Commit
•
476dfe9
0
Parent(s):
Duplicate from michaelfeil/ct2fast-e5-small-v2
Browse filesCo-authored-by: Michael <michaelfeil@users.noreply.huggingface.co>
- .gitattributes +34 -0
- README.md +2750 -0
- config.json +29 -0
- model.bin +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
- vocab.txt +0 -0
- vocabulary.json +0 -0
- vocabulary.txt +0 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.npz filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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README.md
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|
1 |
+
---
|
2 |
+
tags:
|
3 |
+
- ctranslate2
|
4 |
+
- int8
|
5 |
+
- float16
|
6 |
+
- mteb
|
7 |
+
model-index:
|
8 |
+
- name: e5-small-v2
|
9 |
+
results:
|
10 |
+
- task:
|
11 |
+
type: Classification
|
12 |
+
dataset:
|
13 |
+
type: mteb/amazon_counterfactual
|
14 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
15 |
+
config: en
|
16 |
+
split: test
|
17 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
18 |
+
metrics:
|
19 |
+
- type: accuracy
|
20 |
+
value: 77.59701492537313
|
21 |
+
- type: ap
|
22 |
+
value: 41.67064885731708
|
23 |
+
- type: f1
|
24 |
+
value: 71.86465946398573
|
25 |
+
- task:
|
26 |
+
type: Classification
|
27 |
+
dataset:
|
28 |
+
type: mteb/amazon_polarity
|
29 |
+
name: MTEB AmazonPolarityClassification
|
30 |
+
config: default
|
31 |
+
split: test
|
32 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
33 |
+
metrics:
|
34 |
+
- type: accuracy
|
35 |
+
value: 91.265875
|
36 |
+
- type: ap
|
37 |
+
value: 87.67633085349644
|
38 |
+
- type: f1
|
39 |
+
value: 91.24297521425744
|
40 |
+
- task:
|
41 |
+
type: Classification
|
42 |
+
dataset:
|
43 |
+
type: mteb/amazon_reviews_multi
|
44 |
+
name: MTEB AmazonReviewsClassification (en)
|
45 |
+
config: en
|
46 |
+
split: test
|
47 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
48 |
+
metrics:
|
49 |
+
- type: accuracy
|
50 |
+
value: 45.882000000000005
|
51 |
+
- type: f1
|
52 |
+
value: 45.08058870381236
|
53 |
+
- task:
|
54 |
+
type: Retrieval
|
55 |
+
dataset:
|
56 |
+
type: arguana
|
57 |
+
name: MTEB ArguAna
|
58 |
+
config: default
|
59 |
+
split: test
|
60 |
+
revision: None
|
61 |
+
metrics:
|
62 |
+
- type: map_at_1
|
63 |
+
value: 20.697
|
64 |
+
- type: map_at_10
|
65 |
+
value: 33.975
|
66 |
+
- type: map_at_100
|
67 |
+
value: 35.223
|
68 |
+
- type: map_at_1000
|
69 |
+
value: 35.260000000000005
|
70 |
+
- type: map_at_3
|
71 |
+
value: 29.776999999999997
|
72 |
+
- type: map_at_5
|
73 |
+
value: 32.035000000000004
|
74 |
+
- type: mrr_at_1
|
75 |
+
value: 20.982
|
76 |
+
- type: mrr_at_10
|
77 |
+
value: 34.094
|
78 |
+
- type: mrr_at_100
|
79 |
+
value: 35.343
|
80 |
+
- type: mrr_at_1000
|
81 |
+
value: 35.38
|
82 |
+
- type: mrr_at_3
|
83 |
+
value: 29.884
|
84 |
+
- type: mrr_at_5
|
85 |
+
value: 32.141999999999996
|
86 |
+
- type: ndcg_at_1
|
87 |
+
value: 20.697
|
88 |
+
- type: ndcg_at_10
|
89 |
+
value: 41.668
|
90 |
+
- type: ndcg_at_100
|
91 |
+
value: 47.397
|
92 |
+
- type: ndcg_at_1000
|
93 |
+
value: 48.305
|
94 |
+
- type: ndcg_at_3
|
95 |
+
value: 32.928000000000004
|
96 |
+
- type: ndcg_at_5
|
97 |
+
value: 36.998999999999995
|
98 |
+
- type: precision_at_1
|
99 |
+
value: 20.697
|
100 |
+
- type: precision_at_10
|
101 |
+
value: 6.636
|
102 |
+
- type: precision_at_100
|
103 |
+
value: 0.924
|
104 |
+
- type: precision_at_1000
|
105 |
+
value: 0.099
|
106 |
+
- type: precision_at_3
|
107 |
+
value: 14.035
|
108 |
+
