aynı durum, ancak biri daha ayrıntılı veya kapsamı dar. |
| **2** | Aynı konu, farklı içerik. Ortak bir bağlam var, aktarılan bilgi farklı. |
| **1** | Yalnızca gevşek bir bağ var: ortak sözcükler veya ortak alan, farklı anlam. |
| **0** | İlgisiz. |

Fractional scores were used for cases falling between two levels.

## How it was built

The hard part of building an STS dataset is not finding sentences — it is
deciding **which two sentences to put together**.

Pair two Turkish sentences at random and roughly 99% of them score 0. The result
is a dataset that is almost entirely zeros and measures nothing. A usable STS
dataset needs pairs deliberately spread across the whole similarity range, and
that spread has to come from a pairing strategy, not from luck.

Both halves apply the same idea to different material: **find one subject that
two independent sources have each described in their own words.** That yields
genuine paraphrases whose lexical divergence is real rather than manufactured,
and shifting how far apart the two subjects are moves a pair down the scale.

**Half one — news and encyclopedia text (43 pairs).** 527 sentences were
collected from Turkish news RSS feeds and Wikipedia, each annotated with the
context needed to reason about pairing it: which article it came from, which
story it covers, which topic it belongs to. Pairs were then chosen by hand from
that pool, using the metadata to hit the whole similarity range rather than
sampling blindly. The high end comes from different outlets reporting one event;
the low end from different topics.

**Half two — film, cooking and general science (38 pairs).** Sentences collected
from web sources covering the same subject in different places: two descriptions
of one film, two definitions of one natural phenomenon, two notes on one dish.
The high end comes from two sources describing one subject — *Buharlaşma, sıvı
haldeki suyun ısı etkisiyle gaz haline geçmesidir* against *Buharlaşma, bir
sıvının yüzeyinden gaz haline dönüşerek havaya karışması olayıdır*. Descending
the scale, a subject is paired with a neighbouring one (*iklim* against *hava
durumu*, *yıldırım* against *gök gürültüsü*), then with an unrelated one
(*İmam bayıldı* against *Ekvator*). This half reaches everyday domains a news
and encyclopedia corpus does not.

No pre-paired or pre-labelled corpus was used at any stage. Existing Turkish
paraphrase and NLI corpora would have supplied ready-made pairs, but the pairing
decision is the substance of the task, so every pairing here was made by hand.

## The raw pool (`raw_sentences.csv`)

The source for half one: 527 sentences, 352 from news and 175 from Wikipedia,
across 13 topic categories.

| Column | Description |
|---|---|
| `id` | Row identifier |
| `id2` | The sentence this one was paired with, if any |
| `sentence` | The Turkish sentence |
| `domain` | `haber` (news) or `vikipedi` (Wikipedia) |
| `category` | One of 13 topics (`gundem`, `ekonomi`, `spor`, `bilim`, `tarih`, …) |
| `source` | Outlet, or `wikipedia` |
| `doc_id` | **Sentences from the same article share this.** |
| `role` | `baslik` / `spot` for news; `lead1`–`lead3` for Wikipedia |
| `event_id` | **Headlines from _different outlets_ covering the _same story_ share this.** |
| `url` | Source article URL |

`doc_id` and `event_id` are what turn a flat list of sentences into a pairing
resource. Pairing material available in the pool:

| Pairing | Count | Typical similarity |
|---|---|---|
| Cross-outlet, same event (`event_id`) | 20 pairs / 16 events | High — independently written paraphrases |
| Headline + its own lede (`doc_id`, `baslik`+`spot`) | 165 pairs | Medium-high |
| Adjacent Wikipedia lead sentences (consecutive `lead` numbers) | 98 pairs | Medium |
| Non-adjacent sentences from one article | 55 pairs | Low-medium |
| Same `category`, different `doc_id` | many | Low |
| Different `category` and `domain` | many | Zero |

An `event_id` example — the same incident, reported independently by two
newspapers (`E02`):

> **hurriyet** — New York'a giden uçak rahatsızlanan yolcu için İstanbul'a indi
>
> **cumhuriyet** — Dubai-New York seferi yapan uçak İstanbul Havalimanı'na iniş yaptı

The two headlines share barely any wording, yet report the same event. That is
what makes naturally occurring cross-outlet coverage more useful than a
generated paraphrase: the lexical divergence is real, not manufactured.

