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README.md
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|
1 |
+
---
|
2 |
+
license: apache-2.0
|
3 |
+
tags:
|
4 |
+
- sentence-transformers
|
5 |
+
- feature-extraction
|
6 |
+
- sentence-similarity
|
7 |
+
- mteb
|
8 |
+
- arctic
|
9 |
+
- snowflake-arctic-embed
|
10 |
+
- transformers.js
|
11 |
+
- llama-cpp
|
12 |
+
- gguf-my-repo
|
13 |
+
base_model: Snowflake/snowflake-arctic-embed-l
|
14 |
+
pipeline_tag: sentence-similarity
|
15 |
+
model-index:
|
16 |
+
- name: snowflake-arctic-embed-l
|
17 |
+
results:
|
18 |
+
- task:
|
19 |
+
type: Classification
|
20 |
+
dataset:
|
21 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
22 |
+
type: mteb/amazon_counterfactual
|
23 |
+
config: en
|
24 |
+
split: test
|
25 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
26 |
+
metrics:
|
27 |
+
- type: accuracy
|
28 |
+
value: 74.80597014925374
|
29 |
+
- type: ap
|
30 |
+
value: 37.911466766189875
|
31 |
+
- type: f1
|
32 |
+
value: 68.88606927542106
|
33 |
+
- task:
|
34 |
+
type: Classification
|
35 |
+
dataset:
|
36 |
+
name: MTEB AmazonPolarityClassification
|
37 |
+
type: mteb/amazon_polarity
|
38 |
+
config: default
|
39 |
+
split: test
|
40 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
41 |
+
metrics:
|
42 |
+
- type: accuracy
|
43 |
+
value: 78.402275
|
44 |
+
- type: ap
|
45 |
+
value: 73.03294793248114
|
46 |
+
- type: f1
|
47 |
+
value: 78.3147786132161
|
48 |
+
- task:
|
49 |
+
type: Classification
|
50 |
+
dataset:
|
51 |
+
name: MTEB AmazonReviewsClassification (en)
|
52 |
+
type: mteb/amazon_reviews_multi
|
53 |
+
config: en
|
54 |
+
split: test
|
55 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
56 |
+
metrics:
|
57 |
+
- type: accuracy
|
58 |
+
value: 36.717999999999996
|
59 |
+
- type: f1
|
60 |
+
value: 35.918044248787766
|
61 |
+
- task:
|
62 |
+
type: Retrieval
|
63 |
+
dataset:
|
64 |
+
name: MTEB ArguAna
|
65 |
+
type: mteb/arguana
|
66 |
+
config: default
|
67 |
+
split: test
|
68 |
+
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
|
69 |
+
metrics:
|
70 |
+
- type: map_at_1
|
71 |
+
value: 34.495
|
72 |
+
- type: map_at_10
|
73 |
+
value: 50.236000000000004
|
74 |
+
- type: map_at_100
|
75 |
+
value: 50.944
|
76 |
+
- type: map_at_1000
|
77 |
+
value: 50.94499999999999
|
78 |
+
- type: map_at_3
|
79 |
+
value: 45.341
|
80 |
+
- type: map_at_5
|
81 |
+
value: 48.286
|
82 |
+
- type: mrr_at_1
|
83 |
+
value: 35.135
|
84 |
+
- type: mrr_at_10
|
85 |
+
value: 50.471
|
86 |
+
- type: mrr_at_100
|
87 |
+
value: 51.185
|
88 |
+
- type: mrr_at_1000
|
89 |
+
value: 51.187000000000005
|
90 |
+
- type: mrr_at_3
|
91 |
+
value: 45.602
|
92 |
+
- type: mrr_at_5
|
93 |
+
value: 48.468
|
94 |
+
- type: ndcg_at_1
|
95 |
+
value: 34.495
|
96 |
+
- type: ndcg_at_10
|
97 |
+
value: 59.086000000000006
|
98 |
+
- type: ndcg_at_100
|
99 |
+
value: 61.937
|
100 |
+
- type: ndcg_at_1000
|
101 |
+
value: 61.966
|
102 |
+
- type: ndcg_at_3
|
103 |
+
value: 49.062
|
104 |
+
- type: ndcg_at_5
|
105 |
+
value: 54.367
|
106 |
+
- type: precision_at_1
|
107 |
+
value: 34.495
|
108 |
+
- type: precision_at_10
|
109 |
+
value: 8.734
|
110 |
+
- type: precision_at_100
|
111 |
+
value: 0.9939999999999999
|
112 |
+
- type: precision_at_1000
|
113 |
+
value: 0.1
|
114 |
+
- type: precision_at_3
|
115 |
+
value: 19.962
|
116 |
+
- type: precision_at_5
|
117 |
+
value: 14.552000000000001
|
118 |
+
- type: recall_at_1
|
119 |
+
value: 34.495
|
120 |
+
- type: recall_at_10
|
121 |
+
value: 87.33999999999999
|
122 |
+
- type: recall_at_100
|
123 |
+
value: 99.431
|
124 |
+
- type: recall_at_1000
|
125 |
+
value: 99.644
|
126 |
+
- type: recall_at_3
|
127 |
+
value: 59.885999999999996
|
128 |
+
- type: recall_at_5
|
129 |
+
value: 72.76
|
130 |
+
- task:
|
131 |
+
type: Clustering
|
132 |
+
dataset:
|
133 |
+
name: MTEB ArxivClusteringP2P
|
134 |
+
type: mteb/arxiv-clustering-p2p
|
135 |
+
config: default
|
136 |
+
split: test
|
137 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
138 |
+
metrics:
|
139 |
+
- type: v_measure
|
140 |
+
value: 47.46440874635501
|
141 |
+
- task:
|
142 |
+
type: Clustering
|
143 |
+
dataset:
|
144 |
+
name: MTEB ArxivClusteringS2S
|
145 |
+
type: mteb/arxiv-clustering-s2s
|
146 |
+
config: default
|
147 |
+
split: test
|
148 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
149 |
+
metrics:
|
150 |
+
- type: v_measure
|
151 |
+
value: 38.28720154213723
|
152 |
+
- task:
|
153 |
+
type: Reranking
|
154 |
+
dataset:
|
155 |
+
name: MTEB AskUbuntuDupQuestions
|
156 |
+
type: mteb/askubuntudupquestions-reranking
|
157 |
+
config: default
|
158 |
+
split: test
|
159 |
+
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
|
160 |
+
metrics:
|
161 |
+
- type: map
|
162 |
+
value: 60.34614226394902
|
163 |
+
- type: mrr
|
164 |
+
value: 75.05628105351096
|
165 |
+
- task:
|
166 |
+
type: STS
|
167 |
+
dataset:
|
168 |
+
name: MTEB BIOSSES
|
169 |
+
type: mteb/biosses-sts
|
170 |
+
config: default
|
171 |
+
split: test
|
172 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
173 |
+
metrics:
|
174 |
+
- type: cos_sim_pearson
|
175 |
+
value: 87.41072716728198
|
176 |
+
- type: cos_sim_spearman
|
177 |
+
value: 86.34534093114372
|
178 |
+
- type: euclidean_pearson
|
179 |
+
value: 85.34009667750838
|
180 |
+
- type: euclidean_spearman
|
181 |
+
value: 86.34534093114372
|
182 |
+
- type: manhattan_pearson
|
183 |
+
value: 85.2158833586889
|
184 |
+
- type: manhattan_spearman
|
185 |
+
value: 86.60920236509224
|
186 |
+
- task:
|
187 |
+
type: Classification
|
188 |
+
dataset:
|
189 |
+
name: MTEB Banking77Classification
|
190 |
+
type: mteb/banking77
|
191 |
+
config: default
|
192 |
+
split: test
|
193 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
194 |
+
metrics:
|
195 |
+
- type: accuracy
|
196 |
+
value: 80.06493506493507
|
197 |
+
- type: f1
|
198 |
+
value: 79.28108600339833
|
199 |
+
- task:
|
200 |
+
type: Clustering
|
201 |
+
dataset:
|
202 |
+
name: MTEB BigPatentClustering
|
203 |
+
type: jinaai/big-patent-clustering
|
204 |
+
config: default
|
205 |
+
split: test
|
206 |
+
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
|
207 |
+
metrics:
|
208 |
+
- type: v_measure
|
209 |
+
value: 20.545049432417287
|
210 |
+
- task:
|
211 |
+
type: Clustering
|
212 |
+
dataset:
|
213 |
+
name: MTEB BiorxivClusteringP2P
|
214 |
+
type: mteb/biorxiv-clustering-p2p
|
215 |
+
config: default
|
216 |
+
split: test
|
217 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
218 |
+
metrics:
|
219 |
+
- type: v_measure
|
220 |
+
value: 37.54369718479804
|
221 |
+
- task:
|
222 |
+
type: Clustering
|
223 |
+
dataset:
|
224 |
+
name: MTEB BiorxivClusteringS2S
|
225 |
+
type: mteb/biorxiv-clustering-s2s
|
226 |
+
config: default
|
227 |
+
split: test
|
228 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
229 |
+
metrics:
|
230 |
+
- type: v_measure
|
231 |
+
value: 32.64941588219162
|
232 |
+
- task:
|
233 |
+
type: Retrieval
|
234 |
+
dataset:
|
235 |
+
name: MTEB CQADupstackAndroidRetrieval
|
236 |
+
type: mteb/cqadupstack-android
|
237 |
+
config: default
|
238 |
+
split: test
|
239 |
+
revision: f46a197baaae43b4f621051089b82a364682dfeb
|
240 |
+
metrics:
|
241 |
+
- type: map_at_1
|
242 |
+
value: 37.264
|
243 |
+
- type: map_at_10
|
244 |
+
value: 49.43
|
245 |
+
- type: map_at_100
|
246 |
+
value: 50.967
|
247 |
+
- type: map_at_1000
|
248 |
+
value: 51.08200000000001
|
249 |
+
- type: map_at_3
|
250 |
+
value: 45.742
|
251 |
+
- type: map_at_5
|
252 |
+
value: 47.764
|
253 |
+
- type: mrr_at_1
|
254 |
+
value: 44.921
|
255 |
+
- type: mrr_at_10
|
256 |
+
value: 54.879999999999995
|
257 |
+
- type: mrr_at_100
|
258 |
+
value: 55.525000000000006
|
259 |
+
- type: mrr_at_1000
|
260 |
+
value: 55.565
|
261 |
+
- type: mrr_at_3
|
262 |
+
value: 52.480000000000004
|
263 |
+
- type: mrr_at_5
|
264 |
+
value: 53.86
|
265 |
+
- type: ndcg_at_1
|
266 |
+
value: 44.921
|
267 |
+
- type: ndcg_at_10
|
268 |
+
value: 55.664
|
269 |
+
- type: ndcg_at_100
|
270 |
+
value: 60.488
|
271 |
+
- type: ndcg_at_1000
|
272 |
+
value: 62.138000000000005
|
273 |
+
- type: ndcg_at_3
|
274 |
+
value: 50.797000000000004
|
275 |
+
- type: ndcg_at_5
|
276 |
+
value: 52.94799999999999
|
277 |
+
- type: precision_at_1
|
278 |
+
value: 44.921
|
279 |
+
- type: precision_at_10
|
280 |
+
value: 10.587
|
281 |
+
- type: precision_at_100
|
282 |
+
value: 1.629
|
283 |
+
- type: precision_at_1000
|
284 |
+
value: 0.203
|
285 |
+
- type: precision_at_3
|
286 |
+
value: 24.034
|
287 |
+
- type: precision_at_5
|
288 |
+
value: 17.224999999999998
|
289 |
+
- type: recall_at_1
|
290 |
+
value: 37.264
|
291 |
+
- type: recall_at_10
|
292 |
+
value: 67.15
|
293 |
+
- type: recall_at_100
|
294 |
+
value: 86.811
|
295 |
+
- type: recall_at_1000
|
296 |
+
value: 97.172
|
297 |
+
- type: recall_at_3
|
298 |
+
value: 53.15800000000001
|
299 |
+
- type: recall_at_5
|
300 |
+
value: 59.116
|
301 |
+
- task:
|
302 |
+
type: Retrieval
|
303 |
+
dataset:
|
304 |
+
name: MTEB CQADupstackEnglishRetrieval
|
305 |
+
type: mteb/cqadupstack-english
|
306 |
+
config: default
|
307 |
+
split: test
|
308 |
+
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
|
309 |
+
metrics:
|
310 |
+
- type: map_at_1
|
311 |
+
value: 36.237
|
312 |
+
- type: map_at_10
|
313 |
+
value: 47.941
|
314 |
+
- type: map_at_100
|
315 |
+
value: 49.131
|
316 |
+
- type: map_at_1000
|
317 |
+
value: 49.26
|
318 |
+
- type: map_at_3
|
319 |
+
value: 44.561
|
320 |
+
- type: map_at_5
|
321 |
+
value: 46.28
|
322 |
+
- type: mrr_at_1
|
323 |
+
value: 45.605000000000004
|
324 |
+
- type: mrr_at_10
|
325 |
+
value: 54.039
|
326 |
+
- type: mrr_at_100
|
327 |
+
value: 54.653
|
328 |
+
- type: mrr_at_1000
|
329 |
+
value: 54.688
|
330 |
+
- type: mrr_at_3
|
331 |
+
value: 52.006
|
332 |
+
- type: mrr_at_5
|
333 |
+
value: 53.096
|
334 |
+
- type: ndcg_at_1
|
335 |
+
value: 45.605000000000004
|
336 |
+
- type: ndcg_at_10
|
337 |
+
value: 53.916
|
338 |
+
- type: ndcg_at_100
|
339 |
+
value: 57.745999999999995
|
340 |
+
- type: ndcg_at_1000
|
341 |
+
value: 59.492999999999995
|
342 |
+
- type: ndcg_at_3
|
343 |
+
value: 49.774
|
344 |
+
- type: ndcg_at_5
|
345 |
+
value: 51.434999999999995
|
346 |
+
- type: precision_at_1
|
347 |
+
value: 45.605000000000004
|
348 |
+
- type: precision_at_10
|
349 |
+
value: 10.229000000000001
|
350 |
+
- type: precision_at_100
|
351 |
+
value: 1.55
|
352 |
+
- type: precision_at_1000
|
353 |
+
value: 0.2
|
354 |
+
- type: precision_at_3
|
355 |
+
value: 24.098
|
356 |
+
- type: precision_at_5
|
357 |
+
value: 16.726
|
358 |
+
- type: recall_at_1
|
359 |
+
value: 36.237
|
360 |
+
- type: recall_at_10
|
361 |
+
value: 64.03
|
362 |
+
- type: recall_at_100
|
363 |
+
value: 80.423
|
364 |
+
- type: recall_at_1000
|
365 |
+
value: 91.03
|
366 |
+
- type: recall_at_3
|
367 |
+
value: 51.20400000000001
|
368 |
+
- type: recall_at_5
|
369 |
+
value: 56.298
|
370 |
+
- task:
|
371 |
+
type: Retrieval
|
372 |
+
dataset:
|
373 |
+
name: MTEB CQADupstackGamingRetrieval
|
374 |
+
type: mteb/cqadupstack-gaming
|
375 |
+
config: default
|
376 |
+
split: test
|
377 |
+
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
|
378 |
+
metrics:
|
379 |
+
- type: map_at_1
|
380 |
+
value: 47.278
|
381 |
+
- type: map_at_10
|
382 |
+
value: 59.757000000000005
|
383 |
+
- type: map_at_100
|
384 |
+
value: 60.67
|
385 |
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405 |
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406 |
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407 |
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408 |
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409 |
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410 |
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411 |
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417 |
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419 |
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425 |
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437 |
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438 |
