Add dataset card

README.md

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+---
+language:
+- en
+license: mit
+tags:
+- two-tower
+- semantic-search
+- document-retrieval
+- information-retrieval
+- dual-encoder
+---
+
+# mlx7-two-tower-data
+
+This repository contains datasets used for training Two-Tower (Dual Encoder) models for document retrieval.
+
+## Dataset Description
+
+The datasets provided here are structured for training dual encoder models with various sampling strategies:
+
+- **classic_triplets**: 48.2 MB
+- **intra_query_neg**: 47.6 MB
+- **multi_pos_multi_neg**: 126.5 MB
+
+### Dataset Details
+
+- **classic_triplets.parquet**: Standard triplet format with (query, positive_document, negative_document)
+- **intra_query_neg.parquet**: Negative examples selected from within the same query batch
+- **multi_pos_multi_neg.parquet**: Multiple positive and negative examples per query
+
+## Usage
+
+```python
+import pandas as pd
+
+# Load a dataset
+df = pd.read_parquet("classic_triplets.parquet")
+
+# View the schema
+print(df.columns)
+
+# Example of working with the data
+queries = df["q_text"].tolist()
+positive_docs = df["d_pos_text"].tolist()
+negative_docs = df["d_neg_text"].tolist()
+```
+
+## Data Source and Preparation
+
+These datasets are derived from the MS MARCO passage retrieval dataset, processed to create effective training examples for two-tower models.
+
+## Dataset Structure
+
+The datasets follow a common schema with the following fields:
+- `q_text`: Query text
+- `d_pos_text`: Positive (relevant) document text
+- `d_neg_text`: Negative (non-relevant) document text
+
+Additional fields may be present in specific datasets.
+
+## Citation
+
+If you use this dataset in your research, please cite the original MS MARCO dataset:
+
+```
+@article{msmarco,
+  title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
+  author={Nguyen, Tri and Rosenberg, Matthew and Song, Xia and Gao, Jianfeng and Tiwary, Saurabh and Majumder, Rangan and Deng, Li},
+  journal={arXiv preprint arXiv:1611.09268},
+  year={2016}
+}
+```