## Note on two "wrong" clusters

Clusters `E14` and `E16` group stories that are *not* the same event:

- `E14` — two unrelated *yapay zekâ* stories
- `E16` — two different explosions (a volcano in Guatemala, a chemical plant in Brazil), linked by *patlama* and *tahliye edildi*

These were left in deliberately. High lexical overlap with low semantic
similarity is the most valuable kind of row in an STS dataset — it is what
separates a real model from a bag-of-words baseline. The extremes of the scale
are nearly free signal; every model gets paraphrases and unrelated pairs right.

## Files

- **`sts_tr_dataset.parquet`** — the 81 scored pairs, merged
- **`pairs_to_score.csv`** — the 43 pairs of half one, with their averaged scores
- **`sts_data.csv`** — the 38 pairs of half two, with their averaged scores
- **`raw_sentences.csv`** — the 527-sentence source pool behind half one
- **`collect_raw.py`** — the collector
- **`make_dataset.py`** — `extract` builds a scoring sheet from the `id2`
  column; `build` merges the scored CSVs into Parquet

The two halves are kept as separate CSVs and merged at build time, so each row's
provenance stays traceable after the merge.

## Reproducing

```bash
python3 collect_raw.py            # rebuild the raw pool (stdlib only)
python3 make_dataset.py extract   # pairs -> scoring sheet
python3 make_dataset.py build     # scored sheets -> merged parquet
```

RSS feeds only expose current articles, so re-collecting returns different news.
The Wikipedia portion is stable. The pool snapshot was collected **2026-08-05**.

## Known limitations

- **81 pairs is small.** This is a coursework-scale dataset. It is enough to
  sanity-check an embedding model, not to benchmark one — a correlation computed
  over 81 points has wide error bars.
- **Only the averaged score is published.** Each pair was rated by both
  annotators, but the two individual ratings were not retained, so
  inter-annotator agreement cannot be recomputed from this file.
- **The halves differ in composition.** Half one averages 101 characters per
  sentence and has a mean score of 2.35; half two averages 77 characters and a
  mean of 3.04. Since the halves also differ in domain and register, style and
  score are not fully independent.
- **Source URLs were recorded for half one only.** Every row of the raw pool
  carries its outlet and article URL. The web sources behind half two were not
  recorded per sentence, so those 38 pairs are not traceable to a page.
- Within half one, domain is unevenly spread across pairs: 36 news↔news, 7
  news↔Wikipedia, 1 Wikipedia↔Wikipedia.
- Six Wikipedia articles in the pool are missing `lead1` because their first
  sentence exceeded the 210-character collection cap. Check `role` numbers rather
  than assuming any two rows sharing a `doc_id` are adjacent.
- Event clustering in the pool is lexical (rare-token overlap between headlines
  from different outlets). High precision, but not exhaustive.
- The news portion is a single-day snapshot, so topics cluster around whatever
  was in the news that day.

## Sources and licence

- **Wikipedia** lead paragraphs via the Wikimedia REST API — text is
  **CC BY-SA 4.0**.
- **News** headlines and ledes from the public RSS feeds of Hürriyet,
  Cumhuriyet, Anadolu Ajansı, BBC Türkçe, Euronews Türkçe, Habertürk, Milliyet
  and Independent Türkçe. Each row of the raw pool records its outlet and
  article URL.
- **Web pages on film, cooking and general science**, collected by nursimakgul.
  Individual page URLs were not recorded for these 38 pairs.

The repository is licensed **CC BY-SA 4.0**, the licence of the Wikipedia
portion. The remaining sentences are short excerpts included for research and
educational use, in the manner of sentence-pair corpora such as MRPC. Rights in
them remain with their publishers; if you redistribute this data, keep the
`source` and `url` columns of the raw pool intact.