Update README.md
Browse files
README.md
CHANGED
@@ -12,6 +12,3650 @@ tags:
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12 |
- Qwen2-VL
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13 |
- sentence-similarity
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14 |
- vidore
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|
15 |
---
|
16 |
|
17 |
<p align="center">
|
|
|
12 |
- Qwen2-VL
|
13 |
- sentence-similarity
|
14 |
- vidore
|
15 |
+
model-index:
|
16 |
+
- name: gme-Qwen2-VL-7B-Instruct
|
17 |
+
results:
|
18 |
+
- task:
|
19 |
+
type: STS
|
20 |
+
dataset:
|
21 |
+
type: C-MTEB/AFQMC
|
22 |
+
name: MTEB AFQMC
|
23 |
+
config: default
|
24 |
+
split: validation
|
25 |
+
revision: b44c3b011063adb25877c13823db83bb193913c4
|
26 |
+
metrics:
|
27 |
+
- type: cos_sim_pearson
|
28 |
+
value: 64.72351048394194
|
29 |
+
- type: cos_sim_spearman
|
30 |
+
value: 71.66842612591344
|
31 |
+
- type: euclidean_pearson
|
32 |
+
value: 70.0342809043895
|
33 |
+
- type: euclidean_spearman
|
34 |
+
value: 71.66842612323917
|
35 |
+
- type: manhattan_pearson
|
36 |
+
value: 69.94743870947117
|
37 |
+
- type: manhattan_spearman
|
38 |
+
value: 71.53159630946965
|
39 |
+
- task:
|
40 |
+
type: STS
|
41 |
+
dataset:
|
42 |
+
type: C-MTEB/ATEC
|
43 |
+
name: MTEB ATEC
|
44 |
+
config: default
|
45 |
+
split: test
|
46 |
+
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
|
47 |
+
metrics:
|
48 |
+
- type: cos_sim_pearson
|
49 |
+
value: 52.38188106868689
|
50 |
+
- type: cos_sim_spearman
|
51 |
+
value: 55.468235529709766
|
52 |
+
- type: euclidean_pearson
|
53 |
+
value: 56.974786979175086
|
54 |
+
- type: euclidean_spearman
|
55 |
+
value: 55.468231026153745
|
56 |
+
- type: manhattan_pearson
|
57 |
+
value: 56.94467132566259
|
58 |
+
- type: manhattan_spearman
|
59 |
+
value: 55.39037386224014
|
60 |
+
- task:
|
61 |
+
type: Classification
|
62 |
+
dataset:
|
63 |
+
type: mteb/amazon_counterfactual
|
64 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
65 |
+
config: en
|
66 |
+
split: test
|
67 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
68 |
+
metrics:
|
69 |
+
- type: accuracy
|
70 |
+
value: 77.61194029850746
|
71 |
+
- type: ap
|
72 |
+
value: 41.29789064067677
|
73 |
+
- type: f1
|
74 |
+
value: 71.69633278678522
|
75 |
+
- task:
|
76 |
+
type: Classification
|
77 |
+
dataset:
|
78 |
+
type: mteb/amazon_polarity
|
79 |
+
name: MTEB AmazonPolarityClassification
|
80 |
+
config: default
|
81 |
+
split: test
|
82 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
83 |
+
metrics:
|
84 |
+
- type: accuracy
|
85 |
+
value: 97.3258
|
86 |
+
- type: ap
|
87 |
+
value: 95.91845683387056
|
88 |
+
- type: f1
|
89 |
+
value: 97.32526074864263
|
90 |
+
- task:
|
91 |
+
type: Classification
|
92 |
+
dataset:
|
93 |
+
type: mteb/amazon_reviews_multi
|
94 |
+
name: MTEB AmazonReviewsClassification (en)
|
95 |
+
config: en
|
96 |
+
split: test
|
97 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
98 |
+
metrics:
|
99 |
+
- type: accuracy
|
100 |
+
value: 64.794
|
101 |
+
- type: f1
|
102 |
+
value: 63.7329780206882
|
103 |
+
- task:
|
104 |
+
type: Classification
|
105 |
+
dataset:
|
106 |
+
type: mteb/amazon_reviews_multi
|
107 |
+
name: MTEB AmazonReviewsClassification (zh)
|
108 |
+
config: zh
|
109 |
+
split: test
|
110 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
111 |
+
metrics:
|
112 |
+
- type: accuracy
|
113 |
+
value: 55.099999999999994
|
114 |
+
- type: f1
|
115 |
+
value: 53.115528412999666
|
116 |
+
- task:
|
117 |
+
type: Retrieval
|
118 |
+
dataset:
|
119 |
+
type: mteb/arguana
|
120 |
+
name: MTEB ArguAna
|
121 |
+
config: default
|
122 |
+
split: test
|
123 |
+
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
|
124 |
+
metrics:
|
125 |
+
- type: map_at_1
|
126 |
+
value: 40.541
|
127 |
+
- type: map_at_10
|
128 |
+
value: 56.315000000000005
|
129 |
+
- type: map_at_100
|
130 |
+
value: 56.824
|
131 |
+
- type: map_at_1000
|
132 |
+
value: 56.825
|
133 |
+
- type: map_at_3
|
134 |
+
value: 51.778
|
135 |
+
- type: map_at_5
|
136 |
+
value: 54.623
|
137 |
+
- type: mrr_at_1
|
138 |
+
value: 41.038000000000004
|
139 |
+
- type: mrr_at_10
|
140 |
+
value: 56.532000000000004
|
141 |
+
- type: mrr_at_100
|
142 |
+
value: 57.034
|
143 |
+
- type: mrr_at_1000
|
144 |
+
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1038 |
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1039 |
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1105 |
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1107 |
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type: BeIR/cqadupstack
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1108 |
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1173 |
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|
1174 |
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1175 |
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|
1176 |
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type: BeIR/cqadupstack
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1177 |
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|
1245 |
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|
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1311 |
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1313 |
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|
1314 |
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1315 |
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1379 |
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1381 |
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|
1383 |
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type: C-MTEB/CMNLI
|
1384 |
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name: MTEB Cmnli
|
1385 |
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1386 |
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1387 |
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metrics:
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1389 |
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1390 |
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1391 |
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1407 |
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1408 |
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1409 |
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1411 |
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1413 |
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1414 |
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1415 |
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1416 |
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1417 |
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- type: euclidean_recall
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1418 |
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1419 |
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- type: manhattan_accuracy
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1421 |
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1423 |
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1427 |
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1429 |
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1430 |
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1431 |
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1432 |
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1433 |
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- type: max_f1
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1434 |
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1435 |
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- task:
|
1436 |
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1437 |
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dataset:
|
1438 |
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type: C-MTEB/CovidRetrieval
|
1439 |
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name: MTEB CovidRetrieval
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1440 |
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1441 |
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split: dev
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1442 |
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revision: 1271c7809071a13532e05f25fb53511ffce77117
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1443 |
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metrics:
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1444 |
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1445 |
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1446 |
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1447 |
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1448 |
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1449 |
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1450 |
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1452 |
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1453 |
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1454 |
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1455 |
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1456 |
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1458 |
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1459 |
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1460 |
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1461 |
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1462 |
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1463 |
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1464 |
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1465 |
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1466 |
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1467 |
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1468 |
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1469 |
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1470 |
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1471 |
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1472 |
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1473 |
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1474 |
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1475 |
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1476 |
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1477 |
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1478 |
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1479 |
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1480 |
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- type: precision_at_1
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1481 |
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1482 |
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1483 |
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value: 9.231
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1484 |
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- type: precision_at_100
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1485 |
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value: 1.0
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1486 |
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- type: precision_at_1000
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1487 |
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value: 0.101
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1488 |
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- type: precision_at_3
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1489 |
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value: 27.362
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1490 |
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1491 |
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1492 |
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- type: recall_at_1
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1493 |
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1494 |
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1495 |
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1496 |
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- type: recall_at_100
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1497 |
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1498 |
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- type: recall_at_1000
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1499 |
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1500 |
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1501 |
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1502 |
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- type: recall_at_5
|
1503 |
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value: 86.407
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1504 |
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- task:
|
1505 |
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type: Retrieval
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1506 |
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dataset:
|
1507 |
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type: mteb/dbpedia
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1508 |
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name: MTEB DBPedia
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1509 |
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1510 |
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1511 |
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revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
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1512 |
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metrics:
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1513 |
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- type: map_at_1
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1514 |
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value: 9.33
|
1515 |
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- type: map_at_10
|
1516 |
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value: 23.118
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1517 |
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- type: map_at_100
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1518 |
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1519 |
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- type: map_at_1000
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1520 |
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1521 |
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1522 |
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1523 |
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- type: map_at_5
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1524 |
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value: 18.778
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1525 |
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- type: mrr_at_1
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1526 |
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1527 |
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- type: mrr_at_10
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1528 |
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1529 |
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- type: mrr_at_100
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1530 |
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1531 |
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- type: mrr_at_1000
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1532 |
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1533 |
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1534 |
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1535 |
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1536 |
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1537 |
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- type: ndcg_at_1
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1538 |
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1539 |
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- type: ndcg_at_10
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1540 |
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value: 50.781
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1541 |
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- type: ndcg_at_100
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1542 |
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value: 55.537000000000006
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1543 |
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- type: ndcg_at_1000
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1544 |
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value: 62.651
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1545 |
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- type: ndcg_at_3
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1546 |
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1547 |
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1548 |
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1549 |
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1550 |
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value: 75.25
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1551 |
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1552 |
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1553 |
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- type: precision_at_100
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1554 |
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1555 |
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- type: precision_at_1000
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1556 |
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1557 |
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- type: precision_at_3
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1558 |
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1559 |
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- type: precision_at_5
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1560 |
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1561 |
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- type: recall_at_1
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1562 |
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value: 9.33
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1563 |
