Create README.md
Browse files
README.md
ADDED
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|
1 |
+
---
|
2 |
+
tags:
|
3 |
+
- mteb
|
4 |
+
model-index:
|
5 |
+
- name: nomic_classification_nignore50
|
6 |
+
results:
|
7 |
+
- task:
|
8 |
+
type: Classification
|
9 |
+
dataset:
|
10 |
+
type: None
|
11 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
12 |
+
config: en
|
13 |
+
split: test
|
14 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
15 |
+
metrics:
|
16 |
+
- type: accuracy
|
17 |
+
value: 73.34328358208954
|
18 |
+
- type: ap
|
19 |
+
value: 35.68453876944336
|
20 |
+
- type: f1
|
21 |
+
value: 67.06992889373645
|
22 |
+
- task:
|
23 |
+
type: Classification
|
24 |
+
dataset:
|
25 |
+
type: None
|
26 |
+
name: MTEB AmazonPolarityClassification
|
27 |
+
config: default
|
28 |
+
split: test
|
29 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
30 |
+
metrics:
|
31 |
+
- type: accuracy
|
32 |
+
value: 59.672925
|
33 |
+
- type: ap
|
34 |
+
value: 55.848120844301974
|
35 |
+
- type: f1
|
36 |
+
value: 59.45636290076794
|
37 |
+
- task:
|
38 |
+
type: Classification
|
39 |
+
dataset:
|
40 |
+
type: None
|
41 |
+
name: MTEB AmazonReviewsClassification (en)
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42 |
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44 |
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|
46 |
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47 |
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48 |
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|
49 |
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|
50 |
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|
51 |
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type: Retrieval
|
52 |
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|
53 |
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type: None
|
54 |
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name: MTEB ArguAna
|
55 |
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|
56 |
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57 |
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58 |
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|
59 |
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|
60 |
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61 |
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|
62 |
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63 |
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|
64 |
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65 |
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66 |
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70 |
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71 |
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72 |
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73 |
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74 |
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76 |
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77 |
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78 |
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80 |
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81 |
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82 |
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84 |
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93 |
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97 |
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99 |
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|
106 |
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|
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|
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|
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|
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117 |
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|
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value: 53.627
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119 |
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- task:
|
120 |
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type: Clustering
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121 |
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|
122 |
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type: None
|
123 |
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name: MTEB ArxivClusteringP2P
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124 |
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125 |
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126 |
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|
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value: 35.49094114007186
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|
131 |
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|
133 |
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type: None
|
134 |
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|
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140 |
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value: 24.389506327986794
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141 |
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- task:
|
142 |
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type: Reranking
|
143 |
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dataset:
|
144 |
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type: None
|
145 |
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name: MTEB AskUbuntuDupQuestions
|
146 |
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147 |
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|
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|
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153 |
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|
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156 |
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|
157 |
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|
158 |
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name: MTEB BIOSSES
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159 |
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|
163 |
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|
164 |
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value: 81.55829597082581
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175 |
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- task:
|
176 |
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type: Classification
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177 |
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|
178 |
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type: None
|
179 |
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name: MTEB Banking77Classification
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180 |
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181 |
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|
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185 |
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187 |
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188 |
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|
189 |
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190 |
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|
191 |
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|
192 |
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|
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value: 32.120028817225204
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199 |
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|
200 |
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dataset:
|
202 |
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type: None
|
203 |
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name: MTEB BiorxivClusteringS2S
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204 |
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205 |
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206 |
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|
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209 |
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value: 23.27141125132152
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210 |
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- task:
|
211 |
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212 |
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|
213 |
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|
214 |
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name: MTEB CQADupstackAndroidRetrieval
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215 |
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219 |
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220 |
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221 |
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222 |
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223 |
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249 |
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255 |
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257 |
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258 |
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260 |
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value: 1.146
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261 |
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value: 36.7
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279 |
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- task:
|
280 |
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281 |
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|
282 |
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type: None
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283 |
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name: MTEB CQADupstackEnglishRetrieval
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284 |
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285 |
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metrics:
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288 |
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289 |
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value: 17.147000000000002
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290 |
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292 |
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300 |
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301 |
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302 |
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303 |
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304 |
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305 |
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306 |
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308 |
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309 |
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310 |
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311 |
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314 |
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315 |
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316 |
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317 |
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320 |
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322 |
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323 |
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324 |
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325 |
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326 |
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327 |
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328 |
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329 |
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335 |
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336 |
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346 |
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347 |
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348 |
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- task:
|
349 |
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350 |
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|
351 |
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|
352 |
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name: MTEB CQADupstackGamingRetrieval
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353 |
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354 |
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355 |
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356 |
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357 |
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358 |
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359 |
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360 |
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361 |
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366 |
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372 |
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403 |
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404 |
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405 |
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407 |
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409 |
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414 |
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415 |
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416 |
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value: 44.052
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417 |
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- task:
|
418 |
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419 |
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|
420 |
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type: None
|
421 |
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name: MTEB CQADupstackGisRetrieval
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422 |
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|
423 |
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441 |
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443 |
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444 |
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446 |
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447 |
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448 |
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465 |
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466 |
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467 |
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value: 0.092
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470 |
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471 |
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value: 7.457999999999999
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472 |
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473 |
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value: 5.266
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474 |
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475 |
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value: 13.07
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476 |
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value: 27.811000000000003
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480 |
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482 |
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484 |
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485 |
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value: 23.397000000000002
|
486 |
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- task:
|
487 |
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type: Retrieval
|
488 |
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dataset:
|
489 |
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type: None
|
490 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
491 |
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config: default
|
492 |
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split: test
|
493 |
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494 |
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495 |
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496 |
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497 |
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|
