diff --git "a/README.md" "b/README.md"
new file mode 100644--- /dev/null
+++ "b/README.md"
@@ -0,0 +1,25260 @@
+---
+license: cc-by-nc-4.0
+tags:
+- feature-extraction
+- sentence-similarity
+- mteb
+- sentence-transformers
+language:
+ - multilingual
+ - af
+ - am
+ - ar
+ - as
+ - az
+ - be
+ - bg
+ - bn
+ - br
+ - bs
+ - ca
+ - cs
+ - cy
+ - da
+ - de
+ - el
+ - en
+ - eo
+ - es
+ - et
+ - eu
+ - fa
+ - fi
+ - fr
+ - fy
+ - ga
+ - gd
+ - gl
+ - gu
+ - ha
+ - he
+ - hi
+ - hr
+ - hu
+ - hy
+ - id
+ - is
+ - it
+ - ja
+ - jv
+ - ka
+ - kk
+ - km
+ - kn
+ - ko
+ - ku
+ - ky
+ - la
+ - lo
+ - lt
+ - lv
+ - mg
+ - mk
+ - ml
+ - mn
+ - mr
+ - ms
+ - my
+ - ne
+ - nl
+ - no
+ - om
+ - or
+ - pa
+ - pl
+ - ps
+ - pt
+ - ro
+ - ru
+ - sa
+ - sd
+ - si
+ - sk
+ - sl
+ - so
+ - sq
+ - sr
+ - su
+ - sv
+ - sw
+ - ta
+ - te
+ - th
+ - tl
+ - tr
+ - ug
+ - uk
+ - ur
+ - uz
+ - vi
+ - xh
+ - yi
+ - zh
+inference: false
+library_name: transformers
+model-index:
+- name: jina-embeddings-v3
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+ name: MTEB AFQMC (default)
+ revision: b44c3b011063adb25877c13823db83bb193913c4
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+ type: C-MTEB/AFQMC
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+ name: MTEB ArguAna-PL (default)
+ revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
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+ type: clarin-knext/arguana-pl
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+ split: test
+ type: mteb/cqadupstack-android
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ - type: mrr_at_1000
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+ - type: mrr_at_20
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+ - type: mrr_at_3
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+ - type: mrr_at_5
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+ - type: ndcg_at_1
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+ - type: ndcg_at_10
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+ - type: ndcg_at_100
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+ - type: ndcg_at_1000
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+ - type: ndcg_at_3
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+ - type: precision_at_1000
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+ - type: precision_at_5
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+ - type: recall_at_1
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+ - type: recall_at_10
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+ - type: recall_at_100
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+ - type: recall_at_1000
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+ - type: recall_at_20
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+ - type: recall_at_3
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+ - type: recall_at_5
+ value: 58.41
+ - type: main_score
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+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackEnglishRetrieval (default)
+ revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+ split: test
+ type: mteb/cqadupstack-english
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: ndcg_at_100
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+ - type: precision_at_3
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+ - type: recall_at_1
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+ - type: recall_at_20
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - type: main_score
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+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackGamingRetrieval (default)
+ revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+ split: test
+ type: mteb/cqadupstack-gaming
+ metrics:
+ - type: map_at_1
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: ndcg_at_10
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+ - type: ndcg_at_1000
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+ - type: recall_at_1000
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - type: main_score
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+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackGisRetrieval (default)
+ revision: 5003b3064772da1887988e05400cf3806fe491f2
+ split: test
+ type: mteb/cqadupstack-gis
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: mrr_at_3
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+ - type: mrr_at_5
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+ - type: ndcg_at_1
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+ - type: ndcg_at_10
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+ - type: ndcg_at_100
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+ - type: ndcg_at_1000
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+ - type: recall_at_1000
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+ - type: recall_at_20
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - type: main_score
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+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackMathematicaRetrieval (default)
+ revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+ split: test
+ type: mteb/cqadupstack-mathematica
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_20
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+ - type: ndcg_at_1
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+ - type: ndcg_at_10
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+ - type: recall_at_100
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+ - type: recall_at_1000
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+ - type: recall_at_20
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - type: main_score
+ value: 32.129999999999995
+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackPhysicsRetrieval (default)
+ revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+ split: test
+ type: mteb/cqadupstack-physics
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ - type: mrr_at_3
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+ - type: mrr_at_5
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+ - type: ndcg_at_1000
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+ - type: recall_at_20
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - type: main_score
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+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackProgrammersRetrieval (default)
+ revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+ split: test
+ type: mteb/cqadupstack-programmers
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: recall_at_100
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+ - type: recall_at_1000
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - type: main_score
+ value: 43.887
+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackRetrieval (default)
+ revision: CQADupstackRetrieval_is_a_combined_dataset
+ split: test
+ type: CQADupstackRetrieval_is_a_combined_dataset
+ metrics:
+ - type: main_score
+ value: 42.58858333333333
+ - type: ndcg_at_10
+ value: 42.58858333333333
+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackStatsRetrieval (default)
+ revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+ split: test
+ type: mteb/cqadupstack-stats
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - type: main_score
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+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackTexRetrieval (default)
+ revision: 46989137a86843e03a6195de44b09deda022eec7
+ split: test
+ type: mteb/cqadupstack-tex
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: recall_at_1000
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - type: main_score
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+ task:
+ type: Retrieval
+ - dataset:
+ config: default
