README.md draft
#2
by
alaeddine-13
- opened
README.md
ADDED
@@ -0,0 +1,2707 @@
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1 |
+
---
|
2 |
+
pipeline_tag: sentence-similarity
|
3 |
+
tags:
|
4 |
+
- finetuner
|
5 |
+
- mteb
|
6 |
+
- sentence-transformers
|
7 |
+
- feature-extraction
|
8 |
+
- sentence-similarity
|
9 |
+
- alibi
|
10 |
+
datasets:
|
11 |
+
- allenai/c4
|
12 |
+
language: en
|
13 |
+
license: apache-2.0
|
14 |
+
model-index:
|
15 |
+
- name: jina-embedding-b-en-v2
|
16 |
+
results:
|
17 |
+
- task:
|
18 |
+
type: Classification
|
19 |
+
dataset:
|
20 |
+
type: mteb/amazon_counterfactual
|
21 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
22 |
+
config: en
|
23 |
+
split: test
|
24 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
25 |
+
metrics:
|
26 |
+
- type: accuracy
|
27 |
+
value: 73.4179104477612
|
28 |
+
- type: ap
|
29 |
+
value: 35.798378234524705
|
30 |
+
- type: f1
|
31 |
+
value: 67.27708504551819
|
32 |
+
- task:
|
33 |
+
type: Classification
|
34 |
+
dataset:
|
35 |
+
type: mteb/amazon_polarity
|
36 |
+
name: MTEB AmazonPolarityClassification
|
37 |
+
config: default
|
38 |
+
split: test
|
39 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
40 |
+
metrics:
|
41 |
+
- type: accuracy
|
42 |
+
value: 88.977575
|
43 |
+
- type: ap
|
44 |
+
value: 85.00359027707599
|
45 |
+
- type: f1
|
46 |
+
value: 88.9585285941142
|
47 |
+
- task:
|
48 |
+
type: Classification
|
49 |
+
dataset:
|
50 |
+
type: mteb/amazon_reviews_multi
|
51 |
+
name: MTEB AmazonReviewsClassification (en)
|
52 |
+
config: en
|
53 |
+
split: test
|
54 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
55 |
+
metrics:
|
56 |
+
- type: accuracy
|
57 |
+
value: 44.455999999999996
|
58 |
+
- type: f1
|
59 |
+
value: 42.80615676169829
|
60 |
+
- task:
|
61 |
+
type: Retrieval
|
62 |
+
dataset:
|
63 |
+
type: arguana
|
64 |
+
name: MTEB ArguAna
|
65 |
+
config: default
|
66 |
+
split: test
|
67 |
+
revision: None
|
68 |
+
metrics:
|
69 |
+
- type: map_at_1
|
70 |
+
value: 18.919
|
71 |
+
- type: map_at_10
|
72 |
+
value: 33.272
|
73 |
+
- type: map_at_100
|
74 |
+
value: 34.669
|
75 |
+
- type: map_at_1000
|
76 |
+
value: 34.68
|
77 |
+
- type: map_at_3
|
78 |
+
value: 28.011000000000003
|
79 |
+
- type: map_at_5
|
80 |
+
value: 30.767
|
81 |
+
- type: mrr_at_1
|
82 |
+
value: 19.061
|
83 |
+
- type: mrr_at_10
|
84 |
+
value: 33.352
|
85 |
+
- type: mrr_at_100
|
86 |
+
value: 34.75
|
87 |
+
- type: mrr_at_1000
|
88 |
+
value: 34.760999999999996
|
89 |
+
- type: mrr_at_3
|
90 |
+
value: 28.07
|
91 |
+
- type: mrr_at_5
|
92 |
+
value: 30.848
|
93 |
+
- type: ndcg_at_1
|
94 |
+
value: 18.919
|
95 |
+
- type: ndcg_at_10
|
96 |
+
value: 42.138
|
97 |
+
- type: ndcg_at_100
|
98 |
+
value: 48.165
|
99 |
+
- type: ndcg_at_1000
|
100 |
+
value: 48.435
|
101 |
+
- type: ndcg_at_3
|
102 |
+
value: 31.041
|
103 |
+
- type: ndcg_at_5
|
104 |
+
value: 36.015
|
105 |
+
- type: precision_at_1
|
106 |
+
value: 18.919
|
107 |
+
- type: precision_at_10
|
108 |
+
value: 7.098
|
109 |
+
- type: precision_at_100
|
110 |
+
value: 0.9740000000000001
|
111 |
+
- type: precision_at_1000
|
112 |
+
value: 0.1
|
113 |
+
- type: precision_at_3
|
114 |
+
value: 13.276
|
115 |
+
- type: precision_at_5
|
116 |
+
value: 10.384
|
117 |
+
- type: recall_at_1
|
118 |
+
value: 18.919
|
119 |
+
- type: recall_at_10
|
120 |
+
value: 70.982
|
121 |
+
- type: recall_at_100
|
122 |
+
value: 97.44
|
123 |
+
- type: recall_at_1000
|
124 |
+
value: 99.502
|
125 |
+
- type: recall_at_3
|
126 |
+
value: 39.829
|
127 |
+
- type: recall_at_5
|
128 |
+
value: 51.92
|
129 |
+
- task:
|
130 |
+
type: Clustering
|
131 |
+
dataset:
|
132 |
+
type: mteb/arxiv-clustering-p2p
|
133 |
+
name: MTEB ArxivClusteringP2P
|
134 |
+
config: default
|
135 |
+
split: test
|
136 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
137 |
+
metrics:
|
138 |
+
- type: v_measure
|
139 |
+
value: 45.38238451470738
|
140 |
+
- task:
|
141 |
+
type: Clustering
|
142 |
+
dataset:
|
143 |
+
type: mteb/arxiv-clustering-s2s
|
144 |
+
name: MTEB ArxivClusteringS2S
|
145 |
+
config: default
|
146 |
+
split: test
|
147 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
148 |
+
metrics:
|
149 |
+
- type: v_measure
|
150 |
+
value: 37.12265635737745
|
151 |
+
- task:
|
152 |
+
type: Reranking
|
153 |
+
dataset:
|
154 |
+
type: mteb/askubuntudupquestions-reranking
|
155 |
+
name: MTEB AskUbuntuDupQuestions
|
156 |
+
config: default
|
157 |
+
split: test
|
158 |
+
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
|
159 |
+
metrics:
|
160 |
+
- type: map
|
161 |
+
value: 62.473921100678695
|
162 |
+
- type: mrr
|
163 |
+
value: 75.28195488721803
|
164 |
+
- task:
|
165 |
+
type: STS
|
166 |
+
dataset:
|
167 |
+
type: mteb/biosses-sts
|
168 |
+
name: MTEB BIOSSES
|
169 |
+
config: default
|
170 |
+
split: test
|
171 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
172 |
+
metrics:
|
173 |
+
- type: cos_sim_pearson
|
174 |
+
value: 84.46030780641742
|
175 |
+
- type: cos_sim_spearman
|
176 |
+
value: 83.29647627997147
|
177 |
+
- type: euclidean_pearson
|
178 |
+
value: 83.63127685751004
|
179 |
+
- type: euclidean_spearman
|
180 |
+
value: 83.29647627997147
|
181 |
+
- type: manhattan_pearson
|
182 |
+
value: 83.29505322210208
|
183 |
+
- type: manhattan_spearman
|
184 |
+
value: 82.8398393691656
|
185 |
+
- task:
|
186 |
+
type: Classification
|
187 |
+
dataset:
|
188 |
+
type: mteb/banking77
|
189 |
+
name: MTEB Banking77Classification
|
190 |
+
config: default
|
191 |
+
split: test
|
192 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
193 |
+
metrics:
|
194 |
+
- type: accuracy
|
195 |
+
value: 83.94480519480521
|
196 |
+
- type: f1
|
197 |
+
value: 83.26406143364741
|
198 |
+
- task:
|
199 |
+
type: Clustering
|
200 |
+
dataset:
|
201 |
+
type: mteb/biorxiv-clustering-p2p
|
202 |
+
name: MTEB BiorxivClusteringP2P
|
203 |
+
config: default
|
204 |
+
split: test
|
205 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
206 |
+
metrics:
|
207 |
+
- type: v_measure
|
208 |
+
value: 37.15926312173139
|
209 |
+
- task:
|
210 |
+
type: Clustering
|
211 |
+
dataset:
|
212 |
+
type: mteb/biorxiv-clustering-s2s
|
213 |
+
name: MTEB BiorxivClusteringS2S
|
214 |
+
config: default
|
215 |
+
split: test
|
216 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
217 |
+
metrics:
|
218 |
+
- type: v_measure
|
219 |
+
value: 31.20469085642121
|
220 |
+
- task:
|
221 |
+
type: Retrieval
|
222 |
+
dataset:
|
223 |
+
type: BeIR/cqadupstack
|
224 |
+
name: MTEB CQADupstackAndroidRetrieval
|
225 |
+
config: default
|
226 |
+
split: test
|
227 |
+
revision: None
|
228 |
+
metrics:
|
229 |
+
- type: map_at_1
|
230 |
+
value: 28.462
|
231 |
+
- type: map_at_10
|
232 |
+
value: 39.834
|
233 |
+
- type: map_at_100
|
234 |
+
value: 41.329
|
235 |
+
- type: map_at_1000
|
236 |
+
value: 41.465
|
237 |
+
- type: map_at_3
|
238 |
+
value: 36.586999999999996
|
239 |
+
- type: map_at_5
|
240 |
+
value: 38.239000000000004
|
241 |
+
- type: mrr_at_1
|
242 |
+
value: 34.335
|
243 |
+
- type: mrr_at_10
|
244 |
+
value: 45.493
|
245 |
+
- type: mrr_at_100
|
246 |
+
value: 46.323
|
247 |
+
- type: mrr_at_1000
|
248 |
+
value: 46.37
|
249 |
+
- type: mrr_at_3
|
250 |
+
value: 42.870999999999995
|
251 |
+
- type: mrr_at_5
|
252 |
+
value: 44.502
|
253 |
+
- type: ndcg_at_1
|
254 |
+
value: 34.335
|
255 |
+
- type: ndcg_at_10
|
256 |
+
value: 46.434
|
257 |
+
- type: ndcg_at_100
|
258 |
+
value: 52.013
|
259 |
+
- type: ndcg_at_1000
|
260 |
+
value: 54.079
|
261 |
+
- type: ndcg_at_3
|
262 |
+
value: 41.408
|
263 |
+
- type: ndcg_at_5
|
264 |
+
value: 43.562
|
265 |
+
- type: precision_at_1
|
266 |
+
value: 34.335
|
267 |
+
- type: precision_at_10
|
268 |
+
value: 8.913
|
269 |
+
- type: precision_at_100
|
270 |
+
value: 1.439
|
271 |
+
- type: precision_at_1000
|
272 |
+
value: 0.197
|
273 |
+
- type: precision_at_3
|
274 |
+
value: 20.029
|
275 |
+
- type: precision_at_5
|
276 |
+
value: 14.335
|
277 |
+
- type: recall_at_1
|
278 |
+
value: 28.462
|
279 |
+
- type: recall_at_10
|
280 |
+
value: 59.574000000000005
|
281 |
+
- type: recall_at_100
|
282 |
+
value: 82.631
|
283 |
+
- type: recall_at_1000
|
284 |
+
value: 95.45700000000001
|
285 |
+
- type: recall_at_3
|
286 |
+
value: 45.381
|
287 |
+
- type: recall_at_5
|
288 |
+
value: 51.18000000000001
|
289 |
+
- task:
|
290 |
+
type: Retrieval
|
291 |
+
dataset:
|
292 |
+
type: BeIR/cqadupstack
|
293 |
+
name: MTEB CQADupstackEnglishRetrieval
|
294 |
+
config: default
|
295 |
+
split: test
|
296 |
+
revision: None
|
297 |
+
metrics:
|
298 |
+
- type: map_at_1
|
299 |
+
value: 27.245
|
300 |
+
- type: map_at_10
|
301 |
+
value: 37.156
|
302 |
+
- type: map_at_100
|
303 |
+
value: 38.464999999999996
|
304 |
+
- type: map_at_1000
|
305 |
+
value: 38.607
|
306 |
+
- type: map_at_3
|
307 |
+
value: 34.613
|
308 |
+
- type: map_at_5
|
309 |
+
value: 35.924
|
310 |
+
- type: mrr_at_1
|
311 |
+
value: 34.777
|
312 |
+
- type: mrr_at_10
|
313 |
+
value: 43.425000000000004
|
314 |
+
- type: mrr_at_100
|
315 |
+
value: 44.163000000000004
|
316 |
+
- type: mrr_at_1000
|
317 |
+
value: 44.211
|
318 |
+
- type: mrr_at_3
|
319 |
+
value: 41.391
|
320 |
+
- type: mrr_at_5
|
321 |
+
value: 42.461
|
322 |
+
- type: ndcg_at_1
|
323 |
+
value: 34.777
|
324 |
+
- type: ndcg_at_10
|
325 |
+
value: 42.807
|
326 |
+
- type: ndcg_at_100
|
327 |
+
value: 47.629
|
328 |
+
- type: ndcg_at_1000
|
329 |
+
value: 49.84
|
330 |
+
- type: ndcg_at_3
|
331 |
+
value: 39.28
|
332 |
+
- type: ndcg_at_5
|
333 |
+
value: 40.671
|
334 |
+
- type: precision_at_1
|
335 |
+
value: 34.777
|
336 |
+
- type: precision_at_10
|
337 |
+
value: 8.134
|
338 |
+
- type: precision_at_100
|
339 |
+
value: 1.3599999999999999
|
340 |
+
- type: precision_at_1000
|
341 |
+
value: 0.186
|
342 |
+
- type: precision_at_3
|
343 |
+
value: 19.320999999999998
|
344 |
+
- type: precision_at_5
|
345 |
+
value: 13.286999999999999
|
346 |
+
- type: recall_at_1
|
347 |
+
value: 27.245
|
348 |
+
- type: recall_at_10
|
349 |
+
value: 52.491
|
350 |
+
- type: recall_at_100
|
351 |
+
value: 73.065
|
352 |
+
- type: recall_at_1000
|
353 |
+
value: 86.931
|
354 |
+
- type: recall_at_3
|
355 |
+
value: 41.257
|
356 |
+
- type: recall_at_5
|
357 |
+
value: 45.811
|
358 |
+
- task:
|
359 |
+
type: Retrieval
|
360 |
+
dataset:
|
361 |
+
type: BeIR/cqadupstack
|
362 |
+
name: MTEB CQADupstackGamingRetrieval
|
363 |
+
config: default
|
364 |
+
split: test
|
365 |
+
revision: None
|
366 |
+
metrics:
|
367 |
+
- type: map_at_1
|
368 |
+
value: 37.088
|
369 |
+
- type: map_at_10
|
370 |
+
value: 49.003
|
371 |
+
- type: map_at_100
|
372 |
+
value: 50.017999999999994
|
373 |
+
- type: map_at_1000
|
374 |
+
value: 50.07899999999999
|
375 |
+
- type: map_at_3
|
376 |
+
value: 45.846
|
377 |
+
- type: map_at_5
|
378 |
+
value: 47.733
|
379 |
+
- type: mrr_at_1
|
380 |
+
value: 42.193999999999996
|
381 |
+
- type: mrr_at_10
|
382 |
+
value: 52.522999999999996
|
383 |
+
- type: mrr_at_100
|
384 |
+
value: 53.177
|
385 |
+
- type: mrr_at_1000
|
386 |
+
value: 53.205999999999996
|
387 |
+
- type: mrr_at_3
|
388 |
+
value: 49.916
|
389 |
+
- type: mrr_at_5
|
390 |
+
value: 51.50900000000001
|
391 |
+
- type: ndcg_at_1
|
392 |
+
value: 42.193999999999996
|
393 |
+
- type: ndcg_at_10
|
394 |
+
value: 54.99699999999999
|
395 |
+
- type: ndcg_at_100
|
396 |
+
value: 59.058
|
397 |
+
- type: ndcg_at_1000
|
398 |
+
value: 60.355000000000004
|
399 |
+
- type: ndcg_at_3
|
400 |
+
value: 49.515
|
401 |
+
- type: ndcg_at_5
|
402 |
+
value: 52.412000000000006
|
403 |
+
- type: precision_at_1
|
404 |
+
value: 42.193999999999996
|
405 |
+
- type: precision_at_10
|
406 |
+
value: 8.84
|
407 |
+
- type: precision_at_100
|
408 |
+
value: 1.1820000000000002
|
409 |
+
- type: precision_at_1000
|
410 |
+
value: 0.134
|
411 |
+
- type: precision_at_3
|
412 |
+
value: 21.944
|
413 |
+
- type: precision_at_5
|
414 |
+
value: 15.197
|
415 |
+
- type: recall_at_1
|
416 |
+
value: 37.088
|
417 |
+
- type: recall_at_10
|
418 |
+
value: 69.13
|
419 |
+
- type: recall_at_100
|
420 |
+
value: 86.612
|
421 |
+
- type: recall_at_1000
|
422 |
+
value: 95.946
|
423 |
+
- type: recall_at_3
|
424 |
+
value: 54.76
|
425 |
+
- type: recall_at_5
|
426 |
+
value: 61.76199999999999
|
427 |
+
- task:
|
428 |
+
type: Retrieval
|
429 |
+
dataset:
|
430 |
+
type: BeIR/cqadupstack
|
431 |
+
name: MTEB CQADupstackGisRetrieval
|
432 |
+
config: default
|
433 |
+
split: test
|
434 |
+
revision: None
|
435 |
+
metrics:
|
436 |
+
- type: map_at_1
|
437 |
+
value: 21.816
|
438 |
+
- type: map_at_10
|
439 |
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value: 30.630000000000003
|
440 |
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- type: map_at_100
|
441 |
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value: 31.641000000000002
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442 |
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- type: map_at_1000
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443 |
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value: 31.730999999999998
|
444 |
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- type: map_at_3
|
445 |
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value: 28.153
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446 |
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- type: map_at_5
|
447 |
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value: 29.433
|
448 |
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- type: mrr_at_1
|
449 |
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value: 23.842
|
450 |
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- type: mrr_at_10
|
451 |
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value: 32.432
|
452 |
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- type: mrr_at_100
|
453 |
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value: 33.354
|
454 |
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- type: mrr_at_1000
|
455 |
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value: 33.421
|
456 |
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- type: mrr_at_3
|
457 |
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value: 30.131999999999998
|
458 |
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- type: mrr_at_5
|
459 |
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value: 31.358000000000004
|
460 |
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- type: ndcg_at_1
|
461 |
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value: 23.842
|
462 |
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- type: ndcg_at_10
|
463 |
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value: 35.626000000000005
|
464 |
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- type: ndcg_at_100
|
465 |
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value: 40.855999999999995
|
466 |
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- type: ndcg_at_1000
|
467 |
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value: 43.111
|
468 |
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- type: ndcg_at_3
|
469 |
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value: 30.712
|
470 |
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- type: ndcg_at_5
|
471 |
