Initial upload: ogma-base embedding model

README.md

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+---
+language:
+- en
+license: mit
+tags:
+- mteb
+- sentence-transformers
+- embedding
+- text-embedding
+- ogma
+- axiotic
+- matryoshka
+- small-model
+model-index:
+- name: ogma-base
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/AmazonCounterfactualClassification
+      name: MTEB AmazonCounterfactualClassification
+      config: default
+      split: test
+      revision: 1f7e6a9d6fa6e64c53d146e428565640410c0df1
+    metrics:
+    - type: accuracy
+      value: 73.13
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/AmazonPolarityClassification
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 79.85
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/AmazonReviewsClassification
+      name: MTEB AmazonReviewsClassification
+      config: default
+      split: test
+      revision: 6b5d328eaae8ef408dd7d775040245cf86f92e9d
+    metrics:
+    - type: accuracy
+      value: 39.47
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/ArXivHierarchicalClusteringP2P
+      name: MTEB ArXivHierarchicalClusteringP2P
+      config: default
+      split: test
+      revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
+    metrics:
+    - type: v_measure
+      value: 55.83
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/ArXivHierarchicalClusteringS2S
+      name: MTEB ArXivHierarchicalClusteringS2S
+      config: default
+      split: test
+      revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
+    metrics:
+    - type: v_measure
+      value: 52.73
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/ArguAna
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+    metrics:
+    - type: ndcg_at_10
+      value: 45.0
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/AskUbuntuDupQuestions
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: c5691e3c48741d5f83b5cc8e630653d7a8cfc048
+    metrics:
+    - type: map
+      value: 56.76
+  - task:
+      type: STS
+    dataset:
+      type: mteb/BIOSSES
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cosine_spearman
+      value: 84.15
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/Banking77Classification
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 78.56
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/BiorxivClusteringP2P
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 34.11
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/BiorxivClusteringS2S
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 26.34
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackAndroidRetrieval
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: 9be4c0e46342e8e3aff577a89b9a1ec9bc6b4af3
+    metrics:
+    - type: ndcg_at_10
+      value: 37.28
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackEnglishRetrieval
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+    metrics:
+    - type: ndcg_at_10
+      value: 34.91
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackGamingRetrieval
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+    metrics:
+    - type: ndcg_at_10
+      value: 44.91
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackGisRetrieval
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: 5003b3064772da1887988e05400cf3806fe491f2
+    metrics:
+    - type: ndcg_at_10
+      value: 29.65
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackMathematicaRetrieval
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+    metrics:
+    - type: ndcg_at_10
+      value: 24.91
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackPhysicsRetrieval
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+    metrics:
+    - type: ndcg_at_10
+      value: 34.23
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackProgrammersRetrieval
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+    metrics:
+    - type: ndcg_at_10
+      value: 33.53
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackRetrieval
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: '1'
+    metrics:
+    - type: ndcg_at_10
+      value: 31.05
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackStatsRetrieval
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+    metrics:
+    - type: ndcg_at_10
+      value: 26.66
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackTexRetrieval
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: 46989137a86843e03a6195de44b09deda022eec7
+    metrics:
+    - type: ndcg_at_10
+      value: 21.77
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackUnixRetrieval
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+    metrics:
+    - type: ndcg_at_10
+      value: 29.57
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackWebmastersRetrieval
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+    metrics:
+    - type: ndcg_at_10
+      value: 31.33
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/CQADupstackWordpressRetrieval
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+    metrics:
+    - type: ndcg_at_10
+      value: 23.86
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/ClimateFEVER
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+    metrics:
+    - type: ndcg_at_10
+      value: 28.51
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/DBPedia
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+    metrics:
+    - type: ndcg_at_10
+      value: 36.32
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/EmotionClassification
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 47.69
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/FEVER
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+    metrics:
+    - type: ndcg_at_10
+      value: 60.27
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/FiQA2018
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+    metrics:
+    - type: ndcg_at_10
+      value: 32.59
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/HotpotQA
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+    metrics:
+    - type: ndcg_at_10
+      value: 52.43
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/ImdbClassification
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 73.59
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/MSMARCO