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501 |
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502 |
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503 |
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504 |
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505 |
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506 |
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507 |
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508 |
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509 |
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510 |
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514 |
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515 |
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516 |
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517 |
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518 |
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519 |
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520 |
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521 |
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523 |
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525 |
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528 |
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529 |
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530 |
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531 |
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532 |
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533 |
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534 |
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535 |
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536 |
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537 |
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538 |
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539 |
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540 |
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541 |
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542 |
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543 |
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544 |
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545 |
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546 |
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547 |
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549 |
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551 |
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553 |
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554 |
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555 |
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- task:
|
556 |
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557 |
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dataset:
|
558 |
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type: None
|
559 |
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name: MTEB CQADupstackPhysicsRetrieval
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560 |
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config: default
|
561 |
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split: test
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562 |
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564 |
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565 |
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566 |
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567 |
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569 |
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571 |
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572 |
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574 |
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575 |
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576 |
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578 |
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579 |
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580 |
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584 |
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586 |
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588 |
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594 |
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601 |
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602 |
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|
603 |
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604 |
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605 |
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606 |
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607 |
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609 |
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610 |
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611 |
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612 |
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613 |
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614 |
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615 |
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616 |
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619 |
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621 |
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622 |
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|
623 |
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value: 30.788
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624 |
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- task:
|
625 |
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626 |
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dataset:
|
627 |
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type: None
|
628 |
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name: MTEB CQADupstackProgrammersRetrieval
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629 |
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config: default
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630 |
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split: test
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631 |
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633 |
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634 |
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635 |
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|
636 |
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637 |
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639 |
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641 |
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642 |
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643 |
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644 |
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645 |
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647 |
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649 |
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650 |
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651 |
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652 |
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654 |
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655 |
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657 |
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660 |
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661 |
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663 |
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667 |
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669 |
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671 |
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672 |
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673 |
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675 |
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677 |
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679 |
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680 |
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681 |
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682 |
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683 |
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684 |
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685 |
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686 |
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687 |
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|
688 |
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689 |
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690 |
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691 |
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|
692 |
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value: 26.166
|
693 |
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|
694 |
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|
695 |
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dataset:
|
696 |
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type: mteb/cqadupstack
|
697 |
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name: MTEB CQADupstackRetrieval
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698 |
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699 |
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700 |
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701 |
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702 |
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703 |
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704 |
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705 |
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706 |
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707 |
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708 |
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709 |
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710 |
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719 |
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724 |
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731 |
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733 |
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734 |
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736 |
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738 |
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741 |
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742 |
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|
743 |
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744 |
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745 |
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746 |
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747 |
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748 |
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|
749 |
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750 |
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|
751 |
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752 |
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|
753 |
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755 |
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758 |
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759 |
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760 |
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|
761 |
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value: 26.018083333333337
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762 |
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- task:
|
763 |
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|
764 |
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dataset:
|
765 |
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type: None
|
766 |
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name: MTEB CQADupstackStatsRetrieval
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767 |
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|
768 |
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769 |
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770 |
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773 |
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774 |
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789 |
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799 |
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811 |
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815 |
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827 |
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829 |
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831 |
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- task:
|
832 |
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833 |
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dataset:
|
834 |
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type: None
|
835 |
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name: MTEB CQADupstackTexRetrieval
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836 |
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837 |
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value: 4.136
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888 |
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- type: recall_at_1
|
889 |
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value: 7.148000000000001
|
890 |
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- type: recall_at_10
|
891 |
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value: 18.875
|
892 |
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- type: recall_at_100
|
893 |
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value: 36.321999999999996
|
894 |
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- type: recall_at_1000
|
895 |
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value: 63.273999999999994
|
896 |
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- type: recall_at_3
|
897 |
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value: 12.590000000000002
|
898 |
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- type: recall_at_5
|
899 |
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value: 15.6
|
900 |
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- task:
|
901 |
+
type: Retrieval
|
902 |
+
dataset:
|
903 |
+
type: None
|
904 |
+
name: MTEB CQADupstackUnixRetrieval
|
905 |
+
config: default
|
906 |
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split: test
|
907 |
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revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
|
908 |
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metrics:
|
909 |
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- type: map_at_1
|
910 |
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value: 13.178
|
911 |
+
- type: map_at_10
|
912 |
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value: 17.538
|
913 |
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- type: map_at_100
|
914 |
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value: 18.343999999999998
|
915 |
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- type: map_at_1000
|
916 |
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value: 18.462999999999997
|
917 |
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- type: map_at_3
|
918 |
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value: 15.984000000000002
|
919 |
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- type: map_at_5
|
920 |
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value: 16.78
|
921 |
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- type: mrr_at_1
|
922 |
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value: 15.672
|
923 |
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- type: mrr_at_10
|
924 |
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value: 20.385
|
925 |
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- type: mrr_at_100
|
926 |
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value: 21.160999999999998
|
927 |
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- type: mrr_at_1000
|
928 |
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value: 21.252
|
929 |
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- type: mrr_at_3
|
930 |
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value: 18.766
|
931 |
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- type: mrr_at_5
|
932 |
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value: 19.554
|
933 |
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- type: ndcg_at_1
