Update README.md

README.md

@@ -253,11 +253,12 @@ size_categories:
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 license: apache-2.0
 ---
-# Dataset Card for Dataset Name
+# Redis LangCache Sentence Pairs Dataset
 
 <!-- Provide a quick summary of the dataset. -->
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+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
 
@@ -265,128 +266,138 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 <!-- 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 (`source`, `source_idx`).
+- **Source-specific configs:** `apt`, `mrpc`, `parade`, `paws`, `pit2015`, `qqp`, `sick`, `stsb`.
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
+**Size & splits (overall)**  
+Total **~1.12M** pairs: **~1.05M train**, **8.4k validation**, **62k test**. See per-config sizes in the viewer.
 
 ### Dataset Sources [optional]
 
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **APT (Adversarial Paraphrasing Task)** — [Paper](https://arxiv.org/abs/1907.05774) | [Dataset repo](https://github.com/jfan001/apt)
+- **MRPC (Microsoft Research Paraphrase Corpus)** — [Dataset page](https://www.microsoft.com/en-us/download/details.aspx?id=52398) | [GLUE version](https://huggingface.co/datasets/glue/viewer/mrpc)
+- **PARADE (Paraphrase Identification requiring Domain Knowledge)** — [Paper](https://arxiv.org/abs/2005.13888) | [Dataset repo](https://github.com/vgtomahawk/parade)
+- **PAWS (Paraphrase Adversaries from Word Scrambling)** — [Paper](https://arxiv.org/abs/1904.01130) | [Dataset on Hugging Face](https://huggingface.co/datasets/paws)
+- **PIT2015 (Twitter Paraphrase SemEval 2015)** — [Task page](https://alt.qcri.org/semeval2015/task1/) | [Dataset on Hugging Face](https://huggingface.co/datasets/pit2015)
+- **QQP (Quora Question Pairs)** — [Competition page](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs) | [Dataset on Hugging Face](https://huggingface.co/datasets/qqp)
+- **SICK (Sentences Involving Compositional Knowledge)** — [Dataset page](http://marcobaroni.org/composes/sick.html) | [Dataset on Hugging Face](https://huggingface.co/datasets/sick)
+- **STS-B (Semantic Textual Similarity Benchmark)** — [SemEval 2017 Task page](http://alt.qcri.org/semeval2017/task1/) | [Dataset on Hugging Face](https://huggingface.co/datasets/stsb)
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
+- 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
 
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
-
-## Dataset Structure
+```python
+from datasets import load_dataset
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+# Unified corpus
+ds = load_dataset("aditeyabaral-redis/langcache-sentencepairs-v1", "all")
 
-[More Information Needed]
+# A single source, e.g., PAWS
+paws = load_dataset("aditeyabaral-redis/langcache-sentencepairs-v1", "paws")
 
-## Dataset Creation
+# Columns: sentence1, sentence2, label (+ source, source_idx in 'all')
+```
 
-### Curation Rationale
+### Out-of-Scope Use
 
-<!-- Motivation for the creation of this dataset. -->
+- **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.
 
-[More Information Needed]
+## Dataset Structure
 
-### Source Data
+**Fields**
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+* `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)*:
 
-#### Data Collection and Processing
+  * `source` *(string)* — Upstream dataset identifier.
+  * `source_idx` *(int64)* — Source-local row id.
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+**Splits**
 
-[More Information Needed]
+* `train`, `validation` (where available), `test` — original dataset splits preserved whenever provided by the source.
 
-#### Who are the source data producers?
+**Schemas by config**
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+* `all`: 5 columns (`sentence1`, `sentence2`, `label`, `source`, `source_idx`).
+* All other configs: 3 columns (`sentence1`, `sentence2`, `label`).
 
-[More Information Needed]
+## Dataset Creation
 
-### Annotations [optional]
+### Curation Rationale
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+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.
 
-#### Annotation process
+### Source Data
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+#### Data Collection and Processing
 
-[More Information Needed]
+* 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 annotators?
+#### Who are the source data producers?
 
-<!-- This section describes the people or systems who created the annotations. -->
+Original creators of the upstream datasets (e.g., Microsoft Research for MRPC, Quora for QQP, Google Research for PAWS, etc.).
 
-[More Information Needed]
 
 #### Personal and Sensitive Information
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+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.
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+* **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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- 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.
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
 
-## Citation [optional]
+## Citation
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+If you use this dataset, please cite the Hugging Face entry and the original upstream datasets you rely on.
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+```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 [optional]
+## Dataset Card Authors
 
-[More Information Needed]
+Aditeya Baral
 
 ## Dataset Card Contact
 
-[More Information Needed]
+[[email protected]](mailto:[email protected])