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value: 70.283
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439 |
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|
440 |
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type: Retrieval
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dataset:
|
442 |
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name: MTEB CQADupstackGisRetrieval
|
443 |
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type: mteb/cqadupstack-gis
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445 |
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split: test
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revision: 5003b3064772da1887988e05400cf3806fe491f2
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450 |
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451 |
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452 |
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471 |
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472 |
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474 |
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476 |
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479 |
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482 |
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483 |
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484 |
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485 |
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486 |
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488 |
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489 |
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490 |
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491 |
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492 |
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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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498 |
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499 |
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500 |
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501 |
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502 |
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503 |
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505 |
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506 |
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507 |
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value: 51.449
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508 |
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|
509 |
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type: Retrieval
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510 |
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dataset:
|
511 |
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name: MTEB CQADupstackMathematicaRetrieval
|
512 |
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type: mteb/cqadupstack-mathematica
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513 |
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config: default
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514 |
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split: test
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515 |
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revision: 90fceea13679c63fe563ded68f3b6f06e50061de
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516 |
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metrics:
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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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527 |
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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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548 |
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549 |
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550 |
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551 |
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553 |
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555 |
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556 |
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557 |
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558 |
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559 |
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560 |
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561 |
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562 |
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563 |
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564 |
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565 |
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value: 21.336
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567 |
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568 |
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value: 47.746
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569 |
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571 |
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574 |
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575 |
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576 |
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value: 39.397999999999996
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577 |
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|
578 |
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type: Retrieval
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579 |
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dataset:
|
580 |
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name: MTEB CQADupstackPhysicsRetrieval
|
581 |
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type: mteb/cqadupstack-physics
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582 |
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config: default
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583 |
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split: test
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584 |
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585 |
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metrics:
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586 |
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587 |
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value: 34.424
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588 |
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589 |
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590 |
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591 |
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592 |
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595 |
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596 |
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598 |
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599 |
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600 |
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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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608 |
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610 |
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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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617 |
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618 |
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619 |
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621 |
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622 |
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623 |
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624 |
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625 |
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626 |
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627 |
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628 |
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629 |
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630 |
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631 |
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632 |
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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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638 |
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639 |
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640 |
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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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646 |
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|
647 |
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dataset:
|
649 |
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name: MTEB CQADupstackProgrammersRetrieval
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650 |
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type: mteb/cqadupstack-programmers
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651 |
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652 |
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653 |
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654 |
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655 |
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656 |
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657 |
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658 |
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659 |
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661 |
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662 |
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663 |
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665 |
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667 |
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668 |
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669 |
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671 |
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672 |
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673 |
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677 |
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679 |
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681 |
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689 |
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691 |
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692 |
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693 |
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694 |
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695 |
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696 |
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697 |
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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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714 |
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715 |
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|
716 |
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717 |
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dataset:
|
718 |
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name: MTEB CQADupstackRetrieval
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719 |
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721 |
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722 |
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726 |
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731 |
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766 |
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784 |
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|
785 |
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|
786 |
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dataset:
|
787 |
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name: MTEB CQADupstackStatsRetrieval