|
934 |
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value: 15.672
|
935 |
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- type: ndcg_at_10
|
936 |
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value: 20.785
|
937 |
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- type: ndcg_at_100
|
938 |
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value: 25.089
|
939 |
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- type: ndcg_at_1000
|
940 |
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value: 28.396
|
941 |
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- type: ndcg_at_3
|
942 |
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value: 17.701
|
943 |
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- type: ndcg_at_5
|
944 |
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value: 18.968
|
945 |
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- type: precision_at_1
|
946 |
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value: 15.672
|
947 |
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- type: precision_at_10
|
948 |
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value: 3.5069999999999997
|
949 |
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- type: precision_at_100
|
950 |
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value: 0.633
|
951 |
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- type: precision_at_1000
|
952 |
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value: 0.10300000000000001
|
953 |
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- type: precision_at_3
|
954 |
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value: 8.022
|
955 |
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- type: precision_at_5
|
956 |
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value: 5.634
|
957 |
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- type: recall_at_1
|
958 |
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value: 13.178
|
959 |
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- type: recall_at_10
|
960 |
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value: 28.227999999999998
|
961 |
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- type: recall_at_100
|
962 |
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value: 48.022999999999996
|
963 |
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- type: recall_at_1000
|
964 |
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value: 72.475
|
965 |
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- type: recall_at_3
|
966 |
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value: 19.425
|
967 |
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- type: recall_at_5
|
968 |
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value: 22.783
|
969 |
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- task:
|
970 |
+
type: Retrieval
|
971 |
+
dataset:
|
972 |
+
type: None
|
973 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
974 |
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config: default
|
975 |
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split: test
|
976 |
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revision: 160c094312a0e1facb97e55eeddb698c0abe3571
|
977 |
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metrics:
|
978 |
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- type: map_at_1
|
979 |
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value: 15.662999999999998
|
980 |
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- type: map_at_10
|
981 |
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value: 21.191
|
982 |
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- type: map_at_100
|
983 |
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value: 22.436
|
984 |
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|
985 |
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value: 22.634999999999998
|
986 |
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|
987 |
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value: 19.493
|
988 |
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- type: map_at_5
|
989 |
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value: 20.543
|
990 |
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- type: mrr_at_1
|
991 |
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value: 19.368
|
992 |
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- type: mrr_at_10
|
993 |
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value: 24.866
|
994 |
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- type: mrr_at_100
|
995 |
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value: 25.875999999999998
|
996 |
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- type: mrr_at_1000
|
997 |
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value: 25.958
|
998 |
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- type: mrr_at_3
|
999 |
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value: 23.551
|
1000 |
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- type: mrr_at_5
|
1001 |
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value: 24.233
|
1002 |
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- type: ndcg_at_1
|
1003 |
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value: 19.368
|
1004 |
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- type: ndcg_at_10
|
1005 |
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value: 24.869
|
1006 |
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- type: ndcg_at_100
|
1007 |
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value: 30.54
|
1008 |
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- type: ndcg_at_1000
|
1009 |
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value: 33.994
|
1010 |
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- type: ndcg_at_3
|
1011 |
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value: 22.522000000000002
|
1012 |
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- type: ndcg_at_5
|
1013 |
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value: 23.674
|
1014 |
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- type: precision_at_1
|
1015 |
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value: 19.368
|
1016 |
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- type: precision_at_10
|
1017 |
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value: 4.704
|
1018 |
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- type: precision_at_100
|
1019 |
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value: 1.1400000000000001
|
1020 |
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- type: precision_at_1000
|
1021 |
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value: 0.2
|
1022 |
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- type: precision_at_3
|
1023 |
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value: 10.671999999999999
|
1024 |
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- type: precision_at_5
|
1025 |
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value: 7.668
|
1026 |
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- type: recall_at_1
|
1027 |
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value: 15.662999999999998
|
1028 |
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- type: recall_at_10
|
1029 |
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value: 31.03
|
1030 |
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- type: recall_at_100
|
1031 |
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value: 57.861
|
1032 |
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- type: recall_at_1000
|
1033 |
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value: 81.179
|
1034 |
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- type: recall_at_3
|
1035 |
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value: 23.843
|
1036 |
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- type: recall_at_5
|
1037 |
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value: 27.223999999999997
|
1038 |
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- task:
|
1039 |
+
type: Retrieval
|
1040 |
+
dataset:
|
1041 |
+
type: None
|
1042 |
+
name: MTEB CQADupstackWordpressRetrieval
|
1043 |
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config: default
|
1044 |
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split: test
|
1045 |
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revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
|
1046 |
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metrics:
|
1047 |
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- type: map_at_1
|
1048 |
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value: 11.842
|
1049 |
+
- type: map_at_10
|
1050 |
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value: 15.841
|
1051 |
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- type: map_at_100
|
1052 |
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value: 16.539
|
1053 |
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- type: map_at_1000
|
1054 |
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value: 16.656000000000002
|
1055 |
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- type: map_at_3
|
1056 |
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value: 14.152999999999999
|
1057 |
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- type: map_at_5
|
1058 |
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value: 15.055
|
1059 |
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- type: mrr_at_1
|
1060 |
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value: 13.309000000000001
|
1061 |
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- type: mrr_at_10
|
1062 |
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value: 17.39
|
1063 |
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|
1064 |
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value: 18.102999999999998
|
1065 |
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- type: mrr_at_1000
|
1066 |
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value: 18.209
|
1067 |
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- type: mrr_at_3
|
1068 |
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value: 15.681000000000001
|
1069 |
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- type: mrr_at_5
|
1070 |
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value: 16.614
|
1071 |
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|
1072 |
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value: 13.309000000000001
|
1073 |
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|
1074 |
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value: 18.712999999999997
|
1075 |
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- type: ndcg_at_100
|
1076 |
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value: 22.553
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1077 |
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- type: ndcg_at_1000
|
1078 |
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value: 25.923000000000002
|
1079 |
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|
1080 |
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value: 15.299
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1081 |
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|
1082 |
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value: 16.875
|
1083 |
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- type: precision_at_1
|
1084 |
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value: 13.309000000000001
|
1085 |
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- type: precision_at_10
|
1086 |
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value: 2.994
|
1087 |
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- type: precision_at_100
|
1088 |
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value: 0.529
|
1089 |
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- type: precision_at_1000
|
1090 |
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value: 0.089
|
1091 |
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- type: precision_at_3
|
1092 |
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value: 6.4079999999999995
|
1093 |
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- type: precision_at_5
|
1094 |
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value: 4.695
|
1095 |
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- type: recall_at_1
|
1096 |
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value: 11.842
|
1097 |
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- type: recall_at_10
|
1098 |
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value: 26.358999999999998
|
1099 |
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- type: recall_at_100
|
1100 |
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value: 44.553
|
1101 |
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- type: recall_at_1000
|
1102 |
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value: 70.456
|
1103 |
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- type: recall_at_3
|
1104 |
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value: 17.079
|
1105 |
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- type: recall_at_5
|
1106 |
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value: 20.93
|
1107 |
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- task:
|
1108 |
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type: Retrieval
|
1109 |
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dataset:
|
1110 |
+
type: None
|
1111 |
+
name: MTEB ClimateFEVER
|
1112 |
+
config: default
|
1113 |
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split: test
|
1114 |
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revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
|
1115 |
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metrics:
|
1116 |
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|
1117 |
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value: 6.841
|
1118 |
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- type: map_at_10
|
1119 |
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value: 11.955
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1120 |
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- type: map_at_100
|
1121 |
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value: 13.322000000000001
|
1122 |
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|
1123 |
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value: 13.517000000000001
|
1124 |
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- type: map_at_3
|
1125 |
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value: 9.818
|
1126 |
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|
1127 |
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value: 10.875
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1128 |
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|
1129 |
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value: 15.504999999999999
|
1130 |
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|
1131 |
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value: 24.087
|
1132 |
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- type: mrr_at_100
|
1133 |
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value: 25.149
|
1134 |
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- type: mrr_at_1000
|
1135 |
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value: 25.221
|
1136 |
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- type: mrr_at_3
|
1137 |
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value: 20.923
|
1138 |
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|
1139 |
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value: 22.64
|
1140 |
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- type: ndcg_at_1
|
1141 |
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value: 15.504999999999999
|
1142 |
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- type: ndcg_at_10
|
1143 |
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value: 17.787
|
1144 |
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- type: ndcg_at_100
|
1145 |
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value: 24.032
|
1146 |
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- type: ndcg_at_1000
|
1147 |
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value: 28.058
|
1148 |
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- type: ndcg_at_3
|