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- type: recall_at_10
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1564 |
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1565 |
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- type: recall_at_100
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1566 |
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1567 |
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- type: recall_at_1000
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1568 |
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1569 |
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- type: recall_at_3
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1570 |
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|
1571 |
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- type: recall_at_5
|
1572 |
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value: 21.89
|
1573 |
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- task:
|
1574 |
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|
1575 |
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dataset:
|
1576 |
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type: C-MTEB/DuRetrieval
|
1577 |
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name: MTEB DuRetrieval
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1578 |
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config: default
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1579 |
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split: dev
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1580 |
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revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
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1581 |
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metrics:
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1582 |
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|
1583 |
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value: 25.608999999999998
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1584 |
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- type: map_at_10
|
1585 |
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value: 78.649
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1586 |
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- type: map_at_100
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1587 |
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1588 |
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1589 |
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1590 |
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- type: map_at_3
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1591 |
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1592 |
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- type: map_at_5
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1593 |
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1594 |
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- type: mrr_at_1
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1595 |
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1596 |
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1597 |
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1598 |
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- type: mrr_at_100
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1599 |
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1600 |
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- type: mrr_at_1000
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1601 |
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value: 92.227
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1602 |
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- type: mrr_at_3
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1603 |
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1604 |
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1605 |
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1606 |
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1607 |
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1608 |
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1609 |
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1610 |
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- type: ndcg_at_100
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1611 |
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1612 |
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1613 |
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1614 |
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- type: ndcg_at_3
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1615 |
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1616 |
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1617 |
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1618 |
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- type: precision_at_1
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1619 |
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value: 87.75
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1620 |
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- type: precision_at_10
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1621 |
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1622 |
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- type: precision_at_100
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1623 |
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value: 4.827
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1624 |
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1625 |
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value: 0.49
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1626 |
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1627 |
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value: 75.533
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1628 |
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- type: precision_at_5
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1629 |
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value: 64.01
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1630 |
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- type: recall_at_1
|
1631 |
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value: 25.608999999999998
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1632 |
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- type: recall_at_10
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1633 |
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value: 88.708
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1634 |
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- type: recall_at_100
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1635 |
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value: 98.007
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1636 |
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- type: recall_at_1000
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1637 |
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value: 99.555
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1638 |
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- type: recall_at_3
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1639 |
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value: 57.157000000000004
|
1640 |
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- type: recall_at_5
|
1641 |
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value: 74.118
|
1642 |
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- task:
|
1643 |
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type: Retrieval
|
1644 |
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dataset:
|
1645 |
+
type: C-MTEB/EcomRetrieval
|
1646 |
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name: MTEB EcomRetrieval
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1647 |
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config: default
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1648 |
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split: dev
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1649 |
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revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
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1650 |
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metrics:
|
1651 |
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|
1652 |
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value: 55.800000000000004
|
1653 |
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- type: map_at_10
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1654 |
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value: 65.952
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1655 |
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- type: map_at_100
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1656 |
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value: 66.413
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1657 |
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- type: map_at_1000
|
1658 |
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1659 |
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1660 |
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value: 63.3
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1661 |
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1662 |
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1663 |
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- type: mrr_at_1
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1664 |
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1665 |
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- type: mrr_at_10
|
1666 |
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value: 65.952
|
1667 |
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1668 |
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1669 |
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1670 |
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1671 |
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1672 |
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1673 |
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1674 |
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1675 |
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1676 |
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1677 |
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1678 |
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1679 |
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- type: ndcg_at_100
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1680 |
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1681 |
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1682 |
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|
1683 |
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- type: ndcg_at_3
|
1684 |
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1685 |
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1686 |
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1687 |
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- type: precision_at_1
|
1688 |
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|
1689 |
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|
1690 |
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|
1691 |
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- type: precision_at_100
|
1692 |
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1693 |
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1694 |
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1695 |
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|
1696 |
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value: 24.166999999999998
|
1697 |
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- type: precision_at_5
|
1698 |
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value: 15.939999999999998
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1699 |
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- type: recall_at_1
|
1700 |
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value: 55.800000000000004
|
1701 |
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- type: recall_at_10
|
1702 |
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value: 86.9
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1703 |
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- type: recall_at_100
|
1704 |
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value: 95.5
|
1705 |
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- type: recall_at_1000
|
1706 |
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value: 98.0
|
1707 |
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- type: recall_at_3
|
1708 |
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value: 72.5
|
1709 |
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- type: recall_at_5
|
1710 |
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value: 79.7
|
1711 |
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- task:
|
1712 |
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type: Classification
|
1713 |
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dataset:
|
1714 |
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type: mteb/emotion
|
1715 |
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name: MTEB EmotionClassification
|
1716 |
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config: default
|
1717 |
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split: test
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1718 |
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|
1719 |
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metrics:
|
1720 |
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- type: accuracy
|
1721 |
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value: 67.39500000000001
|
1722 |
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- type: f1
|
1723 |
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value: 62.01837785021389
|
1724 |
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- task:
|
1725 |
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type: Retrieval
|
1726 |
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dataset:
|
1727 |
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type: mteb/fever
|
1728 |
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name: MTEB FEVER
|
1729 |
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config: default
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1730 |
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split: test
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1731 |
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revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
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1732 |
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metrics:
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1733 |
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|
1734 |
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value: 86.27
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1735 |
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- type: map_at_10
|
1736 |
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value: 92.163
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1737 |
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1738 |
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value: 92.351
|
1739 |
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- type: map_at_1000
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1740 |
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1741 |
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- type: map_at_3
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1742 |
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value: 91.36
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1743 |
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- type: map_at_5
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1744 |
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value: 91.888
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1745 |
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- type: mrr_at_1
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1746 |
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1747 |
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- type: mrr_at_10
|
1748 |
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value: 95.789
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1749 |
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1750 |
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value: 95.80300000000001
|
1751 |
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- type: mrr_at_1000
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1752 |
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1753 |
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- type: mrr_at_3
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1754 |
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value: 95.64200000000001
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1755 |
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- type: mrr_at_5
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1756 |
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1757 |
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1758 |
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1759 |
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1760 |
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value: 94.269
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1761 |
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1762 |
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1763 |
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- type: ndcg_at_1000
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1764 |
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value: 94.94
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1765 |
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- type: ndcg_at_3
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1766 |
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value: 93.427
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1767 |
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- type: ndcg_at_5