+      name: MTEB MSMARCO
+      config: default
+      split: test
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+    metrics:
+    - type: ndcg_at_10
+      value: 0
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/MTOPDomainClassification
+      name: MTEB MTOPDomainClassification
+      config: default
+      split: test
+      revision: a76d16fae880597b9c73047b50159220a441cb54
+    metrics:
+    - type: accuracy
+      value: 90.37
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/MTOPIntentClassification
+      name: MTEB MTOPIntentClassification
+      config: default
+      split: test
+      revision: 2992d820f31312593c49a4890430aadadb0f0039
+    metrics:
+    - type: accuracy
+      value: 62.51
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/MassiveIntentClassification
+      name: MTEB MassiveIntentClassification
+      config: default
+      split: test
+      revision: 4672e20407010da34463acc759c162ca9734bca6
+    metrics:
+    - type: accuracy
+      value: 68.19
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/MassiveScenarioClassification
+      name: MTEB MassiveScenarioClassification
+      config: default
+      split: test
+      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
+    metrics:
+    - type: accuracy
+      value: 73.07
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/MedrxivClusteringP2P
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 32.02
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/MedrxivClusteringS2S
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 29.22
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/MindSmallReranking
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 227478e3235572039f4f7661840e059f31ef6eb1
+    metrics:
+    - type: map
+      value: 30.62
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/NFCorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+    metrics:
+    - type: ndcg_at_10
+      value: 30.35
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/NQ
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+    metrics:
+    - type: ndcg_at_10
+      value: 50.71
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/QuoraRetrieval
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
+    metrics:
+    - type: ndcg_at_10
+      value: 60.88
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/RedditClustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 44.67
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/RedditClusteringP2P
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
+    metrics:
+    - type: v_measure
+      value: 53.67
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/SCIDOCS
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
+    metrics:
+    - type: ndcg_at_10
+      value: 16.37
+  - task:
+      type: STS
+    dataset:
+      type: mteb/SICK-R
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
+    metrics:
+    - type: cosine_spearman
+      value: 79.81
+  - task:
+      type: STS
+    dataset:
+      type: mteb/STS12
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cosine_spearman
+      value: 76.03
+  - task:
+      type: STS
+    dataset:
+      type: mteb/STS13
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cosine_spearman
+      value: 85.05
+  - task:
+      type: STS
+    dataset:
+      type: mteb/STS14
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cosine_spearman
+      value: 80.97
+  - task:
+      type: STS
+    dataset:
+      type: mteb/STS15
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cosine_spearman
+      value: 86.88
+  - task:
+      type: STS
+    dataset:
+      type: mteb/STS16
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cosine_spearman
+      value: 83.3
+  - task:
+      type: STS
+    dataset:
+      type: mteb/STSBenchmark
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cosine_spearman
+      value: 86.49
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/SciDocsRR
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: 39b8377811871075eed9de3b8a7e21aaa6acb3d8
+    metrics:
+    - type: map
+      value: 74.1
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/SciFact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: d56462d0e63a25450459c4f213e49ffdb866f7f9
+    metrics:
+    - type: ndcg_at_10
+      value: 59.42
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/SprintDuplicateQuestions
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cosine_ap
+      value: 94.91
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/StackExchangeClustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 52.04
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/StackExchangeClusteringP2P
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 34.14
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/StackOverflowDupQuestions
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: 5debda000fe8e27ebb5c123d38081f92e1847a59
+    metrics:
+    - type: map
+      value: 43.53
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/SummEval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cosine_spearman
+      value: 29.73
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/TRECCOVID
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
+    metrics:
+    - type: ndcg_at_10
+      value: 67.01
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/Touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+    metrics:
+    - type: ndcg_at_10
+      value: 28.58
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/ToxicConversationsClassification
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
+    metrics:
+    - type: accuracy
+      value: 66.23
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/TweetSentimentExtractionClassification
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 62.04
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/TwentyNewsgroupsClustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 41.63
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/TwitterSemEval2015
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cosine_ap
+      value: 70.79
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/TwitterURLCorpus
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cosine_ap
+      value: 85.5
+---
+
+# ogma-base