1149 |
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value: 13.729
|
1150 |
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- type: ndcg_at_5
|
1151 |
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value: 15.165999999999999
|
1152 |
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- type: precision_at_1
|
1153 |
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value: 15.504999999999999
|
1154 |
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- type: precision_at_10
|
1155 |
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value: 5.811
|
1156 |
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- type: precision_at_100
|
1157 |
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value: 1.246
|
1158 |
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- type: precision_at_1000
|
1159 |
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value: 0.197
|
1160 |
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- type: precision_at_3
|
1161 |
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value: 10.228
|
1162 |
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- type: precision_at_5
|
1163 |
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value: 8.155999999999999
|
1164 |
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- type: recall_at_1
|
1165 |
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value: 6.841
|
1166 |
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- type: recall_at_10
|
1167 |
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value: 22.451999999999998
|
1168 |
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- type: recall_at_100
|
1169 |
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value: 44.588
|
1170 |
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- type: recall_at_1000
|
1171 |
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value: 67.806
|
1172 |
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- type: recall_at_3
|
1173 |
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value: 12.824
|
1174 |
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- type: recall_at_5
|
1175 |
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value: 16.59
|
1176 |
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- task:
|
1177 |
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type: Retrieval
|
1178 |
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dataset:
|
1179 |
+
type: None
|
1180 |
+
name: MTEB DBPedia
|
1181 |
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config: default
|
1182 |
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split: test
|
1183 |
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revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
|
1184 |
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metrics:
|
1185 |
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- type: map_at_1
|
1186 |
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value: 4.601999999999999
|
1187 |
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- type: map_at_10
|
1188 |
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value: 10.209
|
1189 |
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- type: map_at_100
|
1190 |
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value: 14.172
|
1191 |
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- type: map_at_1000
|
1192 |
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value: 15.121
|
1193 |
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- type: map_at_3
|
1194 |
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value: 7.59
|
1195 |
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- type: map_at_5
|
1196 |
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value: 8.853
|
1197 |
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- type: mrr_at_1
|
1198 |
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value: 41.75
|
1199 |
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|
1200 |
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value: 51.949
|
1201 |
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|
1202 |
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value: 52.678000000000004
|
1203 |
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|
1204 |
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value: 52.708
|
1205 |
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|
1206 |
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value: 49.542
|
1207 |
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|
1208 |
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value: 51.054
|
1209 |
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- type: ndcg_at_1
|
1210 |
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value: 31.125000000000004
|
1211 |
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|
1212 |
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value: 24.581
|
1213 |
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|
1214 |
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value: 27.894999999999996
|
1215 |
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- type: ndcg_at_1000
|
1216 |
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value: 34.438
|
1217 |
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|
1218 |
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value: 27.877999999999997
|
1219 |
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|
1220 |
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value: 26.273000000000003
|
1221 |
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- type: precision_at_1
|
1222 |
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value: 41.75
|
1223 |
+
- type: precision_at_10
|
1224 |
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value: 21.15
|
1225 |
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- type: precision_at_100
|
1226 |
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value: 6.787999999999999
|
1227 |
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|
1228 |
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value: 1.394
|
1229 |
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|
1230 |
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value: 32.917
|
1231 |
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- type: precision_at_5
|
1232 |
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value: 28.000000000000004
|
1233 |
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- type: recall_at_1
|
1234 |
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value: 4.601999999999999
|
1235 |
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- type: recall_at_10
|
1236 |
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value: 14.434
|
1237 |
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- type: recall_at_100
|
1238 |
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value: 33.838
|
1239 |
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- type: recall_at_1000
|
1240 |
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value: 56.438
|
1241 |
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- type: recall_at_3
|
1242 |
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value: 9.073
|
1243 |
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- type: recall_at_5
|
1244 |
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value: 11.395
|
1245 |
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- task:
|
1246 |
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type: Classification
|
1247 |
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dataset:
|
1248 |
+
type: None
|
1249 |
+
name: MTEB EmotionClassification
|
1250 |
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config: default
|
1251 |
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split: test
|
1252 |
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revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
1253 |
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metrics:
|
1254 |
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- type: accuracy
|
1255 |
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value: 43.364999999999995
|
1256 |
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- type: f1
|
1257 |
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value: 41.05024359810464
|
1258 |
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- task:
|
1259 |
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type: Retrieval
|
1260 |
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dataset:
|
1261 |
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type: None
|
1262 |
+
name: MTEB FEVER
|
1263 |
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config: default
|
1264 |
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split: test
|
1265 |
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revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
|
1266 |
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metrics:
|
1267 |
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|
1268 |
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value: 17.078
|
1269 |
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- type: map_at_10
|
1270 |
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value: 25.590000000000003
|
1271 |
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|
1272 |
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|
1273 |
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|
1274 |
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value: 26.651000000000003
|
1275 |
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|
1276 |
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value: 23.113
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1277 |
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|
1278 |
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value: 24.457
|
1279 |
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|
1280 |
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value: 18.257
|
1281 |
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|
1282 |
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value: 27.171
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1283 |
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|
1284 |
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value: 28.147
|
1285 |
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|
1286 |
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value: 28.206999999999997
|
1287 |
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|
1288 |
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value: 24.607
|
1289 |
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- type: mrr_at_5
|
1290 |
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value: 26.005
|
1291 |
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- type: ndcg_at_1
|
1292 |
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value: 18.257
|
1293 |
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- type: ndcg_at_10
|
1294 |
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value: 30.617
|
1295 |
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- type: ndcg_at_100
|
1296 |
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value: 35.625
|
1297 |
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- type: ndcg_at_1000
|
1298 |
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value: 37.566
|
1299 |
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|
1300 |
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value: 25.488
|
1301 |
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|
1302 |
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1303 |
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1304 |
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value: 18.257
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1305 |
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|
1306 |
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value: 4.877
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1308 |
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value: 0.76
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1310 |
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1311 |
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1314 |
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1316 |
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value: 17.078
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1317 |
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|
1318 |
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|
1320 |
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|
1322 |
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value: 83.134
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1323 |
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|
1324 |
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|
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|
1326 |
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value: 36.629
|
1327 |
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- task:
|
1328 |
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type: Retrieval
|
1329 |
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dataset:
|
1330 |
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type: None
|
1331 |
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name: MTEB FiQA2018
|
1332 |
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config: default
|
1333 |
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split: test
|
1334 |
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revision: 27a168819829fe9bcd655c2df245fb19452e8e06
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1335 |
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metrics:
|
1336 |
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|
1337 |
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value: 7.515
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1338 |
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|
1339 |
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1341 |
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1343 |
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1345 |
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|
1347 |
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1349 |
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1350 |
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|
1351 |
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1352 |
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|
1353 |
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value: 21.288
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1354 |
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1355 |
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1357 |
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1359 |
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1360 |
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|
1361 |
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1362 |
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1363 |
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1364 |
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|
1365 |
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1366 |
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|
1367 |
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1368 |
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|
1369 |
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1371 |
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|
1373 |
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1374 |
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|
1375 |
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value: 4.645
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1376 |
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|
1377 |
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value: 1.028
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1378 |
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|
1379 |
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value: 0.185
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1380 |
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|
1381 |
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value: 8.796
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1382 |
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|
1383 |
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value: 6.728000000000001