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1768 |
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value: 93.914
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1769 |
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1770 |
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value: 92.72399999999999
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1771 |
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- type: precision_at_10
|
1772 |
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value: 11.007
|
1773 |
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- type: precision_at_100
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1774 |
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value: 1.153
|
1775 |
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- type: precision_at_1000
|
1776 |
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value: 0.11800000000000001
|
1777 |
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- type: precision_at_3
|
1778 |
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value: 34.993
|
1779 |
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- type: precision_at_5
|
1780 |
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value: 21.542
|
1781 |
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- type: recall_at_1
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1782 |
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value: 86.27
|
1783 |
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- type: recall_at_10
|
1784 |
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value: 97.031
|
1785 |
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- type: recall_at_100
|
1786 |
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value: 98.839
|
1787 |
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- type: recall_at_1000
|
1788 |
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value: 99.682
|
1789 |
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- type: recall_at_3
|
1790 |
+
value: 94.741
|
1791 |
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- type: recall_at_5
|
1792 |
+
value: 96.03
|
1793 |
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- task:
|
1794 |
+
type: Retrieval
|
1795 |
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dataset:
|
1796 |
+
type: mteb/fiqa
|
1797 |
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name: MTEB FiQA2018
|
1798 |
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config: default
|
1799 |
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split: test
|
1800 |
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revision: 27a168819829fe9bcd655c2df245fb19452e8e06
|
1801 |
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metrics:
|
1802 |
+
- type: map_at_1
|
1803 |
+
value: 29.561999999999998
|
1804 |
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- type: map_at_10
|
1805 |
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value: 48.52
|
1806 |
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- type: map_at_100
|
1807 |
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value: 50.753
|
1808 |
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- type: map_at_1000
|
1809 |
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value: 50.878
|
1810 |
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- type: map_at_3
|
1811 |
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value: 42.406
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1812 |
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- type: map_at_5
|
1813 |
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value: 45.994
|
1814 |
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- type: mrr_at_1
|
1815 |
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value: 54.784
|
1816 |
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- type: mrr_at_10
|
1817 |
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value: 64.51400000000001
|
1818 |
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- type: mrr_at_100
|
1819 |
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value: 65.031
|
1820 |
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- type: mrr_at_1000
|
1821 |
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value: 65.05199999999999
|
1822 |
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- type: mrr_at_3
|
1823 |
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value: 62.474
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1824 |
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- type: mrr_at_5
|
1825 |
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value: 63.562
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1826 |
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- type: ndcg_at_1
|
1827 |
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value: 54.784
|
1828 |
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- type: ndcg_at_10
|
1829 |
+
value: 57.138
|
1830 |
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- type: ndcg_at_100
|
1831 |
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value: 63.666999999999994
|
1832 |
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- type: ndcg_at_1000
|
1833 |
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value: 65.379
|
1834 |
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- type: ndcg_at_3
|
1835 |
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value: 52.589
|
1836 |
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- type: ndcg_at_5
|
1837 |
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value: 54.32599999999999
|
1838 |
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- type: precision_at_1
|
1839 |
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value: 54.784
|
1840 |
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- type: precision_at_10
|
1841 |
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value: 15.693999999999999
|
1842 |
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- type: precision_at_100
|
1843 |
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value: 2.259
|
1844 |
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- type: precision_at_1000
|
1845 |
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value: 0.256
|
1846 |
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- type: precision_at_3
|
1847 |
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value: 34.774
|
1848 |
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- type: precision_at_5
|
1849 |
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value: 25.772000000000002
|
1850 |
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- type: recall_at_1
|
1851 |
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value: 29.561999999999998
|
1852 |
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- type: recall_at_10
|
1853 |
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value: 64.708
|
1854 |
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- type: recall_at_100
|
1855 |
+
value: 87.958
|
1856 |
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- type: recall_at_1000
|
1857 |
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value: 97.882
|
1858 |
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- type: recall_at_3
|
1859 |
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value: 48.394
|
1860 |
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- type: recall_at_5
|
1861 |
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value: 56.101
|
1862 |
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- task:
|
1863 |
+
type: Retrieval
|
1864 |
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dataset:
|
1865 |
+
type: mteb/hotpotqa
|
1866 |
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name: MTEB HotpotQA
|
1867 |
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config: default
|
1868 |
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split: test
|
1869 |
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revision: ab518f4d6fcca38d87c25209f94beba119d02014
|
1870 |
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metrics:
|
1871 |
+
- type: map_at_1
|
1872 |
+
value: 43.72
|
1873 |
+
- type: map_at_10
|
1874 |
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value: 71.905
|
1875 |
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- type: map_at_100
|
1876 |
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value: 72.685
|
1877 |
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- type: map_at_1000
|
1878 |
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value: 72.72800000000001
|
1879 |
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- type: map_at_3
|
1880 |
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value: 68.538
|
1881 |
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- type: map_at_5
|
1882 |
+
value: 70.675
|
1883 |
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- type: mrr_at_1
|
1884 |
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value: 87.441
|
1885 |
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- type: mrr_at_10
|
1886 |
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value: 91.432
|
1887 |
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- type: mrr_at_100
|
1888 |
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value: 91.512
|
1889 |
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- type: mrr_at_1000
|
1890 |
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value: 91.513
|
1891 |
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- type: mrr_at_3
|
1892 |
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value: 90.923
|
1893 |
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- type: mrr_at_5
|
1894 |
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value: 91.252
|
1895 |
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- type: ndcg_at_1
|
1896 |
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value: 87.441
|
1897 |
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- type: ndcg_at_10
|
1898 |
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value: 79.212
|
1899 |
+
- type: ndcg_at_100
|
1900 |
+
value: 81.694
|
1901 |
+
- type: ndcg_at_1000
|
1902 |
+
value: 82.447
|
1903 |
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- type: ndcg_at_3
|
1904 |
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value: 74.746
|
1905 |
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- type: ndcg_at_5
|
1906 |
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value: 77.27199999999999
|
1907 |
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- type: precision_at_1
|
1908 |
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value: 87.441
|
1909 |
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- type: precision_at_10
|
1910 |
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value: 16.42
|
1911 |
+
- type: precision_at_100
|
1912 |
+
value: 1.833
|
1913 |
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- type: precision_at_1000
|
1914 |
+
value: 0.193
|
1915 |
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- type: precision_at_3
|
1916 |
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value: 48.184
|
1917 |
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- type: precision_at_5
|
1918 |
+
value: 30.897999999999996
|
1919 |
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- type: recall_at_1
|
1920 |
+
value: 43.72
|
1921 |
+
- type: recall_at_10
|
1922 |
+
value: 82.1
|
1923 |
+
- type: recall_at_100
|
1924 |
+
value: 91.62700000000001
|
1925 |
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- type: recall_at_1000
|
1926 |
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value: 96.556
|
1927 |
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- type: recall_at_3
|
1928 |
+
value: 72.275
|
1929 |
+
- type: recall_at_5
|
1930 |
+
value: 77.24499999999999
|
1931 |
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- task:
|
1932 |
+
type: Classification
|
1933 |
+
dataset:
|
1934 |
+
type: C-MTEB/IFlyTek-classification
|
1935 |
+
name: MTEB IFlyTek
|
1936 |
+
config: default
|
1937 |
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split: validation
|
1938 |
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revision: 421605374b29664c5fc098418fe20ada9bd55f8a
|
1939 |
+
metrics:
|
1940 |
+
- type: accuracy
|
1941 |
+
value: 54.520969603693736
|
1942 |
+
- type: f1
|
1943 |
+
value: 42.359043311419626
|
1944 |
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- task:
|
1945 |
+
type: Classification
|
1946 |
+
dataset:
|
1947 |
+
type: mteb/imdb
|
1948 |
+
name: MTEB ImdbClassification
|
1949 |
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config: default
|
1950 |
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split: test
|
1951 |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
1952 |
+
metrics:
|
1953 |
+
- type: accuracy
|
1954 |
+
value: 96.72559999999999
|
1955 |
+
- type: ap
|
1956 |
+
value: 95.01759461773742
|
1957 |
+
- type: f1
|
1958 |
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value: 96.72429945397575
|
1959 |
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- task:
|
1960 |
+
type: Classification
|
1961 |
+
dataset:
|
1962 |
+
type: C-MTEB/JDReview-classification
|
1963 |
+
name: MTEB JDReview
|
1964 |
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config: default
|
1965 |
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split: test
|
1966 |
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revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
|
1967 |
+
metrics:
|
1968 |
+
- type: accuracy
|
1969 |
+
value: 90.1688555347092
|
1970 |
+
- type: ap
|
1971 |
+
value: 63.36583667477521
|
1972 |
+
- type: f1
|
1973 |
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value: 85.6845016521436
|
1974 |
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- task:
|
1975 |
+
type: STS
|
1976 |
+
dataset:
|
1977 |
+
type: C-MTEB/LCQMC
|
1978 |
+
name: MTEB LCQMC
|
1979 |
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config: default
|
1980 |
+
split: test
|
1981 |
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revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
|
1982 |
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metrics:
|
1983 |
+
- type: cos_sim_pearson
|
1984 |
+
value: 67.35114066823127
|
1985 |
+
- type: cos_sim_spearman
|
1986 |
+
value: 72.98875207056305
|
1987 |
+
- type: euclidean_pearson
|
1988 |
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value: 71.45620183630378
|
1989 |
+
- type: euclidean_spearman
|
1990 |
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value: 72.98875207022671
|
1991 |
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- type: manhattan_pearson
|
1992 |
+
value: 71.3845159780333
|
1993 |
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- type: manhattan_spearman
|
1994 |
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value: 72.92710990543166
|
1995 |
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- task:
|
1996 |
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type: Reranking
|
1997 |
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dataset:
|
1998 |
+
type: C-MTEB/Mmarco-reranking
|
1999 |
+
name: MTEB MMarcoReranking
|
2000 |
+
config: default
|
2001 |
+
split: dev
|
2002 |
+
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
|
2003 |
+
metrics:
|
2004 |
+
- type: map
|
2005 |
+
value: 32.68592539803807
|
2006 |
+
- type: mrr
|
2007 |
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value: 31.58968253968254
|
2008 |
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- task:
|
2009 |
+
type: Retrieval
|
2010 |
+
dataset:
|
2011 |
+
type: C-MTEB/MMarcoRetrieval
|
2012 |
+
name: MTEB MMarcoRetrieval
|
2013 |
+
config: default
|
2014 |
+
split: dev
|
2015 |
+
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
|
2016 |
+
metrics:
|
2017 |
+
- type: map_at_1
|
2018 |
+
value: 71.242
|
2019 |
+
- type: map_at_10
|
2020 |
+
value: 80.01
|
2021 |
+
- type: map_at_100
|
2022 |
+
value: 80.269
|
2023 |
+
- type: map_at_1000
|
2024 |
+
value: 80.276
|
2025 |
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- type: map_at_3
|
2026 |
+
value: 78.335
|
2027 |
+
- type: map_at_5
|
2028 |
+
value: 79.471
|
2029 |
+
- type: mrr_at_1
|
2030 |
+
value: 73.668
|
2031 |
+
- type: mrr_at_10
|
2032 |
+
value: 80.515
|
2033 |
+
- type: mrr_at_100
|
2034 |
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value: 80.738
|
2035 |
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- type: mrr_at_1000
|
2036 |
+
value: 80.744
|
2037 |
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- type: mrr_at_3
|
2038 |
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value: 79.097
|
2039 |
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- type: mrr_at_5
|
2040 |
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value: 80.045
|
2041 |
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- type: ndcg_at_1
|
2042 |
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value: 73.668
|
2043 |
+
- type: ndcg_at_10
|
2044 |
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value: 83.357
|
2045 |
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- type: ndcg_at_100
|
2046 |
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value: 84.442
|
2047 |
+
- type: ndcg_at_1000
|
2048 |
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value: 84.619
|
2049 |
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- type: ndcg_at_3
|
2050 |
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value: 80.286
|
2051 |
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- type: ndcg_at_5
|
2052 |
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value: 82.155
|
2053 |
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- type: precision_at_1
|
2054 |
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value: 73.668
|
2055 |
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- type: precision_at_10
|
2056 |
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value: 9.905
|
2057 |
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- type: precision_at_100
|
2058 |
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value: 1.043
|
2059 |
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- type: precision_at_1000
|
2060 |
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value: 0.106
|
2061 |
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- type: precision_at_3
|
2062 |
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value: 30.024
|
2063 |
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- type: precision_at_5