+
+**13.3M parameter text embedding model** by [Axiotic AI](https://axiotic.ai), achieving **56.54 average** on MTEB English v1 (54/54 tasks).
+
+12-layer transformer, 256 hidden dim, 128 embedding dim.
+
+## Highlights
+
+- **13.3M parameters** — small enough for CPU inference, edge deployment, and resource-constrained environments
+- **56.54 MTEB average** — outperforms Potion-32M (51.22) despite being significantly smaller
+- **Matryoshka embeddings** — use dimensions [32, 64, 128, 256] for flexible storage/compute tradeoffs
+- **Asymmetric encoding** — dedicated `[QRY]`, `[DOC]`, `[SYM]` task tokens for query-document and symmetric tasks
+- **1024 token context** — handles longer passages than typical small models (Potion: 512)
+- **Pure PyTorch** — no external transformer library dependencies
+
+## Architecture
+
+| Component | Details |
+|-----------|---------|
+| Parameters | 13.3M |
+| Layers | 12 |
+| Hidden dim (d_model) | 256 |
+| Embedding dim (d_embed) | 128 |
+| Output dim (d_output) | 256 |
+| Attention heads | 4 |
+| Max sequence length | 1024 |
+| Matryoshka dims | [32, 64, 128, 256] |
+| Pooling | Mean (mask-aware) |
+| Position encoding | RoPE |
+| FFN | SwiGLU |
+| Normalization | Pre-LayerNorm |
+| Tokenizer | SentencePiece Unigram (30K vocab) |
+| Training | Knowledge distillation from teacher model |
+
+## MTEB Results
+
+### Category-Level Scores
+
+| Category | ogma-base | Potion-32M | Potion-8M | vs Potion-32M |
+|----------|-----------|------------|-----------|---------------|
+| Classification | **67.73** | 66.01 | 64.46 | +1.72 |
+| Clustering | **41.49** | 39.24 | 36.88 | +2.25 |
+| PairClassification | **83.73** | 78.17 | 76.62 | +5.56 |
+| Reranking | **51.25** | 50.92 | 49.73 | +0.33 |
+| Retrieval | **42.36** | 32.21 | 30.43 | +10.15 |
+| STS | **82.83** | 73.86 | 72.93 | +8.97 |
+| Summarization | **29.73** | 29.77 | 29.26 | -0.04 |
+| **Overall** | **56.54** | 51.22 | 49.58 | **+5.32** |
+
+> **Potion scores are locally reproduced** using the same evaluation pipeline and hardware for fair head-to-head comparison. These are not self-reported numbers from the Potion model card.
+
+## Usage
+
+### Quick Start
+
+```python
+import torch
+import numpy as np
+from pathlib import Path
+
+# Load model
+from ogma_model import OgmaModel
+from config import OgmaConfig
+from tokenizer import OgmaTokenizer
+
+# Load from checkpoint directory
+model = OgmaModel.from_checkpoint("path/to/ogma-base", device="cpu")
+model.eval()
+
+# Load tokenizer (uses the SentencePiece model embedded in tokenizer.json)
+# The tokenizer needs the .model file — extract from tokenizer.json or use:
+tokenizer = OgmaTokenizer("path/to/tokenizer.model")
+
+# Encode text
+texts = ["This is a query", "This is a document"]
+encoded = tokenizer.batch_encode(texts, max_length=1024)
+
+token_ids = torch.tensor(encoded["input_ids"])
+attention_mask = torch.tensor(encoded["attention_mask"])
+
+# Use task tokens for asymmetric encoding
+from config import TaskToken
+
+with torch.no_grad():
+    # For symmetric tasks (STS, clustering, classification)
+    embeddings = model.encode(token_ids, attention_mask, task=TaskToken.SYM)
+    
+    # For retrieval — encode queries and documents separately
+    query_embs = model.encode(token_ids[:1], attention_mask[:1], task=TaskToken.QRY)
+    doc_embs = model.encode(token_ids[1:], attention_mask[1:], task=TaskToken.DOC)
+
+print(f"Embedding shape: {embeddings.shape}")  # (2, 256)
+```
+
+### Matryoshka Dimensionality Reduction
+
+```python
+# Full embeddings: 256d
+full_embs = model.encode(token_ids, attention_mask, task=TaskToken.SYM)
+
+# Reduce to any Matryoshka dimension: [32, 64, 128, 256]
+dim = 64
+reduced_embs = torch.nn.functional.normalize(full_embs[:, :dim], p=2, dim=-1)
+# These reduced embeddings are trained to be effective at lower dims
+```
+
+### Loading with safetensors
+
+```python
+import torch
+import yaml
+from safetensors.torch import load_file
+from ogma_model import OgmaModel
+from config import OgmaConfig
+
+# Load config
+with open("path/to/ogma-base/config.json") as f:
+    import json
+    config_dict = json.load(f)
+
+config = OgmaConfig.from_dict(config_dict)
+model = OgmaModel(config)
+
+# Load weights from safetensors
+state_dict = load_file("path/to/ogma-base/model.safetensors")
+model.load_state_dict(state_dict)
+model.eval()
+```
+
+## Task Tokens
+
+Ogma uses task-specific prefix tokens for asymmetric encoding:
+
+| Token | ID | Use Case |
+|-------|-----|----------|
+| `[QRY]` | 4 | Query encoding for retrieval |
+| `[DOC]` | 5 | Document/passage encoding for retrieval |
+| `[SYM]` | 6 | Symmetric tasks (STS, classification, clustering) |
+
+For retrieval tasks, encode queries with `[QRY]` and documents with `[DOC]`. For all other tasks, use `[SYM]`.
+
+## Training
+
+Ogma is trained via **knowledge distillation** from a larger teacher embedding model. The training pipeline:
+
+1. **Tokenizer**: SentencePiece Unigram model trained on the distillation corpus (30K vocab)
+2. **Token embeddings**: PCA-reduced embeddings from the teacher model, providing a strong initialization
+3. **Distillation**: MSE loss between student and teacher embeddings, with Matryoshka loss at multiple dimensions
+4. **Architecture**: Standard transformer encoder with RoPE positional encoding and SwiGLU FFN
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `model.safetensors` | Model weights (safetensors format) |
+| `model.pt` | Model weights (PyTorch format) |
+| `config.json` | Model configuration |
+| `config.yaml` | Original training config |
+| `tokenizer.json` | HuggingFace tokenizer |
+| `tokenizer_config.json` | Tokenizer configuration |
+| `token_embeds_128d.npy` | Pre-computed token embeddings (30K × 128, float16) |
+| `ogma_model.py` | OgmaModel class |
+| `config.py` | OgmaConfig dataclass |
+| `embeddings.py` | Token embedding + RoPE |
+| `pooling.py` | Pooling strategies |
+| `variants/transformer.py` | Transformer encoder variant |
+| `tokenizer.py` | OgmaTokenizer wrapper |
+| `results/` | MTEB result JSONs |
+
+## Citation
+
+```bibtex
+@misc{ogma2026,
+  title={Ogma: Small High-Performance Text Embeddings},
+  author={Axiotic AI},
+  year={2026},
+  url={https://huggingface.co/axiotic/ogma-base}
+}
+```
+
+## License
+
+MIT