+ name: MTEB CQADupstackUnixRetrieval (default)
+ revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+ split: test
+ type: mteb/cqadupstack-unix
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ name: MTEB OnlineShopping (default)
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+ task:
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+ name: MTEB PAC (default)
+ revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
+ split: test
+ type: laugustyniak/abusive-clauses-pl
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+ task:
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+ name: MTEB PAWSX (default)
+ revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
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+ name: MTEB PawsXPairClassification (en)
+ revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+ split: test
+ type: google-research-datasets/paws-x
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+ name: MTEB PawsXPairClassification (de)
+ revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+ split: test
+ type: google-research-datasets/paws-x
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+ config: es
+ name: MTEB PawsXPairClassification (es)
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+ revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
+ split: test
+ type: PL-MTEB/sicke-pl-pairclassification
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+ - type: cosine_ap
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+ - type: cosine_f1_threshold
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+ - type: dot_accuracy
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+ - type: dot_accuracy_threshold
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+ - type: euclidean_accuracy
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+ - type: euclidean_accuracy_threshold
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+ - type: euclidean_f1
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+ - type: euclidean_precision
+ value: 62.625
+ - type: euclidean_recall
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+ - type: main_score
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+ - type: manhattan_f1_threshold
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+ - type: manhattan_precision
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+ - type: manhattan_recall
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+ - type: max_accuracy
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+ - type: max_ap
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+ - type: max_f1
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+ - type: max_precision
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+ - type: max_recall
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+ - type: similarity_accuracy
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+ - type: similarity_accuracy_threshold
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+ - type: similarity_ap
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+ - type: similarity_f1
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+ - type: similarity_f1_threshold
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+ - type: similarity_precision
+ value: 62.4922504649721
+ - type: similarity_recall
+ value: 71.7948717948718
+ task:
+ type: PairClassification
+ - dataset:
+ config: default
+ name: MTEB SICK-R (default)
+ revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
+ split: test
+ type: mteb/sickr-sts
+ metrics:
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+ - type: cosine_spearman
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+ - type: euclidean_pearson
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+ - type: euclidean_spearman
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+ - type: main_score
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+ - type: manhattan_pearson
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+ - type: pearson
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+ - type: spearman
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+ task:
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+ config: default
+ name: MTEB SICK-R-PL (default)
+ revision: fd5c2441b7eeff8676768036142af4cfa42c1339
+ split: test
+ type: PL-MTEB/sickr-pl-sts
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+ - type: euclidean_pearson
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+ - type: euclidean_spearman
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+ - type: spearman
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+ config: default
+ name: MTEB SICKFr (default)
+ revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
+ split: test
+ type: Lajavaness/SICK-fr
+ metrics:
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+ - type: cosine_spearman
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+ - type: euclidean_pearson
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+ config: default
+ name: MTEB STS12 (default)
+ revision: a0d554a64d88156834ff5ae9920b964011b16384
+ split: test
+ type: mteb/sts12-sts
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+ - type: euclidean_pearson
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+ - type: euclidean_spearman
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+ config: default
+ name: MTEB STS13 (default)
+ revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+ split: test
+ type: mteb/sts13-sts
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+ - type: cosine_spearman
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+ - type: euclidean_pearson
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+ - type: euclidean_spearman
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+ - type: main_score
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+ - type: manhattan_pearson
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+ - type: manhattan_spearman
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+ - type: spearman
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+ task:
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+ config: default
+ name: MTEB STS14 (default)
+ revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+ split: test
+ type: mteb/sts14-sts
+ metrics:
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+ - type: euclidean_pearson
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+ - type: euclidean_spearman
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+ - type: manhattan_pearson
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+ - type: pearson
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+ - type: spearman
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+ task:
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+ - dataset:
+ config: default
+ name: MTEB STS15 (default)
+ revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+ split: test
+ type: mteb/sts15-sts
+ metrics:
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+ - type: cosine_spearman
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+ - type: euclidean_pearson
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+ - type: euclidean_spearman
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+ - type: manhattan_pearson
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+ - type: spearman
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+ config: default
+ name: MTEB STS16 (default)
+ revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+ split: test
+ type: mteb/sts16-sts
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+ config: en-en
+ name: MTEB STS17 (en-en)
+ revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+ split: test
+ type: mteb/sts17-crosslingual-sts
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+ - type: euclidean_pearson