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value: 32.912
|
472 |
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- type: precision_at_1
|
473 |
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value: 23.842
|
474 |
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- type: precision_at_10
|
475 |
+
value: 5.627
|
476 |
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- type: precision_at_100
|
477 |
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value: 0.873
|
478 |
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- type: precision_at_1000
|
479 |
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value: 0.11100000000000002
|
480 |
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- type: precision_at_3
|
481 |
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value: 13.333
|
482 |
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- type: precision_at_5
|
483 |
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value: 9.266
|
484 |
+
- type: recall_at_1
|
485 |
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value: 21.816
|
486 |
+
- type: recall_at_10
|
487 |
+
value: 49.370000000000005
|
488 |
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- type: recall_at_100
|
489 |
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value: 73.855
|
490 |
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- type: recall_at_1000
|
491 |
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value: 90.67399999999999
|
492 |
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- type: recall_at_3
|
493 |
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value: 35.85
|
494 |
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- type: recall_at_5
|
495 |
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value: 41.282000000000004
|
496 |
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- task:
|
497 |
+
type: Retrieval
|
498 |
+
dataset:
|
499 |
+
type: BeIR/cqadupstack
|
500 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
501 |
+
config: default
|
502 |
+
split: test
|
503 |
+
revision: None
|
504 |
+
metrics:
|
505 |
+
- type: map_at_1
|
506 |
+
value: 14.402000000000001
|
507 |
+
- type: map_at_10
|
508 |
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value: 21.401999999999997
|
509 |
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- type: map_at_100
|
510 |
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value: 22.425
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511 |
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- type: map_at_1000
|
512 |
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value: 22.561
|
513 |
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- type: map_at_3
|
514 |
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value: 19.238
|
515 |
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- type: map_at_5
|
516 |
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value: 20.213
|
517 |
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- type: mrr_at_1
|
518 |
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value: 17.91
|
519 |
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- type: mrr_at_10
|
520 |
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value: 25.629999999999995
|
521 |
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- type: mrr_at_100
|
522 |
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value: 26.529999999999998
|
523 |
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- type: mrr_at_1000
|
524 |
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value: 26.616
|
525 |
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- type: mrr_at_3
|
526 |
+
value: 23.362
|
527 |
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- type: mrr_at_5
|
528 |
+
value: 24.438
|
529 |
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- type: ndcg_at_1
|
530 |
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value: 17.91
|
531 |
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- type: ndcg_at_10
|
532 |
+
value: 26.161
|
533 |
+
- type: ndcg_at_100
|
534 |
+
value: 31.474000000000004
|
535 |
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- type: ndcg_at_1000
|
536 |
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value: 34.802
|
537 |
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- type: ndcg_at_3
|
538 |
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value: 21.965
|
539 |
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- type: ndcg_at_5
|
540 |
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value: 23.511000000000003
|
541 |
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- type: precision_at_1
|
542 |
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value: 17.91
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543 |
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- type: precision_at_10
|
544 |
+
value: 4.8629999999999995
|
545 |
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- type: precision_at_100
|
546 |
+
value: 0.869
|
547 |
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- type: precision_at_1000
|
548 |
+
value: 0.129
|
549 |
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- type: precision_at_3
|
550 |
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value: 10.655000000000001
|
551 |
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- type: precision_at_5
|
552 |
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value: 7.5120000000000005
|
553 |
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- type: recall_at_1
|
554 |
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value: 14.402000000000001
|
555 |
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- type: recall_at_10
|
556 |
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value: 36.760999999999996
|
557 |
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- type: recall_at_100
|
558 |
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value: 60.549
|
559 |
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- type: recall_at_1000
|
560 |
+
value: 84.414
|
561 |
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- type: recall_at_3
|
562 |
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value: 25.130000000000003
|
563 |
+
- type: recall_at_5
|
564 |
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value: 29.079
|
565 |
+
- task:
|
566 |
+
type: Retrieval
|
567 |
+
dataset:
|
568 |
+
type: BeIR/cqadupstack
|
569 |
+
name: MTEB CQADupstackPhysicsRetrieval
|
570 |
+
config: default
|
571 |
+
split: test
|
572 |
+
revision: None
|
573 |
+
metrics:
|
574 |
+
- type: map_at_1
|
575 |
+
value: 26.176
|
576 |
+
- type: map_at_10
|
577 |
+
value: 35.789
|
578 |
+
- type: map_at_100
|
579 |
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value: 37.092000000000006
|
580 |
+
- type: map_at_1000
|
581 |
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value: 37.206
|
582 |
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- type: map_at_3
|
583 |
+
value: 33.207
|
584 |
+
- type: map_at_5
|
585 |
+
value: 34.436
|
586 |
+
- type: mrr_at_1
|
587 |
+
value: 31.569000000000003
|
588 |
+
- type: mrr_at_10
|
589 |
+
value: 41.219
|
590 |
+
- type: mrr_at_100
|
591 |
+
value: 42.016999999999996
|
592 |
+
- type: mrr_at_1000
|
593 |
+
value: 42.065000000000005
|
594 |
+
- type: mrr_at_3
|
595 |
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value: 39.012
|
596 |
+
- type: mrr_at_5
|
597 |
+
value: 40.22
|
598 |
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- type: ndcg_at_1
|
599 |
+
value: 31.569000000000003
|
600 |
+
- type: ndcg_at_10
|
601 |
+
value: 41.515
|
602 |
+
- type: ndcg_at_100
|
603 |
+
value: 47.125
|
604 |
+
- type: ndcg_at_1000
|
605 |
+
value: 49.314
|
606 |
+
- type: ndcg_at_3
|
607 |
+
value: 37.201
|
608 |
+
- type: ndcg_at_5
|
609 |
+
value: 38.906
|
610 |
+
- type: precision_at_1
|
611 |
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value: 31.569000000000003
|
612 |
+
- type: precision_at_10
|
613 |
+
value: 7.517
|
614 |
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- type: precision_at_100
|
615 |
+
value: 1.225
|
616 |
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- type: precision_at_1000
|
617 |
+
value: 0.161
|
618 |
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- type: precision_at_3
|
619 |
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value: 17.485
|
620 |
+
- type: precision_at_5
|
621 |
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value: 12.089
|
622 |
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- type: recall_at_1
|
623 |
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value: 26.176
|
624 |
+
- type: recall_at_10
|
625 |
+
value: 53.076
|
626 |
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- type: recall_at_100
|
627 |
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value: 77.049
|
628 |
+
- type: recall_at_1000
|
629 |
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value: 91.51
|
630 |
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- type: recall_at_3
|
631 |
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value: 40.82
|
632 |
+
- type: recall_at_5
|
633 |
+
value: 45.479
|
634 |
+
- task:
|
635 |
+
type: Retrieval
|
636 |
+
dataset:
|
637 |
+
type: BeIR/cqadupstack
|
638 |
+
name: MTEB CQADupstackProgrammersRetrieval
|
639 |
+
config: default
|
640 |
+
split: test
|
641 |
+
revision: None
|
642 |
+
metrics:
|
643 |
+
- type: map_at_1
|
644 |
+
value: 22.675
|
645 |
+
- type: map_at_10
|
646 |
+
value: 31.752999999999997
|
647 |
+
- type: map_at_100
|
648 |
+
value: 33.19
|
649 |
+
- type: map_at_1000
|
650 |
+
value: 33.303
|
651 |
+
- type: map_at_3
|
652 |
+
value: 28.89
|
653 |
+
- type: map_at_5
|
654 |
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value: 30.451
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655 |
+
- type: mrr_at_1
|
656 |
+
value: 27.854
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657 |
+
- type: mrr_at_10
|
658 |
+
value: 36.736999999999995
|
659 |
+
- type: mrr_at_100
|
660 |
+
value: 37.783
|
661 |
+
- type: mrr_at_1000
|
662 |
+
value: 37.836
|
663 |
+
- type: mrr_at_3
|
664 |
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value: 34.266000000000005
|
665 |
+
- type: mrr_at_5
|
666 |
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value: 35.577999999999996
|
667 |
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- type: ndcg_at_1
|
668 |
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value: 27.854
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669 |
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|
670 |
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value: 37.391999999999996
|
671 |
+
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|
672 |
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value: 43.682
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673 |
+
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|
674 |
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value: 46.005
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675 |
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|
676 |
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value: 32.66
|
677 |
+
- type: ndcg_at_5
|
678 |
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value: 34.73
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679 |
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- type: precision_at_1
|
680 |
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value: 27.854
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681 |
+
- type: precision_at_10
|
682 |
+
value: 6.963
|
683 |
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- type: precision_at_100
|
684 |
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value: 1.184
|
685 |
+
- type: precision_at_1000
|
686 |
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value: 0.159
|
687 |
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- type: precision_at_3
|
688 |
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value: 15.715000000000002
|
689 |
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- type: precision_at_5
|
690 |
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value: 11.256
|
691 |
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- type: recall_at_1
|
692 |
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value: 22.675
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693 |
+
- type: recall_at_10
|
694 |
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value: 49.15
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695 |
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- type: recall_at_100
|
696 |
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value: 76.542
|
697 |
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- type: recall_at_1000
|
698 |
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value: 92.19000000000001
|
699 |
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- type: recall_at_3
|
700 |
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value: 35.607
|
701 |
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- type: recall_at_5
|
702 |
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value: 41.288000000000004
|
703 |
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- task:
|
704 |
+
type: Retrieval
|
705 |
+
dataset:
|
706 |
+
type: BeIR/cqadupstack
|
707 |
+
name: MTEB CQADupstackRetrieval
|
708 |
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config: default
|
709 |
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split: test
|
710 |
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revision: None
|
711 |
+
metrics:
|
712 |
+
- type: map_at_1
|
713 |
+
value: 23.214499999999997
|
714 |
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- type: map_at_10
|
715 |
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value: 31.979833333333335
|
716 |
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|
717 |
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value: 33.20666666666666
|
718 |
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|
719 |
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value: 33.328583333333334
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720 |
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|
721 |
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722 |
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|
723 |
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value: 30.718083333333336
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724 |
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|
725 |
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726 |
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|
727 |
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value: 35.88433333333333
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728 |
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|
729 |
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|
730 |
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|
731 |
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value: 36.86175
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732 |
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|
733 |
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value: 33.51625