config.json

@@ -0,0 +1,37 @@
+{
+  "architectures": [
+    "OgmaModel"
+  ],
+  "model_type": "ogma",
+  "auto_map": {
+    "AutoModel": "ogma_model.OgmaModel"
+  },
+  "variant": "transformer",
+  "d_embed": 128,
+  "d_model": 256,
+  "d_output": 256,
+  "n_layers": 12,
+  "n_heads": 4,
+  "vocab_size": 30000,
+  "max_seq_len": 1024,
+  "matryoshka_dims": [
+    32,
+    64,
+    128,
+    256
+  ],
+  "pooling": "mean",
+  "ffn_mult": 2.6666666666666665,
+  "conv_kernel_size": 7,
+  "spatial_rank": 32,
+  "n_random_features": 128,
+  "dropout": 0.0,
+  "pad_id": 0,
+  "unk_id": 1,
+  "bos_id": 2,
+  "eos_id": 3,
+  "qry_id": 4,
+  "doc_id": 5,
+  "sym_id": 6,
+  "n_special_tokens": 7
+}

config.py

@@ -0,0 +1,161 @@
+"""Model configuration for Ogma."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from enum import StrEnum
+from typing import Any
+
+__all__ = ["OgmaConfig", "VariantType", "PoolingType", "TaskToken"]
+
+
+class VariantType(StrEnum):
+    """Architecture variant identifiers."""
+
+    TRANSFORMER = "transformer"
+    DEEP_NARROW = "deep_narrow"
+    CONV = "conv"
+    LINEAR_ATTENTION = "linear_attention"
+    MLP_MIXER = "mlp_mixer"
+    TRANSFORMER_RESA = "transformer_resa"
+    GLA = "gla"
+
+
+class PoolingType(StrEnum):
+    """Pooling strategy identifiers."""
+
+    TASK_TOKEN = "task_token"
+    LATENT_ATTENTION = "latent_attention"
+    MEAN = "mean"
+
+
+class TaskToken(StrEnum):
+    """Task token identifiers for asymmetric encoding."""
+
+    QRY = "QRY"
+    DOC = "DOC"
+    SYM = "SYM"
+
+
+@dataclass
+class OgmaConfig:
+    """Configuration for an Ogma model instance.
+
+    Args:
+        variant: Architecture variant to use.
+        d_embed: Token embedding dimension (from teacher PCA).
+        d_model: Internal model dimension after projection.
+        n_layers: Number of fusion layers/blocks.
+        n_heads: Number of attention heads (attention variants only).
+        vocab_size: Vocabulary size for embedding table.
+        max_seq_len: Maximum sequence length.
+        matryoshka_dims: Nested output dimensions for Matryoshka.
+        pooling: Pooling strategy.
+        d_output: Final output dimension.
+        ffn_mult: SwiGLU FFN hidden dimension multiplier.
+        conv_kernel_size: Kernel size for conv variant.
+        spatial_rank: Rank of spatial mixing in MLP mixer.
+        n_random_features: Random features for linear attention.
+        dropout: Dropout rate (0 for inference).
+    """
+
+    variant: VariantType = VariantType.TRANSFORMER
+    d_embed: int = 128
+    d_model: int = 256
+    n_layers: int = 1
+    n_heads: int = 4
+    vocab_size: int = 30_000
+    max_seq_len: int = 512
+    matryoshka_dims: list[int] = field(
+        default_factory=lambda: [32, 64, 128, 256]
+    )
+    pooling: PoolingType = PoolingType.TASK_TOKEN
+    d_output: int = 256
+    ffn_mult: float = 8 / 3  # SwiGLU: 8/3 * d_model ≈ 683 for d=256
+    conv_kernel_size: int = 7
+    spatial_rank: int = 32
+    n_random_features: int = 128
+    dropout: float = 0.0
+
+    # ReSA scorer settings
+    scorer_type: str = "dot"