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1385 |
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1387 |
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value: 21.018
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|
1389 |
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|
1391 |
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1392 |
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|
1393 |
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1394 |
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|
1395 |
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value: 15.612
|
1396 |
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- task:
|
1397 |
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|
1398 |
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dataset:
|
1399 |
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type: None
|
1400 |
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name: MTEB HotpotQA
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1401 |
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config: default
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1402 |
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split: test
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1403 |
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1404 |
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|
1405 |
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|
1406 |
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value: 18.332
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1407 |
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|
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1422 |
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1428 |
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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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1434 |
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1436 |
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1437 |
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1439 |
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1440 |
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1441 |
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1442 |
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value: 36.664
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1443 |
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1444 |
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value: 7.245
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1445 |
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1446 |
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1448 |
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1449 |
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1450 |
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1451 |
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|
1452 |
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value: 12.527
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1453 |
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|
1454 |
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value: 18.332
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1455 |
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|
1456 |
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value: 36.226
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1457 |
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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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- type: recall_at_5
|
1464 |
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value: 31.317
|
1465 |
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- task:
|
1466 |
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type: Classification
|
1467 |
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dataset:
|
1468 |
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type: None
|
1469 |
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name: MTEB ImdbClassification
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1470 |
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1471 |
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1472 |
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1473 |
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metrics:
|
1474 |
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|
1475 |
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value: 58.64719999999999
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1476 |
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- type: ap
|
1477 |
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1479 |
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1480 |
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- task:
|
1481 |
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1482 |
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dataset:
|
1483 |
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type: None
|
1484 |
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name: MTEB MSMARCO
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1485 |
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config: default
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1486 |
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split: dev
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1487 |
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|
1488 |
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metrics:
|
1489 |
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|
1490 |
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value: 6.459
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1491 |
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|
1492 |
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1493 |
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1494 |
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1495 |
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1496 |
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1497 |
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1498 |
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1499 |
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1500 |
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1501 |
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1502 |
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1503 |
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1504 |
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1505 |
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1506 |
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1507 |
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1508 |
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1510 |
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1512 |
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1513 |
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1514 |
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1515 |
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1516 |
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1517 |
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1518 |
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1519 |
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1520 |
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value: 22.235
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1521 |
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1522 |
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value: 10.631
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1523 |
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1524 |
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value: 12.339
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1525 |
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|
1526 |
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value: 6.59
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1527 |
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|
1528 |
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value: 2.461
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1529 |
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|
1530 |
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value: 0.496
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1531 |
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1532 |
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value: 0.077
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1533 |
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1534 |
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value: 4.6850000000000005
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1535 |
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1536 |
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value: 3.6790000000000003
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1537 |
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1538 |
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value: 6.459
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1539 |
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|
1540 |
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value: 23.649
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1541 |
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1542 |
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1543 |
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|
1544 |
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value: 72.51100000000001
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1545 |
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1546 |
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1547 |
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|
1548 |
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value: 17.787
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1549 |
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- task:
|
1550 |
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type: Classification
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1551 |
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dataset:
|
1552 |
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type: None
|
1553 |
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name: MTEB MTOPDomainClassification (en)
|
1554 |
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config: en
|
1555 |
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split: test
|
1556 |
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1557 |
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metrics:
|
1558 |
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|
1559 |
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value: 85.75695394436845
|
1560 |
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|
1561 |
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|
1562 |
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- task:
|
1563 |
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|
1564 |
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dataset:
|
1565 |
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type: None
|
1566 |
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name: MTEB MTOPIntentClassification (en)
|
1567 |
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|
1568 |
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|
1569 |
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1570 |
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metrics:
|
1571 |
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|
1572 |
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|
1573 |
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- type: f1
|
1574 |
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value: 36.95943939246465
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1575 |
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- task:
|
1576 |
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type: Classification
|
1577 |
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dataset:
|
1578 |
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type: None
|
1579 |
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name: MTEB MassiveIntentClassification (en)
|
1580 |
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config: en
|
1581 |
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split: test
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1582 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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1583 |
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metrics:
|
1584 |
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1585 |
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1586 |
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1587 |
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1588 |
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- task:
|
1589 |
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|
1590 |
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dataset:
|
1591 |
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|
1592 |
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name: MTEB MassiveScenarioClassification (en)
|
1593 |
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|
1594 |
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1595 |
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metrics:
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value: 64.28379287155346
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1599 |
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- type: f1
|
1600 |
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value: 62.76816678345901
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1601 |
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- task:
|
1602 |
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type: Clustering
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1603 |
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dataset:
|
1604 |
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type: None
|
1605 |
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name: MTEB MedrxivClusteringP2P
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1606 |
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1607 |
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1608 |
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1609 |
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metrics:
|
1610 |
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1611 |
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value: 29.671378352049466
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1612 |
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- task:
|
1613 |
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type: Clustering
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1614 |
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dataset:
|
1615 |
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type: None
|
1616 |
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name: MTEB MedrxivClusteringS2S
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1617 |
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1618 |
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1619 |
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1620 |
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|
1621 |
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1622 |
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1623 |
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- task:
|
1624 |
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type: Reranking
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1625 |
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dataset:
|
1626 |
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type: None
|
1627 |
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name: MTEB MindSmallReranking
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1628 |
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1629 |
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1630 |
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1631 |
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1632 |
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1633 |
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1635 |
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1636 |
