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Datasets:
heihei
/
prune-to-prosper-data

Tasks:
Feature Extraction
Languages:
English
Size:
1K<n<10K
Tags:
embeddings
pruning
dimensionality-reduction
mteb
sentence-transformers
License:
Dataset card Files
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Upload experiment data: chunk analysis, MTEB results, task similarity

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  1. README.md +99 -0
  2. analyze/gte-large-en-v1.5.json +0 -0
  3. analyze/roberta-large-InBedder.json +0 -0
  4. analyze/stella_en_400M_v5.json +0 -0
  5. experiment_results/all_methods_comparison.json +101 -0
  6. experiment_results/analysis_results.json +0 -0
  7. experiment_results/basis_sensitivity_gte-large.json +113 -0
  8. experiment_results/basis_sensitivity_stella.json +113 -0
  9. experiment_results/magnitude_analysis.json +5492 -0
  10. experiment_results/magnitude_gte_mteb.json +43 -0
  11. experiment_results/magnitude_stella_mteb.json +43 -0
  12. experiment_results/near_optimal_mask_analysis.json +0 -0
  13. experiment_results/reviewer_response_analysis.json +0 -0
  14. experiment_results/round3_deep_analysis.json +233 -0
  15. experiment_results/universal_mask_analysis.json +953 -0
  16. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/AmazonCounterfactualClassification.json +349 -0
  17. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/AmazonReviewsClassification.json +137 -0
  18. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/ArguAna.json +158 -0
  19. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/AskUbuntuDupQuestions.json +26 -0
  20. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/BIOSSES.json +26 -0
  21. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/Banking77Classification.json +73 -0
  22. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/BiorxivClusteringS2S.json +32 -0
  23. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/CQADupstackEnglishRetrieval.json +158 -0
  24. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/EmotionClassification.json +73 -0
  25. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/ImdbClassification.json +95 -0
  26. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/MTOPDomainClassification.json +137 -0
  27. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/MTOPIntentClassification.json +137 -0
  28. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/MassiveIntentClassification.json +137 -0
  29. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/MassiveScenarioClassification.json +137 -0
  30. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/MedrxivClusteringS2S.json +32 -0
  31. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/NFCorpus.json +158 -0
  32. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/SCIDOCS.json +158 -0
  33. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/SICK-R.json +26 -0
  34. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/STS12.json +26 -0
  35. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/STS13.json +26 -0
  36. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/STS14.json +26 -0
  37. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/STS15.json +26 -0
  38. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/STS16.json +26 -0
  39. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/STS17.json +138 -0
  40. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/STSBenchmark.json +26 -0
  41. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/SciDocsRR.json +26 -0
  42. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/SciFact.json +158 -0
  43. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/SprintDuplicateQuestions.json +58 -0
  44. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/StackOverflowDupQuestions.json +26 -0
  45. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/SummEval.json +24 -0
  46. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/ToxicConversationsClassification.json +95 -0
  47. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/TweetSentimentExtractionClassification.json +73 -0
  48. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/TwentyNewsgroupsClustering.json +32 -0
  49. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/TwitterSemEval2015.json +58 -0
  50. mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/TwitterURLCorpus.json +58 -0
README.md ADDED
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+ ---
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+ license: mit
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+ task_categories:
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+ - feature-extraction
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+ language:
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+ - en
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+ tags:
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+ - embeddings
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+ - pruning
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+ - dimensionality-reduction
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+ - mteb
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+ - sentence-transformers
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+ pretty_name: Prune to Prosper - Embedding Dimension Analysis Data
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+ size_categories:
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+ - 1K<n<10K
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+ ---
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+
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+ # Prune to Prosper - Embedding Dimension Analysis Data
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+
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+ This dataset contains experimental data for the paper **"Dimensions Are Interchangeable: Evidence That Task-Aware Embedding Pruning Does Not Outperform Random Selection"**.
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+
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+ ## Dataset Structure
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+
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+ ### `analyze/` — Per-Model Chunk Importance Analysis
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+
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+ Chunk-level importance scores for 3 models evaluated with win_size=2 (512 chunks for 1024-dim models):
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+
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+ | File | Model | Size |
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+ |------|-------|------|
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+ | `gte-large-en-v1.5.json` | GTE-Large | 4.7 MB |
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+ | `stella_en_400M_v5.json` | Stella EN 400M | 4.7 MB |
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+ | `roberta-large-InBedder.json` | Roberta-Large-InBedder | 4.7 MB |
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+
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+ Each file contains per-task chunk importance scores, including:
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+ - `task_name` → task → `split_win_size` → win_size → `chunk_result` (512 scores)
36
+ - `defult_score`, `random_score`, `sort_score` at task level
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+
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+ ### `task_similar/` — Cross-Task Dimension Ranking Transfer
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+
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+ Dimension ranking transfer data for 12 models, showing retention when using task A's ranking to prune for task B.