+    scorer_alpha_init: float = 0.1
+    scorer_hidden: int = 0  # 0 defaults to d_head
+
+    # GLA (Gated Linear Attention) settings
+    gla_expand_k: float = 0.5  # key dim expansion (key_dim = d_model * expand_k)
+    gla_expand_v: float = 1.0  # value dim expansion (value_dim = d_model * expand_v)
+    gla_gate_low_rank_dim: int = 16  # low-rank dim for gating projection
+    gla_gate_logit_normalizer: int = 16  # normalizer for gate logits
+    gla_use_short_conv: bool = True  # whether to use short conv on Q,K,V
+    gla_conv_size: int = 4  # short conv kernel size
+
+    # Special token IDs
+    pad_id: int = 0
+    unk_id: int = 1
+    bos_id: int = 2
+    eos_id: int = 3
+    qry_id: int = 4
+    doc_id: int = 5
+    sym_id: int = 6
+    n_special_tokens: int = 7
+
+    @property
+    def d_head(self) -> int:
+        """Per-head dimension."""
+        return self.d_model // self.n_heads
+
+    @property
+    def ffn_hidden(self) -> int:
+        """SwiGLU FFN hidden dimension."""
+        return int(self.d_model * self.ffn_mult)
+
+    def task_token_id(self, task: TaskToken) -> int:
+        """Return token ID for a task token."""
+        mapping = {
+            TaskToken.QRY: self.qry_id,
+            TaskToken.DOC: self.doc_id,
+            TaskToken.SYM: self.sym_id,
+        }
+        return mapping[task]
+
+    def to_dict(self) -> dict[str, Any]:
+        """Serialize config to dictionary."""
+        return {
+            "variant": self.variant.value,
+            "d_embed": self.d_embed,
+            "d_model": self.d_model,
+            "n_layers": self.n_layers,
+            "n_heads": self.n_heads,
+            "vocab_size": self.vocab_size,
+            "max_seq_len": self.max_seq_len,
+            "matryoshka_dims": self.matryoshka_dims,
+            "pooling": self.pooling.value,
+            "d_output": self.d_output,
+            "ffn_mult": self.ffn_mult,
+            "conv_kernel_size": self.conv_kernel_size,
+            "spatial_rank": self.spatial_rank,
+            "n_random_features": self.n_random_features,
+            "dropout": self.dropout,
+            "scorer_type": self.scorer_type,
+            "scorer_alpha_init": self.scorer_alpha_init,
+            "scorer_hidden": self.scorer_hidden,
+            "gla_expand_k": self.gla_expand_k,
+            "gla_expand_v": self.gla_expand_v,
+            "gla_gate_low_rank_dim": self.gla_gate_low_rank_dim,
+            "gla_gate_logit_normalizer": self.gla_gate_logit_normalizer,
+            "gla_use_short_conv": self.gla_use_short_conv,
+            "gla_conv_size": self.gla_conv_size,
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> OgmaConfig:
+        """Deserialize config from dictionary."""
+        data = dict(data)
+        if "variant" in data:
+            data["variant"] = VariantType(data["variant"])
+        if "pooling" in data:
+            data["pooling"] = PoolingType(data["pooling"])
+        known = {f.name for f in cls.__dataclass_fields__.values()}
+        filtered = {k: v for k, v in data.items() if k in known}
+        return cls(**filtered)

