| # MTEB-lite: selection method |
|
|
| ## 1. Candidate pools (per task, from the eval split mteb uses) |
|
|
| * **English (`eng*`):** MTEB(eng, v2), with each task's English subset and test split. |
| * **Arabic (`ara*`):** the 42 Arabic MMTEB test tasks. Each is restricted to its Arabic hf_subsets (Arabic, and |
| Arabic-English pairs) and to the split our Arabic MMTEB evaluation uses. Multi-subset tasks get their items split evenly over subsets, |
| and their task score is the mean over subsets, as in mteb. Texts are capped at 512 tokens. |
| |
| Per task type: |
| |
| * **Retrieval:** up to 1,000 queries with at least one relevant document (seed 0). Each query keeps its own candidate list: the |
| BM25 top 30 over the full corpus (Okapi k1=0.9, b=0.4) plus its relevant documents (at most 100). Random corpus documents |
| shared by all queries are added: 1,000, or max(200, 1000/#subsets) per subset for multi-subset tasks. |
| * **Reranking:** up to 1,000 samples with their own candidate lists. Arabic MIRACLReranking was cut to 300 queries because of encode cost. |
| * **Classification:** up to 1,000 test examples (label-stratified). The train rows are the ones mteb's undersampling protocol draws (seed 42). |
| * **Pair classification:** up to 1,000 pairs, balanced by label. AP is reweighted to the full label prior. |
| * **STS:** up to 1,000 pairs, stratified by 5 gold-score bins. |
| * **Clustering:** up to 1,000 items, stratified by label; hierarchical label levels are kept. |
| * **Summarization:** all SummEval documents. |
| * **Bitext mining (Arabic):** up to 1,000 gold pairs. Each language pair gets a fixed set of up to 1,000 distractor targets, |
| and parallel datasets reuse the same rows. |
| |
| ## 2. Per-item outcomes (one model x one item) |
| |
| | type | outcome | |
| |---|---| |
| | Retrieval | nDCG@10 of the query | |
| | Reranking | average precision (nDCG@10 for MIRACL-style main scores) | |
| | Classification | fraction of the n_experiments logistic regressions (mteb protocol) that are correct | |
| | Summarization | Spearman(human relevance, max cosine to references) of the document | |
| | Bitext mining | 1 if the gold target is the nearest of target + distractors | |
| | STS | 1 - abs(percentile(cos) - percentile(gold)) | |
| | Pair classification | correctness at the accuracy-optimal cosine threshold | |
| | Clustering | 1 if the item's k-means cluster majority label equals its label | |
|
|
| ## 3. Reference models and full scores |
|
|
| Model families are merge recipes or training runs; every public or base model is its own family. At most 3 models are kept |
| per family (best / median / worst by full score). Full scores come from our own mteb 1.39.7 runs for our models and the Qwen |
| base models. For public models they come from the official MTEB results (English: test split of MTEB(eng, v2); Arabic: the same |
| Arabic subsets / split, available for only 15-31 of the 42 tasks per public model; a model enters a task only where it has a |
| full score). For English, 18 additional public models (BGE, E5, GTE, Arctic-Embed, mxbai, MiniLM / mpnet, LaBSE, GIST, UAE) |