|
734 |
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|
735 |
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value: 34.821416666666664
|
736 |
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|
737 |
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value: 27.328583333333338
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738 |
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|
739 |
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value: 37.24475
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740 |
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- type: ndcg_at_100
|
741 |
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value: 42.63825
|
742 |
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- type: ndcg_at_1000
|
743 |
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value: 45.08266666666667
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744 |
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- type: ndcg_at_3
|
745 |
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value: 32.61783333333334
|
746 |
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|
747 |
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value: 34.631249999999994
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748 |
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- type: precision_at_1
|
749 |
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value: 27.328583333333338
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750 |
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- type: precision_at_10
|
751 |
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value: 6.5873333333333335
|
752 |
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- type: precision_at_100
|
753 |
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value: 1.094916666666667
|
754 |
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- type: precision_at_1000
|
755 |
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756 |
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|
757 |
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value: 15.073499999999997
|
758 |
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- type: precision_at_5
|
759 |
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value: 10.651916666666667
|
760 |
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- type: recall_at_1
|
761 |
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value: 23.214499999999997
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762 |
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- type: recall_at_10
|
763 |
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value: 49.010250000000006
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764 |
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|
765 |
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value: 72.70374999999999
|
766 |
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|
767 |
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value: 89.66041666666666
|
768 |
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- type: recall_at_3
|
769 |
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value: 36.06008333333334
|
770 |
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- type: recall_at_5
|
771 |
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value: 41.289166666666674
|
772 |
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- task:
|
773 |
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type: Retrieval
|
774 |
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dataset:
|
775 |
+
type: BeIR/cqadupstack
|
776 |
+
name: MTEB CQADupstackStatsRetrieval
|
777 |
+
config: default
|
778 |
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split: test
|
779 |
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revision: None
|
780 |
+
metrics:
|
781 |
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- type: map_at_1
|
782 |
+
value: 23.497
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783 |
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- type: map_at_10
|
784 |
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value: 29.176000000000002
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785 |
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|
786 |
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value: 30.218
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787 |
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- type: map_at_1000
|
788 |
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value: 30.317
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789 |
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|
790 |
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value: 27.072000000000003
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791 |
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|
792 |
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value: 28.162
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793 |
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|
794 |
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value: 25.919999999999998
|
795 |
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|
796 |
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value: 31.513
|
797 |
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|
798 |
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value: 32.434000000000005
|
799 |
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|
800 |
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|
801 |
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|
802 |
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value: 29.576
|
803 |
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- type: mrr_at_5
|
804 |
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value: 30.45
|
805 |
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|
806 |
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value: 25.919999999999998
|
807 |
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|
808 |
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value: 32.958999999999996
|
809 |
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- type: ndcg_at_100
|
810 |
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value: 37.937
|
811 |
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|
812 |
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value: 40.455000000000005
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813 |
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|
814 |
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value: 28.969
|
815 |
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|
816 |
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value: 30.552
|
817 |
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- type: precision_at_1
|
818 |
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value: 25.919999999999998
|
819 |
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|
820 |
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value: 5.106999999999999
|
821 |
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- type: precision_at_100
|
822 |
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value: 0.8170000000000001
|
823 |
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- type: precision_at_1000
|
824 |
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value: 0.11100000000000002
|
825 |
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- type: precision_at_3
|
826 |
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value: 12.117
|
827 |
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- type: precision_at_5
|
828 |
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value: 8.373999999999999
|
829 |
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- type: recall_at_1
|
830 |
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value: 23.497
|
831 |
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- type: recall_at_10
|
832 |
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value: 42.506
|
833 |
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- type: recall_at_100
|
834 |
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value: 65.048
|
835 |
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- type: recall_at_1000
|
836 |
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value: 83.545
|
837 |
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- type: recall_at_3
|
838 |
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value: 31.078
|
839 |
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- type: recall_at_5
|
840 |
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value: 35.018
|
841 |
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- task:
|
842 |
+
type: Retrieval
|
843 |
+
dataset:
|
844 |
+
type: BeIR/cqadupstack
|
845 |
+
name: MTEB CQADupstackTexRetrieval
|
846 |
+
config: default
|
847 |
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split: test
|
848 |
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revision: None
|
849 |
+
metrics:
|
850 |
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- type: map_at_1
|
851 |
+
value: 15.267
|
852 |
+
- type: map_at_10
|
853 |
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value: 22.292
|
854 |
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- type: map_at_100
|
855 |
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value: 23.412
|
856 |
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- type: map_at_1000
|
857 |
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value: 23.543
|
858 |
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- type: map_at_3
|
859 |
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value: 19.993
|
860 |
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- type: map_at_5
|
861 |
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value: 21.256
|
862 |
+
- type: mrr_at_1
|
863 |
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value: 18.445
|
864 |
+
- type: mrr_at_10
|
865 |
+
value: 25.698999999999998
|
866 |
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- type: mrr_at_100
|
867 |
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value: 26.682
|
868 |
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- type: mrr_at_1000
|
869 |
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value: 26.764
|
870 |
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- type: mrr_at_3
|
871 |
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value: 23.446
|
872 |
+
- type: mrr_at_5
|
873 |
+
value: 24.757
|
874 |
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- type: ndcg_at_1
|
875 |
+
value: 18.445
|
876 |
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- type: ndcg_at_10
|
877 |
+
value: 26.833000000000002
|
878 |
+
- type: ndcg_at_100
|
879 |
+
value: 32.151999999999994
|
880 |
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- type: ndcg_at_1000
|
881 |
+
value: 35.235
|
882 |
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- type: ndcg_at_3
|
883 |
+
value: 22.597
|
884 |
+
- type: ndcg_at_5
|
885 |
+
value: 24.585
|
886 |
+
- type: precision_at_1
|
887 |
+
value: 18.445
|
888 |
+
- type: precision_at_10
|
889 |
+
value: 4.942
|
890 |
+
- type: precision_at_100
|
891 |
+
value: 0.894
|
892 |
+
- type: precision_at_1000
|
893 |
+
value: 0.135
|
894 |
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- type: precision_at_3
|
895 |
+
value: 10.735999999999999
|
896 |
+
- type: precision_at_5
|
897 |
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value: 7.915
|
898 |
+
- type: recall_at_1
|
899 |
+
value: 15.267
|
900 |
+
- type: recall_at_10
|
901 |
+
value: 37.198
|
902 |
+
- type: recall_at_100
|
903 |
+
value: 60.748999999999995
|
904 |
+
- type: recall_at_1000
|
905 |
+
value: 82.72699999999999
|
906 |
+
- type: recall_at_3
|
907 |
+
value: 25.419000000000004
|
908 |
+
- type: recall_at_5
|
909 |
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value: 30.416999999999998
|
910 |
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- task:
|
911 |
+
type: Retrieval
|
912 |
+
dataset:
|
913 |
+
type: BeIR/cqadupstack
|
914 |
+
name: MTEB CQADupstackUnixRetrieval
|
915 |
+
config: default
|
916 |
+
split: test
|
917 |
+
revision: None
|
918 |
+
metrics:
|
919 |
+
- type: map_at_1
|
920 |
+
value: 22.839000000000002
|
921 |
+
- type: map_at_10
|
922 |
+
value: 31.287
|
923 |
+
- type: map_at_100
|
924 |
+
value: 32.474
|
925 |
+
- type: map_at_1000
|
926 |
+
value: 32.586
|
927 |
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- type: map_at_3
|
928 |
+
value: 28.735
|
929 |
+
- type: map_at_5
|
930 |
+
value: 30.11
|
931 |
+
- type: mrr_at_1
|
932 |
+
value: 26.959
|
933 |
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- type: mrr_at_10
|
934 |
+
value: 34.943000000000005
|
935 |
+
- type: mrr_at_100
|
936 |
+
value: 35.957
|
937 |
+
- type: mrr_at_1000
|
938 |
+
value: 36.022
|
939 |
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- type: mrr_at_3
|
940 |
+
value: 32.572
|
941 |
+
- type: mrr_at_5
|
942 |
+
value: 33.952
|
943 |
+
- type: ndcg_at_1
|
944 |
+
value: 26.959
|
945 |
+
- type: ndcg_at_10
|
946 |
+
value: 36.252
|
947 |
+
- type: ndcg_at_100
|
948 |
+
value: 41.915
|
949 |
+
- type: ndcg_at_1000
|
950 |
+
value: 44.461
|
951 |
+
- type: ndcg_at_3
|
952 |
+
value: 31.532
|
953 |
+
- type: ndcg_at_5
|
954 |
+
value: 33.674
|
955 |
+
- type: precision_at_1
|
956 |
+
value: 26.959
|
957 |
+
- type: precision_at_10
|
958 |
+
value: 6.166
|
959 |
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- type: precision_at_100
|
960 |
+
value: 1.01
|
961 |
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- type: precision_at_1000
|
962 |
+
value: 0.134
|
963 |
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- type: precision_at_3
|
964 |
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value: 14.302999999999999
|
965 |
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- type: precision_at_5
|
966 |
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value: 10.131
|
967 |
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- type: recall_at_1
|
968 |
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value: 22.839000000000002
|
969 |
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- type: recall_at_10
|
970 |
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value: 47.796
|
971 |
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- type: recall_at_100
|
972 |
+
value: 72.68
|
973 |
+
- type: recall_at_1000
|
974 |
+
value: 90.556
|
975 |
+
- type: recall_at_3
|
976 |
+
value: 34.955000000000005
|
977 |
+
- type: recall_at_5
|
978 |
+
value: 40.293
|
979 |
+
- task:
|
980 |
+
type: Retrieval
|
981 |
+
dataset:
|
982 |
+
type: BeIR/cqadupstack
|
983 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
984 |
+
config: default
|
985 |
+
split: test
|
986 |
+
revision: None
|
987 |
+
metrics:
|
988 |
+
- type: map_at_1
|
989 |
+
value: 21.676000000000002
|
990 |
+
- type: map_at_10
|
991 |
+
value: 30.742000000000004
|
992 |
+
- type: map_at_100
|
993 |
+
value: 32.332
|
994 |
+
- type: map_at_1000
|
995 |
+
value: 32.548
|
996 |
+
- type: map_at_3
|
997 |
+
value: 27.560000000000002
|
998 |
+
- type: map_at_5
|
999 |