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- task:
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1637 |
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1638 |
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dataset:
|
1639 |
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|
1640 |
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1641 |
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1642 |
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1643 |
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1644 |
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1645 |
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1646 |
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1647 |
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1660 |
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1695 |
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1700 |
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1703 |
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1704 |
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value: 9.853000000000002
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1705 |
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- task:
|
1706 |
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1707 |
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dataset:
|
1708 |
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type: None
|
1709 |
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name: MTEB NQ
|
1710 |
+
config: default
|
1711 |
+
split: test
|
1712 |
+
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
|
1713 |
+
metrics:
|
1714 |
+
- type: map_at_1
|
1715 |
+
value: 8.408
|
1716 |
+
- type: map_at_10
|
1717 |
+
value: 15.064
|
1718 |
+
- type: map_at_100
|
1719 |
+
value: 16.282
|
1720 |
+
- type: map_at_1000
|
1721 |
+
value: 16.384999999999998
|
1722 |
+
- type: map_at_3
|
1723 |
+
value: 12.605
|
1724 |
+
- type: map_at_5
|
1725 |
+
value: 13.866
|
1726 |
+
- type: mrr_at_1
|
1727 |
+
value: 9.589
|
1728 |
+
- type: mrr_at_10
|
1729 |
+
value: 16.598
|
1730 |
+
- type: mrr_at_100
|
1731 |
+
value: 17.716
|
1732 |
+
- type: mrr_at_1000
|
1733 |
+
value: 17.8
|
1734 |
+
- type: mrr_at_3
|
1735 |
+
value: 14.021
|
1736 |
+
- type: mrr_at_5
|
1737 |
+
value: 15.39
|
1738 |
+
- type: ndcg_at_1
|
1739 |
+
value: 9.589
|
1740 |
+
- type: ndcg_at_10
|
1741 |
+
value: 19.408
|
1742 |
+
- type: ndcg_at_100
|
1743 |
+
value: 25.549
|
1744 |
+
- type: ndcg_at_1000
|
1745 |
+
value: 28.344
|
1746 |
+
- type: ndcg_at_3
|
1747 |
+
value: 14.258000000000001
|
1748 |
+
- type: ndcg_at_5
|
1749 |
+
value: 16.549
|
1750 |
+
- type: precision_at_1
|
1751 |
+
value: 9.589
|
1752 |
+
- type: precision_at_10
|
1753 |
+
value: 3.711
|
1754 |
+
- type: precision_at_100
|
1755 |
+
value: 0.717
|
1756 |
+
- type: precision_at_1000
|
1757 |
+
value: 0.098
|
1758 |
+
- type: precision_at_3
|
1759 |
+
value: 6.769
|
1760 |
+
- type: precision_at_5
|
1761 |
+
value: 5.359
|
1762 |
+
- type: recall_at_1
|
1763 |
+
value: 8.408
|
1764 |
+
- type: recall_at_10
|
1765 |
+
value: 31.442999999999998
|
1766 |
+
- type: recall_at_100
|
1767 |
+
value: 59.931
|
1768 |
+
- type: recall_at_1000
|
1769 |
+
value: 81.40899999999999
|
1770 |
+
- type: recall_at_3
|
1771 |
+
value: 17.654
|
1772 |
+
- type: recall_at_5
|
1773 |
+
value: 22.982
|
1774 |
+
- task:
|
1775 |
+
type: Retrieval
|
1776 |
+
dataset:
|
1777 |
+
type: None
|
1778 |
+
name: MTEB QuoraRetrieval
|
1779 |
+
config: default
|
1780 |
+
split: test
|
1781 |
+
revision: None
|
1782 |
+
metrics:
|
1783 |
+
- type: map_at_1
|
1784 |
+
value: 64.35600000000001
|
1785 |
+
- type: map_at_10
|
1786 |
+
value: 77.12400000000001
|
1787 |
+
- type: map_at_100
|
1788 |
+
value: 77.873
|
1789 |
+
- type: map_at_1000
|
1790 |
+
value: 77.905
|
1791 |
+
- type: map_at_3
|
1792 |
+
value: 74.18599999999999
|
1793 |
+
- type: map_at_5
|
1794 |
+
value: 76.00099999999999
|
1795 |
+
- type: mrr_at_1
|
1796 |
+
value: 74.22
|
1797 |
+
- type: mrr_at_10
|
1798 |
+
value: 81.326
|
1799 |
+
- type: mrr_at_100
|
1800 |
+
value: 81.54
|
1801 |
+
- type: mrr_at_1000
|
1802 |
+
value: 81.545
|
1803 |
+
- type: mrr_at_3
|
1804 |
+
value: 79.912
|
1805 |
+
- type: mrr_at_5
|
1806 |
+
value: 80.833
|
1807 |
+
- type: ndcg_at_1
|
1808 |
+
value: 74.26
|
1809 |
+
- type: ndcg_at_10
|
1810 |
+
value: 81.709
|
1811 |
+
- type: ndcg_at_100
|
1812 |
+
value: 83.688
|
1813 |
+
- type: ndcg_at_1000
|
1814 |
+
value: 84.029
|
1815 |
+
- type: ndcg_at_3
|
1816 |
+
value: 78.214
|
1817 |
+
- type: ndcg_at_5
|
1818 |
+
value: 80.07
|
1819 |
+
- type: precision_at_1
|
1820 |
+
value: 74.26
|
1821 |
+
- type: precision_at_10
|
1822 |
+
value: 12.334
|
1823 |
+
- type: precision_at_100
|
1824 |
+
value: 1.463
|
1825 |
+
- type: precision_at_1000
|
1826 |
+
value: 0.155
|
1827 |
+
- type: precision_at_3
|
1828 |
+
value: 33.977000000000004
|
1829 |
+
- type: precision_at_5
|
1830 |
+
value: 22.442
|
1831 |
+
- type: recall_at_1
|
1832 |
+
value: 64.35600000000001
|
1833 |
+
- type: recall_at_10
|
1834 |
+
value: 90.50200000000001
|
1835 |
+
- type: recall_at_100
|
1836 |
+
value: 97.833
|
1837 |
+
- type: recall_at_1000
|
1838 |
+
value: 99.681
|
1839 |
+
- type: recall_at_3
|
1840 |
+
value: 80.426
|
1841 |
+
- type: recall_at_5
|
1842 |
+
value: 85.627
|
1843 |
+
- task:
|
1844 |
+
type: Clustering
|
1845 |
+
dataset:
|
1846 |
+
type: None
|
1847 |
+
name: MTEB RedditClustering
|
1848 |
+
config: default
|
1849 |
+
split: test
|
1850 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
1851 |
+
metrics:
|
1852 |
+
- type: v_measure
|
1853 |
+
value: 36.774788977863864
|
1854 |
+
- task:
|
1855 |
+
type: Clustering
|
1856 |
+
dataset:
|
1857 |
+
type: None
|
1858 |
+
name: MTEB RedditClusteringP2P
|
1859 |
+
config: default
|
1860 |
+
split: test
|
1861 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
1862 |
+
metrics:
|
1863 |
+
- type: v_measure
|
1864 |
+
value: 46.77683019813603
|
1865 |
+
- task:
|
1866 |
+
type: Retrieval
|
1867 |
+
dataset:
|
1868 |
+
type: None
|
1869 |
+
name: MTEB SCIDOCS
|
1870 |
+
config: default
|
1871 |
+
split: test
|
1872 |
+
revision: None
|
1873 |
+
metrics:
|
1874 |
+
- type: map_at_1
|
1875 |
+
value: 2.988
|
1876 |
+
- type: map_at_10
|
1877 |
+
value: 7.183000000000001
|
1878 |
+
- type: map_at_100
|
1879 |
+
value: 8.522
|
1880 |
+
- type: map_at_1000
|
1881 |
+
value: 8.769
|
1882 |
+
- type: map_at_3
|
1883 |
+
value: 5.29
|
1884 |
+
- type: map_at_5
|
1885 |
+
value: 6.249
|
1886 |
+
- type: mrr_at_1
|
1887 |
+
value: 14.7
|
1888 |
+
- type: mrr_at_10
|
1889 |
+
value: 22.526
|
1890 |
+
- type: mrr_at_100
|
1891 |
+
value: 23.749000000000002
|
1892 |
+
- type: mrr_at_1000
|
1893 |
+
value: 23.827
|
1894 |
+
- type: mrr_at_3
|
1895 |
+
value: 19.617
|
1896 |
+
- type: mrr_at_5
|
1897 |
+
value: 21.322
|
1898 |
+
- type: ndcg_at_1
|
1899 |
+
value: 14.7
|
1900 |
+
- type: ndcg_at_10
|
1901 |
+
value: 12.666
|
1902 |
+
- type: ndcg_at_100
|
1903 |
+
value: 18.999
|
1904 |
+
- type: ndcg_at_1000
|
1905 |
+
value: 24.060000000000002
|
1906 |
+
- type: ndcg_at_3
|
1907 |
+
value: 11.908000000000001
|
1908 |
+
- type: ndcg_at_5
|
1909 |
+
value: 10.517999999999999
|
1910 |
+
- type: precision_at_1
|
1911 |
+
value: 14.7
|
1912 |
+
- type: precision_at_10
|
1913 |
+
value: 6.59
|
1914 |
+
- type: precision_at_100
|
1915 |
+
value: 1.592
|
1916 |
+
- type: precision_at_1000
|
1917 |
+
value: 0.28200000000000003
|
1918 |
+
- type: precision_at_3
|
1919 |
+
value: 11.1
|
1920 |
+
- type: precision_at_5
|
1921 |
+
value: 9.28
|
1922 |
+
- type: recall_at_1
|
1923 |
+
value: 2.988
|
1924 |
+
- type: recall_at_10
|
1925 |
+
value: 13.361999999999998
|
1926 |
+
- type: recall_at_100
|
1927 |
+
value: 32.312999999999995
|
1928 |
+
- type: recall_at_1000
|
1929 |
+
value: 57.293000000000006
|
1930 |
+
- type: recall_at_3
|
1931 |
+
value: 6.768000000000001
|
1932 |
+
- type: recall_at_5
|
1933 |
+
value: 9.408
|
1934 |
+
- task:
|
1935 |
+
type: STS
|
1936 |
+
dataset:
|
1937 |
+
type: None
|
1938 |
+
name: MTEB SICK-R
|
1939 |
+
config: default
|
1940 |
+
split: test
|
1941 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
1942 |
+
metrics:
|
1943 |
+
- type: cos_sim_pearson
|
1944 |
+
value: 74.41889076801608
|
1945 |
+
- type: cos_sim_spearman
|
1946 |
+
value: 64.0943040174807
|
1947 |
+
- type: euclidean_pearson
|
1948 |
+
value: 68.82034211304835
|
1949 |
+
- type: euclidean_spearman
|
1950 |
+
value: 64.09442214998937
|
1951 |
+
- type: manhattan_pearson
|
1952 |
+
value: 67.28965034113492
|
1953 |
+
- type: manhattan_spearman
|
1954 |
+
value: 63.44420264246327
|
1955 |
+
- task:
|
1956 |
+
type: STS
|
1957 |
+
dataset:
|
1958 |
+
type: None
|
1959 |
+
name: MTEB STS12
|
1960 |
+
config: default
|
1961 |
+
split: test
|
1962 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
1963 |
+
metrics:
|
1964 |
+
- type: cos_sim_pearson
|
1965 |
+
value: 74.19404151109595
|
1966 |
+
- type: cos_sim_spearman
|
1967 |
+
value: 67.74306846452986
|
1968 |
+
- type: euclidean_pearson
|
1969 |
+
value: 70.18395120349778
|
1970 |
+
- type: euclidean_spearman
|
1971 |
+
value: 67.74435929212751
|
1972 |
+
- type: manhattan_pearson
|
1973 |
+
value: 68.00233535704764
|
1974 |
+
- type: manhattan_spearman
|
1975 |
+
value: 66.49484254678912
|
1976 |
+
- task:
|
1977 |
+
type: STS
|
1978 |
+
dataset:
|
1979 |
+
type: None
|
1980 |
+
name: MTEB STS13
|
1981 |
+
config: default
|
1982 |
+
split: test
|
1983 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
1984 |
+
metrics:
|
1985 |
+
- type: cos_sim_pearson
|
1986 |
+
value: 76.87296571665527
|
1987 |
+
- type: cos_sim_spearman
|
1988 |
+
value: 77.89561218206916
|
1989 |
+
- type: euclidean_pearson
|
1990 |
+
value: 77.69387561554153
|
1991 |
+
- type: euclidean_spearman
|
1992 |
+
value: 77.89564999542652
|
1993 |
+
- type: manhattan_pearson
|
1994 |
+
value: 77.2962286612409
|
1995 |
+
- type: manhattan_spearman
|
1996 |
+
value: 77.62183070015116
|
1997 |
+
- task:
|
1998 |
+
type: STS
|
1999 |
+
dataset:
|
2000 |
+
type: None
|
2001 |
+
name: MTEB STS14
|
2002 |
+
config: default
|
2003 |
+
split: test
|
2004 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
2005 |
+
metrics:
|
2006 |
+
- type: cos_sim_pearson
|
2007 |
+
value: 77.57868393517904
|
2008 |
+
- type: cos_sim_spearman
|
2009 |
+
value: 74.3463323181872
|
2010 |
+
- type: euclidean_pearson
|
2011 |
+
value: 76.34645562235912
|
2012 |
+
- type: euclidean_spearman
|
2013 |
+
value: 74.34632262739605
|
2014 |
+
- type: manhattan_pearson
|
2015 |
+
value: 75.18605845598414
|
2016 |
+
- type: manhattan_spearman
|
2017 |
+
value: 73.37658913779082
|
2018 |
+
- task:
|
2019 |
+
type: STS
|
2020 |
+
dataset:
|
2021 |
+
type: None
|
2022 |
+
name: MTEB STS15
|
2023 |
+
config: default
|
2024 |
+
split: test
|
2025 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
2026 |
+
metrics:
|
2027 |
+
- type: cos_sim_pearson
|
2028 |
+
value: 81.3634602887491
|
2029 |
+
- type: cos_sim_spearman
|
2030 |
+
value: 82.08782263146122
|
2031 |
+
- type: euclidean_pearson
|
2032 |
+
value: 81.98650592402285
|
2033 |
+
- type: euclidean_spearman
|
2034 |
+
value: 82.08782111739671
|
2035 |
+
- type: manhattan_pearson
|
2036 |
+
value: 81.3116524025808
|
2037 |
+
- type: manhattan_spearman
|
2038 |
+
value: 81.48861424628144
|
2039 |
+
- task:
|
2040 |
+
type: STS
|
2041 |
+
dataset:
|
2042 |
+
type: None
|
2043 |
+
name: MTEB STS16
|
2044 |
+
config: default
|
2045 |
+
split: test
|
2046 |
+
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
2047 |
+
metrics:
|
2048 |
+
- type: cos_sim_pearson
|
2049 |
+
value: 75.87657339764795
|
2050 |
+
- type: cos_sim_spearman
|
2051 |
+
value: 76.85063393951995
|
2052 |
+
- type: euclidean_pearson
|
2053 |
+
value: 76.4294473355708
|
2054 |
+
- type: euclidean_spearman
|
2055 |
+
value: 76.85118422928095
|
2056 |
+
- type: manhattan_pearson
|
2057 |
+
value: 76.64930860894692
|
2058 |
+
- type: manhattan_spearman
|
2059 |
+
value: 77.10013496496948
|
2060 |
+
- task:
|
2061 |
+
type: STS
|
2062 |
+
dataset:
|
2063 |
+
type: None
|
2064 |
+
name: MTEB STS17 (en-en)
|
2065 |
+
config: en-en
|
2066 |
+
split: test
|
2067 |
+
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
2068 |
+
metrics:
|
2069 |
+
- type: cos_sim_pearson
|
2070 |
+
value: 83.34326345648941
|
2071 |
+
- type: cos_sim_spearman
|
2072 |
+
value: 83.88446362725742
|
2073 |
+
- type: euclidean_pearson
|
2074 |
+
value: 83.46533085232362
|
2075 |
+
- type: euclidean_spearman
|
2076 |
+
value: 83.8853378609005
|
2077 |
+
- type: manhattan_pearson
|
2078 |
+
value: 83.1484584186202
|
2079 |
+
- type: manhattan_spearman
|
2080 |
+
value: 83.95137720105474
|
2081 |
+
- task:
|
2082 |
+
type: STS
|
2083 |
+
dataset:
|
2084 |
+
type: None
|
2085 |
+
name: MTEB STS22 (en)
|
2086 |
+
config: en
|
2087 |
+
split: test
|
2088 |
+
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
|
2089 |
+
metrics:
|
2090 |
+
- type: cos_sim_pearson
|
2091 |
+
value: 63.83321542257857
|
2092 |
+
- type: cos_sim_spearman
|
2093 |
+
value: 60.82336431262449
|
2094 |
+
- type: euclidean_pearson
|
2095 |
+
value: 63.11807243264151
|
2096 |
+
- type: euclidean_spearman
|
2097 |
+
value: 60.82336431262449
|
2098 |
+
- type: manhattan_pearson
|
2099 |
+
value: 62.08662855750554
|
2100 |
+
- type: manhattan_spearman
|
2101 |
+
value: 59.96327210016991
|
2102 |
+
- task:
|
2103 |
+
type: STS
|
2104 |
+
dataset:
|
2105 |
+
type: None
|
2106 |
+
name: MTEB STSBenchmark
|
2107 |
+
config: default
|
2108 |
+
split: test
|
2109 |
+
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
2110 |
+
metrics:
|
2111 |
+
- type: cos_sim_pearson
|
2112 |
+
value: 78.52565754458857
|
2113 |
+
- type: cos_sim_spearman
|
2114 |
+
value: 77.31638441143197
|
2115 |
+
- type: euclidean_pearson
|
2116 |
+
value: 78.14871856195552
|
2117 |
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- type: euclidean_spearman
|
2118 |
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value: 77.31637278095369
|
2119 |
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- type: manhattan_pearson
|
2120 |
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value: 77.18966645743699
|
2121 |
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- type: manhattan_spearman
|
2122 |
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value: 76.60273070312653
|
2123 |
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- task:
|
2124 |
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type: Reranking
|
2125 |
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dataset:
|
2126 |
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type: None
|
2127 |
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name: MTEB SciDocsRR
|
2128 |
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config: default
|
2129 |
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split: test
|
2130 |
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revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
2131 |
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metrics:
|
2132 |
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- type: map
|
2133 |
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value: 72.10656484834338
|
2134 |
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- type: mrr
|
2135 |
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value: 90.98610811846106
|
2136 |
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- task:
|
2137 |
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type: Retrieval
|
2138 |
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dataset:
|
2139 |
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type: None
|
2140 |
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name: MTEB SciFact
|
2141 |
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config: default
|
2142 |
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split: test
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2143 |
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revision: 0228b52cf27578f30900b9e5271d331663a030d7
|
2144 |
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metrics:
|
2145 |
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- type: map_at_1
|
2146 |
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value: 39.75
|
2147 |
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- type: map_at_10
|
2148 |
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value: 47.549
|
2149 |
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- type: map_at_100
|
2150 |
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value: 48.579