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+ - type: manhattan_pearson
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+ - type: pearson
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+ - type: spearman
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+ config: es-en
+ name: MTEB STS17 (es-en)
+ revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+ split: test
+ type: mteb/sts17-crosslingual-sts
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+ - type: euclidean_pearson
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+ - type: euclidean_spearman
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+ - type: manhattan_pearson
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+ - type: pearson
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+ - type: spearman
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+ task:
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+ name: MTEB STS17 (en-de)
+ revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+ split: test
+ type: mteb/sts17-crosslingual-sts
+ metrics:
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+ - type: euclidean_pearson
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+ - type: main_score
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+ - type: manhattan_pearson
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+ name: MTEB STS17 (es-es)
+ revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+ split: test
+ type: mteb/sts17-crosslingual-sts
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+ - type: euclidean_pearson
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+ - type: euclidean_spearman
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+ - type: main_score
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+ - type: spearman
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+ task:
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+ config: fr-en
+ name: MTEB STS17 (fr-en)
+ revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+ split: test
+ type: mteb/sts17-crosslingual-sts
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+ - type: euclidean_pearson
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+ config: zh-en
+ name: MTEB STS22 (zh-en)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
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+ config: zh
+ name: MTEB STS22 (zh)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
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+ - type: euclidean_pearson
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+ name: MTEB STS22 (es)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
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+ - type: euclidean_pearson
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+ name: MTEB STS22 (fr)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
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+ name: MTEB STS22 (de-fr)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
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+ name: MTEB STS22 (pl-en)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
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+ name: MTEB STS22 (pl)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
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+ name: MTEB STS22 (en)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
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+ name: MTEB STS22 (ru)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
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+ name: MTEB STS22 (de)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
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+ name: MTEB STS22 (fr-pl)
+ revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+ split: test
+ type: mteb/sts22-crosslingual-sts
+ metrics:
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+---
+
+
+
+
+ +
+ + ++The embedding model trained by Jina AI. +
+ ++jina-embeddings-v3: Multilingual Embeddings With Task LoRA +
+ +## Quick Start + +[Blog](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/#parameter-dimensions) | [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.jina-embeddings-v3) | [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-kdi3xkt62lo32) | [API](https://jina.ai/embeddings) + + +## Intended Usage & Model Info + + +`jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications. +Based on the [Jina-XLM-RoBERTa architecture](https://huggingface.co/jinaai/xlm-roberta-flash-implementation), +this model supports Rotary Position Embeddings to handle long input sequences up to **8192 tokens**. +Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently. + +### Key Features: +- **Extended Sequence Length:** Supports up to 8192 tokens with RoPE. +- **Task-Specific Embedding:** Customize embeddings through the `task` argument with the following options: + - `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks + - `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks + - `separation`: Used for embeddings in clustering and re-ranking applications + - `classification`: Used for embeddings in classification tasks + - `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks +- **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application. + +### Supported Languages: +While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages: +**Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, +Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, +Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.** + +## Usage + +**+ +### Why Use Mean Pooling? + +Mean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level. +This approach has been shown to produce high-quality sentence embeddings. + +We provide an `encode` function that handles this for you automatically. + +However, if you're working with the model directly, outside of the `encode` function, +you'll need to apply mean pooling manually. Here's how you can do it: + + +```python +import torch +import torch.nn.functional as F +from transformers import AutoTokenizer, AutoModel + + +def mean_pooling(model_output, attention_mask): + token_embeddings = model_output[0] + input_mask_expanded = ( + attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() + ) + return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp( + input_mask_expanded.sum(1), min=1e-9 + ) + + +sentences = ["How is the weather today?", "What is the current weather like today?"] + +tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3") +model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True) + +encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt") +task = 'retrieval.query' +task_id = model._adaptation_map[task] +adapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32) +with torch.no_grad(): + model_output = model(**encoded_input, adapter_mask=adapter_mask) + +embeddings = mean_pooling(model_output, encoded_input["attention_mask"]) +embeddings = F.normalize(embeddings, p=2, dim=1) + +``` + +
++ +You can use ONNX for efficient inference with `jina-embeddings-v3`: +```python +import onnxruntime +import numpy as np +from transformers import AutoTokenizer, PretrainedConfig + +# Load tokenizer and model config +tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3') +config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3') + +# Tokenize input +input_text = tokenizer('sample text', return_tensors='np') + +# ONNX session +model_path = 'jina-embeddings-v3/onnx/model.onnx' +session = onnxruntime.InferenceSession(model_path) + +# Prepare inputs for ONNX model +task_type = 'text-matching' +task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64) +inputs = { + 'input_ids': input_text['input_ids'], + 'attention_mask': input_text['attention_mask'], + 'task_id': task_id +} + +# Run model +outputs = session.run(None, inputs) +``` + +
+