+
value: 29.331000000000003
|
1000 |
+
- type: mrr_at_1
|
1001 |
+
value: 25.099
|
1002 |
+
- type: mrr_at_10
|
1003 |
+
value: 34.538999999999994
|
1004 |
+
- type: mrr_at_100
|
1005 |
+
value: 35.629
|
1006 |
+
- type: mrr_at_1000
|
1007 |
+
value: 35.687000000000005
|
1008 |
+
- type: mrr_at_3
|
1009 |
+
value: 31.621
|
1010 |
+
- type: mrr_at_5
|
1011 |
+
value: 33.419
|
1012 |
+
- type: ndcg_at_1
|
1013 |
+
value: 25.099
|
1014 |
+
- type: ndcg_at_10
|
1015 |
+
value: 36.741
|
1016 |
+
- type: ndcg_at_100
|
1017 |
+
value: 42.964
|
1018 |
+
- type: ndcg_at_1000
|
1019 |
+
value: 45.754
|
1020 |
+
- type: ndcg_at_3
|
1021 |
+
value: 31.356
|
1022 |
+
- type: ndcg_at_5
|
1023 |
+
value: 33.934999999999995
|
1024 |
+
- type: precision_at_1
|
1025 |
+
value: 25.099
|
1026 |
+
- type: precision_at_10
|
1027 |
+
value: 7.115
|
1028 |
+
- type: precision_at_100
|
1029 |
+
value: 1.46
|
1030 |
+
- type: precision_at_1000
|
1031 |
+
value: 0.23800000000000002
|
1032 |
+
- type: precision_at_3
|
1033 |
+
value: 14.954
|
1034 |
+
- type: precision_at_5
|
1035 |
+
value: 11.067
|
1036 |
+
- type: recall_at_1
|
1037 |
+
value: 21.676000000000002
|
1038 |
+
- type: recall_at_10
|
1039 |
+
value: 49.546
|
1040 |
+
- type: recall_at_100
|
1041 |
+
value: 76.544
|
1042 |
+
- type: recall_at_1000
|
1043 |
+
value: 94.39999999999999
|
1044 |
+
- type: recall_at_3
|
1045 |
+
value: 34.67
|
1046 |
+
- type: recall_at_5
|
1047 |
+
value: 41.528999999999996
|
1048 |
+
- task:
|
1049 |
+
type: Retrieval
|
1050 |
+
dataset:
|
1051 |
+
type: BeIR/cqadupstack
|
1052 |
+
name: MTEB CQADupstackWordpressRetrieval
|
1053 |
+
config: default
|
1054 |
+
split: test
|
1055 |
+
revision: None
|
1056 |
+
metrics:
|
1057 |
+
- type: map_at_1
|
1058 |
+
value: 17.431
|
1059 |
+
- type: map_at_10
|
1060 |
+
value: 24.694
|
1061 |
+
- type: map_at_100
|
1062 |
+
value: 25.884
|
1063 |
+
- type: map_at_1000
|
1064 |
+
value: 25.996999999999996
|
1065 |
+
- type: map_at_3
|
1066 |
+
value: 22.203
|
1067 |
+
- type: map_at_5
|
1068 |
+
value: 23.329
|
1069 |
+
- type: mrr_at_1
|
1070 |
+
value: 19.039
|
1071 |
+
- type: mrr_at_10
|
1072 |
+
value: 26.459
|
1073 |
+
- type: mrr_at_100
|
1074 |
+
value: 27.560000000000002
|
1075 |
+
- type: mrr_at_1000
|
1076 |
+
value: 27.636
|
1077 |
+
- type: mrr_at_3
|
1078 |
+
value: 24.03
|
1079 |
+
- type: mrr_at_5
|
1080 |
+
value: 25.213
|
1081 |
+
- type: ndcg_at_1
|
1082 |
+
value: 19.039
|
1083 |
+
- type: ndcg_at_10
|
1084 |
+
value: 29.220000000000002
|
1085 |
+
- type: ndcg_at_100
|
1086 |
+
value: 34.854
|
1087 |
+
- type: ndcg_at_1000
|
1088 |
+
value: 37.580999999999996
|
1089 |
+
- type: ndcg_at_3
|
1090 |
+
value: 24.218999999999998
|
1091 |
+
- type: ndcg_at_5
|
1092 |
+
value: 26.125
|
1093 |
+
- type: precision_at_1
|
1094 |
+
value: 19.039
|
1095 |
+
- type: precision_at_10
|
1096 |
+
value: 4.861
|
1097 |
+
- type: precision_at_100
|
1098 |
+
value: 0.826
|
1099 |
+
- type: precision_at_1000
|
1100 |
+
value: 0.116
|
1101 |
+
- type: precision_at_3
|
1102 |
+
value: 10.290000000000001
|
1103 |
+
- type: precision_at_5
|
1104 |
+
value: 7.394
|
1105 |
+
- type: recall_at_1
|
1106 |
+
value: 17.431
|
1107 |
+
- type: recall_at_10
|
1108 |
+
value: 41.525
|
1109 |
+
- type: recall_at_100
|
1110 |
+
value: 67.121
|
1111 |
+
- type: recall_at_1000
|
1112 |
+
value: 87.575
|
1113 |
+
- type: recall_at_3
|
1114 |
+
value: 27.794
|
1115 |
+
- type: recall_at_5
|
1116 |
+
value: 32.332
|
1117 |
+
- task:
|
1118 |
+
type: Retrieval
|
1119 |
+
dataset:
|
1120 |
+
type: climate-fever
|
1121 |
+
name: MTEB ClimateFEVER
|
1122 |
+
config: default
|
1123 |
+
split: test
|
1124 |
+
revision: None
|
1125 |
+
metrics:
|
1126 |
+
- type: map_at_1
|
1127 |
+
value: 10.767
|
1128 |
+
- type: map_at_10
|
1129 |
+
value: 17.456
|
1130 |
+
- type: map_at_100
|
1131 |
+
value: 19.097
|
1132 |
+
- type: map_at_1000
|
1133 |
+
value: 19.272
|
1134 |
+
- type: map_at_3
|
1135 |
+
value: 14.530000000000001
|
1136 |
+
- type: map_at_5
|
1137 |
+
value: 15.943999999999999
|
1138 |
+
- type: mrr_at_1
|
1139 |
+
value: 23.583000000000002
|
1140 |
+
- type: mrr_at_10
|
1141 |
+
value: 33.391
|
1142 |
+
- type: mrr_at_100
|
1143 |
+
value: 34.43
|
1144 |
+
- type: mrr_at_1000
|
1145 |
+
value: 34.479
|
1146 |
+
- type: mrr_at_3
|
1147 |
+
value: 30.239
|
1148 |
+
- type: mrr_at_5
|
1149 |
+
value: 31.923000000000002
|
1150 |
+
- type: ndcg_at_1
|
1151 |
+
value: 23.583000000000002
|
1152 |
+
- type: ndcg_at_10
|
1153 |
+
value: 24.84
|
1154 |
+
- type: ndcg_at_100
|
1155 |
+
value: 31.749
|
1156 |
+
- type: ndcg_at_1000
|
1157 |
+
value: 35.161
|
1158 |
+
- type: ndcg_at_3
|
1159 |
+
value: 19.906
|
1160 |
+
- type: ndcg_at_5
|
1161 |
+
value: 21.543
|
1162 |
+
- type: precision_at_1
|
1163 |
+
value: 23.583000000000002
|
1164 |
+
- type: precision_at_10
|
1165 |
+
value: 7.739
|
1166 |
+
- type: precision_at_100
|
1167 |
+
value: 1.5110000000000001
|
1168 |
+
- type: precision_at_1000
|
1169 |
+
value: 0.215
|
1170 |
+
- type: precision_at_3
|
1171 |
+
value: 14.506
|
1172 |
+
- type: precision_at_5
|
1173 |
+
value: 11.179
|
1174 |
+
- type: recall_at_1
|
1175 |
+
value: 10.767
|
1176 |
+
- type: recall_at_10
|
1177 |
+
value: 30.270000000000003
|
1178 |
+
- type: recall_at_100
|
1179 |
+
value: 54.467
|
1180 |
+
- type: recall_at_1000
|
1181 |
+
value: 73.71799999999999
|
1182 |
+
- type: recall_at_3
|
1183 |
+
value: 18.251
|
1184 |
+
- type: recall_at_5
|
1185 |
+
value: 22.831000000000003
|
1186 |
+
- task:
|
1187 |
+
type: Retrieval
|
1188 |
+
dataset:
|
1189 |
+
type: dbpedia-entity
|
1190 |
+
name: MTEB DBPedia
|
1191 |
+
config: default
|
1192 |
+
split: test
|
1193 |
+
revision: None
|
1194 |
+
metrics:
|
1195 |
+
- type: map_at_1
|
1196 |
+
value: 6.493
|
1197 |
+
- type: map_at_10
|
1198 |
+
value: 15.290999999999999
|
1199 |
+
- type: map_at_100
|
1200 |
+
value: 21.523999999999997
|
1201 |
+
- type: map_at_1000
|
1202 |
+
value: 22.980999999999998
|
1203 |
+
- type: map_at_3
|
1204 |
+
value: 11.015
|
1205 |
+
- type: map_at_5
|
1206 |
+
value: 12.631
|
1207 |
+
- type: mrr_at_1
|
1208 |
+
value: 55.50000000000001
|
1209 |
+
- type: mrr_at_10
|
1210 |
+
value: 65.068
|
1211 |
+
- type: mrr_at_100
|
1212 |
+
value: 65.608
|
1213 |
+
- type: mrr_at_1000
|
1214 |
+
value: 65.622
|
1215 |
+
- type: mrr_at_3
|
1216 |
+
value: 62.625
|
1217 |
+
- type: mrr_at_5
|
1218 |
+
value: 64.2
|
1219 |
+
- type: ndcg_at_1
|
1220 |
+
value: 44.875
|
1221 |
+
- type: ndcg_at_10
|
1222 |
+
value: 35.046
|
1223 |
+
- type: ndcg_at_100
|
1224 |
+
value: 38.662
|
1225 |
+
- type: ndcg_at_1000
|
1226 |
+
value: 45.916000000000004
|
1227 |
+
- type: ndcg_at_3
|
1228 |
+
value: 38.888
|
1229 |
+
- type: ndcg_at_5
|
1230 |
+
value: 36.411
|
1231 |
+
- type: precision_at_1
|
1232 |
+
value: 55.50000000000001
|
1233 |
+
- type: precision_at_10
|
1234 |
+
value: 28.175
|
1235 |
+
- type: precision_at_100
|
1236 |
+
value: 8.938
|
1237 |
+
- type: precision_at_1000
|
1238 |
+
value: 1.894
|
1239 |
+
- type: precision_at_3
|
1240 |
+
value: 41.917
|
1241 |
+
- type: precision_at_5
|
1242 |
+
value: 34.949999999999996
|
1243 |
+
- type: recall_at_1
|
1244 |
+
value: 6.493
|
1245 |
+
- type: recall_at_10
|
1246 |
+
value: 20.992
|
1247 |
+
- type: recall_at_100
|
1248 |
+
value: 44.138
|
1249 |
+
- type: recall_at_1000
|
1250 |
+
value: 67.181
|
1251 |
+
- type: recall_at_3
|
1252 |
+
value: 12.546
|
1253 |
+
- type: recall_at_5
|
1254 |
+
value: 15.552
|
1255 |
+
- task:
|
1256 |
+
type: Classification
|
1257 |
+
dataset:
|
1258 |
+
type: mteb/emotion
|
1259 |
+
name: MTEB EmotionClassification
|
1260 |
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config: default
|
1261 |
+
split: test
|
1262 |
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revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
1263 |
+
metrics:
|
1264 |
+
- type: accuracy
|
1265 |
+
value: 45.955
|
1266 |
+
- type: f1
|
1267 |
+
value: 40.97084067876041
|
1268 |
+
- task:
|
1269 |
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type: Retrieval
|
1270 |
+
dataset:
|
1271 |
+
type: fever
|
1272 |
+
name: MTEB FEVER
|
1273 |
+
config: default
|
1274 |
+
split: test
|
1275 |
+
revision: None
|
1276 |
+
metrics:
|
1277 |
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value: 43.765
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value: 56.566
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1281 |
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1282 |
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1286 |
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1287 |
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1290 |
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1291 |
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1292 |
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1294 |
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1299 |
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1300 |
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1301 |
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1302 |
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value: 47.03
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1303 |
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|
1304 |
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value: 63.227
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1305 |
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|
1306 |
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value: 65.846
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1307 |
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1308 |
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1309 |
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1310 |
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1311 |
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1312 |
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1313 |
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1314 |
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1315 |
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1316 |
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value: 8.831
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1317 |
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1318 |
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value: 1.027
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1319 |
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1320 |
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1321 |
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1322 |
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value: 23.642
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1323 |
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1324 |
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value: 15.884
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1325 |
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1326 |
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value: 43.765
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1327 |
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1328 |
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value: 80.537
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1329 |
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1330 |
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value: 92.06400000000001
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1331 |
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- type: recall_at_1000
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1332 |
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value: 96.054
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1333 |
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1334 |
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value: 65.27199999999999
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1335 |
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|
1336 |
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value: 72.71
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1337 |
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- task:
|
1338 |
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type: Retrieval
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1339 |
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dataset:
|
1340 |
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type: fiqa
|
1341 |
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name: MTEB FiQA2018
|
1342 |
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config: default
|
1343 |
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split: test
|
1344 |
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revision: None
|
1345 |
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metrics:
|
1346 |
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- type: map_at_1
|
1347 |
+
value: 20.684
|
1348 |
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- type: map_at_10
|
1349 |
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value: 33.393
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1350 |
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1351 |
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1352 |
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1353 |
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1354 |
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1355 |
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value: 28.810000000000002
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1356 |
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1357 |
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value: 31.484
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1358 |
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1359 |
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value: 41.049
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1360 |
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|
1361 |
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value: 49.736999999999995
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1362 |
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1363 |
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value: 50.541000000000004
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1364 |
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1365 |
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value: 50.575
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1366 |
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1367 |
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value: 47.094
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1368 |
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|
1369 |
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value: 48.768
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1370 |
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1371 |
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value: 41.049
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1372 |
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|
1373 |
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value: 41.338