|
2064 |
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value: 19.017
|
2065 |
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- type: recall_at_1
|
2066 |
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value: 71.242
|
2067 |
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- type: recall_at_10
|
2068 |
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value: 93.11
|
2069 |
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- type: recall_at_100
|
2070 |
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value: 97.85000000000001
|
2071 |
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- type: recall_at_1000
|
2072 |
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value: 99.21900000000001
|
2073 |
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- type: recall_at_3
|
2074 |
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value: 85.137
|
2075 |
+
- type: recall_at_5
|
2076 |
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value: 89.548
|
2077 |
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- task:
|
2078 |
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type: Retrieval
|
2079 |
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dataset:
|
2080 |
+
type: mteb/msmarco
|
2081 |
+
name: MTEB MSMARCO
|
2082 |
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config: default
|
2083 |
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split: dev
|
2084 |
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revision: c5a29a104738b98a9e76336939199e264163d4a0
|
2085 |
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metrics:
|
2086 |
+
- type: map_at_1
|
2087 |
+
value: 22.006999999999998
|
2088 |
+
- type: map_at_10
|
2089 |
+
value: 34.994
|
2090 |
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- type: map_at_100
|
2091 |
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value: 36.183
|
2092 |
+
- type: map_at_1000
|
2093 |
+
value: 36.227
|
2094 |
+
- type: map_at_3
|
2095 |
+
value: 30.75
|
2096 |
+
- type: map_at_5
|
2097 |
+
value: 33.155
|
2098 |
+
- type: mrr_at_1
|
2099 |
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value: 22.679
|
2100 |
+
- type: mrr_at_10
|
2101 |
+
value: 35.619
|
2102 |
+
- type: mrr_at_100
|
2103 |
+
value: 36.732
|
2104 |
+
- type: mrr_at_1000
|
2105 |
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value: 36.77
|
2106 |
+
- type: mrr_at_3
|
2107 |
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value: 31.44
|
2108 |
+
- type: mrr_at_5
|
2109 |
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value: 33.811
|
2110 |
+
- type: ndcg_at_1
|
2111 |
+
value: 22.679
|
2112 |
+
- type: ndcg_at_10
|
2113 |
+
value: 42.376000000000005
|
2114 |
+
- type: ndcg_at_100
|
2115 |
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value: 48.001
|
2116 |
+
- type: ndcg_at_1000
|
2117 |
+
value: 49.059999999999995
|
2118 |
+
- type: ndcg_at_3
|
2119 |
+
value: 33.727000000000004
|
2120 |
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- type: ndcg_at_5
|
2121 |
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value: 38.013000000000005
|
2122 |
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- type: precision_at_1
|
2123 |
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value: 22.679
|
2124 |
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- type: precision_at_10
|
2125 |
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value: 6.815
|
2126 |
+
- type: precision_at_100
|
2127 |
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value: 0.962
|
2128 |
+
- type: precision_at_1000
|
2129 |
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value: 0.105
|
2130 |
+
- type: precision_at_3
|
2131 |
+
value: 14.441
|
2132 |
+
- type: precision_at_5
|
2133 |
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value: 10.817
|
2134 |
+
- type: recall_at_1
|
2135 |
+
value: 22.006999999999998
|
2136 |
+
- type: recall_at_10
|
2137 |
+
value: 65.158
|
2138 |
+
- type: recall_at_100
|
2139 |
+
value: 90.997
|
2140 |
+
- type: recall_at_1000
|
2141 |
+
value: 98.996
|
2142 |
+
- type: recall_at_3
|
2143 |
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value: 41.646
|
2144 |
+
- type: recall_at_5
|
2145 |
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value: 51.941
|
2146 |
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- task:
|
2147 |
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type: Classification
|
2148 |
+
dataset:
|
2149 |
+
type: mteb/mtop_domain
|
2150 |
+
name: MTEB MTOPDomainClassification (en)
|
2151 |
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config: en
|
2152 |
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split: test
|
2153 |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
2154 |
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metrics:
|
2155 |
+
- type: accuracy
|
2156 |
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value: 97.55129958960327
|
2157 |
+
- type: f1
|
2158 |
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value: 97.43464802675416
|
2159 |
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- task:
|
2160 |
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type: Classification
|
2161 |
+
dataset:
|
2162 |
+
type: mteb/mtop_intent
|
2163 |
+
name: MTEB MTOPIntentClassification (en)
|
2164 |
+
config: en
|
2165 |
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split: test
|
2166 |
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
2167 |
+
metrics:
|
2168 |
+
- type: accuracy
|
2169 |
+
value: 90.4719562243502
|
2170 |
+
- type: f1
|
2171 |
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value: 70.76460034443902
|
2172 |
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- task:
|
2173 |
+
type: Classification
|
2174 |
+
dataset:
|
2175 |
+
type: mteb/amazon_massive_intent
|
2176 |
+
name: MTEB MassiveIntentClassification (en)
|
2177 |
+
config: en
|
2178 |
+
split: test
|
2179 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
2180 |
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metrics:
|
2181 |
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- type: accuracy
|
2182 |
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value: 83.49024882313383
|
2183 |
+
- type: f1
|
2184 |
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value: 81.44067057564666
|
2185 |
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- task:
|
2186 |
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type: Classification
|
2187 |
+
dataset:
|
2188 |
+
type: mteb/amazon_massive_intent
|
2189 |
+
name: MTEB MassiveIntentClassification (zh-CN)
|
2190 |
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config: zh-CN
|
2191 |
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split: test
|
2192 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
2193 |
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metrics:
|
2194 |
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- type: accuracy
|
2195 |
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value: 79.88231338264963
|
2196 |
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- type: f1
|
2197 |
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value: 77.13536609019927
|
2198 |
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- task:
|
2199 |
+
type: Classification
|
2200 |
+
dataset:
|
2201 |
+
type: mteb/amazon_massive_scenario
|
2202 |
+
name: MTEB MassiveScenarioClassification (en)
|
2203 |
+
config: en
|
2204 |
+
split: test
|
2205 |
+
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
2206 |
+
metrics:
|
2207 |
+
- type: accuracy
|
2208 |
+
value: 87.23268325487558
|
2209 |
+
- type: f1
|
2210 |
+
value: 86.36737921996752
|
2211 |
+
- task:
|
2212 |
+
type: Classification
|
2213 |
+
dataset:
|
2214 |
+
type: mteb/amazon_massive_scenario
|
2215 |
+
name: MTEB MassiveScenarioClassification (zh-CN)
|
2216 |
+
config: zh-CN
|
2217 |
+
split: test
|
2218 |
+
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
2219 |
+
metrics:
|
2220 |
+
- type: accuracy
|
2221 |
+
value: 84.50571620712844
|
2222 |
+
- type: f1
|
2223 |
+
value: 83.4128768262944
|
2224 |
+
- task:
|
2225 |
+
type: Retrieval
|
2226 |
+
dataset:
|
2227 |
+
type: C-MTEB/MedicalRetrieval
|
2228 |
+
name: MTEB MedicalRetrieval
|
2229 |
+
config: default
|
2230 |
+
split: dev
|
2231 |
+
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
|
2232 |
+
metrics:
|
2233 |
+
- type: map_at_1
|
2234 |
+
value: 56.89999999999999
|
2235 |
+
- type: map_at_10
|
2236 |
+
value: 63.438
|
2237 |
+
- type: map_at_100
|
2238 |
+
value: 63.956
|
2239 |
+
- type: map_at_1000
|
2240 |
+
value: 63.991
|
2241 |
+
- type: map_at_3
|
2242 |
+
value: 61.983
|
2243 |
+
- type: map_at_5
|
2244 |
+
value: 62.778
|
2245 |
+
- type: mrr_at_1
|
2246 |
+
value: 56.99999999999999
|
2247 |
+
- type: mrr_at_10
|
2248 |
+
value: 63.483000000000004
|
2249 |
+
- type: mrr_at_100
|
2250 |
+
value: 63.993
|
2251 |
+
- type: mrr_at_1000
|
2252 |
+
value: 64.02799999999999
|
2253 |
+
- type: mrr_at_3
|
2254 |
+
value: 62.017
|
2255 |
+
- type: mrr_at_5
|
2256 |
+
value: 62.812
|
2257 |
+
- type: ndcg_at_1
|
2258 |
+
value: 56.89999999999999
|
2259 |
+
- type: ndcg_at_10
|
2260 |
+
value: 66.61
|
2261 |
+
- type: ndcg_at_100
|
2262 |
+
value: 69.387
|
2263 |
+
- type: ndcg_at_1000
|
2264 |
+
value: 70.327
|
2265 |
+
- type: ndcg_at_3
|
2266 |
+
value: 63.583999999999996
|
2267 |
+
- type: ndcg_at_5
|
2268 |
+
value: 65.0
|
2269 |
+
- type: precision_at_1
|
2270 |
+
value: 56.89999999999999
|
2271 |
+
- type: precision_at_10
|
2272 |
+
value: 7.66
|
2273 |
+
- type: precision_at_100
|
2274 |
+
value: 0.902
|
2275 |
+
- type: precision_at_1000
|
2276 |
+
value: 0.098
|
2277 |
+
- type: precision_at_3
|
2278 |
+
value: 22.733
|
2279 |
+
- type: precision_at_5
|
2280 |
+
value: 14.32
|
2281 |
+
- type: recall_at_1
|
2282 |
+
value: 56.89999999999999
|
2283 |
+
- type: recall_at_10
|
2284 |
+
value: 76.6
|
2285 |
+
- type: recall_at_100
|
2286 |
+
value: 90.2
|
2287 |
+
- type: recall_at_1000
|
2288 |
+
value: 97.6
|
2289 |
+
- type: recall_at_3
|
2290 |
+
value: 68.2
|
2291 |
+
- type: recall_at_5
|
2292 |
+
value: 71.6
|
2293 |
+
- task:
|
2294 |
+
type: Clustering
|
2295 |
+
dataset:
|
2296 |
+
type: mteb/medrxiv-clustering-p2p
|
2297 |
+
name: MTEB MedrxivClusteringP2P
|
2298 |
+
config: default
|
2299 |
+
split: test
|
2300 |
+
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
2301 |
+
metrics:
|
2302 |
+
- type: v_measure
|
2303 |
+
value: 40.32149153753394
|
2304 |
+
- task:
|
2305 |
+
type: Clustering
|
2306 |
+
dataset:
|
2307 |
+
type: mteb/medrxiv-clustering-s2s
|
2308 |
+
name: MTEB MedrxivClusteringS2S
|
2309 |
+
config: default
|
2310 |
+
split: test
|
2311 |
+
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
2312 |
+
metrics:
|
2313 |
+
- type: v_measure
|
2314 |
+
value: 39.40319973495386
|
2315 |
+
- task:
|
2316 |
+
type: Reranking
|
2317 |
+
dataset:
|
2318 |
+
type: mteb/mind_small
|
2319 |
+
name: MTEB MindSmallReranking
|
2320 |
+
config: default
|
2321 |
+
split: test
|
2322 |
+
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
2323 |
+
metrics:
|
2324 |
+
- type: map
|
2325 |
+
value: 33.9769104898534
|
2326 |
+
- type: mrr
|
2327 |
+
value: 35.32831430710564
|
2328 |
+
- task:
|
2329 |
+
type: Classification
|
2330 |
+
dataset:
|
2331 |
+
type: C-MTEB/MultilingualSentiment-classification
|
2332 |
+
name: MTEB MultilingualSentiment
|
2333 |
+
config: default
|
2334 |
+
split: validation
|
2335 |
+
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
|
2336 |
+
metrics:
|
2337 |
+
- type: accuracy
|
2338 |
+
value: 81.80666666666667
|
2339 |
+
- type: f1
|
2340 |
+
value: 81.83278699395508
|
2341 |
+
- task:
|
2342 |
+
type: Retrieval
|
2343 |
+
dataset:
|
2344 |
+
type: mteb/nfcorpus
|
2345 |
+
name: MTEB NFCorpus
|
2346 |
+
config: default
|
2347 |
+
split: test
|
2348 |
+
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
|
2349 |
+
metrics:
|
2350 |
+
- type: map_at_1
|
2351 |
+
value: 6.3
|
2352 |
+
- type: map_at_10
|
2353 |
+
value: 14.151
|
2354 |
+
- type: map_at_100
|
2355 |
+
value: 18.455
|
2356 |
+
- type: map_at_1000
|
2357 |
+
value: 20.186999999999998
|
2358 |
+
- type: map_at_3
|
2359 |
+
value: 10.023
|
2360 |
+
- type: map_at_5
|
2361 |
+
value: 11.736
|
2362 |
+
- type: mrr_at_1
|
2363 |
+
value: 49.536
|
2364 |
+
- type: mrr_at_10
|
2365 |
+
value: 58.516
|
2366 |
+
- type: mrr_at_100
|
2367 |
+
value: 59.084
|
2368 |
+
- type: mrr_at_1000
|
2369 |
+
value: 59.114
|
2370 |
+
- type: mrr_at_3
|
2371 |
+
value: 56.45
|
2372 |
+
- type: mrr_at_5
|
2373 |
+
value: 57.642
|
2374 |
+
- type: ndcg_at_1
|
2375 |
+
value: 47.522999999999996
|
2376 |
+
- type: ndcg_at_10
|
2377 |
+
value: 38.4
|
2378 |
+
- type: ndcg_at_100
|
2379 |
+
value: 35.839999999999996
|
2380 |
+
- type: ndcg_at_1000
|
2381 |
+
value: 44.998
|
2382 |
+
- type: ndcg_at_3
|
2383 |
+
value: 43.221
|
2384 |
+
- type: ndcg_at_5
|
2385 |
+
value: 40.784
|
2386 |
+
- type: precision_at_1
|
2387 |
+
value: 49.536
|
2388 |
+
- type: precision_at_10
|
2389 |
+
value: 28.977999999999998
|
2390 |
+
- type: precision_at_100
|
2391 |
+
value: 9.378
|
2392 |
+
- type: precision_at_1000
|
2393 |
+
value: 2.2769999999999997
|
2394 |
+
- type: precision_at_3
|
2395 |
+
value: 40.454
|
2396 |
+
- type: precision_at_5
|
2397 |
+
value: 35.418
|
2398 |
+
- type: recall_at_1
|
2399 |
+
value: 6.3
|
2400 |
+
- type: recall_at_10
|
2401 |
+
value: 19.085
|
2402 |
+
- type: recall_at_100
|
2403 |
+
value: 38.18
|
2404 |
+
- type: recall_at_1000
|
2405 |
+
value: 71.219
|
2406 |
+
- type: recall_at_3
|
2407 |
+
value: 11.17
|
2408 |
+
- type: recall_at_5
|
2409 |
+
value: 13.975999999999999
|
2410 |
+
- task:
|
2411 |
+
type: Retrieval
|
2412 |
+
dataset:
|
2413 |
+
type: mteb/nq
|
2414 |
+
name: MTEB NQ
|
2415 |
+
config: default
|
2416 |
+
split: test
|
2417 |
+
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
|
2418 |
+
metrics:
|
2419 |
+
- type: map_at_1
|
2420 |
+
value: 43.262
|
2421 |
+
- type: map_at_10
|
2422 |
+
value: 60.387
|
2423 |
+
- type: map_at_100
|
2424 |
+
value: 61.102000000000004
|
2425 |
+
- type: map_at_1000
|
2426 |
+
value: 61.111000000000004
|
2427 |
+
- type: map_at_3
|
2428 |
+
value: 56.391999999999996
|
2429 |
+
- type: map_at_5
|
2430 |
+
value: 58.916000000000004
|
2431 |
+
- type: mrr_at_1
|
2432 |
+
value: 48.725
|
2433 |
+
- type: mrr_at_10
|
2434 |
+
value: 62.812999999999995
|
2435 |
+
- type: mrr_at_100
|
2436 |
+
value: 63.297000000000004
|
2437 |
+
- type: mrr_at_1000
|
2438 |
+
value: 63.304
|
2439 |
+
- type: mrr_at_3
|
2440 |
+
value: 59.955999999999996
|
2441 |
+
- type: mrr_at_5
|
2442 |
+
value: 61.785999999999994
|
2443 |
+
- type: ndcg_at_1
|
2444 |
+
value: 48.696
|
2445 |
+
- type: ndcg_at_10
|
2446 |
+
value: 67.743
|
2447 |
+
- type: ndcg_at_100
|
2448 |
+
value: 70.404
|
2449 |
+
- type: ndcg_at_1000
|
2450 |
+
value: 70.60600000000001
|
2451 |
+
- type: ndcg_at_3
|
2452 |
+
value: 60.712999999999994
|
2453 |
+
- type: ndcg_at_5
|
2454 |
+
value: 64.693
|
2455 |
+
- type: precision_at_1
|
2456 |
+
value: 48.696
|
2457 |
+
- type: precision_at_10
|
2458 |
+
value: 10.513
|
2459 |
+
- type: precision_at_100
|
2460 |
+
value: 1.196
|
2461 |
+
- type: precision_at_1000
|
2462 |
+
value: 0.121
|
2463 |
+
- type: precision_at_3
|
2464 |
+
value: 27.221
|
2465 |
+
- type: precision_at_5
|
2466 |
+
value: 18.701999999999998
|
2467 |
+
- type: recall_at_1
|
2468 |
+
value: 43.262
|
2469 |
+
- type: recall_at_10
|
2470 |
+
value: 87.35300000000001
|
2471 |
+
- type: recall_at_100
|
2472 |
+
value: 98.31299999999999
|
2473 |
+
- type: recall_at_1000
|
2474 |
+
value: 99.797
|
2475 |
+
- type: recall_at_3
|
2476 |
+
value: 69.643
|
2477 |
+
- type: recall_at_5
|
2478 |
+
value: 78.645
|
2479 |
+
- task:
|
2480 |
+
type: PairClassification
|
2481 |
+
dataset:
|
2482 |
+
type: C-MTEB/OCNLI
|
2483 |
+
name: MTEB Ocnli
|
2484 |
+
config: default
|
2485 |
+
split: validation
|
2486 |
+
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
|
2487 |
+
metrics:
|
2488 |
+
- type: cos_sim_accuracy
|
2489 |
+
value: 72.65836491608013
|
2490 |
+
- type: cos_sim_ap
|
2491 |
+
value: 78.75807247519593
|
2492 |
+
- type: cos_sim_f1
|
2493 |
+
value: 74.84662576687117
|
2494 |
+
- type: cos_sim_precision
|
2495 |
+
value: 63.97003745318352
|
2496 |
+
- type: cos_sim_recall
|
2497 |
+
value: 90.17951425554382
|
2498 |
+
- type: dot_accuracy
|
2499 |
+
value: 72.65836491608013
|
2500 |
+
- type: dot_ap
|
2501 |
+
value: 78.75807247519593
|
2502 |
+
- type: dot_f1
|
2503 |
+
value: 74.84662576687117
|
2504 |
+
- type: dot_precision
|
2505 |
+
value: 63.97003745318352
|
2506 |
+
- type: dot_recall
|
2507 |
+
value: 90.17951425554382
|
2508 |
+
- type: euclidean_accuracy
|
2509 |
+
value: 72.65836491608013
|
2510 |
+
- type: euclidean_ap
|
2511 |
+
value: 78.75807247519593
|
2512 |
+
- type: euclidean_f1
|
2513 |
+
value: 74.84662576687117
|
2514 |
+
- type: euclidean_precision
|
2515 |
+
value: 63.97003745318352
|
2516 |
+
- type: euclidean_recall
|
2517 |
+
value: 90.17951425554382
|
2518 |
+
- type: manhattan_accuracy
|
2519 |
+
value: 72.00866269626421
|
2520 |
+
- type: manhattan_ap
|
2521 |
+
value: 78.34663376353235
|
2522 |
+
- type: manhattan_f1
|
2523 |
+
value: 74.13234613604813
|
2524 |
+
- type: manhattan_precision
|
2525 |
+
value: 65.98023064250413
|
2526 |
+
- type: manhattan_recall
|
2527 |
+
value: 84.58289334741288
|
2528 |
+
- type: max_accuracy
|
2529 |
+
value: 72.65836491608013
|
2530 |
+
- type: max_ap
|
2531 |
+
value: 78.75807247519593
|
2532 |
+
- type: max_f1
|
2533 |
+
value: 74.84662576687117
|
2534 |
+
- task:
|
2535 |
+
type: Classification
|
2536 |
+
dataset:
|
2537 |
+
type: C-MTEB/OnlineShopping-classification
|
2538 |
+
name: MTEB OnlineShopping
|
2539 |
+
config: default
|
2540 |
+
split: test
|
2541 |
+
revision: e610f2ebd179a8fda30ae534c3878750a96db120
|
2542 |
+
metrics:
|
2543 |
+
- type: accuracy
|
2544 |
+
value: 94.46999999999998
|
2545 |
+
- type: ap
|
2546 |
+
value: 93.56401511160975
|
2547 |
+
- type: f1
|
2548 |
+
value: 94.46692790889986
|
2549 |
+
- task:
|
2550 |
+
type: STS
|
2551 |
+
dataset:
|
2552 |
+
type: C-MTEB/PAWSX
|
2553 |
+
name: MTEB PAWSX
|
2554 |
+
config: default
|
2555 |
+
split: test
|
2556 |
+
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
|
2557 |
+
metrics:
|
2558 |
+
- type: cos_sim_pearson
|
2559 |
+
value: 46.851404503762474
|
2560 |
+
- type: cos_sim_spearman
|
2561 |
+
value: 52.74603680597415
|
2562 |
+
- type: euclidean_pearson
|
2563 |
+
value: 51.596358967977295
|
2564 |
+
- type: euclidean_spearman
|
2565 |
+
value: 52.74603680597415
