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Add dataset card

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