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2151 |
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- type: map_at_1000
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2152 |
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value: 48.653
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2153 |
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|
2154 |
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value: 45.135
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2155 |
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|
2156 |
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value: 46.444
|
2157 |
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- type: mrr_at_1
|
2158 |
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value: 42.0
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2159 |
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|
2160 |
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value: 49.418
|
2161 |
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2162 |
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value: 50.253
|
2163 |
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|
2164 |
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value: 50.319
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2165 |
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|
2166 |
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value: 47.221999999999994
|
2167 |
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|
2168 |
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value: 48.439
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2169 |
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- type: ndcg_at_1
|
2170 |
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value: 42.0
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2171 |
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2172 |
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value: 51.922999999999995
|
2173 |
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- type: ndcg_at_100
|
2174 |
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value: 56.607
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2175 |
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- type: ndcg_at_1000
|
2176 |
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value: 58.3
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2177 |
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|
2178 |
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2179 |
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2180 |
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value: 49.446
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2181 |
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|
2182 |
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value: 42.0
|
2183 |
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- type: precision_at_10
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2184 |
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value: 7.066999999999999
|
2185 |
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- type: precision_at_100
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2186 |
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value: 0.963
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2187 |
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- type: precision_at_1000
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2188 |
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value: 0.11
|
2189 |
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- type: precision_at_3
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2190 |
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value: 18.556
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2191 |
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- type: precision_at_5
|
2192 |
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value: 12.4
|
2193 |
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- type: recall_at_1
|
2194 |
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value: 39.75
|
2195 |
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- type: recall_at_10
|
2196 |
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value: 63.727999999999994
|
2197 |
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- type: recall_at_100
|
2198 |
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value: 84.983
|
2199 |
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- type: recall_at_1000
|
2200 |
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value: 97.8
|
2201 |
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- type: recall_at_3
|
2202 |
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value: 51.233
|
2203 |
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- type: recall_at_5
|
2204 |
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value: 56.494
|
2205 |
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- task:
|
2206 |
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type: PairClassification
|
2207 |
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dataset:
|
2208 |
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type: None
|
2209 |
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name: MTEB SprintDuplicateQuestions
|
2210 |
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config: default
|
2211 |
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split: test
|
2212 |
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revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
2213 |
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metrics:
|
2214 |
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- type: cos_sim_accuracy
|
2215 |
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value: 99.77623762376237
|
2216 |
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- type: cos_sim_ap
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2217 |
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value: 94.03474205524832
|
2218 |
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- type: cos_sim_f1
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2219 |
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value: 88.17533129459734
|
2220 |
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- type: cos_sim_precision
|
2221 |
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value: 89.91683991683992
|
2222 |
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- type: cos_sim_recall
|
2223 |
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value: 86.5
|
2224 |
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- type: dot_accuracy
|
2225 |
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value: 99.77623762376237
|
2226 |
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- type: dot_ap
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2227 |
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value: 94.03474205524832
|
2228 |
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- type: dot_f1
|
2229 |
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value: 88.17533129459734
|
2230 |
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- type: dot_precision
|
2231 |
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value: 89.91683991683992
|
2232 |
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- type: dot_recall
|
2233 |
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value: 86.5
|
2234 |
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- type: euclidean_accuracy
|
2235 |
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value: 99.77623762376237
|
2236 |
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- type: euclidean_ap
|
2237 |
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value: 94.03474205524832
|
2238 |
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- type: euclidean_f1
|
2239 |
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value: 88.17533129459734
|
2240 |
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- type: euclidean_precision
|
2241 |
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value: 89.91683991683992
|
2242 |
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- type: euclidean_recall
|
2243 |
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value: 86.5
|
2244 |
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- type: manhattan_accuracy
|
2245 |
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value: 99.77722772277228
|
2246 |
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- type: manhattan_ap
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2247 |
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value: 94.18601558420747
|
2248 |
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- type: manhattan_f1
|
2249 |
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value: 88.37209302325581
|
2250 |
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- type: manhattan_precision
|
2251 |
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value: 91.44385026737967
|
2252 |
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- type: manhattan_recall
|
2253 |
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value: 85.5
|
2254 |
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- type: max_accuracy
|
2255 |
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value: 99.77722772277228
|
2256 |
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- type: max_ap
|
2257 |
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value: 94.18601558420747
|
2258 |
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- type: max_f1
|
2259 |
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value: 88.37209302325581
|
2260 |
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- task:
|
2261 |
+
type: Clustering
|
2262 |
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dataset:
|
2263 |
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type: None
|
2264 |
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name: MTEB StackExchangeClustering
|
2265 |
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config: default
|
2266 |
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split: test
|
2267 |
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revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
2268 |
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metrics:
|
2269 |
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- type: v_measure
|
2270 |
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value: 39.59223629185949
|
2271 |
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- task:
|
2272 |
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type: Clustering
|
2273 |
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dataset:
|
2274 |
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type: None
|
2275 |
+
name: MTEB StackExchangeClusteringP2P
|
2276 |
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config: default
|
2277 |
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split: test
|
2278 |
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revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
2279 |
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metrics:
|
2280 |
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- type: v_measure
|
2281 |
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value: 29.72427682478714
|
2282 |
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- task:
|
2283 |
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type: Reranking
|
2284 |
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dataset:
|
2285 |
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type: None
|
2286 |
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name: MTEB StackOverflowDupQuestions
|
2287 |
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config: default
|
2288 |
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split: test
|
2289 |
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revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
2290 |
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metrics:
|
2291 |
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- type: map
|
2292 |
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value: 42.58781744864796
|
2293 |
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- type: mrr
|
2294 |
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value: 43.04660311094135
|
2295 |
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- task:
|
2296 |
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type: Summarization
|
2297 |
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dataset:
|
2298 |
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type: None
|
2299 |
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name: MTEB SummEval
|
2300 |
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config: default
|
2301 |
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split: test
|
2302 |
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revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
2303 |
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metrics:
|
2304 |
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- type: cos_sim_pearson
|
2305 |
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value: 29.162875776593765
|
2306 |
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- type: cos_sim_spearman
|
2307 |
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value: 29.469578163520644
|
2308 |
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- type: dot_pearson
|
2309 |
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value: 29.16287568844527
|
2310 |
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- type: dot_spearman
|
2311 |
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value: 29.491226084003042
|
2312 |
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- task:
|
2313 |
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type: Retrieval
|
2314 |
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dataset:
|
2315 |
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type: None
|
2316 |
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name: MTEB TRECCOVID
|
2317 |
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config: default
|
2318 |
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split: test
|
2319 |
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revision: None
|
2320 |
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metrics:
|
2321 |
+
- type: map_at_1
|
2322 |
+
value: 0.15
|
2323 |
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- type: map_at_10
|
2324 |
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value: 0.732
|
2325 |
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2326 |
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value: 3.402
|
2327 |
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- type: map_at_1000
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2328 |
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value: 8.562
|
2329 |
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- type: map_at_3
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2330 |
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value: 0.311
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2331 |
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- type: map_at_5
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2332 |
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value: 0.45799999999999996
|
2333 |
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- type: mrr_at_1
|
2334 |
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value: 60.0
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2335 |
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2336 |
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value: 66.297
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2337 |
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- type: mrr_at_100
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2338 |
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value: 67.486
|
2339 |
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- type: mrr_at_1000
|
2340 |
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value: 67.495
|
2341 |
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- type: mrr_at_3
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2342 |
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value: 64.667
|
2343 |
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- type: mrr_at_5
|