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1374 |
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|
1375 |
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value: 48.386
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1376 |
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|
1377 |
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1378 |
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1379 |
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value: 37.208000000000006
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1380 |
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|
1381 |
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value: 38.788
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1382 |
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|
1383 |
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value: 41.049
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1384 |
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- type: precision_at_10
|
1385 |
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value: 11.466
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1386 |
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- type: precision_at_100
|
1387 |
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value: 1.8769999999999998
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1388 |
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- type: precision_at_1000
|
1389 |
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value: 0.23800000000000002
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1390 |
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|
1391 |
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value: 24.691
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1392 |
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- type: precision_at_5
|
1393 |
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value: 18.519
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1394 |
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- type: recall_at_1
|
1395 |
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value: 20.684
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1396 |
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- type: recall_at_10
|
1397 |
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value: 48.431000000000004
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1398 |
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- type: recall_at_100
|
1399 |
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value: 74.331
|
1400 |
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- type: recall_at_1000
|
1401 |
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value: 91.268
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1402 |
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- type: recall_at_3
|
1403 |
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value: 33.267
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1404 |
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- type: recall_at_5
|
1405 |
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value: 40.313
|
1406 |
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- task:
|
1407 |
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type: Retrieval
|
1408 |
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dataset:
|
1409 |
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type: hotpotqa
|
1410 |
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name: MTEB HotpotQA
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1411 |
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config: default
|
1412 |
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split: test
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1413 |
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revision: None
|
1414 |
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metrics:
|
1415 |
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- type: map_at_1
|
1416 |
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value: 32.242
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1417 |
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|
1418 |
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value: 47.49
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1419 |
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1420 |
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1421 |
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1422 |
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value: 48.489
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1423 |
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1424 |
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value: 44.519
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1425 |
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1426 |
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value: 46.298
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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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1435 |
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1436 |
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value: 69.899
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1437 |
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1438 |
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value: 70.791
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1439 |
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1440 |
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1441 |
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1442 |
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value: 56.274
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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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value: 51.636
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1449 |
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1450 |
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value: 54.089
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1451 |
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1452 |
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value: 64.483
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1453 |
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|
1454 |
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value: 11.858
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1455 |
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1456 |
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value: 1.47
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1457 |
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1458 |
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value: 0.169
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1459 |
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1460 |
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value: 32.635999999999996
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1461 |
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- type: precision_at_5
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1462 |
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value: 21.521
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1463 |
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1464 |
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value: 32.242
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1465 |
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|
1466 |
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value: 59.291000000000004
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1467 |
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- type: recall_at_100
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1468 |
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value: 73.518
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1469 |
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- type: recall_at_1000
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1470 |
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value: 84.747
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1471 |
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- type: recall_at_3
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1472 |
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value: 48.953
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1473 |
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- type: recall_at_5
|
1474 |
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value: 53.801
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1475 |
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- task:
|
1476 |
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type: Classification
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1477 |
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dataset:
|
1478 |
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type: mteb/imdb
|
1479 |
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name: MTEB ImdbClassification
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1480 |
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config: default
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1481 |
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split: test
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1482 |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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1483 |
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metrics:
|
1484 |
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- type: accuracy
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1485 |
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value: 80.9492
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1486 |
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- type: ap
|
1487 |
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value: 75.30846930618502
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1488 |
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1489 |
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value: 80.89150705991759
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1490 |
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- task:
|
1491 |
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type: Retrieval
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1492 |
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dataset:
|
1493 |
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type: msmarco
|
1494 |
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name: MTEB MSMARCO
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1495 |
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config: default
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1496 |
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split: dev
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1497 |
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revision: None
|
1498 |
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metrics:
|
1499 |
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- type: map_at_1
|
1500 |
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value: 22.033
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1501 |
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- type: map_at_10
|
1502 |
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1503 |
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1504 |
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value: 35.536
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1505 |
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1506 |
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value: 35.583
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1507 |
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1508 |
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value: 30.562
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1509 |
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1510 |
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value: 32.667
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1511 |
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1512 |
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value: 22.708000000000002
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1513 |
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1514 |
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1515 |
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1516 |
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value: 36.105
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1517 |
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1518 |
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value: 36.147
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1519 |
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1520 |
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value: 31.256
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1521 |
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1522 |
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value: 33.322
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1523 |
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1524 |
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value: 22.708000000000002
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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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value: 22.708000000000002
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1537 |
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1538 |
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value: 6.519
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1539 |
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1540 |
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value: 0.9390000000000001
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1541 |
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1542 |
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value: 0.104
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1543 |
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1544 |
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value: 14.302999999999999
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1545 |
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1546 |
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value: 10.481
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1547 |
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1548 |
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value: 22.033
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1549 |
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|
1550 |
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value: 62.348000000000006
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1551 |
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1552 |
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value: 88.771
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1553 |