|
788 |
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type: mteb/cqadupstack-stats
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789 |
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795 |
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|
856 |
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name: MTEB CQADupstackTexRetrieval
|
857 |
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type: mteb/cqadupstack-tex
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864 |
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902 |
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921 |
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922 |
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|
923 |
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dataset:
|
925 |
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name: MTEB CQADupstackUnixRetrieval
|
926 |
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type: mteb/cqadupstack-unix
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932 |
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953 |
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955 |
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959 |
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962 |
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989 |
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990 |
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value: 51.184
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991 |
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|
992 |
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993 |
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dataset:
|
994 |
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name: MTEB CQADupstackWebmastersRetrieval
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995 |
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type: mteb/cqadupstack-webmasters
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996 |
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997 |
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998 |
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999 |
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1000 |
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1004 |
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1006 |
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1008 |
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1010 |
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1012 |
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1014 |
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1015 |
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1016 |
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1017 |
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1018 |
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1019 |
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1022 |
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1042 |
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1046 |
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1058 |
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|
1059 |
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1060 |
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|
1061 |
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1062 |
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dataset:
|
1063 |
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name: MTEB CQADupstackWordpressRetrieval
|
1064 |
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type: mteb/cqadupstack-wordpress
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1065 |
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1067 |
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1071 |
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1099 |
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1101 |
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1111 |
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1123 |
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1127 |
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1128 |
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1129 |
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|
1130 |
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1131 |
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dataset:
|
1132 |
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name: MTEB ClimateFEVER
|
1133 |
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type: mteb/climate-fever
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1134 |
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1135 |
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1140 |
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1153 |
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1155 |
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1156 |
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1158 |
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1175 |
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1177 |
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1178 |
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|
1179 |
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1189 |
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1190 |
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1191 |
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1195 |
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1196 |
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- type: recall_at_5
|
1197 |
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value: 37.141999999999996
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1198 |
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|
1199 |
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type: Retrieval
|
1200 |
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dataset:
|
1201 |
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name: MTEB DBPedia
|
1202 |
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type: mteb/dbpedia
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1203 |
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1204 |
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1205 |
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1206 |
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1207 |
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1208 |
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1209 |
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1212 |
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1254 |
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1265 |
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1266 |
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1267 |
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|
1268 |
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1269 |
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|
1270 |
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name: MTEB EmotionClassification
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1271 |
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1272 |
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1279 |
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1281 |
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1283 |
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name: MTEB FEVER
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1284 |
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1290 |
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1291 |
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1320 |
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1330 |
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1334 |
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1336 |
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1338 |
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1342 |
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1344 |
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1346 |
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1347 |
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1348 |
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1349 |
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- task:
|
1350 |
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1351 |
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dataset:
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1352 |
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name: MTEB FiQA2018
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1353 |
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1355 |
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1358 |
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1359 |
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1360 |
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1361 |
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1363 |
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1365 |
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1369 |
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1371 |
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1373 |
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1375 |
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1381 |
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1383 |
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1384 |
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1385 |
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1386 |
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1387 |
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1388 |
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1389 |
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1390 |
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1391 |
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1393 |
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1395 |
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1396 |
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1397 |
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1398 |
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1399 |
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1400 |
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1401 |
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1402 |
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1403 |
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1404 |
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1405 |
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1406 |
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1407 |
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1408 |
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1409 |
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1410 |
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1411 |
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1412 |
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1413 |
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1414 |
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1415 |
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1416 |
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1417 |
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1418 |
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- task:
|
1419 |
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1420 |
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dataset:
|
1421 |
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name: MTEB HotpotQA
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1422 |
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1423 |
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1424 |
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1425 |
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1426 |
