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--- |
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license: gfdl |
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task_categories: |
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- sentence-similarity |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Wiki Sim |
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## Overview |
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This semi-synthetic dataset is derived from `wikimedia/wikipedia`. |
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Each row contains 1-3 references sentences extracted from the original dataset. |
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For each reference sentence, we use an optimized DSPy program to generate 4 similar sentences: |
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- *Synonym* (Replace words with synonyms to maintain the same meaning.) |
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- *Paraphrase* (Rephrase the sentence using a different structure while keeping the same idea.) |
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- *Conceptual Overlap* (Express a related concept differently without changing the core meaning.) |
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- *Contextual Meaning* (Modify the sentence to derive meaning from context, preserving the original intent.) |
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Additionally, we score each result using `cross-encoder/stsb-roberta-large`. |
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We use this to mine hard negatives from different contiguous sentences in the original passage, retaining the most similar result. |
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## Purpose |
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We aim to expand training for small models like [WordLlama](https://github.com/dleemiller/WordLlama), |
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general embedding models, and targeting benchmarks like stsb and similarity tasks differing from NLI or QnA. |
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## Dataset |
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The colums of the dataset include: |
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`synonym` |
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`paraphrase` |
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`conceptual_overlap` |
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`contextual_meaning` |
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`reference` |
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`negative` |
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`negative_score` |
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`model_id` |
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`cross_encoder` |
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`synonym_score` |
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`paraphrase_score` |
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`conceptual_overlap_score` |
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`contextual_meaning_score` |
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where `reference` and `negative` are derived from `wikimedia/wikipedia`, |
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and the similarity text columns are synthetically derived. |
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We filter all rows where negative scores exceed any of the similarity scores. |
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## Results |
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