config.yaml

@@ -0,0 +1,19 @@
+conv_kernel_size: 7
+d_embed: 128
+d_model: 256
+d_output: 256
+dropout: 0.0
+ffn_mult: 2.6666666666666665
+matryoshka_dims:
+- 32
+- 64
+- 128
+- 256
+max_seq_len: 1024
+n_heads: 4
+n_layers: 12
+n_random_features: 128
+pooling: mean
+spatial_rank: 32
+variant: transformer
+vocab_size: 30000

embeddings.py

@@ -0,0 +1,143 @@
+"""Token embeddings, task token embeddings, and RoPE for Ogma."""
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+
+from ogma.model.config import OgmaConfig
+
+__all__ = ["TokenEmbedding", "RotaryPositionalEncoding"]
+
+
+class TokenEmbedding(nn.Module):
+    """Token embedding with optional linear projection.
+
+    Loads a vocab_size x d_embed embedding table and projects to d_model.
+    Includes 3 learnable task token embeddings ([QRY], [DOC], [SYM]).
+    """
+
+    def __init__(self, config: OgmaConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.embed = nn.Embedding(
+            config.vocab_size + config.n_special_tokens,
+            config.d_embed,
+            padding_idx=config.pad_id,
+        )
+        if config.d_embed != config.d_model:
+            self.proj = nn.Linear(config.d_embed, config.d_model)
+        else:
+            self.proj = nn.Identity()  # type: ignore[assignment]
+
+        # Task token embeddings are learned separately at d_model
+        self.task_tokens = nn.Embedding(3, config.d_model)
+
+    def forward(
+        self,
+        token_ids: torch.Tensor,
+        task_token_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        """Embed tokens and prepend task token.
+
+        Args:
+            token_ids: (B, S) token IDs.
+            task_token_ids: (B,) task token IDs (4=QRY, 5=DOC, 6=SYM).
+
+        Returns:
+            (B, S+1, d_model) embeddings with task token prepended.
+        """
+        # Embed and project regular tokens
+        x = self.embed(token_ids)  # (B, S, d_embed)
+        x = self.proj(x)  # (B, S, d_model)
+
+        # Get task token embeddings (map 4,5,6 -> 0,1,2)
+        task_idx = task_token_ids - self.config.qry_id  # (B,)
+        task_emb = self.task_tokens(task_idx)  # (B, d_model)
+        task_emb = task_emb.unsqueeze(1)  # (B, 1, d_model)
+
+        # Prepend task token
+        return torch.cat([task_emb, x], dim=1)  # (B, S+1, d_model)
+
+    def load_pretrained_embeddings(
+        self, embeddings: torch.Tensor
+    ) -> None:
+        """Load pre-computed token embeddings (e.g., from teacher PCA).
+
+        Args:
+            embeddings: (vocab_size, d_embed) tensor.
+        """
+        with torch.no_grad():
+            n = min(embeddings.shape[0], self.config.vocab_size)
+            start = self.config.n_special_tokens
+            self.embed.weight[start : n + start] = embeddings[:n]
+
+
+class RotaryPositionalEncoding(nn.Module):
+    """Rotary Position Embedding (RoPE). Zero trainable parameters."""
+
+    def __init__(self, dim: int, max_seq_len: int = 512) -> None:
+        super().__init__()
+        inv_freq = 1.0 / (
+            10000.0 ** (torch.arange(0, dim, 2).float() / dim)
+        )
+        self.register_buffer("inv_freq", inv_freq)
+        self._build_cache(max_seq_len)
+
+    def _build_cache(self, seq_len: int) -> None:
+        inv_freq: torch.Tensor = self.inv_freq  # type: ignore[assignment]
+        t = torch.arange(seq_len, dtype=inv_freq.dtype)
+        freqs = torch.outer(t, inv_freq)
+        cos_cached = freqs.cos()
+        sin_cached = freqs.sin()
+        self.register_buffer("cos_cached", cos_cached, persistent=False)
+        self.register_buffer("sin_cached", sin_cached, persistent=False)
+
+    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """Return cos and sin for sequence length of x.
+
+        Args:
+            x: (B, S, ...) tensor to determine sequence length.
+
+        Returns:
+            Tuple of (cos, sin) each of shape (S, d_head//2).
+        """
+        seq_len = x.shape[1]
+        cos: torch.Tensor = self.cos_cached  # type: ignore[assignment]
+        sin: torch.Tensor = self.sin_cached  # type: ignore[assignment]
+        if seq_len > cos.shape[0]:
+            self._build_cache(seq_len)
+            cos = self.cos_cached  # type: ignore[assignment]
+            sin = self.sin_cached  # type: ignore[assignment]
+        return cos[:seq_len], sin[:seq_len]
+
+
+def apply_rope(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Apply rotary embeddings to query and key tensors.
+
+    Args:
+        q: (B, n_heads, S, d_head) query tensor.
+        k: (B, n_heads, S, d_head) key tensor.
+        cos: (S, d_head//2) cosine cache.
+        sin: (S, d_head//2) sine cache.
+
+    Returns:
+        Rotated (q, k) tensors.
+    """
+
+    def _rotate(x: torch.Tensor) -> torch.Tensor:
+        x1 = x[..., : x.shape[-1] // 2]
+        x2 = x[..., x.shape[-1] // 2 :]
+        cos_exp = cos.unsqueeze(0).unsqueeze(0)  # (1, 1, S, d_head//2)
+        sin_exp = sin.unsqueeze(0).unsqueeze(0)
+        return torch.cat(
+            [x1 * cos_exp - x2 * sin_exp, x2 * cos_exp + x1 * sin_exp],
+            dim=-1,
+        )
+
+    return _rotate(q), _rotate(k)

