README.md

---
dataset_info:
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configs:
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  data_files:
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    path: all/train-*
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    path: all/validation-*
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- config_name: stsb
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task_categories:
- text-classification
- sentence-similarity
- text-ranking
- text-retrieval
tags:
- english
- sentence-similarity
- sentence-pair-classification
- semantic-retrieval
- re-ranking
- information-retrieval
- embedding-training
- semantic-search
- paraphrase-detection
language:
- en
size_categories:
- 1M<n<10M
license: apache-2.0
pretty_name: RedisLangCache SentecePairs v1
---
# Redis LangCache Sentence Pairs Dataset

<!-- Provide a quick summary of the dataset. -->

A large, consolidated collection of English sentence pairs for training and evaluating semantic similarity, retrieval, and re-ranking models. 
It merges widely used benchmarks into a single schema with consistent fields and ready-made splits.

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

- **Name:** langcache-sentencepairs-v1
- **Summary:** Sentence-pair dataset created to fine-tune encoder-based embedding and re-ranking models. It combines multiple high-quality corpora spanning diverse styles (short questions, long paraphrases, Twitter, adversarial pairs, technical queries, news headlines, etc.), with both positive and negative examples and preserved splits.
- **Curated by:** Redis
- **Shared by:** Aditeya Baral
- **Language(s):** English
- **License:** Apache-2.0
- **Homepage / Repository:** https://huggingface.co/datasets/redis/langcache-sentencepairs-v1
  
**Configs and coverage**

- **`all`**: Unified view over all sources with extra metadata columns (`id`, `source`, `source_idx`).
- **Source-specific configs:** `apt`, `mrpc`, `parade`, `paws`, `pit2015`, `qqp`, `sick`, `stsb`.

**Size & splits (overall)**  
Total **~1.12M** pairs: **~1.05M train**, **8.4k validation**, **62k test**. See per-config sizes in the viewer.

### Dataset Sources

- **APT (Adversarial Paraphrasing Task)** — [Paper](https://aclanthology.org/2021.acl-long.552/) | [Dataset](https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt)
- **MRPC (Microsoft Research Paraphrase Corpus)** — [Paper](https://aclanthology.org/I05-5002.pdf) | [Dataset](https://huggingface.co/datasets/glue/viewer/mrpc)
- **PARADE (Paraphrase Identification requiring Domain Knowledge)** — [Paper](https://aclanthology.org/2020.emnlp-main.611/) | [Dataset](https://github.com/heyunh2015/PARADE_dataset)
- **PAWS (Paraphrase Adversaries from Word Scrambling)** — [Paper](https://arxiv.org/abs/1904.01130) | [Dataset](https://huggingface.co/datasets/paws)
- **PIT2015 (SemEval 2015 Twitter Paraphrase)** — [Website](https://alt.qcri.org/semeval2015/task1/) | [Dataset](https://github.com/cocoxu/SemEval-PIT2015)
- **QQP (Quora Question Pairs)** — [Website](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs) | [Dataset](https://huggingface.co/datasets/glue/viewer/qqp)
- **SICK (Sentences Involving Compositional Knowledge)** — [Website](http://marcobaroni.org/composes/sick.html) | [Dataset](https://zenodo.org/records/2787612)
- **STS-B (Semantic Textual Similarity Benchmark)** — [Website](https://alt.qcri.org/semeval2017/task1/) | [Dataset](https://huggingface.co/datasets/nyu-mll/glue/viewer/stsb)

## Uses

- Train/fine-tune sentence encoders for **semantic retrieval** and **re-ranking**.
- Supervised **sentence-pair classification** tasks like paraphrase detection.
- Evaluation of **semantic similarity** and building general-purpose retrieval and ranking systems.