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1427 |
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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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1438 |
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1439 |
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1440 |
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1441 |
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1442 |
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1443 |
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1444 |
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1445 |
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1446 |
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1447 |
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1448 |
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1449 |
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1450 |
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1451 |
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1452 |
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1453 |
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1454 |
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1455 |
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1456 |
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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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1464 |
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1466 |
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1467 |
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1468 |
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1469 |
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1470 |
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1471 |
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1472 |
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1473 |
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1474 |
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1475 |
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1476 |
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1477 |
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1478 |
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1479 |
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1480 |
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1481 |
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1482 |
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1483 |
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1484 |
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1485 |
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1486 |
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1487 |
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- task:
|
1488 |
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type: Classification
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1489 |
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dataset:
|
1490 |
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name: MTEB ImdbClassification
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1491 |
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1492 |
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1494 |
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1495 |
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1496 |
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1497 |
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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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dataset:
|
1505 |
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name: MTEB MSMARCO
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1506 |
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type: mteb/msmarco
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1507 |
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1508 |
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1509 |
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1510 |
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|
1511 |
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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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1521 |
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1522 |
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1523 |
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1524 |
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1525 |
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1526 |
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1527 |
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1528 |
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1529 |
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1530 |
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1531 |
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1532 |
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1533 |
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1534 |
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1535 |
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1536 |
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1537 |
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1538 |
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1539 |
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1540 |
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1541 |
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1542 |
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1543 |
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1544 |
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1545 |
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1546 |
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1547 |
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1548 |
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1549 |
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|
1550 |
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1551 |
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1552 |
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1553 |
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1554 |
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1555 |
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1556 |
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1557 |
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1558 |
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1559 |
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1560 |
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1561 |
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1562 |
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1563 |
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1564 |
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1565 |
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1566 |
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1567 |
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1568 |
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1569 |
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1570 |
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1571 |
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|
1572 |
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type: Classification
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1573 |
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dataset:
|
1574 |
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name: MTEB MTOPDomainClassification (en)
|
1575 |
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1576 |
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1577 |
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1578 |
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1579 |
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1580 |
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1581 |
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1582 |
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1583 |
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1584 |
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1585 |
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1586 |
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dataset:
|
1587 |
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name: MTEB MTOPIntentClassification (en)
|
1588 |
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type: mteb/mtop_intent
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1589 |
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1590 |
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1591 |
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1596 |
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1597 |
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1598 |
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type: Classification
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1599 |
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dataset:
|
1600 |
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name: MTEB MasakhaNEWSClassification (eng)
|
1601 |
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type: masakhane/masakhanews
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1602 |
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1603 |
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1604 |
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1605 |
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1606 |
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1607 |
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1608 |
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1609 |
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1610 |
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1611 |
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type: Clustering
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1612 |
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dataset:
|
1613 |
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name: MTEB MasakhaNEWSClusteringP2P (eng)
|
1614 |
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type: masakhane/masakhanews
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1615 |
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1616 |
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1617 |
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1618 |
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1619 |
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- type: v_measure
|
1620 |
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value: 44.84052695201612
|
1621 |
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- type: v_measure
|
1622 |
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value: 21.443971229936494
|
1623 |
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- task:
|
1624 |
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type: Classification
|
1625 |
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dataset:
|
1626 |
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name: MTEB MassiveIntentClassification (en)
|
1627 |
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type: mteb/amazon_massive_intent
|
1628 |
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config: en
|
1629 |
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split: test
|
1630 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
1631 |
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metrics:
|
1632 |
+
- type: accuracy
|
1633 |
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value: 65.79354404841965
|
1634 |
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- type: f1
|
1635 |
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value: 63.17260074126185
|
1636 |
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- task:
|
1637 |
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type: Classification
|
1638 |
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dataset:
|
1639 |
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name: MTEB MassiveScenarioClassification (en)
|
1640 |
+
type: mteb/amazon_massive_scenario
|
1641 |
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config: en
|
1642 |
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split: test
|
1643 |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
1644 |
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metrics:
|
1645 |
+
- type: accuracy
|
1646 |
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value: 71.09616677874916
|
1647 |
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- type: f1
|
1648 |
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value: 69.74285784421075
|
1649 |
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- task:
|
1650 |
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type: Clustering
|
1651 |
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dataset:
|
1652 |
+
name: MTEB MedrxivClusteringP2P
|
1653 |
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type: mteb/medrxiv-clustering-p2p
|
1654 |
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config: default
|
1655 |
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split: test
|
1656 |
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
1657 |
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metrics:
|
1658 |
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- type: v_measure
|
1659 |
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value: 31.474709231086184
|
1660 |
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- task:
|
1661 |
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type: Clustering
|
1662 |
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dataset:
|
1663 |
+
name: MTEB MedrxivClusteringS2S
|
1664 |
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type: mteb/medrxiv-clustering-s2s
|
1665 |
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config: default
|
1666 |
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split: test
|
1667 |
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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1668 |
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metrics:
|
1669 |
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- type: v_measure
|
1670 |
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value: 28.93630367824217
|
1671 |
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- task:
|
1672 |
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type: Reranking
|
1673 |
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dataset:
|
1674 |
+
name: MTEB MindSmallReranking
|
1675 |
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type: mteb/mind_small
|
1676 |
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config: default
|
1677 |
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split: test
|
1678 |
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revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
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1679 |
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metrics:
|
1680 |
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- type: map
|
1681 |
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value: 29.08234393834005
|
1682 |
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- type: mrr
|
1683 |
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value: 29.740466971605432
|
1684 |
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- task:
|
1685 |
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type: Retrieval
|
1686 |
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dataset:
|
1687 |
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name: MTEB NFCorpus
|
1688 |
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type: mteb/nfcorpus
|
1689 |
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config: default
|
1690 |
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split: test
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1691 |
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revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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1692 |
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metrics:
|
1693 |
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- type: map_at_1
|
1694 |
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value: 6.2059999999999995
|
1695 |
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- type: map_at_10
|
1696 |
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value: 14.442
|
1697 |
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- type: map_at_100
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1698 |
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value: 18.005
|
1699 |
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- type: map_at_1000
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1700 |
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value: 19.488
|
1701 |
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|
1702 |
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value: 10.666
|
1703 |
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- type: map_at_5
|
1704 |
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value: 12.45
|
1705 |
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- type: mrr_at_1
|
1706 |
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value: 47.678
|
1707 |
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- type: mrr_at_10
|
1708 |
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value: 57.519
|
1709 |
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- type: mrr_at_100
|
1710 |
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value: 58.13700000000001
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1711 |
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- type: mrr_at_1000
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1712 |
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value: 58.167
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1713 |
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- type: mrr_at_3
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1714 |
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value: 55.779
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1715 |
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- type: mrr_at_5
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1716 |
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value: 56.940000000000005
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1717 |
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- type: ndcg_at_1
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1718 |
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value: 45.82
|
1719 |
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- type: ndcg_at_10
|
1720 |
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value: 37.651
|
1721 |
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- type: ndcg_at_100
|
1722 |
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value: 34.001999999999995
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1723 |
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- type: ndcg_at_1000
|
1724 |
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value: 42.626
|
1725 |
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- type: ndcg_at_3
|
1726 |
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value: 43.961
|
1727 |
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- type: ndcg_at_5
|
1728 |
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value: 41.461
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1729 |
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- type: precision_at_1
|
1730 |
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value: 47.678
|
1731 |
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- type: precision_at_10
|
1732 |
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value: 27.584999999999997
|
1733 |
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- type: precision_at_100
|
1734 |
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value: 8.455
|
1735 |
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- type: precision_at_1000
|
1736 |
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value: 2.118
|
1737 |
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- type: precision_at_3
|
1738 |
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value: 41.692
|
1739 |
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- type: precision_at_5
|
1740 |
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value: 36.161
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1741 |
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- type: recall_at_1
|
1742 |
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value: 6.2059999999999995
|
1743 |
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- type: recall_at_10
|
1744 |
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value: 18.599
|
1745 |
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- type: recall_at_100
|
1746 |
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value: 33.608
|
1747 |
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- type: recall_at_1000
|
1748 |
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value: 65.429
|
1749 |
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- type: recall_at_3
|
1750 |
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value: 12.126000000000001
|
1751 |
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- type: recall_at_5
|
1752 |
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value: 14.902000000000001
|
1753 |
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- task:
|
1754 |
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type: Retrieval
|
1755 |
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dataset:
|
1756 |
+
name: MTEB NQ
|
1757 |
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type: mteb/nq
|
1758 |
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config: default
|
1759 |
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split: test
|
1760 |
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revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
|
1761 |
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metrics:
|
1762 |
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- type: map_at_1
|
1763 |
+
value: 39.117000000000004
|
1764 |
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- type: map_at_10
|
1765 |
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value: 55.535000000000004
|
1766 |
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- type: map_at_100
|
1767 |
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value: 56.32899999999999
|
1768 |
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- type: map_at_1000
|
1769 |
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value: 56.34400000000001
|
1770 |
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- type: map_at_3
|
1771 |
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value: 51.439
|
1772 |
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- type: map_at_5
|
1773 |
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value: 53.89699999999999
|
1774 |
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- type: mrr_at_1
|
1775 |
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value: 43.714
|
1776 |
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- type: mrr_at_10
|
1777 |
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value: 58.05200000000001
|
1778 |
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- type: mrr_at_100
|
1779 |
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value: 58.582
|
1780 |
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- type: mrr_at_1000
|
1781 |