|
2566 |
+
- type: manhattan_pearson
|
2567 |
+
value: 51.81838023379299
|
2568 |
+
- type: manhattan_spearman
|
2569 |
+
value: 52.79611669731429
|
2570 |
+
- task:
|
2571 |
+
type: STS
|
2572 |
+
dataset:
|
2573 |
+
type: C-MTEB/QBQTC
|
2574 |
+
name: MTEB QBQTC
|
2575 |
+
config: default
|
2576 |
+
split: test
|
2577 |
+
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
|
2578 |
+
metrics:
|
2579 |
+
- type: cos_sim_pearson
|
2580 |
+
value: 31.928376136347016
|
2581 |
+
- type: cos_sim_spearman
|
2582 |
+
value: 34.38497204533162
|
2583 |
+
- type: euclidean_pearson
|
2584 |
+
value: 32.658432953090674
|
2585 |
+
- type: euclidean_spearman
|
2586 |
+
value: 34.38497204533162
|
2587 |
+
- type: manhattan_pearson
|
2588 |
+
value: 32.887190283203054
|
2589 |
+
- type: manhattan_spearman
|
2590 |
+
value: 34.69496960849327
|
2591 |
+
- task:
|
2592 |
+
type: Retrieval
|
2593 |
+
dataset:
|
2594 |
+
type: mteb/quora
|
2595 |
+
name: MTEB QuoraRetrieval
|
2596 |
+
config: default
|
2597 |
+
split: test
|
2598 |
+
revision: None
|
2599 |
+
metrics:
|
2600 |
+
- type: map_at_1
|
2601 |
+
value: 69.952
|
2602 |
+
- type: map_at_10
|
2603 |
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value: 84.134
|
2604 |
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- type: map_at_100
|
2605 |
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value: 84.795
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2606 |
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- type: map_at_1000
|
2607 |
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value: 84.809
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2608 |
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- type: map_at_3
|
2609 |
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value: 81.085
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2610 |
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- type: map_at_5
|
2611 |
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value: 82.976
|
2612 |
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- type: mrr_at_1
|
2613 |
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value: 80.56
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2614 |
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- type: mrr_at_10
|
2615 |
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value: 87.105
|
2616 |
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- type: mrr_at_100
|
2617 |
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value: 87.20700000000001
|
2618 |
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- type: mrr_at_1000
|
2619 |
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value: 87.208
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2620 |
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- type: mrr_at_3
|
2621 |
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value: 86.118
|
2622 |
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- type: mrr_at_5
|
2623 |
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value: 86.79299999999999
|
2624 |
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- type: ndcg_at_1
|
2625 |
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value: 80.57
|
2626 |
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- type: ndcg_at_10
|
2627 |
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value: 88.047
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2628 |
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- type: ndcg_at_100
|
2629 |
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value: 89.266
|
2630 |
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- type: ndcg_at_1000
|
2631 |
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value: 89.34299999999999
|
2632 |
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- type: ndcg_at_3
|
2633 |
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value: 85.052
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2634 |
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- type: ndcg_at_5
|
2635 |
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value: 86.68299999999999
|
2636 |
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- type: precision_at_1
|
2637 |
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value: 80.57
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2638 |
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- type: precision_at_10
|
2639 |
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value: 13.439
|
2640 |
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- type: precision_at_100
|
2641 |
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value: 1.536
|
2642 |
+
- type: precision_at_1000
|
2643 |
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value: 0.157
|
2644 |
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- type: precision_at_3
|
2645 |
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value: 37.283
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2646 |
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- type: precision_at_5
|
2647 |
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value: 24.558
|
2648 |
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- type: recall_at_1
|
2649 |
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value: 69.952
|
2650 |
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- type: recall_at_10
|
2651 |
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value: 95.599
|
2652 |
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- type: recall_at_100
|
2653 |
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value: 99.67099999999999
|
2654 |
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- type: recall_at_1000
|
2655 |
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value: 99.983
|
2656 |
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- type: recall_at_3
|
2657 |
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value: 87.095
|
2658 |
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- type: recall_at_5
|
2659 |
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value: 91.668
|
2660 |
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- task:
|
2661 |
+
type: Clustering
|
2662 |
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dataset:
|
2663 |
+
type: mteb/reddit-clustering
|
2664 |
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name: MTEB RedditClustering
|
2665 |
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config: default
|
2666 |
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split: test
|
2667 |
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revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
2668 |
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metrics:
|
2669 |
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- type: v_measure
|
2670 |
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value: 70.12802769698337
|
2671 |
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- task:
|
2672 |
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type: Clustering
|
2673 |
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dataset:
|
2674 |
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type: mteb/reddit-clustering-p2p
|
2675 |
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name: MTEB RedditClusteringP2P
|
2676 |
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config: default
|
2677 |
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split: test
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2678 |
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revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
2679 |
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metrics:
|
2680 |
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- type: v_measure
|
2681 |
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value: 71.19047621740276
|
2682 |
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- task:
|
2683 |
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type: Retrieval
|
2684 |
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dataset:
|
2685 |
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type: mteb/scidocs
|
2686 |
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name: MTEB SCIDOCS
|
2687 |
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config: default
|
2688 |
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split: test
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2689 |
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revision: None
|
2690 |
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metrics:
|
2691 |
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- type: map_at_1
|
2692 |
+
value: 6.208
|
2693 |
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- type: map_at_10
|
2694 |
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value: 17.036
|
2695 |
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- type: map_at_100
|
2696 |
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value: 20.162
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2697 |
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- type: map_at_1000
|
2698 |
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value: 20.552
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2699 |
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- type: map_at_3
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2700 |
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value: 11.591999999999999
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2701 |
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- type: map_at_5
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2702 |
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value: 14.349
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2703 |
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- type: mrr_at_1
|
2704 |
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value: 30.599999999999998
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2705 |
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- type: mrr_at_10
|
2706 |
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value: 43.325
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2707 |
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- type: mrr_at_100
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2708 |
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value: 44.281
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2709 |
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- type: mrr_at_1000
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2710 |
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value: 44.31
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2711 |
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- type: mrr_at_3
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2712 |
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value: 39.300000000000004
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2713 |
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- type: mrr_at_5
|
2714 |
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value: 41.730000000000004
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2715 |
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- type: ndcg_at_1
|
2716 |
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value: 30.599999999999998
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2717 |
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- type: ndcg_at_10
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2718 |
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value: 27.378000000000004
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2719 |
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- type: ndcg_at_100
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2720 |
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value: 37.768
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2721 |
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- type: ndcg_at_1000
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2722 |
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value: 43.275000000000006
|
2723 |
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- type: ndcg_at_3
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2724 |
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value: 25.167
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2725 |
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- type: ndcg_at_5
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2726 |
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value: 22.537
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2727 |
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- type: precision_at_1
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2728 |
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value: 30.599999999999998
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2729 |
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- type: precision_at_10
|
2730 |
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value: 14.46
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2731 |
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- type: precision_at_100
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2732 |
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value: 2.937
|
2733 |
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- type: precision_at_1000
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2734 |
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value: 0.424
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2735 |
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- type: precision_at_3
|
2736 |
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value: 23.666999999999998
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2737 |
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- type: precision_at_5
|
2738 |
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value: 20.14
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2739 |
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- type: recall_at_1
|
2740 |
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value: 6.208
|
2741 |
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- type: recall_at_10
|
2742 |
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value: 29.29
|
2743 |
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- type: recall_at_100
|
2744 |
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value: 59.565
|
2745 |
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- type: recall_at_1000
|
2746 |
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value: 85.963
|
2747 |
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- type: recall_at_3
|
2748 |
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value: 14.407
|
2749 |
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- type: recall_at_5
|
2750 |
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value: 20.412
|
2751 |
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- task:
|
2752 |
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type: STS
|
2753 |
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dataset:
|
2754 |
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type: mteb/sickr-sts
|
2755 |
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name: MTEB SICK-R
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2756 |
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config: default
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2757 |
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split: test
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2758 |
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revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
2759 |
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metrics:
|
2760 |
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- type: cos_sim_pearson
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2761 |
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value: 82.65489797062479
|
2762 |
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- type: cos_sim_spearman
|
2763 |
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value: 75.34808277034776
|
2764 |
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- type: euclidean_pearson
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2765 |
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value: 79.28097508609059
|
2766 |
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- type: euclidean_spearman
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2767 |
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value: 75.3480824481771
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2768 |
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- type: manhattan_pearson
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2769 |
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value: 78.83529262858895
|
2770 |
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- type: manhattan_spearman
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2771 |
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value: 74.96318170787025
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2772 |
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- task:
|
2773 |
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type: STS
|
2774 |
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dataset:
|
2775 |
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type: mteb/sts12-sts
|
2776 |
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name: MTEB STS12
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2777 |
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config: default
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2778 |
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split: test
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2779 |
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revision: a0d554a64d88156834ff5ae9920b964011b16384
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2780 |
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metrics:
|
2781 |
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- type: cos_sim_pearson
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2782 |
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value: 85.06920163624117
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2783 |
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- type: cos_sim_spearman