| were scored in selection v3 to add diversity. |
|
|
| **`eng`** (28 models, 15 families): |
|
|
| * A-v3@6750: kept A-lin0.6, A-lin0.5, A-ties-d0.1-l0.5 (of 5) |
| * AS3: kept AS3-dareties-p0.9-l1.0, AS3-ties-d0.3-l1.0, AS3-ties-d0.3-l0.5 (of 4) |
| * AV2: kept AV2-dareties-p0.9-l1.0, AV2-lin1.0, AV2-lin0.5 (of 6) |
| * W: kept W-A0.7-S3-0.3-l0.5 (of 1) |
| * all-MiniLM-L6-v2: kept all-MiniLM-L6-v2 (of 1) |
| * bge-m3: kept bge-m3 (of 1) |
| * e5-base-v2: kept e5-base-v2 (of 1) |
| * embeddinggemma-300m: kept embeddinggemma-300m (of 1) |
| * multilingual-e5-base: kept multilingual-e5-base (of 1) |
| * multilingual-e5-small: kept multilingual-e5-small (of 1) |
| * qwen3-0.6b: kept qwen3-0.6b, qwen3-0.6b+norm (of 2) |
| * qwen3-4b: kept qwen3-4b (of 1) |
| * v1-F/P: kept F-lin1.0, F-lin0.5, F-ties-d0.1-l0.5 (of 7) |
| * v4: kept V-lin0.7, S-lin0.5, V-ties-d0.1-l0.5 (of 5) |
| * v5: kept S5-lin0.5, V5-ties-d0.3-l0.7, V5-ties-d0.1-l0.5 (of 5) |
| **`eng-super`** (46 models, 33 families): |
|
|
| * A-v3@6750: kept A-lin0.6, A-lin0.5, A-ties-d0.1-l0.5 (of 5) |
| * AS3: kept AS3-dareties-p0.9-l1.0, AS3-ties-d0.3-l1.0, AS3-ties-d0.3-l0.5 (of 4) |
| * AV2: kept AV2-dareties-p0.9-l1.0, AV2-lin1.0, AV2-lin0.5 (of 6) |
| * GIST-small-Embedding-v0: kept GIST-small-Embedding-v0 (of 1) |
| * LaBSE: kept LaBSE (of 1) |
| * UAE-Large-V1: kept UAE-Large-V1 (of 1) |
| * W: kept W-A0.7-S3-0.3-l0.5 (of 1) |
| * all-MiniLM-L12-v2: kept all-MiniLM-L12-v2 (of 1) |
| * all-MiniLM-L6-v2: kept all-MiniLM-L6-v2 (of 1) |
| * all-mpnet-base-v2: kept all-mpnet-base-v2 (of 1) |
| * arctic-embed-l: kept arctic-embed-l (of 1) |
| * arctic-embed-m-v1.5: kept arctic-embed-m-v1.5 (of 1) |
| * arctic-embed-xs: kept arctic-embed-xs (of 1) |
| * bge-base-en-v1.5: kept bge-base-en-v1.5 (of 1) |
| * bge-large-en-v1.5: kept bge-large-en-v1.5 (of 1) |
| * bge-m3: kept bge-m3 (of 1) |
| * bge-small-en-v1.5: kept bge-small-en-v1.5 (of 1) |
| * e5-base-v2: kept e5-base-v2 (of 1) |
| * e5-large-v2: kept e5-large-v2 (of 1) |
| * e5-small-v2: kept e5-small-v2 (of 1) |
| * embeddinggemma-300m: kept embeddinggemma-300m (of 1) |
| * gte-large: kept gte-large (of 1) |
| * gte-small: kept gte-small (of 1) |
| * multilingual-e5-base: kept multilingual-e5-base (of 1) |
| * multilingual-e5-large: kept multilingual-e5-large (of 1) |
| * multilingual-e5-small: kept multilingual-e5-small (of 1) |
| * mxbai-embed-large-v1: kept mxbai-embed-large-v1 (of 1) |
| * paraphrase-multilingual-mpnet-base-v2: kept paraphrase-multilingual-mpnet-base-v2 (of 1) |
| * qwen3-0.6b: kept qwen3-0.6b, qwen3-0.6b+norm (of 2) |
| * qwen3-4b: kept qwen3-4b (of 1) |
| * v1-F/P: kept F-lin1.0, F-lin0.5, F-ties-d0.1-l0.5 (of 7) |
| * v4: kept V-lin0.7, S-lin0.5, V-ties-d0.1-l0.5 (of 5) |
| * v5: kept S5-lin0.5, V5-ties-d0.3-l0.7, V5-ties-d0.1-l0.5 (of 5) |
| **`ara-super`** (39 models, 21 families): |
|
|
| * A-v3@6750: kept A-dare-p0.9-l1.0, A-lin0.6, A-lin0.5 (of 3) |
| * AS3: kept AS3-dareties-p0.9-l1.0, AS3-ties-d0.3-l1.0, AS3-ties-d0.3-l0.5 (of 3) |
| * AV2: kept AV2-dareties-p0.9-l1.0, AV2-lin1.0, AV2-lin0.5 (of 3) |