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value: 58.592
|
1782 |
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- type: mrr_at_3
|
1783 |
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value: 54.896
|
1784 |
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- type: mrr_at_5
|
1785 |
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value: 56.874
|
1786 |
+
- type: ndcg_at_1
|
1787 |
+
value: 43.685
|
1788 |
+
- type: ndcg_at_10
|
1789 |
+
value: 63.108
|
1790 |
+
- type: ndcg_at_100
|
1791 |
+
value: 66.231
|
1792 |
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- type: ndcg_at_1000
|
1793 |
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value: 66.583
|
1794 |
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- type: ndcg_at_3
|
1795 |
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value: 55.659000000000006
|
1796 |
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- type: ndcg_at_5
|
1797 |
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value: 59.681
|
1798 |
+
- type: precision_at_1
|
1799 |
+
value: 43.685
|
1800 |
+
- type: precision_at_10
|
1801 |
+
value: 9.962
|
1802 |
+
- type: precision_at_100
|
1803 |
+
value: 1.174
|
1804 |
+
- type: precision_at_1000
|
1805 |
+
value: 0.121
|
1806 |
+
- type: precision_at_3
|
1807 |
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value: 24.961
|
1808 |
+
- type: precision_at_5
|
1809 |
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value: 17.352
|
1810 |
+
- type: recall_at_1
|
1811 |
+
value: 39.117000000000004
|
1812 |
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- type: recall_at_10
|
1813 |
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value: 83.408
|
1814 |
+
- type: recall_at_100
|
1815 |
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value: 96.553
|
1816 |
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- type: recall_at_1000
|
1817 |
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value: 99.136
|
1818 |
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- type: recall_at_3
|
1819 |
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value: 64.364
|
1820 |
+
- type: recall_at_5
|
1821 |
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value: 73.573
|
1822 |
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- task:
|
1823 |
+
type: Classification
|
1824 |
+
dataset:
|
1825 |
+
name: MTEB NewsClassification
|
1826 |
+
type: ag_news
|
1827 |
+
config: default
|
1828 |
+
split: test
|
1829 |
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revision: eb185aade064a813bc0b7f42de02595523103ca4
|
1830 |
+
metrics:
|
1831 |
+
- type: accuracy
|
1832 |
+
value: 78.87763157894737
|
1833 |
+
- type: f1
|
1834 |
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value: 78.69611753876177
|
1835 |
+
- task:
|
1836 |
+
type: PairClassification
|
1837 |
+
dataset:
|
1838 |
+
name: MTEB OpusparcusPC (en)
|
1839 |
+
type: GEM/opusparcus
|
1840 |
+
config: en
|
1841 |
+
split: test
|
1842 |
+
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
|
1843 |
+
metrics:
|
1844 |
+
- type: cos_sim_accuracy
|
1845 |
+
value: 99.89816700610999
|
1846 |
+
- type: cos_sim_ap
|
1847 |
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value: 100
|
1848 |
+
- type: cos_sim_f1
|
1849 |
+
value: 99.9490575649516
|
1850 |
+
- type: cos_sim_precision
|
1851 |
+
value: 100
|
1852 |
+
- type: cos_sim_recall
|
1853 |
+
value: 99.89816700610999
|
1854 |
+
- type: dot_accuracy
|
1855 |
+
value: 99.89816700610999
|
1856 |
+
- type: dot_ap
|
1857 |
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value: 100
|
1858 |
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- type: dot_f1
|
1859 |
+
value: 99.9490575649516
|
1860 |
+
- type: dot_precision
|
1861 |
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value: 100
|
1862 |
+
- type: dot_recall
|
1863 |
+
value: 99.89816700610999
|
1864 |
+
- type: euclidean_accuracy
|
1865 |
+
value: 99.89816700610999
|
1866 |
+
- type: euclidean_ap
|
1867 |
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value: 100
|
1868 |
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- type: euclidean_f1
|
1869 |
+
value: 99.9490575649516
|
1870 |
+
- type: euclidean_precision
|
1871 |
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value: 100
|
1872 |
+
- type: euclidean_recall
|
1873 |
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value: 99.89816700610999
|
1874 |
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- type: manhattan_accuracy
|
1875 |
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value: 99.89816700610999
|
1876 |
+
- type: manhattan_ap
|
1877 |
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value: 100
|
1878 |
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- type: manhattan_f1
|
1879 |
+
value: 99.9490575649516
|
1880 |
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- type: manhattan_precision
|
1881 |
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value: 100
|
1882 |
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- type: manhattan_recall
|
1883 |
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value: 99.89816700610999
|
1884 |
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- type: max_accuracy
|
1885 |
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value: 99.89816700610999
|
1886 |
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- type: max_ap
|
1887 |
+
value: 100
|
1888 |
+
- type: max_f1
|
1889 |
+
value: 99.9490575649516
|
1890 |
+
- task:
|
1891 |
+
type: PairClassification
|
1892 |
+
dataset:
|
1893 |
+
name: MTEB PawsX (en)
|
1894 |
+
type: paws-x
|
1895 |
+
config: en
|
1896 |
+
split: test
|
1897 |
+
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
|
1898 |
+
metrics:
|
1899 |
+
- type: cos_sim_accuracy
|
1900 |
+
value: 62
|
1901 |
+
- type: cos_sim_ap
|
1902 |
+
value: 62.26837791655737
|
1903 |
+
- type: cos_sim_f1
|
1904 |
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value: 62.607449856733524
|
1905 |
+
- type: cos_sim_precision
|
1906 |
+
value: 46.36604774535809
|
1907 |
+
- type: cos_sim_recall
|
1908 |
+
value: 96.36163175303197
|
1909 |
+
- type: dot_accuracy
|
1910 |
+
value: 62
|
1911 |
+
- type: dot_ap
|
1912 |
+
value: 62.26736459439965
|
1913 |
+
- type: dot_f1
|
1914 |
+
value: 62.607449856733524
|
1915 |
+
- type: dot_precision
|
1916 |
+
value: 46.36604774535809
|
1917 |
+
- type: dot_recall
|
1918 |
+
value: 96.36163175303197
|
1919 |
+
- type: euclidean_accuracy
|
1920 |
+
value: 62
|
1921 |
+
- type: euclidean_ap
|
1922 |
+
value: 62.26826112548132
|
1923 |
+
- type: euclidean_f1
|
1924 |
+
value: 62.607449856733524
|
1925 |
+
- type: euclidean_precision
|
1926 |
+
value: 46.36604774535809
|
1927 |
+
- type: euclidean_recall
|
1928 |
+
value: 96.36163175303197
|
1929 |
+
- type: manhattan_accuracy
|
1930 |
+
value: 62
|
1931 |
+
- type: manhattan_ap
|
1932 |
+
value: 62.26223761507973
|
1933 |
+
- type: manhattan_f1
|
1934 |
+
value: 62.585034013605444
|
1935 |
+
- type: manhattan_precision
|
1936 |
+
value: 46.34146341463415
|
1937 |
+
- type: manhattan_recall
|
1938 |
+
value: 96.36163175303197
|
1939 |
+
- type: max_accuracy
|
1940 |
+
value: 62
|
1941 |
+
- type: max_ap
|
1942 |
+
value: 62.26837791655737
|
1943 |
+
- type: max_f1
|
1944 |
+
value: 62.607449856733524
|
1945 |
+
- task:
|
1946 |
+
type: Retrieval
|
1947 |
+
dataset:
|
1948 |
+
name: MTEB QuoraRetrieval
|
1949 |
+
type: mteb/quora
|
1950 |
+
config: default
|
1951 |
+
split: test
|
1952 |
+
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
|
1953 |
+
metrics:
|
1954 |
+
- type: map_at_1
|
1955 |
+
value: 69.90899999999999
|
1956 |
+
- type: map_at_10
|
1957 |
+
value: 83.56700000000001
|
1958 |
+
- type: map_at_100
|
1959 |
+
value: 84.19200000000001
|
1960 |
+
- type: map_at_1000
|
1961 |
+
value: 84.212
|
1962 |
+
- type: map_at_3
|
1963 |
+
value: 80.658
|
1964 |
+
- type: map_at_5
|
1965 |
+
value: 82.473
|
1966 |
+
- type: mrr_at_1
|
1967 |
+
value: 80.4
|
1968 |
+
- type: mrr_at_10
|
1969 |
+
value: 86.699
|
1970 |
+
- type: mrr_at_100
|
1971 |
+
value: 86.798
|
1972 |
+
- type: mrr_at_1000
|
1973 |
+
value: 86.80099999999999
|
1974 |
+
- type: mrr_at_3
|
1975 |
+
value: 85.677
|
1976 |
+
- type: mrr_at_5
|
1977 |
+
value: 86.354
|
1978 |
+
- type: ndcg_at_1
|
1979 |
+
value: 80.43
|
1980 |
+
- type: ndcg_at_10
|
1981 |
+
value: 87.41
|
1982 |
+
- type: ndcg_at_100
|
1983 |
+
value: 88.653
|
1984 |
+
- type: ndcg_at_1000
|
1985 |
+
value: 88.81599999999999
|
1986 |
+
- type: ndcg_at_3
|
1987 |
+
value: 84.516
|
1988 |
+
- type: ndcg_at_5
|
1989 |
+
value: 86.068
|
1990 |
+
- type: precision_at_1
|
1991 |
+
value: 80.43
|
1992 |
+
- type: precision_at_10
|
1993 |
+
value: 13.234000000000002
|
1994 |
+
- type: precision_at_100
|
1995 |
+
value: 1.513
|
1996 |
+
- type: precision_at_1000
|
1997 |
+
value: 0.156
|
1998 |
+
- type: precision_at_3
|
1999 |
+
value: 36.93
|
2000 |
+
- type: precision_at_5
|
2001 |
+
value: 24.26
|
2002 |
+
- type: recall_at_1
|
2003 |
+
value: 69.90899999999999
|
2004 |
+
- type: recall_at_10
|
2005 |
+
value: 94.687
|
2006 |
+
- type: recall_at_100
|
2007 |
+
value: 98.96000000000001
|
2008 |
+
- type: recall_at_1000
|
2009 |
+
value: 99.79599999999999
|
2010 |
+
- type: recall_at_3
|
2011 |
+
value: 86.25699999999999
|
2012 |
+
- type: recall_at_5
|
2013 |
+
value: 90.70700000000001
|
2014 |
+
- task:
|
2015 |
+
type: Clustering
|
2016 |
+
dataset:
|
2017 |
+
name: MTEB RedditClustering
|
2018 |
+
type: mteb/reddit-clustering
|
2019 |
+
config: default
|
2020 |
+
split: test
|
2021 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
2022 |
+
metrics:
|
2023 |
+
- type: v_measure
|
2024 |
+
value: 46.02256865360266
|
2025 |
+
- task:
|
2026 |
+
type: Clustering
|
2027 |
+
dataset:
|
2028 |
+
name: MTEB RedditClusteringP2P
|
2029 |
+
type: mteb/reddit-clustering-p2p
|
2030 |
+
config: default
|
2031 |
+
split: test
|
2032 |
+
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
|
2033 |
+
metrics:
|
2034 |
+
- type: v_measure
|
2035 |
+
value: 62.43157528757563
|
2036 |
+
- task:
|
2037 |
+
type: Retrieval
|
2038 |
+
dataset:
|
2039 |
+
name: MTEB SCIDOCS
|
2040 |
+
type: mteb/scidocs
|
2041 |
+
config: default
|
2042 |
+
split: test
|
2043 |
+
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
|
2044 |
+
metrics:
|
2045 |
+
- type: map_at_1
|
2046 |
+
value: 5.093
|
2047 |
+
- type: map_at_10
|
2048 |
+
value: 12.982
|
2049 |
+
- type: map_at_100
|
2050 |
+
value: 15.031
|
2051 |
+
- type: map_at_1000
|
2052 |
+
value: 15.334
|
2053 |
+
- type: map_at_3
|
2054 |
+
value: 9.339
|
2055 |
+
- type: map_at_5
|
2056 |
+
value: 11.183
|
2057 |
+
- type: mrr_at_1
|
2058 |
+
value: 25.1
|
2059 |
+
- type: mrr_at_10
|
2060 |
+
value: 36.257
|
2061 |
+
- type: mrr_at_100
|
2062 |
+
value: 37.351
|
2063 |
+
- type: mrr_at_1000
|
2064 |
+
value: 37.409
|
2065 |
+
- type: mrr_at_3
|
2066 |
+
value: 33.050000000000004
|
2067 |
+
- type: mrr_at_5
|
2068 |
+
value: 35.205
|
2069 |
+
- type: ndcg_at_1
|
2070 |
+
value: 25.1
|
2071 |
+
- type: ndcg_at_10
|
2072 |
+
value: 21.361
|
2073 |
+
- type: ndcg_at_100
|
2074 |
+
value: 29.396
|
2075 |
+
- type: ndcg_at_1000
|
2076 |
+
value: 34.849999999999994
|
2077 |
+
- type: ndcg_at_3
|
2078 |
+
value: 20.704
|
2079 |
+
- type: ndcg_at_5
|
2080 |
+
value: 18.086
|
2081 |
+
- type: precision_at_1
|
2082 |
+
value: 25.1
|
2083 |
+
- type: precision_at_10
|
2084 |
+
value: 10.94
|
2085 |
+
- type: precision_at_100
|
2086 |
+
value: 2.257
|
2087 |
+
- type: precision_at_1000
|
2088 |
+
value: 0.358
|
2089 |
+
- type: precision_at_3
|
2090 |
+
value: 19.467000000000002
|
2091 |
+
- type: precision_at_5
|
2092 |
+
value: 15.98
|
2093 |
+
- type: recall_at_1
|
2094 |
+
value: 5.093
|
2095 |
+
- type: recall_at_10
|
2096 |
+
value: 22.177
|
2097 |
+
- type: recall_at_100
|
2098 |
+
value: 45.842
|
2099 |
+
- type: recall_at_1000
|
2100 |
+
value: 72.598
|
2101 |
+
- type: recall_at_3
|
2102 |
+
value: 11.833
|
2103 |
+
- type: recall_at_5
|
2104 |
+
value: 16.173000000000002
|
2105 |
+
- task:
|
2106 |
+
type: STS