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|
1554 |
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value: 97.782
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1555 |
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|
1556 |
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value: 41.331
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1557 |
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|
1558 |
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value: 50.32600000000001
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1559 |
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- task:
|
1560 |
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|
1561 |
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dataset:
|
1562 |
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type: mteb/mtop_domain
|
1563 |
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name: MTEB MTOPDomainClassification (en)
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1564 |
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config: en
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1565 |
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split: test
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1566 |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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1567 |
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metrics:
|
1568 |
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- type: accuracy
|
1569 |
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value: 92.69037847697219
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1570 |
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- type: f1
|
1571 |
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1572 |
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- task:
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1573 |
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1574 |
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dataset:
|
1575 |
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type: mteb/mtop_intent
|
1576 |
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name: MTEB MTOPIntentClassification (en)
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1577 |
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1578 |
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split: test
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1579 |
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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metrics:
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1581 |
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1582 |
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value: 61.12859097127223
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1583 |
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1584 |
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value: 44.859837744275346
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1585 |
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- task:
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1586 |
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1587 |
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dataset:
|
1588 |
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type: mteb/amazon_massive_intent
|
1589 |
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name: MTEB MassiveIntentClassification (en)
|
1590 |
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config: en
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1591 |
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1592 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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1593 |
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metrics:
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1594 |
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1595 |
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value: 67.59246805648958
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1596 |
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1597 |
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1598 |
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- task:
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1599 |
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1600 |
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dataset:
|
1601 |
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type: mteb/amazon_massive_scenario
|
1602 |
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name: MTEB MassiveScenarioClassification (en)
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1603 |
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config: en
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1604 |
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1605 |
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1606 |
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metrics:
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1607 |
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1608 |
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value: 72.82447881640888
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1609 |
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- type: f1
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1610 |
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value: 71.74294810351809
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1611 |
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- task:
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1612 |
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type: Clustering
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1613 |
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dataset:
|
1614 |
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type: mteb/medrxiv-clustering-p2p
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1615 |
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name: MTEB MedrxivClusteringP2P
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1616 |
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config: default
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1617 |
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1618 |
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1619 |
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metrics:
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1620 |
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1621 |
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value: 32.623627054114884
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1622 |
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- task:
|
1623 |
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type: Clustering
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1624 |
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dataset:
|
1625 |
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type: mteb/medrxiv-clustering-s2s
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1626 |
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name: MTEB MedrxivClusteringS2S
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1627 |
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1628 |
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1629 |
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metrics:
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1631 |
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1632 |
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value: 28.715250618201516
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1633 |
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- task:
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1634 |
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type: Reranking
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1635 |
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dataset:
|
1636 |
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type: mteb/mind_small
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1637 |
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name: MTEB MindSmallReranking
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1638 |
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1639 |
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1640 |
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1641 |
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1646 |
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- task:
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1647 |
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1648 |
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dataset:
|
1649 |
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type: nfcorpus
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1650 |
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name: MTEB NFCorpus
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1651 |
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config: default
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1652 |
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split: test
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1653 |
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revision: None
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1654 |
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metrics:
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1655 |
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1656 |
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value: 5.702
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1657 |
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1687 |
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- type: ndcg_at_3
|
1688 |
+
value: 38.015
|
1689 |
+
- type: ndcg_at_5
|
1690 |
+
value: 35.574
|
1691 |
+
- type: precision_at_1
|
1692 |
+
value: 44.891999999999996
|
1693 |
+
- type: precision_at_10
|
1694 |
+
value: 23.375
|
1695 |
+
- type: precision_at_100
|
1696 |
+
value: 7.545
|
1697 |
+
- type: precision_at_1000
|
1698 |
+
value: 2.052
|
1699 |
+
- type: precision_at_3
|
1700 |
+
value: 35.088
|
1701 |
+
- type: precision_at_5
|
1702 |
+
value: 30.154999999999998
|
1703 |
+
- type: recall_at_1
|
1704 |
+
value: 5.702
|
1705 |
+
- type: recall_at_10
|
1706 |
+
value: 15.421000000000001
|
1707 |
+
- type: recall_at_100
|
1708 |
+
value: 30.708999999999996
|
1709 |
+
- type: recall_at_1000
|
1710 |
+
value: 62.487
|
1711 |
+
- type: recall_at_3
|
1712 |
+
value: 9.966999999999999
|
1713 |
+
- type: recall_at_5
|
1714 |
+
value: 12.059000000000001
|
1715 |
+
- task:
|
1716 |
+
type: Retrieval
|
1717 |
+
dataset:
|
1718 |
+
type: nq
|
1719 |
+
name: MTEB NQ
|
1720 |
+
config: default
|
1721 |
+
split: test
|
1722 |
+
revision: None
|
1723 |
+
metrics:
|
1724 |
+
- type: map_at_1
|
1725 |
+
value: 39.117000000000004
|
1726 |
+
- type: map_at_10
|
1727 |
+
value: 54.041
|
1728 |
+
- type: map_at_100
|
1729 |
+
value: 54.845
|
1730 |
+
- type: map_at_1000
|
1731 |
+
value: 54.876999999999995
|
1732 |
+
- type: map_at_3
|
1733 |
+
value: 50.339999999999996
|
1734 |
+
- type: map_at_5
|
1735 |
+
value: 52.678999999999995
|
1736 |
+
- type: mrr_at_1
|
1737 |
+
value: 43.627
|
1738 |
+
- type: mrr_at_10
|
1739 |
+
value: 56.752
|
1740 |
+
- type: mrr_at_100
|
1741 |
+
value: 57.32899999999999
|
1742 |
+
- type: mrr_at_1000
|
1743 |
+
value: 57.35
|
1744 |
+
- type: mrr_at_3
|
1745 |
+
value: 53.818999999999996
|
1746 |
+
- type: mrr_at_5
|
1747 |
+
value: 55.684999999999995
|
1748 |
+
- type: ndcg_at_1
|
1749 |
+
value: 43.627
|
1750 |
+
- type: ndcg_at_10
|
1751 |
+
value: 60.934
|
1752 |
+
- type: ndcg_at_100
|
1753 |
+
value: 64.277
|
1754 |
+
- type: ndcg_at_1000
|
1755 |
+
value: 64.97
|
1756 |
+
- type: ndcg_at_3
|
1757 |
+
value: 54.164
|
1758 |
+
- type: ndcg_at_5
|
1759 |
+
value: 57.994
|
1760 |
+
- type: precision_at_1
|
1761 |
+
value: 43.627
|
1762 |
+
- type: precision_at_10
|
1763 |
+
value: 9.383
|
1764 |
+
- type: precision_at_100
|
1765 |
+
value: 1.131
|
1766 |
+
- type: precision_at_1000
|
1767 |
+
value: 0.12
|
1768 |
+
- type: precision_at_3
|
1769 |
+
value: 23.919
|
1770 |
+
- type: precision_at_5
|
1771 |
+
value: 16.541
|
1772 |
+
- type: recall_at_1
|
1773 |
+
value: 39.117000000000004
|
1774 |
+
- type: recall_at_10
|
1775 |
+
value: 79.012
|
1776 |
+
- type: recall_at_100
|
1777 |
+
value: 93.395
|
1778 |
+
- type: recall_at_1000
|
1779 |
+
value: 98.494
|
1780 |
+
- type: recall_at_3
|
1781 |
+
value: 61.714999999999996
|
1782 |
+
- type: recall_at_5
|
1783 |
+
value: 70.55799999999999
|
1784 |
+
- task:
|
1785 |
+
type: Retrieval
|
1786 |
+
dataset:
|
1787 |
+
type: quora
|
1788 |
+
name: MTEB QuoraRetrieval
|
1789 |
+
config: default
|
1790 |
+
split: test
|
1791 |
+
revision: None
|
1792 |
+
metrics:
|
1793 |
+
- type: map_at_1
|
1794 |
+
value: 70.832
|
1795 |
+
- type: map_at_10
|
1796 |
+
value: 84.82300000000001
|
1797 |
+
- type: map_at_100
|
1798 |
+
value: 85.44500000000001
|
1799 |
+
- type: map_at_1000
|
1800 |
+
value: 85.461
|
1801 |
+
- type: map_at_3
|
1802 |
+
value: 81.917
|
1803 |
+
- type: map_at_5
|
1804 |
+
value: 83.734
|
1805 |
+
- type: mrr_at_1
|
1806 |
+
value: 81.61
|
1807 |
+
- type: mrr_at_10
|
1808 |
+
value: 87.75500000000001
|
1809 |
+
- type: mrr_at_100
|
1810 |
+
value: 87.85300000000001
|
1811 |
+
- type: mrr_at_1000
|
1812 |
+
value: 87.854
|
1813 |
+
- type: mrr_at_3
|
1814 |
+
value: 86.855
|
1815 |
+
- type: mrr_at_5
|
1816 |
+
value: 87.465
|
1817 |
+
- type: ndcg_at_1
|
1818 |
+
value: 81.58999999999999
|
1819 |
+
- type: ndcg_at_10
|
1820 |
+
value: 88.536
|
1821 |
+
- type: ndcg_at_100
|
1822 |
+
value: 89.714
|
1823 |
+
- type: ndcg_at_1000
|
1824 |
+
value: 89.80799999999999
|
1825 |
+
- type: ndcg_at_3
|
1826 |
+
value: 85.8
|
1827 |
+
- type: ndcg_at_5
|
1828 |
+
value: 87.286
|
1829 |
+
- type: precision_at_1
|
1830 |
+
value: 81.58999999999999
|
1831 |
+
- type: precision_at_10
|
1832 |
+
value: 13.438
|
1833 |
+
- type: precision_at_100
|
1834 |
+
value: 1.5310000000000001
|
1835 |
+
- type: precision_at_1000
|
1836 |
+
value: 0.157
|
1837 |
+
- type: precision_at_3
|
1838 |
+
value: 37.563
|
1839 |
+
- type: precision_at_5
|
1840 |
+
value: 24.65
|
1841 |
+
- type: recall_at_1
|
1842 |
+
value: 70.832
|
1843 |
+
- type: recall_at_10
|
1844 |
+
value: 95.574
|
1845 |
+
- type: recall_at_100
|
1846 |
+
value: 99.575
|
1847 |
+
- type: recall_at_1000
|
1848 |
+
value: 99.99
|
1849 |
+
- type: recall_at_3
|
1850 |
+
value: 87.61
|
1851 |
+
- type: recall_at_5
|
1852 |
+
value: 91.9
|
1853 |
+
- task:
|
1854 |
+
type: Clustering
|
1855 |
+
dataset:
|
1856 |
+
type: mteb/reddit-clustering
|
1857 |
+
name: MTEB RedditClustering
|
1858 |
+
config: default
|
1859 |
+
split: test
|
1860 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
1861 |
+
metrics:
|
1862 |
+
- type: v_measure
|
1863 |
+
value: 54.4131741738767
|
1864 |
+
- task:
|
1865 |
+
type: Clustering
|
1866 |
+
dataset:
|
1867 |
+
type: mteb/reddit-clustering-p2p
|
1868 |
+
name: MTEB RedditClusteringP2P
|
1869 |
+
config: default
|
1870 |
+
split: test
|