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2784 |
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value: 77.24549887905519
|
2785 |
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- type: euclidean_pearson
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2786 |
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value: 85.58740280635266
|
2787 |
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- type: euclidean_spearman
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2788 |
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value: 77.24652170306867
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2789 |
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- type: manhattan_pearson
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2790 |
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value: 85.77917470895854
|
2791 |
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- type: manhattan_spearman
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2792 |
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value: 77.54426264008778
|
2793 |
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- task:
|
2794 |
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type: STS
|
2795 |
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dataset:
|
2796 |
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type: mteb/sts13-sts
|
2797 |
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name: MTEB STS13
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2798 |
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config: default
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2799 |
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split: test
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2800 |
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
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2801 |
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metrics:
|
2802 |
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- type: cos_sim_pearson
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2803 |
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value: 80.9762185094084
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2804 |
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- type: cos_sim_spearman
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2805 |
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value: 80.98090253728394
|
2806 |
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- type: euclidean_pearson
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2807 |
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value: 80.88451512135202
|
2808 |
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- type: euclidean_spearman
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2809 |
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value: 80.98090253728394
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2810 |
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- type: manhattan_pearson
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2811 |
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value: 80.7606664599805
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2812 |
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- type: manhattan_spearman
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2813 |
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value: 80.87197716950068
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2814 |
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- task:
|
2815 |
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type: STS
|
2816 |
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dataset:
|
2817 |
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type: mteb/sts14-sts
|
2818 |
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name: MTEB STS14
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2819 |
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config: default
|
2820 |
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split: test
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2821 |
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revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
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2822 |
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metrics:
|
2823 |
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- type: cos_sim_pearson
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2824 |
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value: 81.91239166620251
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2825 |
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- type: cos_sim_spearman
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2826 |
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value: 76.36798509005328
|
2827 |
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- type: euclidean_pearson
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2828 |
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value: 80.6393872615655
|
2829 |
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- type: euclidean_spearman
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2830 |
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value: 76.36798836339655
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2831 |
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- type: manhattan_pearson
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2832 |
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value: 80.50765898709096
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2833 |
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- type: manhattan_spearman
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2834 |
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value: 76.31958999372227
|
2835 |
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- task:
|
2836 |
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type: STS
|
2837 |
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dataset:
|
2838 |
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type: mteb/sts15-sts
|
2839 |
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name: MTEB STS15
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2840 |
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config: default
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2841 |
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split: test
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2842 |
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revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
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2843 |
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metrics:
|
2844 |
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- type: cos_sim_pearson
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2845 |
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value: 83.68800355225011
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2846 |
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- type: cos_sim_spearman
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2847 |
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value: 84.47549220803403
|
2848 |
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- type: euclidean_pearson
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2849 |
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value: 83.86859896384159
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2850 |
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- type: euclidean_spearman
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2851 |
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2852 |
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- type: manhattan_pearson
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2853 |
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value: 83.74201103044383
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2854 |
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- type: manhattan_spearman
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2855 |
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value: 84.39903759718152
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2856 |
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- task:
|
2857 |
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type: STS
|
2858 |
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dataset:
|
2859 |
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type: mteb/sts16-sts
|
2860 |
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name: MTEB STS16
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2861 |
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config: default
|
2862 |
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split: test
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2863 |
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revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
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2864 |
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metrics:
|
2865 |
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- type: cos_sim_pearson
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2866 |
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value: 78.24197302553398
|
2867 |
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- type: cos_sim_spearman
|
2868 |
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value: 79.44526946553684
|
2869 |
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- type: euclidean_pearson
|
2870 |
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value: 79.12747636563053
|
2871 |
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- type: euclidean_spearman
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2872 |
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value: 79.44526946553684
|
2873 |
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- type: manhattan_pearson
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2874 |
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value: 78.94407504115144
|
2875 |
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- type: manhattan_spearman
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2876 |
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value: 79.24858249553934
|
2877 |
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- task:
|
2878 |
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type: STS
|
2879 |
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dataset:
|
2880 |
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type: mteb/sts17-crosslingual-sts
|
2881 |
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name: MTEB STS17 (en-en)
|
2882 |
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config: en-en
|
2883 |
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split: test
|
2884 |
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
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2885 |
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metrics:
|
2886 |
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- type: cos_sim_pearson
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2887 |
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value: 89.15329071763895
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2888 |
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- type: cos_sim_spearman
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value: 88.67251952242073
|
2890 |
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- type: euclidean_pearson
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2891 |
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value: 89.16908249259637
|
2892 |
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- type: euclidean_spearman
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2893 |
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|
2894 |
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- type: manhattan_pearson
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value: 89.1279735094785
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2896 |
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2897 |
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2898 |
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- task:
|
2899 |
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type: STS
|
2900 |
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dataset:
|
2901 |
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type: mteb/sts22-crosslingual-sts
|
2902 |
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name: MTEB STS22 (en)
|
2903 |
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config: en
|
2904 |
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split: test
|
2905 |
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revision: eea2b4fe26a775864c896887d910b76a8098ad3f
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2906 |
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metrics:
|
2907 |
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- type: cos_sim_pearson
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2908 |
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value: 69.44962535524695
|
2909 |
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- type: cos_sim_spearman
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2910 |
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value: 71.75861316291065
|
2911 |
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- type: euclidean_pearson
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2912 |
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value: 72.42347748883483
|
2913 |
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- type: euclidean_spearman
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2914 |
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|
2915 |
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- type: manhattan_pearson
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2916 |
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value: 72.57545073534365
|
2917 |
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- type: manhattan_spearman
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2918 |
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|
2919 |
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- task:
|
2920 |
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type: STS
|
2921 |
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dataset:
|
2922 |
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type: mteb/sts22-crosslingual-sts
|
2923 |
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name: MTEB STS22 (zh)
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2924 |
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config: zh
|
2925 |
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split: test
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2926 |
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revision: eea2b4fe26a775864c896887d910b76a8098ad3f
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2927 |
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metrics:
|
2928 |
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- type: cos_sim_pearson
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2929 |
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value: 68.9945443484093
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2930 |
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- type: cos_sim_spearman
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2931 |
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value: 71.46807157842791
|
2932 |
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- type: euclidean_pearson
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2933 |
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value: 69.24911748374225
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2934 |
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- type: euclidean_spearman
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2935 |
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2936 |
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- type: manhattan_pearson
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2937 |
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2938 |
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2940 |
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- task:
|
2941 |
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type: STS
|
2942 |
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dataset:
|
2943 |
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type: C-MTEB/STSB
|
2944 |
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name: MTEB STSB
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2945 |
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2946 |
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split: test
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2947 |
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revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
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2948 |
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metrics:
|
2949 |
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value: 77.39283860361535
|
2951 |
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- type: cos_sim_spearman
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2952 |
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|
2953 |
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- type: euclidean_pearson
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2954 |
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|
2955 |
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2956 |
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2957 |
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- type: manhattan_pearson
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2958 |
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2959 |
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2960 |
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|
2961 |
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- task:
|
2962 |
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type: STS
|
2963 |
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dataset:
|
2964 |
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type: mteb/stsbenchmark-sts
