Update README.md

README.md

@@ -6,4 +6,50 @@ language:
 - en
 size_categories:
 - 100K<n<1M
----
+---
+
+# Wiki Sim
+
+## Overview
+This semi-synthetic dataset is derived from `wikimedia/wikipedia`.
+Each row contains 1-3 references sentences extracted from the original dataset.
+
+For each reference sentence, we use an optimized DSPy program to generate 4 similar sentences:
+- *Synonym* (Replace words with synonyms to maintain the same meaning.)
+- *Paraphrase* (Rephrase the sentence using a different structure while keeping the same idea.)
+- *Conceptual Overlap* (Express a related concept differently without changing the core meaning.)
+- *Contextual Meaning* (Modify the sentence to derive meaning from context, preserving the original intent.)
+
+Additionally, we score each result using `cross-encoder/stsb-roberta-large`.
+We use this to mine hard negatives from different contiguous sentences in the original passage, retaining the most similar result.
+
+## Purpose
+
+We aim to expand training for small models like [WordLlama](https://github.com/dleemiller/WordLlama),
+general embedding models, and targeting benchmarks like stsb and similarity tasks differing from NLI or QnA.
+
+## Dataset
+
+The colums of the dataset include:
+
+`synonym`
+`paraphrase`
+`conceptual_overlap`
+`contextual_meaning`
+`reference`
+`negative`
+`negative_score`
+`model_id`
+`cross_encoder`
+`synonym_score`
+`paraphrase_score`
+`conceptual_overlap_score`
+`contextual_meaning_score`
+
+where `reference` and `negative` are derived from `wikimedia/wikipedia`,
+and the similarity text columns are synthetically derived.
+
+We filter all rows where negative scores exceed any of the similarity scores.
+
+## Results
+