|
2107 |
+
dataset:
|
2108 |
+
name: MTEB SICK-R
|
2109 |
+
type: mteb/sickr-sts
|
2110 |
+
config: default
|
2111 |
+
split: test
|
2112 |
+
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
|
2113 |
+
metrics:
|
2114 |
+
- type: cos_sim_pearson
|
2115 |
+
value: 73.56535226754596
|
2116 |
+
- type: cos_sim_spearman
|
2117 |
+
value: 69.32425977603488
|
2118 |
+
- type: euclidean_pearson
|
2119 |
+
value: 71.32425703470898
|
2120 |
+
- type: euclidean_spearman
|
2121 |
+
value: 69.32425217267013
|
2122 |
+
- type: manhattan_pearson
|
2123 |
+
value: 71.25897281394246
|
2124 |
+
- type: manhattan_spearman
|
2125 |
+
value: 69.27132577049578
|
2126 |
+
- task:
|
2127 |
+
type: STS
|
2128 |
+
dataset:
|
2129 |
+
name: MTEB STS12
|
2130 |
+
type: mteb/sts12-sts
|
2131 |
+
config: default
|
2132 |
+
split: test
|
2133 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
2134 |
+
metrics:
|
2135 |
+
- type: cos_sim_pearson
|
2136 |
+
value: 69.66387868726018
|
2137 |
+
- type: cos_sim_spearman
|
2138 |
+
value: 67.85470749045027
|
2139 |
+
- type: euclidean_pearson
|
2140 |
+
value: 66.62075098063795
|
2141 |
+
- type: euclidean_spearman
|
2142 |
+
value: 67.85470749045027
|
2143 |
+
- type: manhattan_pearson
|
2144 |
+
value: 66.61455061901262
|
2145 |
+
- type: manhattan_spearman
|
2146 |
+
value: 67.87229618498695
|
2147 |
+
- task:
|
2148 |
+
type: STS
|
2149 |
+
dataset:
|
2150 |
+
name: MTEB STS13
|
2151 |
+
type: mteb/sts13-sts
|
2152 |
+
config: default
|
2153 |
+
split: test
|
2154 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
2155 |
+
metrics:
|
2156 |
+
- type: cos_sim_pearson
|
2157 |
+
value: 75.65731331392575
|
2158 |
+
- type: cos_sim_spearman
|
2159 |
+
value: 77.48991626780108
|
2160 |
+
- type: euclidean_pearson
|
2161 |
+
value: 77.19884738623692
|
2162 |
+
- type: euclidean_spearman
|
2163 |
+
value: 77.48985836619045
|
2164 |
+
- type: manhattan_pearson
|
2165 |
+
value: 77.0656684243772
|
2166 |
+
- type: manhattan_spearman
|
2167 |
+
value: 77.30289226582691
|
2168 |
+
- task:
|
2169 |
+
type: STS
|
2170 |
+
dataset:
|
2171 |
+
name: MTEB STS14
|
2172 |
+
type: mteb/sts14-sts
|
2173 |
+
config: default
|
2174 |
+
split: test
|
2175 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
2176 |
+
metrics:
|
2177 |
+
- type: cos_sim_pearson
|
2178 |
+
value: 69.37003253666457
|
2179 |
+
- type: cos_sim_spearman
|
2180 |
+
value: 69.77157648098141
|
2181 |
+
- type: euclidean_pearson
|
2182 |
+
value: 69.39543876030432
|
2183 |
+
- type: euclidean_spearman
|
2184 |
+
value: 69.77157648098141
|
2185 |
+
- type: manhattan_pearson
|
2186 |
+
value: 69.29901600459745
|
2187 |
+
- type: manhattan_spearman
|
2188 |
+
value: 69.65074167527128
|
2189 |
+
- task:
|
2190 |
+
type: STS
|
2191 |
+
dataset:
|
2192 |
+
name: MTEB STS15
|
2193 |
+
type: mteb/sts15-sts
|
2194 |
+
config: default
|
2195 |
+
split: test
|
2196 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
2197 |
+
metrics:
|
2198 |
+
- type: cos_sim_pearson
|
2199 |
+
value: 78.56777256540136
|
2200 |
+
- type: cos_sim_spearman
|
2201 |
+
value: 80.16458787843023
|
2202 |
+
- type: euclidean_pearson
|
2203 |
+
value: 80.16475730686916
|
2204 |
+
- type: euclidean_spearman
|
2205 |
+
value: 80.16458787843023
|
2206 |
+
- type: manhattan_pearson
|
2207 |
+
value: 80.12814463670401
|
2208 |
+
- type: manhattan_spearman
|
2209 |
+
value: 80.1357907984809
|
2210 |
+
- task:
|
2211 |
+
type: STS
|
2212 |
+
dataset:
|
2213 |
+
name: MTEB STS16
|
2214 |
+
type: mteb/sts16-sts
|
2215 |
+
config: default
|
2216 |
+
split: test
|
2217 |
+
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
2218 |
+
metrics:
|
2219 |
+
- type: cos_sim_pearson
|
2220 |
+
value: 76.09572350919031
|
2221 |
+
- type: cos_sim_spearman
|
2222 |
+
value: 77.94490233429326
|
2223 |
+
- type: euclidean_pearson
|
2224 |
+
value: 78.36595251203524
|
2225 |
+
- type: euclidean_spearman
|
2226 |
+
value: 77.94490233429326
|
2227 |
+
- type: manhattan_pearson
|
2228 |
+
value: 78.41538768125166
|
2229 |
+
- type: manhattan_spearman
|
2230 |
+
value: 78.01244379569542
|
2231 |
+
- task:
|
2232 |
+
type: STS
|
2233 |
+
dataset:
|
2234 |
+
name: MTEB STS17 (en-en)
|
2235 |
+
type: mteb/sts17-crosslingual-sts
|
2236 |
+
config: en-en
|
2237 |
+
split: test
|
2238 |
+
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
2239 |
+
metrics:
|
2240 |
+
- type: cos_sim_pearson
|
2241 |
+
value: 80.7843552187951
|
2242 |
+
- type: cos_sim_spearman
|
2243 |
+
value: 82.28085055047386
|
2244 |
+
- type: euclidean_pearson
|
2245 |
+
value: 82.37373672515267
|
2246 |
+
- type: euclidean_spearman
|
2247 |
+
value: 82.28085055047386
|
2248 |
+
- type: manhattan_pearson
|
2249 |
+
value: 82.39387241346917
|
2250 |
+
- type: manhattan_spearman
|
2251 |
+
value: 82.36503339515906
|
2252 |
+
- task:
|
2253 |
+
type: STS
|
2254 |
+
dataset:
|
2255 |
+
name: MTEB STS22 (en)
|
2256 |
+
type: mteb/sts22-crosslingual-sts
|
2257 |
+
config: en
|
2258 |
+
split: test
|
2259 |
+
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
|
2260 |
+
metrics:
|
2261 |
+
- type: cos_sim_pearson
|
2262 |
+
value: 68.29963929962095
|
2263 |
+
- type: cos_sim_spearman
|
2264 |
+
value: 67.96868942546051
|
2265 |
+
- type: euclidean_pearson
|
2266 |
+
value: 68.93524903869285
|
2267 |
+
- type: euclidean_spearman
|
2268 |
+
value: 67.96868942546051
|
2269 |
+
- type: manhattan_pearson
|
2270 |
+
value: 68.79144468444811
|
2271 |
+
- type: manhattan_spearman
|
2272 |
+
value: 67.69311483884324
|
2273 |
+
- task:
|
2274 |
+
type: STS
|
2275 |
+
dataset:
|
2276 |
+
name: MTEB STSBenchmark
|
2277 |
+
type: mteb/stsbenchmark-sts
|
2278 |
+
config: default
|
2279 |
+
split: test
|
2280 |
+
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
2281 |
+
metrics:
|
2282 |
+
- type: cos_sim_pearson
|
2283 |
+
value: 72.84789696700685
|
2284 |
+
- type: cos_sim_spearman
|
2285 |
+
value: 75.67875747588545
|
2286 |
+
- type: euclidean_pearson
|
2287 |
+
value: 75.07752300463038
|
2288 |
+
- type: euclidean_spearman
|
2289 |
+
value: 75.67875747588545
|
2290 |
+
- type: manhattan_pearson
|
2291 |
+
value: 74.97934248140928
|
2292 |
+
- type: manhattan_spearman
|
2293 |
+
value: 75.62525644178724
|
2294 |
+
- task:
|
2295 |
+
type: STS
|
2296 |
+
dataset:
|
2297 |
+
name: MTEB STSBenchmarkMultilingualSTS (en)
|
2298 |
+
type: PhilipMay/stsb_multi_mt
|
2299 |
+
config: en
|
2300 |
+
split: test
|
2301 |
+
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
|
2302 |
+
metrics:
|
2303 |
+
- type: cos_sim_pearson
|
2304 |
+
value: 72.84789702519309
|
2305 |
+
- type: cos_sim_spearman
|
2306 |
+
value: 75.67875747588545
|
2307 |
+
- type: euclidean_pearson
|
2308 |
+
value: 75.07752310061133
|
2309 |
+
- type: euclidean_spearman
|
2310 |
+
value: 75.67875747588545
|
2311 |
+
- type: manhattan_pearson
|
2312 |
+
value: 74.97934257159595
|
2313 |
+
- type: manhattan_spearman
|
2314 |
+
value: 75.62525644178724
|
2315 |
+
- task:
|
2316 |
+
type: Reranking
|
2317 |
+
dataset:
|
2318 |
+
name: MTEB SciDocsRR
|
2319 |
+
type: mteb/scidocs-reranking
|
2320 |
+
config: default
|
2321 |
+
split: test
|
2322 |
+
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
2323 |
+
metrics:
|
2324 |
+
- type: map
|
2325 |
+
value: 81.55557720431086
|
2326 |
+
- type: mrr
|
2327 |
+
value: 94.91178665198272
|
2328 |
+
- task:
|
2329 |
+
type: Retrieval
|
2330 |
+
dataset:
|
2331 |
+
name: MTEB SciFact
|
2332 |
+
type: mteb/scifact
|
2333 |
+
config: default
|
2334 |
+
split: test
|
2335 |
+
revision: 0228b52cf27578f30900b9e5271d331663a030d7
|
2336 |
+
metrics:
|
2337 |
+
- type: map_at_1
|
2338 |
+
value: 59.260999999999996
|
2339 |
+
- type: map_at_10
|
2340 |
+
value: 69.36099999999999
|
2341 |
+
- type: map_at_100
|
2342 |
+
value: 69.868
|
2343 |
+
- type: map_at_1000
|
2344 |
+
value: 69.877
|
2345 |
+
- type: map_at_3
|
2346 |
+
value: 66.617
|
2347 |
+
- type: map_at_5
|
2348 |
+
value: 68.061
|
2349 |
+
- type: mrr_at_1
|
2350 |
+
value: 62.333000000000006
|
2351 |
+
- type: mrr_at_10
|
2352 |
+
value: 70.533
|
2353 |
+
- type: mrr_at_100
|
2354 |
+
value: 70.966
|
2355 |
+
- type: mrr_at_1000
|
2356 |
+
value: 70.975
|
2357 |
+
- type: mrr_at_3
|
2358 |
+
value: 68.667
|
2359 |
+
- type: mrr_at_5
|
2360 |
+
value: 69.717
|
2361 |
+
- type: ndcg_at_1
|
2362 |
+
value: 62.333000000000006
|
2363 |
+
- type: ndcg_at_10
|
2364 |
+
value: 73.82300000000001
|
2365 |
+
- type: ndcg_at_100
|
2366 |
+
value: 76.122
|
2367 |
+
- type: ndcg_at_1000
|
2368 |
+
value: 76.374
|
2369 |
+
- type: ndcg_at_3
|
2370 |
+
value: 69.27499999999999
|
2371 |
+
- type: ndcg_at_5
|
2372 |
+
value: 71.33
|
2373 |
+
- type: precision_at_1
|
2374 |
+
value: 62.333000000000006
|
2375 |
+
- type: precision_at_10
|
2376 |
+
value: 9.8
|
2377 |
+
- type: precision_at_100
|
2378 |
+
value: 1.097
|
2379 |
+
- type: precision_at_1000
|
2380 |
+
value: 0.11199999999999999
|
2381 |
+
- type: precision_at_3
|
2382 |
+
value: 26.889000000000003
|
2383 |
+
- type: precision_at_5
|
2384 |
+
value: 17.599999999999998
|
2385 |
+
- type: recall_at_1
|
2386 |
+
value: 59.260999999999996
|
2387 |
+
- type: recall_at_10
|
2388 |
+
value: 86.2
|
2389 |
+
- type: recall_at_100
|
2390 |
+
value: 96.667
|
2391 |
+
- type: recall_at_1000
|
2392 |
+
value: 98.667
|
2393 |
+
- type: recall_at_3
|
2394 |
+
value: 74.006
|
2395 |
+
- type: recall_at_5
|
2396 |
+
value: 79.167
|
2397 |
+
- task:
|
2398 |
+
type: PairClassification
|
2399 |
+
dataset:
|
2400 |
+
name: MTEB SprintDuplicateQuestions
|
2401 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
2402 |
+
config: default
|
2403 |
+
split: test
|
2404 |
+
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
2405 |
+
metrics:
|
2406 |
+
- type: cos_sim_accuracy
|
2407 |
+
value: 99.81881188118813
|
2408 |
+
- type: cos_sim_ap
|
2409 |
+
value: 95.20169041096409
|
2410 |
+
- type: cos_sim_f1
|
2411 |
+
value: 90.76224129227664
|
2412 |
+
- type: cos_sim_precision
|
2413 |
+
value: 91.64118246687055
|
2414 |
+
- type: cos_sim_recall
|
2415 |
+
value: 89.9
|
2416 |
+
- type: dot_accuracy
|
2417 |
+
value: 99.81881188118813
|
2418 |
+
- type: dot_ap
|
2419 |
+
value: 95.20169041096409
|
2420 |
+
- type: dot_f1
|
2421 |
+
value: 90.76224129227664
|
2422 |
+
- type: dot_precision
|
2423 |
+
value: 91.64118246687055
|
2424 |
+
- type: dot_recall
|
2425 |
+
value: 89.9
|
2426 |
+
- type: euclidean_accuracy
|
2427 |
+
value: 99.81881188118813
|
2428 |
+
- type: euclidean_ap
|
2429 |
+
value: 95.2016904109641
|
2430 |
+
- type: euclidean_f1
|
2431 |
+
value: 90.76224129227664
|
2432 |
+
- type: euclidean_precision
|
2433 |
+
value: 91.64118246687055
|
2434 |
+
- type: euclidean_recall
|
2435 |
+
value: 89.9
|
2436 |
+
- type: manhattan_accuracy
|
2437 |
+
value: 99.81881188118813
|
2438 |
+
- type: manhattan_ap
|
2439 |
+
value: 95.22680188132777
|
2440 |
+
- type: manhattan_f1
|
2441 |
+
value: 90.79013588324108
|
2442 |
+
- type: manhattan_precision
|
2443 |
+
value: 91.38804457953394
|
2444 |
+
- type: manhattan_recall
|
2445 |
+
value: 90.2
|
2446 |
+
- type: max_accuracy
|
2447 |
+
value: 99.81881188118813
|
2448 |
+
- type: max_ap
|
2449 |
+
value: 95.22680188132777
|
2450 |
+
- type: max_f1
|
2451 |
+
value: 90.79013588324108
|
2452 |
+
- task:
|
2453 |
+
type: Clustering
|
2454 |
+
dataset:
|
2455 |
+
name: MTEB StackExchangeClustering
|
2456 |
+
type: mteb/stackexchange-clustering
|
2457 |
+
config: default
|
2458 |
+
split: test
|
2459 |
+
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
2460 |
+
metrics:
|
2461 |
+
- type: v_measure
|
2462 |
+
value: 57.8638628701308
|
2463 |
+
- task:
|
2464 |
+
type: Clustering
|
2465 |
+
dataset:
|
2466 |
+
name: MTEB StackExchangeClusteringP2P
|
2467 |
+
type: mteb/stackexchange-clustering-p2p
|
2468 |
+
config: default
|
2469 |
+
split: test
|
2470 |
+
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
2471 |
+
metrics:
|
2472 |
+
- type: v_measure
|
2473 |
+
value: 37.82028248106046
|
2474 |
+
- task:
|
2475 |
+
type: Reranking
|
2476 |
+
dataset:
|
2477 |
+
name: MTEB StackOverflowDupQuestions
|
2478 |
+