model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:010499a3a946b0d272b46eb3dd32e818584a968f27f1134c72f5259401559f35
+size 53317199

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3761ec794444f889d18bf9dc3378f3a9c3474a1d7b64fcf6ba3f2041ec64848
+size 53288344

ogma_model.py

@@ -0,0 +1,203 @@
+"""OgmaModel — top-level model wrapping any architecture variant."""
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ogma.model.config import OgmaConfig, TaskToken, VariantType
+from ogma.model.embeddings import TokenEmbedding
+from ogma.model.pooling import create_pooling
+from ogma.model.variants.conv import ConvVariant
+from ogma.model.variants.deep_narrow import DeepNarrowVariant
+from ogma.model.variants.linear_attention import LinearAttentionVariant
+from ogma.model.variants.mlp_mixer import MLPMixerVariant
+from ogma.model.variants.transformer import TransformerVariant
+from ogma.model.variants.transformer_resa import TransformerReSAVariant
+from ogma.model.variants.gla import GLAVariant
+
+__all__ = ["OgmaModel"]
+
+MAX_PARAMS = 10_000_000
+
+
+def _build_variant(config: OgmaConfig) -> nn.Module:
+    """Instantiate the appropriate architecture variant."""
+    if config.variant == VariantType.TRANSFORMER:
+        return TransformerVariant(config)
+    elif config.variant == VariantType.DEEP_NARROW:
+        return DeepNarrowVariant(config)
+    elif config.variant == VariantType.CONV:
+        return ConvVariant(config)
+    elif config.variant == VariantType.LINEAR_ATTENTION:
+        return LinearAttentionVariant(config)
+    elif config.variant == VariantType.MLP_MIXER:
+        return MLPMixerVariant(config)
+    elif config.variant == VariantType.TRANSFORMER_RESA:
+        return TransformerReSAVariant(config)
+    elif config.variant == VariantType.GLA:
+        return GLAVariant(config)
+    raise ValueError(f"Unknown variant: {config.variant}")
+
+
+class OgmaModel(nn.Module):
+    """Ogma embedding model.
+
+    Wraps any architecture variant with shared embedding, pooling, and
+    normalization. Produces L2-normalized embeddings at d_output dimensions,
+    Matryoshka-compatible at configured sub-dimensions.
+    """
+
+    def __init__(self, config: OgmaConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.embedding = TokenEmbedding(config)
+        self.variant = _build_variant(config)
+        self.pooling = create_pooling(config)
+
+        # Output projection if variant output != d_output
+        needs_proj = (
+            config.variant == VariantType.DEEP_NARROW
+            and config.d_model != config.d_output
+        )
+        # DeepNarrowVariant already has output_proj, so no extra needed here
+        if not needs_proj and config.d_model != config.d_output:
+            self.output_proj: nn.Module = nn.Linear(
+                config.d_model, config.d_output
+            )
+        else:
+            self.output_proj = nn.Identity()
+
+    def forward(
+        self,
+        token_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        task_token_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        """Forward pass producing L2-normalized embeddings.
+
+        Args:
+            token_ids: (B, S) token IDs.
+            attention_mask: (B, S) attention mask (1=valid, 0=pad).
+            task_token_ids: (B,) task token IDs (4=QRY, 5=DOC, 6=SYM).
+
+        Returns:
+            (B, d_output) L2-normalized embeddings.
+        """
+        # Embed tokens with task token prepended -> (B, S+1, d_model)
+        x = self.embedding(token_ids, task_token_ids)
+
+        # Extend attention mask for prepended task token
+        task_mask = torch.ones(
+            attention_mask.shape[0], 1,
+            device=attention_mask.device,
+            dtype=attention_mask.dtype,
+        )
+        extended_mask = torch.cat([task_mask, attention_mask], dim=1)
+
+        # Run through variant
+        x = self.variant(x, extended_mask)
+
+        # Pool
+        x = self.pooling(x, extended_mask)
+
+        # Project if needed
+        x = self.output_proj(x)
+
+        # L2 normalize
+        return F.normalize(x, p=2, dim=-1)
+
+    def encode(
+        self,
+        token_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        task: TaskToken = TaskToken.SYM,
+    ) -> torch.Tensor:
+        """Encode tokens with a specified task mode.
+
+        Args:
+            token_ids: (B, S) token IDs.
+            attention_mask: (B, S) attention mask.
+            task: Task token to use.
+
+        Returns:
+            (B, d_output) L2-normalized embeddings.
+        """
+        task_ids = torch.full(
+            (token_ids.shape[0],),
+            self.config.task_token_id(task),
+            device=token_ids.device,
+            dtype=torch.long,
+        )
+        return self.forward(token_ids, attention_mask, task_ids)
+
+    def param_count(self) -> int:
+        """Count total trainable parameters."""
+        return sum(p.numel() for p in self.parameters() if p.requires_grad)
+
+    def assert_param_budget(self) -> None:
+        """Assert model is under the 10M parameter budget."""
+        count = self.param_count()
+        assert count < MAX_PARAMS, (
+            f"Model has {count:,} params, exceeds {MAX_PARAMS:,} budget"
+        )
+
+    @classmethod
+    def from_config(cls, config: OgmaConfig) -> OgmaModel:
+        """Factory method to build a model from config."""
+        model = cls(config)
+        model.assert_param_budget()
+        return model
+
+    @classmethod
+    def from_checkpoint(
+        cls,
+        path: str,
+        device: str = "cpu",
+    ) -> OgmaModel:
+        """Load model from a checkpoint directory.
+
+        Args:
+            path: Path to checkpoint directory containing config.yaml
+                and model.pt.
+            device: Device to load model to.
+
+        Returns:
+            Loaded OgmaModel.
+        """
+        from pathlib import Path
+
+        import yaml
+
+        ckpt_path = Path(path)
+        with open(ckpt_path / "config.yaml") as f:
+            config_dict = yaml.safe_load(f)
+        config = OgmaConfig.from_dict(config_dict)
+
+        model = cls(config)
+        state_dict = torch.load(
+            ckpt_path / "model.pt",
+            map_location=device,
+            weights_only=True,
+        )
+        model.load_state_dict(state_dict)
+        model.to(device)
+        model.eval()
+        return model
+
+    def save_checkpoint(self, path: str) -> None:
+        """Save model checkpoint.
+
+        Args:
+            path: Directory to save config.yaml and model.pt.
+        """
+        from pathlib import Path
+
+        import yaml
+
+        ckpt_path = Path(path)
+        ckpt_path.mkdir(parents=True, exist_ok=True)
+        with open(ckpt_path / "config.yaml", "w") as f:
+            yaml.dump(self.config.to_dict(), f, default_flow_style=False)
+        torch.save(self.state_dict(), ckpt_path / "model.pt")

