Datasets:
Formats:
parquet
Languages:
English
Size:
1M - 10M
ArXiv:
Tags:
english
sentence-similarity
sentence-pair-classification
semantic-retrieval
re-ranking
information-retrieval
License:
| dataset_info: | |
| - config_name: all | |
| features: | |
| - name: id | |
| dtype: string | |
| - name: source_idx | |
| dtype: int32 | |
| - name: source | |
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| - name: sentence1 | |
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| configs: | |
| - config_name: all | |
| data_files: | |
| - split: train | |
| path: all/train-* | |
| - split: validation | |
| path: all/validation-* | |
| - split: test | |
| path: all/test-* | |
| - config_name: apt | |
| data_files: | |
| - split: train | |
| path: apt/train-* | |
| - split: test | |
| path: apt/test-* | |
| - config_name: mrpc | |
| data_files: | |
| - split: train | |
| path: mrpc/train-* | |
| - split: validation | |
| path: mrpc/validation-* | |
| - split: test | |
| path: mrpc/test-* | |
| - config_name: parade | |
| data_files: | |
| - split: train | |
| path: parade/train-* | |
| - split: validation | |
| path: parade/validation-* | |
| - split: test | |
| path: parade/test-* | |
| - config_name: paws | |
| data_files: | |
| - split: train | |
| path: paws/train-* | |
| - split: test | |
| path: paws/test-* | |
| - config_name: pit2015 | |
| data_files: | |
| - split: train | |
| path: pit2015/train-* | |
| - split: validation | |
| path: pit2015/validation-* | |
| - split: test | |
| path: pit2015/test-* | |
| - config_name: qqp | |
| data_files: | |
| - split: train | |
| path: qqp/train-* | |
| - split: test | |
| path: qqp/test-* | |
| - config_name: sick | |
| data_files: | |
| - split: train | |
| path: sick/train-* | |
| - split: validation | |
| path: sick/validation-* | |
| - split: test | |
| path: sick/test-* | |
| - config_name: stsb | |
| data_files: | |
| - split: train | |
| path: stsb/train-* | |
| - split: validation | |
| path: stsb/validation-* | |
| - split: test | |
| path: stsb/test-* | |
| 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]) |