### Direct Use

```python
from datasets import load_dataset

# Unified corpus
ds = load_dataset("aditeyabaral-redis/langcache-sentencepairs-v1", "all")

# A single source, e.g., PAWS
paws = load_dataset("aditeyabaral-redis/langcache-sentencepairs-v1", "paws")

# Columns: sentence1, sentence2, label (+ idx, source_idx in 'all')
```

### Out-of-Scope Use

- **Non-English or multilingual modeling:** The dataset is entirely in English and will not perform well for training or evaluating multilingual models.  
- **Uncalibrated similarity regression:** The STS-B portion has been integerized in this release, so it should not be used for fine-grained regression tasks requiring the original continuous similarity scores.

## Dataset Structure

**Fields**

* `sentence1` *(string)* — First sentence.
* `sentence2` *(string)* — Second sentence.
* `label` *(int64)* — Task label. `1` ≈ paraphrase/similar, `0` ≈ non-paraphrase/dissimilar. For sources with continuous similarity (e.g., STS-B), labels are integerized in this release; consult the source subset if you need original continuous scores.
* *(config `all` only)*:

  * `id` *(string)* — Dataset identifier. Follows the pattern `langcache_{split}_{row number}`.
  * `source` *(string)* — Source dataset name.
  * `source_idx` *(int64)* — Source-local row id.

**Splits**

* `train`, `validation` (where available), `test` — original dataset splits preserved whenever provided by the source.

**Schemas by config**

* `all`: 5 columns (`idx`, `source_idx`, `sentence1`, `sentence2`, `label`).
* All other configs: 3 columns (`sentence1`, `sentence2`, `label`).

## Dataset Creation

### Curation Rationale

To fine-tune stronger encoder models for retrieval and re-ranking, we curated a large, diverse pool of labeled sentence pairs (positives & negatives) covering multiple real-world styles and domains. 
Consolidating canonical benchmarks into a single schema reduces engineering overhead and encourages generalization beyond any single dataset.

### Source Data

#### Data Collection and Processing

* Ingested each selected dataset and **preserved original splits** when available.
* Normalized to a common schema; no manual relabeling was performed.
* Merged into `all` with added `source` and `source_idx` for traceability.

#### Who are the source data producers?

Original creators of the upstream datasets (e.g., Microsoft Research for MRPC, Quora for QQP, Google Research for PAWS, etc.).


#### Personal and Sensitive Information

The corpus may include public-text sentences that mention people, organizations, or places (e.g., news, Wikipedia, tweets). It is **not** intended for identifying or inferring sensitive attributes of individuals. If you require strict PII controls, filter or exclude sources accordingly before downstream use.


## Bias, Risks, and Limitations

* **Label noise:** Some sources include **noisily labeled** pairs (e.g., PAWS large weakly-labeled set).
* **Granularity mismatch:** STS-B's continuous similarity is represented as integers here; treat with care if you need fine-grained scoring.
* **English-only:** Not suitable for multilingual evaluation without adaptation.

### Recommendations

- Use the `all` configuration for large-scale training, but be aware that some datasets dominate in size (e.g., PAWS, QQP). Apply **sampling or weighting** if you want balanced learning across domains.  
- Treat **STS-B labels** with caution: they are integerized in this release. For regression-style similarity scoring, use the original STS-B dataset.  
- This dataset is **best suited for training retrieval and re-ranking models**. Avoid re-purposing it for unrelated tasks (e.g., user profiling, sensitive attribute prediction, or multilingual training).  
- Track the `source` field (in the `all` config) during training to analyze how performance varies by dataset type, which can guide fine-tuning or domain adaptation.


## Citation

If you use this dataset, please cite the Hugging Face entry and the original upstream datasets you rely on.

**BibTeX:**

```bibtex
@misc{langcache_sentencepairs_v1_2025,
  title        = {langcache-sentencepairs-v1},
  author       = {Baral, Aditeya and Redis},
  howpublished = {\url{https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1}},
  year         = {2025},
  note         = {Version 1}
}
```

## Dataset Card Authors

Aditeya Baral

## Dataset Card Contact

[[email protected]](mailto:[email protected])