|
2965 |
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2966 |
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2967 |
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2968 |
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2969 |
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metrics:
|
2970 |
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|
2971 |
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|
2972 |
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- type: cos_sim_spearman
|
2973 |
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2974 |
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|
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2980 |
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2981 |
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|
2982 |
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- task:
|
2983 |
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type: Reranking
|
2984 |
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dataset:
|
2985 |
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type: mteb/scidocs-reranking
|
2986 |
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name: MTEB SciDocsRR
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2987 |
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config: default
|
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2989 |
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2990 |
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metrics:
|
2991 |
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2996 |
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2997 |
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|
2998 |
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type: mteb/scifact
|
2999 |
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name: MTEB SciFact
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3000 |
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3001 |
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3005 |
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|
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3036 |
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3037 |
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3051 |
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3053 |
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3054 |
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3055 |
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3056 |
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3057 |
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value: 97.167
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3058 |
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- type: recall_at_1000
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3059 |
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3060 |
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3061 |
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3062 |
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|
3063 |
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value: 73.411
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3064 |
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- task:
|
3065 |
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type: PairClassification
|
3066 |
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dataset:
|
3067 |
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type: mteb/sprintduplicatequestions-pairclassification
|
3068 |
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name: MTEB SprintDuplicateQuestions
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3069 |
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config: default
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split: test
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3072 |
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metrics:
|
3073 |
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3074 |
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value: 99.90693069306931
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3075 |
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- type: cos_sim_ap
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3079 |
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- type: cos_sim_recall
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3087 |
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3089 |
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3090 |
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3091 |
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- type: dot_recall
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3092 |
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3093 |
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- type: euclidean_accuracy
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3094 |
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3095 |
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- type: euclidean_ap
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3096 |
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3097 |
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- type: euclidean_f1
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3098 |
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3099 |
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- type: euclidean_precision
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3100 |
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3101 |
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- type: euclidean_recall
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3102 |
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3103 |
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- type: manhattan_accuracy
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3104 |
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- type: manhattan_ap
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- type: manhattan_f1
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3109 |
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- type: manhattan_precision
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3110 |
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3111 |
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- type: manhattan_recall
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3112 |
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value: 94.8
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3113 |
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- type: max_accuracy
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3114 |
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value: 99.90693069306931
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3115 |
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- type: max_ap
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3116 |
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3117 |
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- type: max_f1
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3118 |
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value: 95.27638190954774
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3119 |
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- task:
|
3120 |
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type: Clustering
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3121 |
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dataset:
|
3122 |
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type: mteb/stackexchange-clustering
|
3123 |
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name: MTEB StackExchangeClustering
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3124 |
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config: default
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split: test
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3126 |
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3127 |
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metrics:
|
3128 |
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- type: v_measure
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3129 |
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value: 78.89230351770412
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3130 |
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- task:
|
3131 |
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type: Clustering
|
3132 |
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dataset:
|
3133 |
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type: mteb/stackexchange-clustering-p2p
|
3134 |
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name: MTEB StackExchangeClusteringP2P
|
3135 |
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config: default
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3136 |
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split: test
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3137 |
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revision: 815ca46b2622cec33ccafc3735d572c266efdb44
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3138 |
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metrics:
|
3139 |
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- type: v_measure
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3140 |
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value: 47.52328347080355
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3141 |
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- task:
|
3142 |
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type: Reranking
|
3143 |
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dataset:
|
3144 |
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type: mteb/stackoverflowdupquestions-reranking
|
3145 |
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name: MTEB StackOverflowDupQuestions
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3146 |
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config: default
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3147 |
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3148 |
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3149 |
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metrics:
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3150 |
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- type: map
|
3151 |
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3152 |
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- type: mrr
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3153 |
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3154 |
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- task:
|
3155 |
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type: Summarization
|
3156 |
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dataset:
|
3157 |
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|
3158 |
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name: MTEB SummEval
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3159 |
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split: test
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3161 |
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3162 |
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metrics:
|
3163 |
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- type: cos_sim_pearson
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3164 |
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value: 30.047929797503592
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3165 |
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- type: cos_sim_spearman
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3166 |
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value: 29.465371781983567
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3167 |
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- type: dot_pearson
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value: 30.047927690552335
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3169 |
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- type: dot_spearman
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3170 |
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value: 29.465371781983567
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3171 |
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- task:
|
3172 |
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type: Reranking
|
3173 |
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dataset:
|
3174 |
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type: C-MTEB/T2Reranking
|
3175 |
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name: MTEB T2Reranking
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3176 |
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config: default
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3177 |
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split: dev
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3178 |
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revision: 76631901a18387f85eaa53e5450019b87ad58ef9
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3179 |
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metrics:
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3180 |
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- type: map
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3181 |
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3183 |
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3184 |
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- task:
|
3185 |
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type: Retrieval
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3186 |
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dataset:
|
3187 |
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type: C-MTEB/T2Retrieval
|
3188 |
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name: MTEB T2Retrieval
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3189 |
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config: default
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3190 |
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split: dev
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3191 |
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3192 |
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metrics:
|
3193 |
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3194 |
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value: 28.608
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3195 |
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- type: map_at_10
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3196 |
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value: 81.266
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3197 |
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3198 |
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value: 84.714
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3199 |
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3200 |
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3201 |
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3202 |
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3203 |
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3204 |
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value: 70.14
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3205 |
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3206 |
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value: 91.881
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3207 |
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3208 |
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value: 94.11699999999999
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3209 |
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- type: mrr_at_100
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3210 |
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3211 |
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3212 |
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value: 94.181
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3213 |
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3214 |
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3215 |
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3216 |
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value: 93.997
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3217 |
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- type: ndcg_at_1
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3218 |
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3219 |
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3220 |
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3221 |
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3222 |
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value: 90.904
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3223 |
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3224 |
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value: 91.326
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3225 |
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3226 |
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3227 |
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3228 |
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3229 |
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3230 |
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3231 |
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3232 |
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3233 |
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3234 |
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value: 5.082
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3235 |
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3236 |
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3237 |
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3238 |
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value: 77.62400000000001
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3239 |
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- type: precision_at_5
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3240 |
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value: 65.269
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3241 |