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+
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+ Each JSON file contains task pairs with:
43
+ - Source task dimension ranking
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+ - Target task retention ratio
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+ - Spearman rank correlation between rankings
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+
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+ ### `mteb/` — MTEB Evaluation Results
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+
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+ Full MTEB benchmark results for 13 embedding models:
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+
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+ - `gte-large-en-v1.5/`, `stella_en_400M_v5/`, `roberta-large-InBedder/` (detailed models)
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+ - `bge-m3/`, `gte-base/`, `gtr-t5-large/`, `instructor-large/` (additional models)
53
+ - `mxbai-embed-large-v1/`, `Qwen3-Embedding-0.6B/` (recent models)
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+ - `roberta-large/`, `bart-base/` (non-contrastive models)
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+ - `gte-Qwen2-1.5B-instruct/`, `jina-embeddings-v3/` (instruction-tuned models)
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+
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+ ### `experiment_results/` — Analysis Outputs
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+
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+ Key experimental analysis results:
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+
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+ | File | Description |
62
+ |------|-------------|
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+ | `analysis_results.json` (906K) | Main chunk analysis results |
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+ | `near_optimal_mask_analysis.json` (1.7M) | Near-optimal mask degeneracy analysis |
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+ | `universal_mask_analysis.json` | Universal mask transfer experiment |
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+ | `basis_sensitivity_gte-large.json` | Basis independence for GTE-Large |
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+ | `basis_sensitivity_stella.json` | Basis independence for Stella |
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+ | `magnitude_analysis.json` | Magnitude pruning analysis |
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+ | `reviewer_response_analysis.json` | Reviewer response experiments |
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+ | `all_methods_comparison.json` | All 5 methods comparison |
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+
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+ ## Key Findings (from this data)
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+
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+ 1. **Normalized entropy = 0.988–0.993**: Dimension importance is nearly uniform
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+ 2. **Optimized-Random gap = +2.2–5.0%**: Task-aware pruning barely helps
76
+ 3. **Cross-task retention = 95–100%**: Despite ρ ≈ 0.001 ranking correlation
77
+ 4. **Basis independence**: Sequential-Random gap < 1% under all tested rotations
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+ 5. **31.6% of random masks within 1% of oracle**: Near-optimal mask degeneracy
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+
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+ ## Usage
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+
82
+ ```python
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+ import json
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+
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+ # Load chunk importance for GTE-Large
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+ with open("analyze/gte-large-en-v1.5.json") as f:
87
+ data = json.load(f)
88
+
89
+ # Get chunk importance for a specific task
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+ task = "Banking77Classification"
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+ scores = data["task_name"][task]["split_win_size"]["2"]["chunk_result"]
92
+ print(f"Number of chunks: {len(scores)}")
93
+ print(f"Top-10 most important chunks: {sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:10]}")
94
+ ```
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+
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+ ## Related
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+
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+ - Paper: [GitHub - ngyygm/prune-to-prosper](https://github.com/ngyygm/prune-to-prosper)
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+ - MTEB Benchmark: https://github.com/embeddings-benchmark/mteb
analyze/gte-large-en-v1.5.json ADDED
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analyze/roberta-large-InBedder.json ADDED
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analyze/stella_en_400M_v5.json ADDED
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experiment_results/all_methods_comparison.json ADDED
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+ {
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+ "stella_en_400M_v5": {
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+ "n_tasks": 35,
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+ "method_retentions": {
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+ "random": {
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+ "mean": 0.971749493973195,
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+ "std": 0.02627436070327371,
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+ "median": 0.9827825326419692,
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+ "n": 35
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+ },
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+ "sort": {
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+ "mean": 0.9715775965917639,
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+ "std": 0.026984832940630223,
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+ "median": 0.981970230600288,
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+ "n": 35
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+ },
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+ "best": {
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+ "mean": 1.0007410629217461,
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+ "std": 0.04646155324948694,
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+ "median": 0.9982193203739427,
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+ "poor": {
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+ "median": 0.9432192860068165,
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+ "magnitude": {
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+ "median": 0.9810207931120601,
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+ "n": 34
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+ }
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+ }
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+ },
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+ "gte-large-en-v1.5": {
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+ "n_tasks": 35,
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+ "method_retentions": {
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+ "random": {
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+ "mean": 0.9754736578880594,
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+ "std": 0.023576838771943998,
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+ "median": 0.9851487956302152,
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+ "n": 35
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+ },
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+ "sort": {
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+ "mean": 0.9751268134426317,
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+ "std": 0.024112270719923265,
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+ "median": 0.9855162431272502,
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+ },
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+ "best": {
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+ "mean": 0.9993951506919497,
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+ "poor": {
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+ "median": 0.9539908695731897,
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+ "n": 35
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+ },
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+ "magnitude": {
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+ "mean": 0.967351796792346,
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+ "std": 0.030493137641166807,
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+ "median": 0.9781631896185621,
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+ "n": 34
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+ }
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+ }
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+ },
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+ "roberta-large-InBedder": {
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+ "n_tasks": 35,
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+ "method_retentions": {
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+ "random": {
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+ "mean": 1.0029221090316334,
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+ "std": 0.09443798630875845,
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+ "median": 0.9788351309707242,
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+ "sort": {
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+ "median": 0.9887315804680729,
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+ "median": 0.9068418412140975,
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+ "n": 35
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+ }
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+ }
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+ }
101
+ }
experiment_results/analysis_results.json ADDED
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experiment_results/basis_sensitivity_gte-large.json ADDED
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+ }
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experiment_results/basis_sensitivity_stella.json ADDED
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mteb/Qwen3-Embedding-0.6B/no_model_name_available/no_revision_available/TwitterURLCorpus.json ADDED
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