| * FV: kept FV-ties-d0.1-l0.5, FV-ties-d0.3-l0.7 (of 2) |
| * LaBSE: kept LaBSE (of 1) |
| * Vq: kept Vq-lin0.6, Vq-lin0.4, Vq-lin0.7 (of 3) |
| * W: kept W-A0.7-V2-0.3-l0.5, W-F0.7-V0.3-l0.7, W-A0.7-S3-0.3-l0.5 (of 3) |
| * all-MiniLM-L6-v2: kept all-MiniLM-L6-v2 (of 1) |
| * bge-m3: kept bge-m3 (of 1) |
| * e5-base-v2: kept e5-base-v2 (of 1) |
| * embeddinggemma-300m: kept embeddinggemma-300m (of 1) |
| * multilingual-e5-base: kept multilingual-e5-base (of 1) |
| * multilingual-e5-large: kept multilingual-e5-large (of 1) |
| * multilingual-e5-small: kept multilingual-e5-small (of 1) |
| * paraphrase-multilingual-MiniLM-L12-v2: kept paraphrase-multilingual-MiniLM-L12-v2 (of 1) |
| * paraphrase-multilingual-mpnet-base-v2: kept paraphrase-multilingual-mpnet-base-v2 (of 1) |
| * qwen3-0.6b: kept qwen3-0.6b, qwen3-0.6b+norm (of 2) |
| * qwen3-4b: kept qwen3-4b (of 1) |
| * v1-F/P: kept F-lin1.0, F-lin0.4, F-lin0.5 (of 3) |
| * v4: kept V-lin1.0, V-lin0.5, V-lin0.1 (of 3) |
| * v5: kept V5-lin1.0, S5-lin0.5, V5-lin0.7 (of 3) |
|
|
| ## 4. Item models and selection |
|
|
| The model ability is theta = the full-task score, standardised over the training models. Per item: |
|
|
| * **continuous outcomes:** a linear-Gaussian factor model, y = a * theta + b + e; |
| * **binary outcomes:** 2PL, sigmoid(alpha * theta + beta), L2-regularised. |
|
|
| Candidates evaluated: |
|
|
| * **anchor**: tinyBenchmarks anchor points. K-means groups the items' outcome vectors over the training models; the anchor is the |
| item nearest to each centroid, and its weight is the cluster's share of the pool. |
| * **fisher**: a greedy maximum of sum over models of log(eps + information). |
| * **estimators**: the weighted subset mean; p-IRT (MAP theta from the selected items, with the rest of the pool predicted); and gp, their average. |
| * **set-metric tasks**: stratified random, or anchor points on the per-item outcomes. Each estimate is mapped by a per-task |
| linear calibration fitted on the reference models. |
|
|
| ## 5. Validation protocol |
|
|
| * **family-level LOMO:** for every family, select + calibrate without it and predict its models. |
| * **distinct-model LOMO:** the same, one reference model at a time. |
| * **metrics:** Kendall tau / Pearson / MAE of the predicted vs true benchmark macro (mean over type means) and mean (over |
| tasks), for the held-out models with all full scores. We also report tau on resolvable pairs (difference of at least 0.5 points). |
| * **baselines:** uniform random K (metrics averaged over 10 single draws) and the whole pool. |
|
|
| ## 6. Chosen variants |
|
|
| * `eng` (selection v2): per-item tasks **anchor/mean**, set-metric tasks **anchorset**, K = 100 |
| * `eng-super` (selection v3): per-item tasks **fisher/mean**, set-metric tasks **anchorset**, K = 20 |
| * `ara-super` (selection v4): per-item tasks **anchor/gp**, set-metric tasks **strat**, K = 20 |
|
|
| Variants were chosen among {anchor, fisher} x {mean, p-IRT, gp} x {stratified, anchor set} on the same LOMO folds, |
| which makes the numbers mildly optimistic. |
|
|