pooling.py

@@ -0,0 +1,99 @@
+"""Pooling strategies for Ogma."""
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ogma.model.config import OgmaConfig, PoolingType
+
+__all__ = [
+    "create_pooling",
+    "TaskTokenPooling",
+    "LatentAttentionPooling",
+    "MeanPooling",
+]
+
+
+def create_pooling(config: OgmaConfig) -> nn.Module:
+    """Factory for pooling layers."""
+    if config.pooling == PoolingType.TASK_TOKEN:
+        return TaskTokenPooling()
+    elif config.pooling == PoolingType.LATENT_ATTENTION:
+        return LatentAttentionPooling(config.d_model)
+    elif config.pooling == PoolingType.MEAN:
+        return MeanPooling()
+    raise ValueError(f"Unknown pooling type: {config.pooling}")
+
+
+class TaskTokenPooling(nn.Module):
+    """Use the output at position 0 (task token) as the sentence embedding."""
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Extract task token output.
+
+        Args:
+            x: (B, S, D) sequence outputs.
+            attention_mask: unused, for interface compatibility.
+
+        Returns:
+            (B, D) pooled output.
+        """
+        return x[:, 0, :]
+
+
+class LatentAttentionPooling(nn.Module):
+    """Learned query vector attends over all token outputs."""
+
+    def __init__(self, d_model: int) -> None:
+        super().__init__()
+        self.query = nn.Parameter(torch.randn(d_model))
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Attend over sequence with learned query.
+
+        Args:
+            x: (B, S, D) sequence outputs.
+            attention_mask: (B, S) mask where 1=valid, 0=pad.
+
+        Returns:
+            (B, D) pooled output.
+        """
+        # (B, S)
+        scores = torch.matmul(x, self.query) / (x.shape[-1] ** 0.5)
+        if attention_mask is not None:
+            scores = scores.masked_fill(attention_mask == 0, float("-inf"))
+        weights = F.softmax(scores, dim=-1)  # (B, S)
+        return torch.bmm(weights.unsqueeze(1), x).squeeze(1)  # (B, D)
+
+
+class MeanPooling(nn.Module):
+    """Average all token outputs (excluding padding)."""
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Mean pool over valid tokens.
+
+        Args:
+            x: (B, S, D) sequence outputs.
+            attention_mask: (B, S) mask where 1=valid, 0=pad.
+
+        Returns:
+            (B, D) pooled output.
+        """
+        if attention_mask is None:
+            return x.mean(dim=1)
+        mask = attention_mask.unsqueeze(-1).float()  # (B, S, 1)
+        return (x * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-9)

results/AmazonCounterfactualClassification.json

@@ -0,0 +1,526 @@
+{
+  "dataset_revision": "1f7e6a9d6fa6e64c53d146e428565640410c0df1",
+  "task_name": "AmazonCounterfactualClassification",
+  "mteb_version": "2.10.7",
+  "scores": {
+    "validation": [
+      {
+        "scores_per_experiment": [
+          {
+            "accuracy": 0.753754,
+            "f1": 0.623225,
+            "f1_weighted": 0.800371,
+            "precision": 0.619778,
+            "precision_weighted": 0.902615,
+            "recall": 0.78357,
+            "recall_weighted": 0.753754,
+            "ap": 0.23613,
+            "ap_weighted": 0.23613
+          },
+          {
+            "accuracy": 0.704204,
+            "f1": 0.571116,
+            "f1_weighted": 0.761959,
+            "precision": 0.586908,
+            "precision_weighted": 0.884058,
+            "recall": 0.716256,
+            "recall_weighted": 0.704204,
+            "ap": 0.184202,
+            "ap_weighted": 0.184202
+          },
+          {
+            "accuracy": 0.728228,
+            "f1": 0.611557,
+            "f1_weighted": 0.781609,
+            "precision": 0.620103,
+            "precision_weighted": 0.910621,
+            "recall": 0.802519,
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+            "ap": 0.240132,
+            "ap_weighted": 0.240132
+          },
+          {
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+            "f1": 0.545546,
+            "f1_weighted": 0.727054,
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+            "recall": 0.717726,
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+            "ap": 0.178635,
+            "ap_weighted": 0.178635
+          },
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+            "recall": 0.759363,
+            "recall_weighted": 0.71021,
+            "ap": 0.209328,
+            "ap_weighted": 0.209328
+          },
+          {
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+          },
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+            "ap_weighted": 0.200863
+          },
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+          },
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+          },
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+          }
+        ],
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+        "hf_subset": "en-ext",
+        "languages": [
+          "eng-Latn"
+        ]
+      },
+      {
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+        "languages": [
+          "eng-Latn"
+        ]
+      }
+    ],
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+        ]
+      },
+      {
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results/ArXivHierarchicalClusteringP2P.json

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results/ArXivHierarchicalClusteringS2S.json

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results/ArguAna.json

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results/BIOSSES.json

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results/Banking77Classification.json

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results/BiorxivClusteringP2P.json

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results/BiorxivClusteringS2S.json

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results/CQADupstackAndroidRetrieval.json

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+}

results/CQADupstackEnglishRetrieval.json

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results/CQADupstackGamingRetrieval.json

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+}

results/CQADupstackGisRetrieval.json

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results/CQADupstackMathematicaRetrieval.json

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+}

results/CQADupstackPhysicsRetrieval.json

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+}

results/CQADupstackProgrammersRetrieval.json

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+}

results/CQADupstackRetrieval.json

@@ -0,0 +1,20 @@
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+}

results/CQADupstackStatsRetrieval.json

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+}

results/CQADupstackTexRetrieval.json

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results/CQADupstackUnixRetrieval.json

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+}

results/CQADupstackWebmastersRetrieval.json

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+}

results/CQADupstackWordpressRetrieval.json

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+}

results/ClimateFEVER.json

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results/DBPedia.json

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results/EmotionClassification.json

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results/FEVER.json

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+}

results/FiQA2018.json

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results/HotpotQA.json

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results/ImdbClassification.json

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results/MSMARCO.json

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results/MTOPDomainClassification.json

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results/MassiveIntentClassification.json

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results/MassiveScenarioClassification.json

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results/MedrxivClusteringP2P.json

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results/MedrxivClusteringS2S.json

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+}

results/NFCorpus.json

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results/NQ.json

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