type: mteb/stackoverflowdupquestions-reranking
|
2479 |
+
config: default
|
2480 |
+
split: test
|
2481 |
+
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
2482 |
+
metrics:
|
2483 |
+
- type: map
|
2484 |
+
value: 50.870860210170946
|
2485 |
+
- type: mrr
|
2486 |
+
value: 51.608084521687466
|
2487 |
+
- task:
|
2488 |
+
type: Summarization
|
2489 |
+
dataset:
|
2490 |
+
name: MTEB SummEval
|
2491 |
+
type: mteb/summeval
|
2492 |
+
config: default
|
2493 |
+
split: test
|
2494 |
+
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
2495 |
+
metrics:
|
2496 |
+
- type: cos_sim_pearson
|
2497 |
+
value: 31.60384207444685
|
2498 |
+
- type: cos_sim_spearman
|
2499 |
+
value: 30.84047452209471
|
2500 |
+
- type: dot_pearson
|
2501 |
+
value: 31.60384104417333
|
2502 |
+
- type: dot_spearman
|
2503 |
+
value: 30.84047452209471
|
2504 |
+
- task:
|
2505 |
+
type: Retrieval
|
2506 |
+
dataset:
|
2507 |
+
name: MTEB TRECCOVID
|
2508 |
+
type: mteb/trec-covid
|
2509 |
+
config: default
|
2510 |
+
split: test
|
2511 |
+
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
|
2512 |
+
metrics:
|
2513 |
+
- type: map_at_1
|
2514 |
+
value: 0.246
|
2515 |
+
- type: map_at_10
|
2516 |
+
value: 2.051
|
2517 |
+
- type: map_at_100
|
2518 |
+
value: 13.129
|
2519 |
+
- type: map_at_1000
|
2520 |
+
value: 31.56
|
2521 |
+
- type: map_at_3
|
2522 |
+
value: 0.681
|
2523 |
+
- type: map_at_5
|
2524 |
+
value: 1.105
|
2525 |
+
- type: mrr_at_1
|
2526 |
+
value: 94
|
2527 |
+
- type: mrr_at_10
|
2528 |
+
value: 97
|
2529 |
+
- type: mrr_at_100
|
2530 |
+
value: 97
|
2531 |
+
- type: mrr_at_1000
|
2532 |
+
value: 97
|
2533 |
+
- type: mrr_at_3
|
2534 |
+
value: 97
|
2535 |
+
- type: mrr_at_5
|
2536 |
+
value: 97
|
2537 |
+
- type: ndcg_at_1
|
2538 |
+
value: 87
|
2539 |
+
- type: ndcg_at_10
|
2540 |
+
value: 80.716
|
2541 |
+
- type: ndcg_at_100
|
2542 |
+
value: 63.83
|
2543 |
+
- type: ndcg_at_1000
|
2544 |
+
value: 56.215
|
2545 |
+
- type: ndcg_at_3
|
2546 |
+
value: 84.531
|
2547 |
+
- type: ndcg_at_5
|
2548 |
+
value: 84.777
|
2549 |
+
- type: precision_at_1
|
2550 |
+
value: 94
|
2551 |
+
- type: precision_at_10
|
2552 |
+
value: 84.6
|
2553 |
+
- type: precision_at_100
|
2554 |
+
value: 66.03999999999999
|
2555 |
+
- type: precision_at_1000
|
2556 |
+
value: 24.878
|
2557 |
+
- type: precision_at_3
|
2558 |
+
value: 88.667
|
2559 |
+
- type: precision_at_5
|
2560 |
+
value: 89.60000000000001
|
2561 |
+
- type: recall_at_1
|
2562 |
+
value: 0.246
|
2563 |
+
- type: recall_at_10
|
2564 |
+
value: 2.2079999999999997
|
2565 |
+
- type: recall_at_100
|
2566 |
+
value: 15.895999999999999
|
2567 |
+
- type: recall_at_1000
|
2568 |
+
value: 52.683
|
2569 |
+
- type: recall_at_3
|
2570 |
+
value: 0.7040000000000001
|
2571 |
+
- type: recall_at_5
|
2572 |
+
value: 1.163
|
2573 |
+
- task:
|
2574 |
+
type: Retrieval
|
2575 |
+
dataset:
|
2576 |
+
name: MTEB Touche2020
|
2577 |
+
type: mteb/touche2020
|
2578 |
+
config: default
|
2579 |
+
split: test
|
2580 |
+
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
|
2581 |
+
metrics:
|
2582 |
+
- type: map_at_1
|
2583 |
+
value: 3.852
|
2584 |
+
- type: map_at_10
|
2585 |
+
value: 14.316
|
2586 |
+
- type: map_at_100
|
2587 |
+
value: 20.982
|
2588 |
+
- type: map_at_1000
|
2589 |
+
value: 22.58
|
2590 |
+
- type: map_at_3
|
2591 |
+
value: 7.767
|
2592 |
+
- type: map_at_5
|
2593 |
+
value: 10.321
|
2594 |
+
- type: mrr_at_1
|
2595 |
+
value: 51.019999999999996
|
2596 |
+
- type: mrr_at_10
|
2597 |
+
value: 66.365
|
2598 |
+
- type: mrr_at_100
|
2599 |
+
value: 66.522
|
2600 |
+
- type: mrr_at_1000
|
2601 |
+
value: 66.522
|
2602 |
+
- type: mrr_at_3
|
2603 |
+
value: 62.925
|
2604 |
+
- type: mrr_at_5
|
2605 |
+
value: 64.762
|
2606 |
+
- type: ndcg_at_1
|
2607 |
+
value: 46.939
|
2608 |
+
- type: ndcg_at_10
|
2609 |
+
value: 34.516999999999996
|
2610 |
+
- type: ndcg_at_100
|
2611 |
+
value: 44.25
|
2612 |
+
- type: ndcg_at_1000
|
2613 |
+
value: 54.899
|
2614 |
+
- type: ndcg_at_3
|
2615 |
+
value: 40.203
|
2616 |
+
- type: ndcg_at_5
|
2617 |
+
value: 37.004
|
2618 |
+
- type: precision_at_1
|
2619 |
+
value: 51.019999999999996
|
2620 |
+
- type: precision_at_10
|
2621 |
+
value: 29.796
|
2622 |
+
- type: precision_at_100
|
2623 |
+
value: 8.633000000000001
|
2624 |
+
- type: precision_at_1000
|
2625 |
+
value: 1.584
|
2626 |
+
- type: precision_at_3
|
2627 |
+
value: 40.816
|
2628 |
+
- type: precision_at_5
|
2629 |
+
value: 35.918
|
2630 |
+
- type: recall_at_1
|
2631 |
+
value: 3.852
|
2632 |
+
- type: recall_at_10
|
2633 |
+
value: 20.891000000000002
|
2634 |
+
- type: recall_at_100
|
2635 |
+
value: 52.428
|
2636 |
+
- type: recall_at_1000
|
2637 |
+
value: 84.34899999999999
|
2638 |
+
- type: recall_at_3
|
2639 |
+
value: 8.834
|
2640 |
+
- type: recall_at_5
|
2641 |
+
value: 12.909
|
2642 |
+
- task:
|
2643 |
+
type: Classification
|
2644 |
+
dataset:
|
2645 |
+
name: MTEB ToxicConversationsClassification
|
2646 |
+
type: mteb/toxic_conversations_50k
|
2647 |
+
config: default
|
2648 |
+
split: test
|
2649 |
+
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
|
2650 |
+
metrics:
|
2651 |
+
- type: accuracy
|
2652 |
+
value: 64.7092
|
2653 |
+
- type: ap
|
2654 |
+
value: 11.972915012305819
|
2655 |
+
- type: f1
|
2656 |
+
value: 49.91050149892115
|
2657 |
+
- task:
|
2658 |
+
type: Classification
|
2659 |
+
dataset:
|
2660 |
+
name: MTEB TweetSentimentExtractionClassification
|
2661 |
+
type: mteb/tweet_sentiment_extraction
|
2662 |
+
config: default
|
2663 |
+
split: test
|
2664 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
2665 |
+
metrics:
|
2666 |
+
- type: accuracy
|
2667 |
+
value: 56.737408036219584
|
2668 |
+
- type: f1
|
2669 |
+
value: 57.07235266246011
|
2670 |
+
- task:
|
2671 |
+
type: Clustering
|
2672 |
+
dataset:
|
2673 |
+
name: MTEB TwentyNewsgroupsClustering
|
2674 |
+
type: mteb/twentynewsgroups-clustering
|
2675 |
+
config: default
|
2676 |
+
split: test
|
2677 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
2678 |
+
metrics:
|
2679 |
+
- type: v_measure
|
2680 |
+
value: 35.9147539025798
|
2681 |
+
- task:
|
2682 |
+
type: PairClassification
|
2683 |
+
dataset:
|
2684 |
+
name: MTEB TwitterSemEval2015
|
2685 |
+
type: mteb/twittersemeval2015-pairclassification
|
2686 |
+
config: default
|
2687 |
+
split: test
|
2688 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
2689 |
+
metrics:
|
2690 |
+
- type: cos_sim_accuracy
|
2691 |
+
value: 82.52369315133814
|
2692 |
+
- type: cos_sim_ap
|
2693 |
+
value: 62.34858091376534
|
2694 |
+
- type: cos_sim_f1
|
2695 |
+
value: 58.18225190839694
|
2696 |
+
- type: cos_sim_precision
|
2697 |
+
value: 53.09098824553766
|
2698 |
+
- type: cos_sim_recall
|
2699 |
+
value: 64.35356200527704
|
2700 |
+
- type: dot_accuracy
|
2701 |
+
value: 82.52369315133814
|
2702 |
+
- type: dot_ap
|
2703 |
+
value: 62.34857753814992
|
2704 |
+
- type: dot_f1
|
2705 |
+
value: 58.18225190839694
|
2706 |
+
- type: dot_precision
|
2707 |
+
value: 53.09098824553766
|
2708 |
+
- type: dot_recall
|
2709 |
+
value: 64.35356200527704
|
2710 |
+
- type: euclidean_accuracy
|
2711 |
+
value: 82.52369315133814
|
2712 |
+
- type: euclidean_ap
|
2713 |
+
value: 62.34857756663386
|
2714 |
+
- type: euclidean_f1
|
2715 |
+
value: 58.18225190839694
|
2716 |
+
- type: euclidean_precision
|
2717 |
+
value: 53.09098824553766
|
2718 |
+
- type: euclidean_recall
|
2719 |
+
value: 64.35356200527704
|
2720 |
+
- type: manhattan_accuracy
|
2721 |
+
value: 82.49389044525243
|
2722 |
+
- type: manhattan_ap
|
2723 |
+
value: 62.32245347238179
|
2724 |
+
- type: manhattan_f1
|
2725 |
+
value: 58.206309819213054
|
2726 |
+
- type: manhattan_precision
|
2727 |
+
value: 52.70704044511021
|
2728 |
+
- type: manhattan_recall
|
2729 |
+
value: 64.9868073878628
|
2730 |
+
- type: max_accuracy
|
2731 |
+
value: 82.52369315133814
|
2732 |
+
- type: max_ap
|
2733 |
+
value: 62.34858091376534
|
2734 |
+
- type: max_f1
|
2735 |
+
value: 58.206309819213054
|
2736 |
+
- task:
|
2737 |
+
type: PairClassification
|
2738 |
+
dataset:
|
2739 |
+
name: MTEB TwitterURLCorpus
|
2740 |
+
type: mteb/twitterurlcorpus-pairclassification
|
2741 |
+
config: default
|
2742 |
+
split: test
|
2743 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
2744 |
+
metrics:
|
2745 |
+
- type: cos_sim_accuracy
|
2746 |
+
value: 88.34555827220863
|
2747 |
+
- type: cos_sim_ap
|
2748 |
+
value: 84.84152481680071
|
2749 |
+
- type: cos_sim_f1
|
2750 |
+
value: 76.860456739428
|
2751 |
+
- type: cos_sim_precision
|
2752 |
+
value: 72.21470150263978
|
2753 |
+
- type: cos_sim_recall
|
2754 |
+
value: 82.14505697566985
|
2755 |
+
- type: dot_accuracy
|
2756 |
+
value: 88.34555827220863
|
2757 |
+
- type: dot_ap
|
2758 |
+
value: 84.84152743322608
|
2759 |
+
- type: dot_f1
|
2760 |
+
value: 76.860456739428
|
2761 |
+
- type: dot_precision
|
2762 |
+
value: 72.21470150263978
|
2763 |
+
- type: dot_recall
|
2764 |
+
value: 82.14505697566985
|
2765 |
+
- type: euclidean_accuracy
|
2766 |
+
value: 88.34555827220863
|
2767 |
+
- type: euclidean_ap
|
2768 |
+
value: 84.84152589453169
|
2769 |
+
- type: euclidean_f1
|
2770 |
+
value: 76.860456739428
|
2771 |
+
- type: euclidean_precision
|
2772 |
+
value: 72.21470150263978
|
2773 |
+
- type: euclidean_recall
|
2774 |
+
value: 82.14505697566985
|
2775 |
+
- type: manhattan_accuracy
|
2776 |
+
value: 88.38242713548337
|
2777 |
+
- type: manhattan_ap
|
2778 |
+
value: 84.8112124970968
|
2779 |
+
- type: manhattan_f1
|
2780 |
+
value: 76.83599206057487
|
2781 |
+
- type: manhattan_precision
|
2782 |
+
value: 73.51244900829934
|
2783 |
+
- type: manhattan_recall
|
2784 |
+
value: 80.47428395441946
|
2785 |
+
- type: max_accuracy
|
2786 |
+
value: 88.38242713548337
|
2787 |
+
- type: max_ap
|
2788 |
+
value: 84.84152743322608
|
2789 |
+
- type: max_f1
|
2790 |
+
value: 76.860456739428
|
2791 |
+
- task:
|
2792 |
+
type: Clustering
|
2793 |
+
dataset:
|
2794 |
+
name: MTEB WikiCitiesClustering
|
2795 |
+
type: jinaai/cities_wiki_clustering
|
2796 |
+
config: default
|
2797 |
+
split: test
|
2798 |
+
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
|
2799 |
+
metrics:
|
2800 |
+
- type: v_measure
|
2801 |
+
value: 85.5314389263015
|
2802 |
+
---
|
2803 |
+
|
2804 |
+
# radia/snowflake-arctic-embed-l-Q4_K_M-GGUF
|
2805 |
+
This model was converted to GGUF format from [`Snowflake/snowflake-arctic-embed-l`](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
|
2806 |
+
Refer to the [original model card](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) for more details on the model.
|
2807 |
+
|
2808 |
+
## Use with llama.cpp
|
2809 |
+
Install llama.cpp through brew (works on Mac and Linux)
|
2810 |
+
|
2811 |
+
```bash
|
2812 |
+
brew install llama.cpp
|
2813 |
+
|
2814 |
+
```
|
2815 |
+
Invoke the llama.cpp server or the CLI.
|
2816 |
+
|
2817 |
+
### CLI:
|
2818 |
+
```bash
|
2819 |
+
llama --hf-repo radia/snowflake-arctic-embed-l-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-l-q4_k_m.gguf -p "The meaning to life and the universe is"
|
2820 |
+
```
|
2821 |
+
|
2822 |
+
### Server:
|
2823 |
+
```bash
|
2824 |
+
llama-server --hf-repo radia/snowflake-arctic-embed-l-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-l-q4_k_m.gguf -c 2048
|
2825 |
+
```
|
2826 |
+
|
2827 |
+
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
|
2828 |
+
|
2829 |
+
Step 1: Clone llama.cpp from GitHub.
|
2830 |
+
```
|
2831 |
+
git clone https://github.com/ggerganov/llama.cpp
|
2832 |
+
```
|
2833 |
+
|
2834 |
+
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
|
2835 |
+
```
|
2836 |
+
cd llama.cpp && LLAMA_CURL=1 make
|
2837 |
+
```
|
2838 |
+
|
2839 |
+
Step 3: Run inference through the main binary.
|
2840 |
+
```
|
2841 |
+
./main --hf-repo radia/snowflake-arctic-embed-l-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-l-q4_k_m.gguf -p "The meaning to life and the universe is"
|
2842 |
+
```
|
2843 |
+
or
|
2844 |
+
```
|
2845 |
+
./server --hf-repo radia/snowflake-arctic-embed-l-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-l-q4_k_m.gguf -c 2048
|
2846 |
+
```
|