1871 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
1872 |
+
metrics:
|
1873 |
+
- type: v_measure
|
1874 |
+
value: 59.816632341901865
|
1875 |
+
- task:
|
1876 |
+
type: Retrieval
|
1877 |
+
dataset:
|
1878 |
+
type: scidocs
|
1879 |
+
name: MTEB SCIDOCS
|
1880 |
+
config: default
|
1881 |
+
split: test
|
1882 |
+
revision: None
|
1883 |
+
metrics:
|
1884 |
+
- type: map_at_1
|
1885 |
+
value: 4.857
|
1886 |
+
- type: map_at_10
|
1887 |
+
value: 11.937000000000001
|
1888 |
+
- type: map_at_100
|
1889 |
+
value: 14.143
|
1890 |
+
- type: map_at_1000
|
1891 |
+
value: 14.451
|
1892 |
+
- type: map_at_3
|
1893 |
+
value: 8.376999999999999
|
1894 |
+
- type: map_at_5
|
1895 |
+
value: 10.172
|
1896 |
+
- type: mrr_at_1
|
1897 |
+
value: 23.799999999999997
|
1898 |
+
- type: mrr_at_10
|
1899 |
+
value: 34.134
|
1900 |
+
- type: mrr_at_100
|
1901 |
+
value: 35.285
|
1902 |
+
- type: mrr_at_1000
|
1903 |
+
value: 35.33
|
1904 |
+
- type: mrr_at_3
|
1905 |
+
value: 30.833
|
1906 |
+
- type: mrr_at_5
|
1907 |
+
value: 32.828
|
1908 |
+
- type: ndcg_at_1
|
1909 |
+
value: 23.799999999999997
|
1910 |
+
- type: ndcg_at_10
|
1911 |
+
value: 20.0
|
1912 |
+
- type: ndcg_at_100
|
1913 |
+
value: 28.486
|
1914 |
+
- type: ndcg_at_1000
|
1915 |
+
value: 33.781
|
1916 |
+
- type: ndcg_at_3
|
1917 |
+
value: 18.726000000000003
|
1918 |
+
- type: ndcg_at_5
|
1919 |
+
value: 16.587
|
1920 |
+
- type: precision_at_1
|
1921 |
+
value: 23.799999999999997
|
1922 |
+
- type: precision_at_10
|
1923 |
+
value: 10.39
|
1924 |
+
- type: precision_at_100
|
1925 |
+
value: 2.263
|
1926 |
+
- type: precision_at_1000
|
1927 |
+
value: 0.35300000000000004
|
1928 |
+
- type: precision_at_3
|
1929 |
+
value: 17.333000000000002
|
1930 |
+
- type: precision_at_5
|
1931 |
+
value: 14.56
|
1932 |
+
- type: recall_at_1
|
1933 |
+
value: 4.857
|
1934 |
+
- type: recall_at_10
|
1935 |
+
value: 21.02
|
1936 |
+
- type: recall_at_100
|
1937 |
+
value: 45.932
|
1938 |
+
- type: recall_at_1000
|
1939 |
+
value: 71.693
|
1940 |
+
- type: recall_at_3
|
1941 |
+
value: 10.552
|
1942 |
+
- type: recall_at_5
|
1943 |
+
value: 14.760000000000002
|
1944 |
+
- task:
|
1945 |
+
type: STS
|
1946 |
+
dataset:
|
1947 |
+
type: mteb/sickr-sts
|
1948 |
+
name: MTEB SICK-R
|
1949 |
+
config: default
|
1950 |
+
split: test
|
1951 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
1952 |
+
metrics:
|
1953 |
+
- type: cos_sim_pearson
|
1954 |
+
value: 85.00513539036214
|
1955 |
+
- type: cos_sim_spearman
|
1956 |
+
value: 79.19581558052613
|
1957 |
+
- type: euclidean_pearson
|
1958 |
+
value: 82.46689229301268
|
1959 |
+
- type: euclidean_spearman
|
1960 |
+
value: 79.19581263972574
|
1961 |
+
- type: manhattan_pearson
|
1962 |
+
value: 82.46839559537645
|
1963 |
+
- type: manhattan_spearman
|
1964 |
+
value: 79.19301791744469
|
1965 |
+
- task:
|
1966 |
+
type: STS
|
1967 |
+
dataset:
|
1968 |
+
type: mteb/sts12-sts
|
1969 |
+
name: MTEB STS12
|
1970 |
+
config: default
|
1971 |
+
split: test
|
1972 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
1973 |
+
metrics:
|
1974 |
+
- type: cos_sim_pearson
|
1975 |
+
value: 82.44111721768361
|
1976 |
+
- type: cos_sim_spearman
|
1977 |
+
value: 73.14524004507561
|
1978 |
+
- type: euclidean_pearson
|
1979 |
+
value: 78.70346379990235
|
1980 |
+
- type: euclidean_spearman
|
1981 |
+
value: 73.14518679640568
|
1982 |
+
- type: manhattan_pearson
|
1983 |
+
value: 78.68478215009414
|
1984 |
+
- type: manhattan_spearman
|
1985 |
+
value: 73.10912398034866
|
1986 |
+
- task:
|
1987 |
+
type: STS
|
1988 |
+
dataset:
|
1989 |
+
type: mteb/sts13-sts
|
1990 |
+
name: MTEB STS13
|
1991 |
+
config: default
|
1992 |
+
split: test
|
1993 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
1994 |
+
metrics:
|
1995 |
+
- type: cos_sim_pearson
|
1996 |
+
value: 82.17030364533524
|
1997 |
+
- type: cos_sim_spearman
|
1998 |
+
value: 82.88382996129783
|
1999 |
+
- type: euclidean_pearson
|
2000 |
+
value: 82.25266887145027
|
2001 |
+
- type: euclidean_spearman
|
2002 |
+
value: 82.88382996129783
|
2003 |
+
- type: manhattan_pearson
|
2004 |
+
value: 82.21831434263969
|
2005 |
+
- type: manhattan_spearman
|
2006 |
+
value: 82.83144970048046
|
2007 |
+
- task:
|
2008 |
+
type: STS
|
2009 |
+
dataset:
|
2010 |
+
type: mteb/sts14-sts
|
2011 |
+
name: MTEB STS14
|
2012 |
+
config: default
|
2013 |
+
split: test
|
2014 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
2015 |
+
metrics:
|
2016 |
+
- type: cos_sim_pearson
|
2017 |
+
value: 80.73413303490618
|
2018 |
+
- type: cos_sim_spearman
|
2019 |
+
value: 76.95203008005365
|
2020 |
+
- type: euclidean_pearson
|
2021 |
+
value: 79.09169854088067
|
2022 |
+
- type: euclidean_spearman
|
2023 |
+
value: 76.95202489005659
|
2024 |
+
- type: manhattan_pearson
|
2025 |
+
value: 79.04289364751341
|
2026 |
+
- type: manhattan_spearman
|
2027 |
+
value: 76.89976809512328
|
2028 |
+
- task:
|
2029 |
+
type: STS
|
2030 |
+
dataset:
|
2031 |
+
type: mteb/sts15-sts
|
2032 |
+
name: MTEB STS15
|
2033 |
+
config: default
|
2034 |
+
split: test
|
2035 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
2036 |
+
metrics:
|
2037 |
+
- type: cos_sim_pearson
|
2038 |
+
value: 86.84421416279349
|
2039 |
+
- type: cos_sim_spearman
|
2040 |
+
value: 87.67393507190887
|
2041 |
+
- type: euclidean_pearson
|
2042 |
+
value: 86.81662915280972
|
2043 |
+
- type: euclidean_spearman
|
2044 |
+
value: 87.67395576051472
|
2045 |
+
- type: manhattan_pearson
|
2046 |
+
value: 86.76502179645067
|
2047 |
+
- type: manhattan_spearman
|
2048 |
+
value: 87.60931601838358
|
2049 |
+
- task:
|
2050 |
+
type: STS
|
2051 |
+
dataset:
|
2052 |
+
type: mteb/sts16-sts
|
2053 |
+
name: MTEB STS16
|
2054 |
+
config: default
|
2055 |
+
split: test
|
2056 |
+
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
2057 |
+
metrics:
|
2058 |
+
- type: cos_sim_pearson
|
2059 |
+
value: 83.47603001840406
|
2060 |
+
- type: cos_sim_spearman
|
2061 |
+
value: 84.57363689562743
|
2062 |
+
- type: euclidean_pearson
|
2063 |
+
value: 83.62746191773213
|
2064 |
+
- type: euclidean_spearman
|
2065 |
+
value: 84.57363689562743
|
2066 |
+
- type: manhattan_pearson
|
2067 |
+
value: 83.5049257196953
|
2068 |
+
- type: manhattan_spearman
|
2069 |
+
value: 84.43576972291818
|
2070 |
+
- task:
|
2071 |
+
type: STS
|
2072 |
+
dataset:
|
2073 |
+
type: mteb/sts17-crosslingual-sts
|
2074 |
+
name: MTEB STS17 (en-en)
|
2075 |
+
config: en-en
|
2076 |
+
split: test
|
2077 |
+
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
2078 |
+
metrics:
|
2079 |
+
- type: cos_sim_pearson
|
2080 |
+
value: 89.17222804445805
|
2081 |
+
- type: cos_sim_spearman
|
2082 |
+
value: 89.04642204765032
|
2083 |
+
- type: euclidean_pearson
|
2084 |
+
value: 88.93412366747594
|
2085 |
+
- type: euclidean_spearman
|
2086 |
+
value: 89.04642204765032
|
2087 |
+
- type: manhattan_pearson
|
2088 |
+
value: 88.88891722217033
|
2089 |
+
- type: manhattan_spearman
|
2090 |
+
value: 88.95405155642727
|
2091 |
+
- task:
|
2092 |
+
type: STS
|
2093 |
+
dataset:
|
2094 |
+
type: mteb/sts22-crosslingual-sts
|
2095 |
+
name: MTEB STS22 (en)
|
2096 |
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config: en
|
2097 |
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split: test
|
2098 |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
2099 |
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metrics:
|
2100 |
+
- type: cos_sim_pearson
|
2101 |
+
value: 63.4232873899918
|
2102 |
+
- type: cos_sim_spearman
|
2103 |
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value: 62.53261852485254
|
2104 |
+
- type: euclidean_pearson
|
2105 |
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value: 63.95808586267597
|
2106 |
+
- type: euclidean_spearman
|
2107 |
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value: 62.53261852485254
|
2108 |
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- type: manhattan_pearson
|
2109 |
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value: 64.07446205165546
|
2110 |
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- type: manhattan_spearman
|
2111 |
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value: 62.86514483815617
|
2112 |
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- task:
|
2113 |
+
type: STS
|
2114 |
+
dataset:
|
2115 |
+
type: mteb/stsbenchmark-sts
|
2116 |
+
name: MTEB STSBenchmark
|
2117 |
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config: default
|
2118 |
+
split: test
|
2119 |
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revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
2120 |
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metrics:
|
2121 |
+
- type: cos_sim_pearson
|
2122 |
+
value: 84.324835033109
|
2123 |
+
- type: cos_sim_spearman
|
2124 |
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value: 84.75551248417419
|
2125 |
+
- type: euclidean_pearson
|
2126 |
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value: 84.98725144123726
|
2127 |
+
- type: euclidean_spearman
|
2128 |
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value: 84.75551248417419
|
2129 |
+
- type: manhattan_pearson
|
2130 |
+
value: 84.9546533100131
|
2131 |
+
- type: manhattan_spearman
|
2132 |
+
value: 84.73671830914728
|
2133 |
+
- task:
|
2134 |
+
type: Reranking
|
2135 |
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dataset:
|
2136 |
+
type: mteb/scidocs-reranking
|
2137 |
+
name: MTEB SciDocsRR
|
2138 |
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config: default
|
2139 |
+
split: test
|
2140 |
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revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
2141 |
+
metrics:
|
2142 |
+
- type: map
|
2143 |
+
value: 83.62940531539546
|
2144 |
+
- type: mrr
|
2145 |
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value: 95.50283503714876
|
2146 |
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- task:
|
2147 |
+
type: Retrieval
|
2148 |
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dataset:
|
2149 |
+
type: scifact
|
2150 |
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name: MTEB SciFact
|
2151 |
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config: default
|
2152 |
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split: test
|
2153 |
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revision: None
|
2154 |
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metrics:
|
2155 |
+
- type: map_at_1
|
2156 |
+
value: 52.428
|
2157 |
+
- type: map_at_10
|
2158 |
+
value: 62.731
|
2159 |
+
- type: map_at_100
|
2160 |
+
value: 63.327
|
2161 |
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- type: map_at_1000
|
2162 |
+
value: 63.356
|
2163 |
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- type: map_at_3
|
2164 |
+
value: 60.17400000000001
|
2165 |
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- type: map_at_5
|
2166 |
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value: 61.461
|
2167 |
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- type: mrr_at_1
|
2168 |
+
value: 55.333
|
2169 |
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- type: mrr_at_10
|
2170 |
+
value: 63.788999999999994
|
2171 |
+
- type: mrr_at_100
|
2172 |
+
value: 64.27000000000001
|
2173 |
+
- type: mrr_at_1000
|
2174 |
+
value: 64.298
|
2175 |
+
- type: mrr_at_3
|
2176 |
+
value: 61.944
|
2177 |
+
- type: mrr_at_5
|
2178 |
+
value: 62.861
|
2179 |
+
- type: ndcg_at_1
|
2180 |
+
value: 55.333
|
2181 |
+
- type: ndcg_at_10
|
2182 |
+
value: 67.309
|
2183 |
+
- type: ndcg_at_100
|
2184 |
+
value: 70.033
|
2185 |
+
- type: ndcg_at_1000
|
2186 |
+
value: 70.842
|
2187 |
+
- type: ndcg_at_3
|
2188 |
+
value: 63.05500000000001
|
2189 |
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- type: ndcg_at_5
|
2190 |
+
value: 64.8
|
2191 |
+
- type: precision_at_1
|
2192 |
+
value: 55.333
|
2193 |
+
- type: precision_at_10
|
2194 |
+
value: 9.1
|
2195 |
+
- type: precision_at_100
|
2196 |
+
value: 1.057
|
2197 |
+
- type: precision_at_1000
|
2198 |
+
value: 0.11199999999999999
|
2199 |
+
- type: precision_at_3
|
2200 |
+
value: 25.111
|
2201 |
+
- type: precision_at_5
|
2202 |
+
value: 16.333000000000002
|
2203 |
+
- type: recall_at_1
|
2204 |
+
value: 52.428
|
2205 |
+
- type: recall_at_10
|
2206 |
+
value: 80.156
|
2207 |
+
- type: recall_at_100
|
2208 |
+
value: 92.833
|
2209 |
+
- type: recall_at_1000
|
2210 |
+
value: 99.333
|
2211 |
+
- type: recall_at_3
|
2212 |
+
value: 68.73899999999999
|
2213 |
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- type: recall_at_5
|
2214 |
+
value: 73.13300000000001
|
2215 |
+
- task:
|
2216 |
+
type: PairClassification
|
2217 |
+
dataset:
|
2218 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
2219 |
+
name: MTEB SprintDuplicateQuestions
|
2220 |
+
config: default
|
2221 |
+
split: test
|
2222 |
+
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
2223 |
+
metrics:
|
2224 |
+
- type: cos_sim_accuracy
|
2225 |
+
value: 99.8069306930693
|
2226 |
+
- type: cos_sim_ap
|
2227 |
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value: 94.89496931806809
|
2228 |
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- type: cos_sim_f1
|
2229 |
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value: 90.0763358778626
|
2230 |
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- type: cos_sim_precision
|
2231 |
+
value: 91.70984455958549
|
2232 |
+
- type: cos_sim_recall
|
2233 |
+
value: 88.5
|
2234 |
+
- type: dot_accuracy
|
2235 |
+
value: 99.8069306930693
|
2236 |
+
- type: dot_ap
|
2237 |
+
value: 94.89495820622456
|
2238 |
+
- type: dot_f1
|
2239 |
+
value: 90.0763358778626
|
2240 |
+
- type: dot_precision
|
2241 |
+
value: 91.70984455958549
|
2242 |
+
- type: dot_recall
|
2243 |
+
value: 88.5
|
2244 |
+
- type: euclidean_accuracy
|
2245 |
+
value: 99.8069306930693
|
2246 |
+
- type: euclidean_ap
|
2247 |
+
value: 94.8949693180681
|
2248 |
+
- type: euclidean_f1
|
2249 |
+
value: 90.0763358778626
|
2250 |
+
- type: euclidean_precision
|
2251 |
+
value: 91.70984455958549
|
2252 |
+
- type: euclidean_recall
|
2253 |
+
value: 88.5
|
2254 |
+
- type: manhattan_accuracy
|
2255 |
+
value: 99.8009900990099
|
2256 |
+
- type: manhattan_ap
|
2257 |
+
value: 94.81699021810266
|
2258 |
+
- type: manhattan_f1
|
2259 |
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value: 89.82278481012658
|
2260 |
+
- type: manhattan_precision
|
2261 |
+
value: 90.97435897435898
|
2262 |
+
- type: manhattan_recall
|
2263 |
+
value: 88.7
|
2264 |
+
- type: max_accuracy
|
2265 |
+
value: 99.8069306930693
|
2266 |
+
- type: max_ap
|
2267 |
+
value: 94.8949693180681
|
2268 |
+
- type: max_f1
|
2269 |
+
value: 90.0763358778626
|
2270 |
+
- task:
|
2271 |
+
type: Clustering
|
2272 |
+
dataset:
|
2273 |
+
type: mteb/stackexchange-clustering
|
2274 |
+
name: MTEB StackExchangeClustering
|
2275 |
+
config: default
|
2276 |
+
split: test
|
2277 |
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revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
2278 |
+
metrics:
|
2279 |
+
- type: v_measure
|
2280 |
+
value: 58.95255708336027
|
2281 |
+
- task:
|
2282 |
+
type: Clustering
|
2283 |
+
dataset:
|
2284 |
+
type: mteb/stackexchange-clustering-p2p
|
2285 |
+
name: MTEB StackExchangeClusteringP2P
|
2286 |
+
config: default
|
2287 |
+
split: test
|
2288 |
+