2344 |
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value: 65.567
|
2345 |
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- type: ndcg_at_1
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2346 |
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value: 53.0
|
2347 |
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- type: ndcg_at_10
|
2348 |
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value: 39.839999999999996
|
2349 |
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- type: ndcg_at_100
|
2350 |
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value: 28.401
|
2351 |
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- type: ndcg_at_1000
|
2352 |
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value: 24.781
|
2353 |
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- type: ndcg_at_3
|
2354 |
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value: 45.173
|
2355 |
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- type: ndcg_at_5
|
2356 |
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value: 42.543
|
2357 |
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- type: precision_at_1
|
2358 |
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value: 57.99999999999999
|
2359 |
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- type: precision_at_10
|
2360 |
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value: 41.199999999999996
|
2361 |
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- type: precision_at_100
|
2362 |
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value: 29.299999999999997
|
2363 |
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- type: precision_at_1000
|
2364 |
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value: 12.06
|
2365 |
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- type: precision_at_3
|
2366 |
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value: 46.666999999999994
|
2367 |
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- type: precision_at_5
|
2368 |
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value: 44.0
|
2369 |
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- type: recall_at_1
|
2370 |
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value: 0.15
|
2371 |
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- type: recall_at_10
|
2372 |
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value: 0.928
|
2373 |
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- type: recall_at_100
|
2374 |
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value: 6.021
|
2375 |
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- type: recall_at_1000
|
2376 |
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value: 23.61
|
2377 |
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- type: recall_at_3
|
2378 |
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value: 0.332
|
2379 |
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- type: recall_at_5
|
2380 |
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value: 0.521
|
2381 |
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- task:
|
2382 |
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type: Retrieval
|
2383 |
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dataset:
|
2384 |
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type: None
|
2385 |
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name: MTEB Touche2020
|
2386 |
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config: default
|
2387 |
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split: test
|
2388 |
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revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
|
2389 |
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metrics:
|
2390 |
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- type: map_at_1
|
2391 |
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value: 1.6969999999999998
|
2392 |
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- type: map_at_10
|
2393 |
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value: 7.6579999999999995
|
2394 |
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- type: map_at_100
|
2395 |
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value: 14.119000000000002
|
2396 |
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|
2397 |
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value: 15.695999999999998
|
2398 |
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- type: map_at_3
|
2399 |
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value: 3.599
|
2400 |
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- type: map_at_5
|
2401 |
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value: 5.076
|
2402 |
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- type: mrr_at_1
|
2403 |
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value: 26.531
|
2404 |
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- type: mrr_at_10
|
2405 |
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value: 43.033
|
2406 |
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- type: mrr_at_100
|
2407 |
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value: 43.957
|
2408 |
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- type: mrr_at_1000
|
2409 |
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value: 43.976
|
2410 |
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- type: mrr_at_3
|
2411 |
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value: 38.435
|
2412 |
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- type: mrr_at_5
|
2413 |
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value: 42.211
|
2414 |
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- type: ndcg_at_1
|
2415 |
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value: 24.490000000000002
|
2416 |
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- type: ndcg_at_10
|
2417 |
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value: 22.114
|
2418 |
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- type: ndcg_at_100
|
2419 |
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value: 35.583999999999996
|
2420 |
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- type: ndcg_at_1000
|
2421 |
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value: 46.697
|
2422 |
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- type: ndcg_at_3
|
2423 |
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value: 23.521
|
2424 |
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|
2425 |
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value: 23.363
|
2426 |
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- type: precision_at_1
|
2427 |
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value: 26.531
|
2428 |
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- type: precision_at_10
|
2429 |
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value: 20.408
|
2430 |
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- type: precision_at_100
|
2431 |
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value: 8.265
|
2432 |
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- type: precision_at_1000
|
2433 |
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value: 1.551
|
2434 |
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- type: precision_at_3
|
2435 |
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value: 25.169999999999998
|
2436 |
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- type: precision_at_5
|
2437 |
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value: 24.490000000000002
|
2438 |
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- type: recall_at_1
|
2439 |
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value: 1.6969999999999998
|
2440 |
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- type: recall_at_10
|
2441 |
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value: 14.554
|
2442 |
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- type: recall_at_100
|
2443 |
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value: 49.858000000000004
|
2444 |
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- type: recall_at_1000
|
2445 |
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value: 83.635
|
2446 |
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- type: recall_at_3
|
2447 |
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value: 4.822
|
2448 |
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- type: recall_at_5
|
2449 |
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value: 7.933
|
2450 |
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- task:
|
2451 |
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type: Classification
|
2452 |
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dataset:
|
2453 |
+
type: None
|
2454 |
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name: MTEB ToxicConversationsClassification
|
2455 |
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config: default
|
2456 |
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split: test
|
2457 |
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revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
2458 |
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metrics:
|
2459 |
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- type: accuracy
|
2460 |
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value: 68.9474
|
2461 |
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- type: ap
|
2462 |
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value: 13.286323018191338
|
2463 |
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- type: f1
|
2464 |
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value: 52.98797630385014
|
2465 |
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- task:
|
2466 |
+
type: Classification
|
2467 |
+
dataset:
|
2468 |
+
type: None
|
2469 |
+
name: MTEB TweetSentimentExtractionClassification
|
2470 |
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config: default
|
2471 |
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split: test
|
2472 |
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revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
2473 |
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metrics:
|
2474 |
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- type: accuracy
|
2475 |
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value: 47.993774759479344
|
2476 |
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- type: f1
|
2477 |
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value: 48.15808612169315
|
2478 |
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- task:
|
2479 |
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type: Clustering
|
2480 |
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dataset:
|
2481 |
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type: None
|
2482 |
+
name: MTEB TwentyNewsgroupsClustering
|
2483 |
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config: default
|
2484 |
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split: test
|
2485 |
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revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
2486 |
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metrics:
|
2487 |
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- type: v_measure
|
2488 |
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value: 34.53065734021412
|
2489 |
+
- task:
|
2490 |
+
type: PairClassification
|
2491 |
+
dataset:
|
2492 |
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type: None
|
2493 |
+
name: MTEB TwitterSemEval2015
|
2494 |
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config: default
|
2495 |
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split: test
|
2496 |
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revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
2497 |
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metrics:
|
2498 |
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- type: cos_sim_accuracy
|
2499 |
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value: 82.61906181081243
|
2500 |
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- type: cos_sim_ap
|
2501 |
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value: 61.91869955453384
|
2502 |
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- type: cos_sim_f1
|
2503 |
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value: 59.794798377475544
|
2504 |
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- type: cos_sim_precision
|
2505 |
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value: 54.57317073170732
|
2506 |
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- type: cos_sim_recall
|
2507 |
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value: 66.12137203166228
|
2508 |
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- type: dot_accuracy
|
2509 |
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value: 82.61906181081243
|
2510 |
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- type: dot_ap
|
2511 |
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value: 61.91869955453384
|
2512 |
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- type: dot_f1
|
2513 |
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value: 59.794798377475544
|
2514 |
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- type: dot_precision
|
2515 |
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value: 54.57317073170732
|
2516 |
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- type: dot_recall
|
2517 |
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value: 66.12137203166228
|
2518 |
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- type: euclidean_accuracy
|
2519 |
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value: 82.61906181081243
|
2520 |
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- type: euclidean_ap
|
2521 |
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value: 61.91869955453384
|
2522 |
+
- type: euclidean_f1
|
2523 |
+
value: 59.794798377475544
|
2524 |
+
- type: euclidean_precision
|
2525 |
+
value: 54.57317073170732
|
2526 |
+
- type: euclidean_recall
|
2527 |
+
value: 66.12137203166228
|
2528 |
+
- type: manhattan_accuracy
|
2529 |
+
value: 81.89187578232104
|
2530 |
+
- type: manhattan_ap
|
2531 |
+
value: 59.62564983212156
|
2532 |
+
- type: manhattan_f1
|
2533 |
+
value: 57.711442786069654
|
2534 |
+
- type: manhattan_precision
|
2535 |
+
value: 52.364574376612204
|
2536 |
+
- type: manhattan_recall
|
2537 |
+
value: 64.27440633245382
|
2538 |
+
- type: max_accuracy
|
2539 |
+
value: 82.61906181081243
|
2540 |
+
- type: max_ap
|
2541 |
+
value: 61.91869955453384
|
2542 |
+
- type: max_f1
|
2543 |
+
value: 59.794798377475544
|
2544 |
+
- task:
|
2545 |
+
type: PairClassification
|
2546 |
+
dataset:
|
2547 |
+
type: None
|
2548 |
+
name: MTEB TwitterURLCorpus
|
2549 |
+
config: default
|
2550 |
+
split: test
|
2551 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
2552 |
+
metrics:
|
2553 |
+
- type: cos_sim_accuracy
|
2554 |
+
value: 87.49563395040167
|
2555 |
+
- type: cos_sim_ap
|
2556 |
+
value: 82.90539955967866
|
2557 |
+
- type: cos_sim_f1
|
2558 |
+
value: 74.9081462237892
|
2559 |
+
- type: cos_sim_precision
|
2560 |
+
value: 72.30780253636169
|
2561 |
+
- type: cos_sim_recall
|
2562 |
+
value: 77.70249461040962
|
2563 |
+
- type: dot_accuracy
|
2564 |
+
value: 87.49563395040167
|
2565 |
+
- type: dot_ap
|
2566 |
+
value: 82.90539958269324
|
2567 |
+
- type: dot_f1
|
2568 |
+
value: 74.9081462237892
|
2569 |
+
- type: dot_precision
|
2570 |
+
value: 72.30780253636169
|
2571 |
+
- type: dot_recall
|
2572 |
+
value: 77.70249461040962
|
2573 |
+
- type: euclidean_accuracy
|
2574 |
+
value: 87.49563395040167
|
2575 |
+
- type: euclidean_ap
|
2576 |
+
value: 82.9054181491746
|
2577 |
+
- type: euclidean_f1
|
2578 |
+
value: 74.9081462237892
|
2579 |
+
- type: euclidean_precision
|
2580 |
+
value: 72.30780253636169
|
2581 |
+
- type: euclidean_recall
|
2582 |
+
value: 77.70249461040962
|
2583 |
+
- type: manhattan_accuracy
|
2584 |
+
value: 87.37920596111304
|
2585 |
+
- type: manhattan_ap
|
2586 |
+
value: 82.78475210950901
|
2587 |
+
- type: manhattan_f1
|
2588 |
+
value: 74.73338499575631
|
2589 |
+
- type: manhattan_precision
|
2590 |
+
value: 71.7596201544894
|
2591 |
+
- type: manhattan_recall
|
2592 |
+
value: 77.96427471512165
|
2593 |
+
- type: max_accuracy
|
2594 |
+
value: 87.49563395040167
|
2595 |
+
- type: max_ap
|
2596 |
+
value: 82.9054181491746
|
2597 |
+
- type: max_f1
|
2598 |
+
value: 74.9081462237892
|
2599 |
+
---
|