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3242 |
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value: 28.608
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3243 |
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|
3244 |
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value: 87.06
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3245 |
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3246 |
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value: 96.815
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3247 |
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- type: recall_at_1000
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3248 |
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3249 |
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- type: recall_at_3
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3250 |
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value: 58.506
|
3251 |
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- type: recall_at_5
|
3252 |
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value: 73.21600000000001
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3253 |
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- task:
|
3254 |
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type: Classification
|
3255 |
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dataset:
|
3256 |
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type: C-MTEB/TNews-classification
|
3257 |
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name: MTEB TNews
|
3258 |
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config: default
|
3259 |
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split: validation
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3260 |
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3261 |
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metrics:
|
3262 |
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- type: accuracy
|
3263 |
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3264 |
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- type: f1
|
3265 |
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|
3266 |
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- task:
|
3267 |
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type: Retrieval
|
3268 |
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dataset:
|
3269 |
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type: mteb/trec-covid
|
3270 |
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name: MTEB TRECCOVID
|
3271 |
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config: default
|
3272 |
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split: test
|
3273 |
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revision: None
|
3274 |
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metrics:
|
3275 |
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- type: map_at_1
|
3276 |
+
value: 0.181
|
3277 |
+
- type: map_at_10
|
3278 |
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value: 1.2
|
3279 |
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3280 |
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3281 |
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3282 |
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3283 |
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3284 |
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3285 |
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3286 |
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3287 |
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3288 |
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3289 |
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3290 |
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3291 |
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3292 |
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3293 |
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3294 |
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3295 |
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3296 |
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3297 |
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3298 |
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3299 |
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3300 |
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3301 |
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3302 |
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3303 |
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3304 |
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3305 |
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3306 |
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3307 |
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3308 |
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3309 |
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3310 |
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3311 |
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3312 |
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|
3313 |
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3314 |
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value: 55.2
|
3315 |
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3316 |
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3317 |
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3318 |
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|
3319 |
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3320 |
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3321 |
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3322 |
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value: 60.8
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3323 |
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- type: recall_at_1
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3324 |
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value: 0.181
|
3325 |
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- type: recall_at_10
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3326 |
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value: 1.471
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3327 |
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- type: recall_at_100
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3328 |
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|
3329 |
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- type: recall_at_1000
|
3330 |
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value: 37.667
|
3331 |
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- type: recall_at_3
|
3332 |
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value: 0.49300000000000005
|
3333 |
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- type: recall_at_5
|
3334 |
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value: 0.7979999999999999
|
3335 |
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- task:
|
3336 |
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type: Clustering
|
3337 |
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dataset:
|
3338 |
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type: C-MTEB/ThuNewsClusteringP2P
|
3339 |
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name: MTEB ThuNewsClusteringP2P
|
3340 |
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config: default
|
3341 |
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split: test
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3342 |
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3343 |
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metrics:
|
3344 |
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- type: v_measure
|
3345 |
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value: 78.68783858143624
|
3346 |
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- task:
|
3347 |
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type: Clustering
|
3348 |
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dataset:
|
3349 |
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type: C-MTEB/ThuNewsClusteringS2S
|
3350 |
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name: MTEB ThuNewsClusteringS2S
|
3351 |
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config: default
|
3352 |
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split: test
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3353 |
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3354 |
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metrics:
|
3355 |
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|
3356 |
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value: 77.04148998956299
|
3357 |
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- task:
|
3358 |
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type: Retrieval
|
3359 |
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dataset:
|
3360 |
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type: mteb/touche2020
|
3361 |
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name: MTEB Touche2020
|
3362 |
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|
3363 |
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split: test
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3364 |
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3365 |
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metrics:
|
3366 |
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3367 |
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value: 1.936
|
3368 |
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- type: map_at_10
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3369 |
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value: 8.942
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3370 |
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3378 |
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3392 |
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- type: ndcg_at_10
|
3393 |
+
value: 23.262
|
3394 |
+
- type: ndcg_at_100
|
3395 |
+
value: 34.959
|
3396 |
+
- type: ndcg_at_1000
|
3397 |
+
value: 47.258
|
3398 |
+
- type: ndcg_at_3
|
3399 |
+
value: 25.27
|
3400 |
+
- type: ndcg_at_5
|
3401 |
+
value: 24.246000000000002
|
3402 |
+
- type: precision_at_1
|
3403 |
+
value: 26.531
|
3404 |
+
- type: precision_at_10
|
3405 |
+
value: 20.408
|
3406 |
+
- type: precision_at_100
|
3407 |
+
value: 7.306
|
3408 |
+
- type: precision_at_1000
|
3409 |
+
value: 1.541
|
3410 |
+
- type: precision_at_3
|
3411 |
+
value: 26.531
|
3412 |
+
- type: precision_at_5
|
3413 |
+
value: 24.082
|
3414 |
+
- type: recall_at_1
|
3415 |
+
value: 1.936
|
3416 |
+
- type: recall_at_10
|
3417 |
+
value: 15.712000000000002
|
3418 |
+
- type: recall_at_100
|
3419 |
+
value: 45.451
|
3420 |
+
- type: recall_at_1000
|
3421 |
+
value: 83.269
|
3422 |
+
- type: recall_at_3
|
3423 |
+
value: 6.442
|
3424 |
+
- type: recall_at_5
|
3425 |
+
value: 9.151
|
3426 |
+
- task:
|
3427 |
+
type: Classification
|
3428 |
+
dataset:
|
3429 |
+
type: mteb/toxic_conversations_50k
|
3430 |
+
name: MTEB ToxicConversationsClassification
|
3431 |
+
config: default
|
3432 |
+
split: test
|
3433 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
3434 |
+
metrics:
|
3435 |
+
- type: accuracy
|
3436 |
+
value: 86.564
|
3437 |
+
- type: ap
|
3438 |
+
value: 34.58766846081731
|
3439 |
+
- type: f1
|
3440 |
+
value: 72.32759831978161
|
3441 |
+
- task:
|
3442 |
+
type: Classification
|
3443 |
+
dataset:
|
3444 |
+
type: mteb/tweet_sentiment_extraction
|
3445 |
+
name: MTEB TweetSentimentExtractionClassification
|
3446 |
+
config: default
|
3447 |
+
split: test
|
3448 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
3449 |
+
metrics:
|
3450 |
+
- type: accuracy
|
3451 |
+
value: 77.80418788907753
|
3452 |
+
- type: f1
|
3453 |
+
value: 78.1047638421972
|
3454 |
+
- task:
|
3455 |
+
type: Clustering
|
3456 |
+
dataset:
|
3457 |
+
type: mteb/twentynewsgroups-clustering
|
3458 |
+
name: MTEB TwentyNewsgroupsClustering
|
3459 |
+
config: default
|
3460 |
+
split: test
|
3461 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
3462 |
+
metrics:
|
3463 |
+
- type: v_measure
|
3464 |
+
value: 59.20888659980063
|
3465 |
+
- task:
|
3466 |
+
type: PairClassification
|
3467 |
+
dataset:
|
3468 |
+
type: mteb/twittersemeval2015-pairclassification
|
3469 |
+
name: MTEB TwitterSemEval2015
|
3470 |
+
config: default
|
3471 |
+
split: test
|
3472 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
3473 |
+
metrics:
|
3474 |
+
- type: cos_sim_accuracy
|
3475 |
+
value: 85.45627943017226
|
3476 |
+
- type: cos_sim_ap
|
3477 |
+
value: 72.25550061847534
|
3478 |
+
- type: cos_sim_f1
|
3479 |
+
value: 66.0611487783037
|
3480 |
+
- type: cos_sim_precision
|
3481 |
+
value: 64.11720884032779
|
3482 |
+
- type: cos_sim_recall
|
3483 |
+
value: 68.12664907651715
|
3484 |
+
- type: dot_accuracy
|
3485 |
+
value: 85.45627943017226
|
3486 |
+
- type: dot_ap
|
3487 |
+
value: 72.25574305366213
|
3488 |
+
- type: dot_f1
|
3489 |
+
value: 66.0611487783037
|
3490 |
+
- type: dot_precision
|
3491 |
+
value: 64.11720884032779
|
3492 |
+
- type: dot_recall
|
3493 |
+
value: 68.12664907651715
|
3494 |
+
- type: euclidean_accuracy
|
3495 |
+
value: 85.45627943017226
|
3496 |
+
- type: euclidean_ap
|
3497 |
+
value: 72.2557084446673
|
3498 |
+
- type: euclidean_f1
|
3499 |
+
value: 66.0611487783037
|
3500 |
+
- type: euclidean_precision
|
3501 |
+
value: 64.11720884032779
|
3502 |
+
- type: euclidean_recall
|
3503 |
+
value: 68.12664907651715
|
3504 |
+
- type: manhattan_accuracy
|
3505 |
+
value: 85.32514752339513
|
3506 |
+
- type: manhattan_ap
|
3507 |
+
value: 71.52919143472248
|
3508 |
+
- type: manhattan_f1
|
3509 |
+
value: 65.60288251190322
|
3510 |
+
- type: manhattan_precision
|
3511 |
+
value: 64.02913840743531
|
3512 |
+
- type: manhattan_recall
|
3513 |
+
value: 67.25593667546174
|
3514 |
+
- type: max_accuracy
|
3515 |
+
value: 85.45627943017226
|
3516 |
+
- type: max_ap
|
3517 |
+
value: 72.25574305366213
|
3518 |
+
- type: max_f1
|
3519 |
+
value: 66.0611487783037
|
3520 |
+
- task:
|
3521 |
+
type: PairClassification
|
3522 |
+
dataset:
|
3523 |
+
type: mteb/twitterurlcorpus-pairclassification
|
3524 |
+
name: MTEB TwitterURLCorpus
|
3525 |
+
config: default
|
3526 |
+
split: test
|
3527 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
3528 |
+
metrics:
|
3529 |
+
- type: cos_sim_accuracy
|
3530 |
+
value: 88.34167733923235
|
3531 |
+
- type: cos_sim_ap
|
3532 |
+
value: 84.58587730660244
|
3533 |
+
- type: cos_sim_f1
|
3534 |
+
value: 77.14170010676287
|
3535 |
+
- type: cos_sim_precision
|
3536 |
+
value: 73.91181657848324
|
3537 |
+
- type: cos_sim_recall
|
3538 |
+
value: 80.66676932553126
|
3539 |
+
- type: dot_accuracy
|
3540 |
+
value: 88.34167733923235
|
3541 |
+
- type: dot_ap
|
3542 |
+
value: 84.58585083616217
|
3543 |
+
- type: dot_f1
|
3544 |
+
value: 77.14170010676287
|
3545 |
+
- type: dot_precision
|
3546 |
+
value: 73.91181657848324
|
3547 |
+
- type: dot_recall
|
3548 |
+
value: 80.66676932553126
|
3549 |
+
- type: euclidean_accuracy
|
3550 |
+
value: 88.34167733923235
|
3551 |
+
- type: euclidean_ap
|
3552 |
+
value: 84.5858781355044
|
3553 |
+
- type: euclidean_f1
|
3554 |
+
value: 77.14170010676287
|
3555 |
+
- type: euclidean_precision
|
3556 |
+
value: 73.91181657848324
|
3557 |
+
- type: euclidean_recall
|
3558 |
+
value: 80.66676932553126
|
3559 |
+
- type: manhattan_accuracy
|
3560 |
+
value: 88.28152287809989
|
3561 |
+
- type: manhattan_ap
|
3562 |
+
value: 84.53184837110165
|
3563 |
+
- type: manhattan_f1
|
3564 |
+
value: 77.13582823915313
|
3565 |
+
- type: manhattan_precision
|
3566 |
+
value: 74.76156069364161
|
3567 |
+
- type: manhattan_recall
|
3568 |
+
value: 79.66584539574993
|
3569 |
+
- type: max_accuracy
|
3570 |
+
value: 88.34167733923235
|
3571 |
+
- type: max_ap
|
3572 |
+
value: 84.5858781355044
|
3573 |
+
- type: max_f1
|
3574 |
+
value: 77.14170010676287
|
3575 |
+
- task:
|
3576 |
+
type: Retrieval
|
3577 |
+
dataset:
|
3578 |
+
type: C-MTEB/VideoRetrieval
|
3579 |
+
name: MTEB VideoRetrieval
|
3580 |
+
config: default
|
3581 |
+
split: dev
|
3582 |
+
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
|
3583 |
+
metrics:
|
3584 |
+
- type: map_at_1
|
3585 |
+
value: 66.10000000000001
|
3586 |
+
- type: map_at_10
|
3587 |
+
value: 75.238
|
3588 |
+
- type: map_at_100
|
3589 |
+
value: 75.559
|
3590 |
+
- type: map_at_1000
|
3591 |
+
value: 75.565
|
3592 |
+
- type: map_at_3
|
3593 |
+
value: 73.68299999999999
|
3594 |
+
- type: map_at_5
|
3595 |
+
value: 74.63300000000001
|
3596 |
+
- type: mrr_at_1
|
3597 |
+
value: 66.10000000000001
|
3598 |
+
- type: mrr_at_10
|
3599 |
+
value: 75.238
|
3600 |
+
- type: mrr_at_100
|
3601 |
+
value: 75.559
|
3602 |
+
- type: mrr_at_1000
|
3603 |
+
value: 75.565
|
3604 |
+
- type: mrr_at_3
|
3605 |
+
value: 73.68299999999999
|
3606 |
+
- type: mrr_at_5
|
3607 |
+
value: 74.63300000000001
|
3608 |
+
- type: ndcg_at_1
|
3609 |
+
value: 66.10000000000001
|
3610 |
+
- type: ndcg_at_10
|
3611 |
+
value: 79.25999999999999
|
3612 |
+
- type: ndcg_at_100
|
3613 |
+
value: 80.719
|
3614 |
+
- type: ndcg_at_1000
|
3615 |
+
value: 80.862
|
3616 |
+
- type: ndcg_at_3
|
3617 |
+
value: 76.08200000000001
|
3618 |
+
- type: ndcg_at_5
|
3619 |
+
value: 77.782
|
3620 |
+
- type: precision_at_1
|
3621 |
+
value: 66.10000000000001
|
3622 |
+
- type: precision_at_10
|
3623 |
+
value: 9.17
|
3624 |
+
- type: precision_at_100
|
3625 |
+
value: 0.983
|
3626 |
+
- type: precision_at_1000
|
3627 |
+
value: 0.099
|
3628 |
+
- type: precision_at_3
|
3629 |
+
value: 27.667
|
3630 |
+
- type: precision_at_5
|
3631 |
+
value: 17.419999999999998
|
3632 |
+
- type: recall_at_1
|
3633 |
+
value: 66.10000000000001
|
3634 |
+
- type: recall_at_10
|
3635 |
+
value: 91.7
|
3636 |
+
- type: recall_at_100
|
3637 |
+
value: 98.3
|
3638 |
+
- type: recall_at_1000
|
3639 |
+
value: 99.4
|
3640 |
+
- type: recall_at_3
|
3641 |
+
value: 83.0
|
3642 |
+
- type: recall_at_5
|
3643 |
+
value: 87.1
|
3644 |
+
- task:
|
3645 |
+
type: Classification
|
3646 |
+
dataset:
|
3647 |
+
type: C-MTEB/waimai-classification
|
3648 |
+
name: MTEB Waimai
|
3649 |
+
config: default
|
3650 |
+
split: test
|
3651 |
+
revision: 339287def212450dcaa9df8c22bf93e9980c7023
|
3652 |
+
metrics:
|
3653 |
+
- type: accuracy
|
3654 |
+
value: 91.13
|
3655 |
+
- type: ap
|
3656 |
+
value: 79.55231335947015
|
3657 |
+
- type: f1
|
3658 |
+
value: 89.63091922203914
|
3659 |
---
|
3660 |
|
3661 |
<p align="center">
|