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
2289 |
+
metrics:
|
2290 |
+
- type: v_measure
|
2291 |
+
value: 34.26328409998647
|
2292 |
+
- task:
|
2293 |
+
type: Reranking
|
2294 |
+
dataset:
|
2295 |
+
type: mteb/stackoverflowdupquestions-reranking
|
2296 |
+
name: MTEB StackOverflowDupQuestions
|
2297 |
+
config: default
|
2298 |
+
split: test
|
2299 |
+
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
2300 |
+
metrics:
|
2301 |
+
- type: map
|
2302 |
+
value: 52.324949351182134
|
2303 |
+
- type: mrr
|
2304 |
+
value: 53.08798329938036
|
2305 |
+
- task:
|
2306 |
+
type: Summarization
|
2307 |
+
dataset:
|
2308 |
+
type: mteb/summeval
|
2309 |
+
name: MTEB SummEval
|
2310 |
+
config: default
|
2311 |
+
split: test
|
2312 |
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revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
2313 |
+
metrics:
|
2314 |
+
- type: cos_sim_pearson
|
2315 |
+
value: 30.286127875761963
|
2316 |
+
- type: cos_sim_spearman
|
2317 |
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value: 30.85723241148158
|
2318 |
+
- type: dot_pearson
|
2319 |
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value: 30.28613033184199
|
2320 |
+
- type: dot_spearman
|
2321 |
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value: 30.85723241148158
|
2322 |
+
- task:
|
2323 |
+
type: Retrieval
|
2324 |
+
dataset:
|
2325 |
+
type: trec-covid
|
2326 |
+
name: MTEB TRECCOVID
|
2327 |
+
config: default
|
2328 |
+
split: test
|
2329 |
+
revision: None
|
2330 |
+
metrics:
|
2331 |
+
- type: map_at_1
|
2332 |
+
value: 0.199
|
2333 |
+
- type: map_at_10
|
2334 |
+
value: 1.633
|
2335 |
+
- type: map_at_100
|
2336 |
+
value: 8.813
|
2337 |
+
- type: map_at_1000
|
2338 |
+
value: 21.015
|
2339 |
+
- type: map_at_3
|
2340 |
+
value: 0.577
|
2341 |
+
- type: map_at_5
|
2342 |
+
value: 0.907
|
2343 |
+
- type: mrr_at_1
|
2344 |
+
value: 72.0
|
2345 |
+
- type: mrr_at_10
|
2346 |
+
value: 82.667
|
2347 |
+
- type: mrr_at_100
|
2348 |
+
value: 82.667
|
2349 |
+
- type: mrr_at_1000
|
2350 |
+
value: 82.667
|
2351 |
+
- type: mrr_at_3
|
2352 |
+
value: 80.667
|
2353 |
+
- type: mrr_at_5
|
2354 |
+
value: 82.667
|
2355 |
+
- type: ndcg_at_1
|
2356 |
+
value: 67.0
|
2357 |
+
- type: ndcg_at_10
|
2358 |
+
value: 65.377
|
2359 |
+
- type: ndcg_at_100
|
2360 |
+
value: 50.693
|
2361 |
+
- type: ndcg_at_1000
|
2362 |
+
value: 45.449
|
2363 |
+
- type: ndcg_at_3
|
2364 |
+
value: 67.78800000000001
|
2365 |
+
- type: ndcg_at_5
|
2366 |
+
value: 67.19000000000001
|
2367 |
+
- type: precision_at_1
|
2368 |
+
value: 72.0
|
2369 |
+
- type: precision_at_10
|
2370 |
+
value: 70.6
|
2371 |
+
- type: precision_at_100
|
2372 |
+
value: 52.0
|
2373 |
+
- type: precision_at_1000
|
2374 |
+
value: 20.316000000000003
|
2375 |
+
- type: precision_at_3
|
2376 |
+
value: 72.667
|
2377 |
+
- type: precision_at_5
|
2378 |
+
value: 72.39999999999999
|
2379 |
+
- type: recall_at_1
|
2380 |
+
value: 0.199
|
2381 |
+
- type: recall_at_10
|
2382 |
+
value: 1.8800000000000001
|
2383 |
+
- type: recall_at_100
|
2384 |
+
value: 12.195
|
2385 |
+
- type: recall_at_1000
|
2386 |
+
value: 42.612
|
2387 |
+
- type: recall_at_3
|
2388 |
+
value: 0.608
|
2389 |
+
- type: recall_at_5
|
2390 |
+
value: 1.004
|
2391 |
+
- task:
|
2392 |
+
type: Retrieval
|
2393 |
+
dataset:
|
2394 |
+
type: webis-touche2020
|
2395 |
+
name: MTEB Touche2020
|
2396 |
+
config: default
|
2397 |
+
split: test
|
2398 |
+
revision: None
|
2399 |
+
metrics:
|
2400 |
+
- type: map_at_1
|
2401 |
+
value: 2.34
|
2402 |
+
- type: map_at_10
|
2403 |
+
value: 7.983
|
2404 |
+
- type: map_at_100
|
2405 |
+
value: 14.488999999999999
|
2406 |
+
- type: map_at_1000
|
2407 |
+
value: 16.133
|
2408 |
+
- type: map_at_3
|
2409 |
+
value: 4.312
|
2410 |
+
- type: map_at_5
|
2411 |
+
value: 6.3420000000000005
|
2412 |
+
- type: mrr_at_1
|
2413 |
+
value: 26.531
|
2414 |
+
- type: mrr_at_10
|
2415 |
+
value: 41.558
|
2416 |
+
- type: mrr_at_100
|
2417 |
+
value: 42.211999999999996
|
2418 |
+
- type: mrr_at_1000
|
2419 |
+
value: 42.211999999999996
|
2420 |
+
- type: mrr_at_3
|
2421 |
+
value: 36.054
|
2422 |
+
- type: mrr_at_5
|
2423 |
+
value: 39.217999999999996
|
2424 |
+
- type: ndcg_at_1
|
2425 |
+
value: 23.469
|
2426 |
+
- type: ndcg_at_10
|
2427 |
+
value: 21.077
|
2428 |
+
- type: ndcg_at_100
|
2429 |
+
value: 35.497
|
2430 |
+
- type: ndcg_at_1000
|
2431 |
+
value: 47.282000000000004
|
2432 |
+
- type: ndcg_at_3
|
2433 |
+
value: 20.906
|
2434 |
+
- type: ndcg_at_5
|
2435 |
+
value: 21.78
|
2436 |
+
- type: precision_at_1
|
2437 |
+
value: 26.531
|
2438 |
+
- type: precision_at_10
|
2439 |
+
value: 18.570999999999998
|
2440 |
+
- type: precision_at_100
|
2441 |
+
value: 7.673000000000001
|
2442 |
+
- type: precision_at_1000
|
2443 |
+
value: 1.551
|
2444 |
+
- type: precision_at_3
|
2445 |
+
value: 21.769
|
2446 |
+
- type: precision_at_5
|
2447 |
+
value: 22.448999999999998
|
2448 |
+
- type: recall_at_1
|
2449 |
+
value: 2.34
|
2450 |
+
- type: recall_at_10
|
2451 |
+
value: 14.154
|
2452 |
+
- type: recall_at_100
|
2453 |
+
value: 48.355
|
2454 |
+
- type: recall_at_1000
|
2455 |
+
value: 84.872
|
2456 |
+
- type: recall_at_3
|
2457 |
+
value: 5.19
|
2458 |
+
- type: recall_at_5
|
2459 |
+
value: 9.211
|
2460 |
+
- task:
|
2461 |
+
type: Classification
|
2462 |
+
dataset:
|
2463 |
+
type: mteb/toxic_conversations_50k
|
2464 |
+
name: MTEB ToxicConversationsClassification
|
2465 |
+
config: default
|
2466 |
+
split: test
|
2467 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
2468 |
+
metrics:
|
2469 |
+
- type: accuracy
|
2470 |
+
value: 71.9318
|
2471 |
+
- type: ap
|
2472 |
+
value: 14.755439516631267
|
2473 |
+
- type: f1
|
2474 |
+
value: 55.39101096477449
|
2475 |
+
- task:
|
2476 |
+
type: Classification
|
2477 |
+
dataset:
|
2478 |
+
type: mteb/tweet_sentiment_extraction
|
2479 |
+
name: MTEB TweetSentimentExtractionClassification
|
2480 |
+
config: default
|
2481 |
+
split: test
|
2482 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
2483 |
+
metrics:
|
2484 |
+
- type: accuracy
|
2485 |
+
value: 61.06395019807584
|
2486 |
+
- type: f1
|
2487 |
+
value: 61.18513886850968
|
2488 |
+
- task:
|
2489 |
+
type: Clustering
|
2490 |
+
dataset:
|
2491 |
+
type: mteb/twentynewsgroups-clustering
|
2492 |
+
name: MTEB TwentyNewsgroupsClustering
|
2493 |
+
config: default
|
2494 |
+
split: test
|
2495 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
2496 |
+
metrics:
|
2497 |
+
- type: v_measure
|
2498 |
+
value: 43.68814723462553
|
2499 |
+
- task:
|
2500 |
+
type: PairClassification
|
2501 |
+
dataset:
|
2502 |
+
type: mteb/twittersemeval2015-pairclassification
|
2503 |
+
name: MTEB TwitterSemEval2015
|
2504 |
+
config: default
|
2505 |
+
split: test
|
2506 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
2507 |
+
metrics:
|
2508 |
+
- type: cos_sim_accuracy
|
2509 |
+
value: 85.8258329856351
|
2510 |
+
- type: cos_sim_ap
|
2511 |
+
value: 73.51953909054856
|
2512 |
+
- type: cos_sim_f1
|
2513 |
+
value: 68.17958783120707
|
2514 |
+
- type: cos_sim_precision
|
2515 |
+
value: 63.70930765703806
|
2516 |
+
- type: cos_sim_recall
|
2517 |
+
value: 73.3245382585752
|
2518 |
+
- type: dot_accuracy
|
2519 |
+
value: 85.8258329856351
|
2520 |
+
- type: dot_ap
|
2521 |
+
value: 73.51954936569123
|
2522 |
+
- type: dot_f1
|
2523 |
+
value: 68.17958783120707
|
2524 |
+
- type: dot_precision
|
2525 |
+
value: 63.70930765703806
|
2526 |
+
- type: dot_recall
|
2527 |
+
value: 73.3245382585752
|
2528 |
+
- type: euclidean_accuracy
|
2529 |
+
value: 85.8258329856351
|
2530 |
+
- type: euclidean_ap
|
2531 |
+
value: 73.51954390509214
|
2532 |
+
- type: euclidean_f1
|
2533 |
+
value: 68.17958783120707
|
2534 |
+
- type: euclidean_precision
|
2535 |
+
value: 63.70930765703806
|
2536 |
+
- type: euclidean_recall
|
2537 |
+
value: 73.3245382585752
|
2538 |
+
- type: manhattan_accuracy
|
2539 |
+
value: 85.8258329856351
|
2540 |
+
- type: manhattan_ap
|
2541 |
+
value: 73.44954175022839
|
2542 |
+
- type: manhattan_f1
|
2543 |
+
value: 68.08816482989938
|
2544 |
+
- type: manhattan_precision
|
2545 |
+
value: 62.351908731899954
|
2546 |
+
- type: manhattan_recall
|
2547 |
+
value: 74.9868073878628
|
2548 |
+
- type: max_accuracy
|
2549 |
+
value: 85.8258329856351
|
2550 |
+
- type: max_ap
|
2551 |
+
value: 73.51954936569123
|
2552 |
+
- type: max_f1
|
2553 |
+
value: 68.17958783120707
|
2554 |
+
- task:
|
2555 |
+
type: PairClassification
|
2556 |
+
dataset:
|
2557 |
+
type: mteb/twitterurlcorpus-pairclassification
|
2558 |
+
name: MTEB TwitterURLCorpus
|
2559 |
+
config: default
|
2560 |
+
split: test
|
2561 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
2562 |
+
metrics:
|
2563 |
+
- type: cos_sim_accuracy
|
2564 |
+
value: 88.6094617145962
|
2565 |
+
- type: cos_sim_ap
|
2566 |
+
value: 85.4121913477208
|
2567 |
+
- type: cos_sim_f1
|
2568 |
+
value: 77.61548157484985
|
2569 |
+
- type: cos_sim_precision
|
2570 |
+
value: 74.84627484627485
|
2571 |
+
- type: cos_sim_recall
|
2572 |
+
value: 80.59747459193102
|
2573 |
+
- type: dot_accuracy
|
2574 |
+
value: 88.6094617145962
|
2575 |
+
- type: dot_ap
|
2576 |
+
value: 85.41219830675979
|
2577 |
+
- type: dot_f1
|
2578 |
+
value: 77.61548157484985
|
2579 |
+
- type: dot_precision
|
2580 |
+
value: 74.84627484627485
|
2581 |
+
- type: dot_recall
|
2582 |
+
value: 80.59747459193102
|
2583 |
+
- type: euclidean_accuracy
|
2584 |
+
value: 88.6094617145962
|
2585 |
+
- type: euclidean_ap
|
2586 |
+
value: 85.41219328124808
|
2587 |
+
- type: euclidean_f1
|
2588 |
+
value: 77.61548157484985
|
2589 |
+
- type: euclidean_precision
|
2590 |
+
value: 74.84627484627485
|
2591 |
+
- type: euclidean_recall
|
2592 |
+
value: 80.59747459193102
|
2593 |
+
- type: manhattan_accuracy
|
2594 |
+
value: 88.53960492102301
|
2595 |
+
- type: manhattan_ap
|
2596 |
+
value: 85.35022078482446
|
2597 |
+
- type: manhattan_f1
|
2598 |
+
value: 77.56588974387569
|
2599 |
+
- type: manhattan_precision
|
2600 |
+
value: 74.98742183569324
|
2601 |
+
- type: manhattan_recall
|
2602 |
+
value: 80.3279950723745
|
2603 |
+
- type: max_accuracy
|
2604 |
+
value: 88.6094617145962
|
2605 |
+
- type: max_ap
|
2606 |
+
value: 85.41219830675979
|
2607 |
+
- type: max_f1
|
2608 |
+
value: 77.61548157484985
|
2609 |
+
---
|
2610 |
+
<!-- TODO: add evaluation results here -->
|
2611 |
+
<br><br>
|
2612 |
+
|
2613 |
+
<p align="center">
|
2614 |
+
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
|
2615 |
+
</p>
|
2616 |
+
|
2617 |
+
|
2618 |
+
<p align="center">
|
2619 |
+
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b>
|
2620 |
+
</p>
|
2621 |
+
|
2622 |
+
|
2623 |
+
## Intended Usage & Model Info
|
2624 |
+
|
2625 |
+
`jina-embedding-b-en-v2` is an English, monolingual embedding model supporting 8k sequence length.
|
2626 |
+
It is based on a Bert architecture that supports the symmetric bidirectional variant of ALiBi to support longer sequence length.
|
2627 |
+
The backbone Jina Bert Small model is pretrained on the C4 dataset.
|
2628 |
+
The model is further trained on Jina AI's collection of more than 40 datasets of sentence pairs and hard negatives.
|
2629 |
+
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
|
2630 |
+
|
2631 |
+
The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length thanks to ALiBi.
|
2632 |
+
This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search,...
|
2633 |
+
|
2634 |
+
This model has 33 million parameters, which enables lightning-fast and memory efficient inference on long documents, while still delivering impressive performance.
|
2635 |
+
Additionally, we provide the following embedding models, supporting 8k sequence length as well:
|
2636 |
+
|
2637 |
+
- [`jina-embedding-s-en-v2`](https://huggingface.co/jinaai/jina-embedding-s-en-v2): 33 million parameters.
|
2638 |
+
- [`jina-embedding-b-en-v2`](https://huggingface.co/jinaai/jina-embedding-b-en-v2): 137 million parameters **(you are here)**.
|
2639 |
+
- [`jina-embedding-l-en-v2`](https://huggingface.co/jinaai/jina-embedding-l-en-v2): 435 million parameters.
|
2640 |
+
|
2641 |
+
## Data & Parameters
|
2642 |
+
<!-- TODO: update the paper ID once it is published on arxiv -->
|
2643 |
+
Please checkout our [technical blog](https://arxiv.org/abs/2307.11224).
|
2644 |
+
|
2645 |
+
## Metrics
|
2646 |
+
|
2647 |
+
We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI:
|
2648 |
+
|
2649 |
+
<!-- TODO: add evaluation table here -->
|
2650 |
+
|
2651 |
+
## Usage
|
2652 |
+
|
2653 |
+
You can use Jina Embedding models directly from transformers package:
|
2654 |
+
```python
|
2655 |
+
!pip install transformers
|
2656 |
+
from transformers import AutoModel
|
2657 |
+
from numpy.linalg import norm
|
2658 |
+
|
2659 |
+
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
|
2660 |
+
model = AutoModel.from_pretrained('jinaai/jina-embedding-b-en-v2', trust_remote_code=True) # trust_remote_code is needed to use the encode method
|
2661 |
+
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
|
2662 |
+
print(cos_sim(embeddings[0], embeddings[1]))
|
2663 |
+
```
|
2664 |
+
|
2665 |
+
For long sequences, it's recommended to perform inference using Flash Attention. Using Flash Attention allows you to increase the batch size and throughput for long sequence length.
|
2666 |
+
We include an experimental implementation for Flash Attention, shipped with the model.
|
2667 |
+
Install the following triton version:
|
2668 |
+
`pip install triton==2.0.0.dev20221202`.
|
2669 |
+
Now run the same code above, but make sure to set the parameter `with_flash` to `True` when you load the model. You also have to use either `fp16` or `bf16`:
|
2670 |
+
```python
|
2671 |
+
from transformers import AutoModel
|
2672 |
+
from numpy.linalg import norm
|
2673 |
+
import torch
|
2674 |
+
|
2675 |
+
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
|
2676 |
+
model = AutoModel.from_pretrained('jinaai/jina-embedding-b-en-v2', trust_remote_code=True, with_flash=True, torch_dtype=torch.float16).cuda() # trust_remote_code is needed to use the encode method
|
2677 |
+
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
|
2678 |
+
print(cos_sim(embeddings[0], embeddings[1]))
|
2679 |
+
```
|
2680 |
+
|
2681 |
+
## Fine-tuning
|
2682 |
+
|
2683 |
+
Please consider [Finetuner](https://github.com/jina-ai/finetuner).
|
2684 |
+
|
2685 |
+
## Plans
|
2686 |
+
The development of new multilingual models is currently underway. We will be targeting mainly the German and Spanish languages. The upcoming models will be called `jina-embedding-s/b/l-de/es-v2`.
|
2687 |
+
|
2688 |
+
## Contact
|
2689 |
+
|
2690 |
+
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
|
2691 |
+
|
2692 |
+
## Citation
|
2693 |
+
|
2694 |
+
If you find Jina Embeddings useful in your research, please cite the following paper:
|
2695 |
+
|
2696 |
+
<!-- TODO: update the paper ID once it is published on arxiv -->
|
2697 |
+
``` latex
|
2698 |
+
@misc{günther2023jina,
|
2699 |
+
title={Beyond the 512-Token Barrier: Training General-Purpose Text
|
2700 |
+
Embeddings for Large Documents},
|
2701 |
+
author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang},
|
2702 |
+
year={2023},
|
2703 |
+
eprint={2307.11224},
|
2704 |
+
archivePrefix={arXiv},
|
2705 |
+
primaryClass={cs.CL}
|
2706 |
+
}
|
2707 |
+
```
|