- type: precision_at_5
|
109 |
+
value: 10.398
|
110 |
+
- type: recall_at_1
|
111 |
+
value: 20.697
|
112 |
+
- type: recall_at_10
|
113 |
+
value: 66.35799999999999
|
114 |
+
- type: recall_at_100
|
115 |
+
value: 92.39
|
116 |
+
- type: recall_at_1000
|
117 |
+
value: 99.36
|
118 |
+
- type: recall_at_3
|
119 |
+
value: 42.105
|
120 |
+
- type: recall_at_5
|
121 |
+
value: 51.991
|
122 |
+
- task:
|
123 |
+
type: Clustering
|
124 |
+
dataset:
|
125 |
+
type: mteb/arxiv-clustering-p2p
|
126 |
+
name: MTEB ArxivClusteringP2P
|
127 |
+
config: default
|
128 |
+
split: test
|
129 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
130 |
+
metrics:
|
131 |
+
- type: v_measure
|
132 |
+
value: 42.1169517447068
|
133 |
+
- task:
|
134 |
+
type: Clustering
|
135 |
+
dataset:
|
136 |
+
type: mteb/arxiv-clustering-s2s
|
137 |
+
name: MTEB ArxivClusteringS2S
|
138 |
+
config: default
|
139 |
+
split: test
|
140 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
141 |
+
metrics:
|
142 |
+
- type: v_measure
|
143 |
+
value: 34.79553720107097
|
144 |
+
- task:
|
145 |
+
type: Reranking
|
146 |
+
dataset:
|
147 |
+
type: mteb/askubuntudupquestions-reranking
|
148 |
+
name: MTEB AskUbuntuDupQuestions
|
149 |
+
config: default
|
150 |
+
split: test
|
151 |
+
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
|
152 |
+
metrics:
|
153 |
+
- type: map
|
154 |
+
value: 58.10811337308168
|
155 |
+
- type: mrr
|
156 |
+
value: 71.56410763751482
|
157 |
+
- task:
|
158 |
+
type: STS
|
159 |
+
dataset:
|
160 |
+
type: mteb/biosses-sts
|
161 |
+
name: MTEB BIOSSES
|
162 |
+
config: default
|
163 |
+
split: test
|
164 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
165 |
+
metrics:
|
166 |
+
- type: cos_sim_pearson
|
167 |
+
value: 78.46834918248696
|
168 |
+
- type: cos_sim_spearman
|
169 |
+
value: 79.4289182755206
|
170 |
+
- type: euclidean_pearson
|
171 |
+
value: 76.26662973727008
|
172 |
+
- type: euclidean_spearman
|
173 |
+
value: 78.11744260952536
|
174 |
+
- type: manhattan_pearson
|
175 |
+
value: 76.08175262609434
|
176 |
+
- type: manhattan_spearman
|
177 |
+
value: 78.29395265552289
|
178 |
+
- task:
|
179 |
+
type: Classification
|
180 |
+
dataset:
|
181 |
+
type: mteb/banking77
|
182 |
+
name: MTEB Banking77Classification
|
183 |
+
config: default
|
184 |
+
split: test
|
185 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
186 |
+
metrics:
|
187 |
+
- type: accuracy
|
188 |
+
value: 81.63636363636364
|
189 |
+
- type: f1
|
190 |
+
value: 81.55779952376953
|
191 |
+
- task:
|
192 |
+
type: Clustering
|
193 |
+
dataset:
|
194 |
+
type: mteb/biorxiv-clustering-p2p
|
195 |
+
name: MTEB BiorxivClusteringP2P
|
196 |
+
config: default
|
197 |
+
split: test
|
198 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
199 |
+
metrics:
|
200 |
+
- type: v_measure
|
201 |
+
value: 35.88541137137571
|
202 |
+
- task:
|
203 |
+
type: Clustering
|
204 |
+
dataset:
|
205 |
+
type: mteb/biorxiv-clustering-s2s
|
206 |
+
name: MTEB BiorxivClusteringS2S
|
207 |
+
config: default
|
208 |
+
split: test
|
209 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
210 |
+
metrics:
|
211 |
+
- type: v_measure
|
212 |
+
value: 30.05205685274407
|
213 |
+
- task:
|
214 |
+
type: Retrieval
|
215 |
+
dataset:
|
216 |
+
type: BeIR/cqadupstack
|
217 |
+
name: MTEB CQADupstackAndroidRetrieval
|
218 |
+
config: default
|
219 |
+
split: test
|
220 |
+
revision: None
|
221 |
+
metrics:
|
222 |
+
- type: map_at_1
|
223 |
+
value: 30.293999999999997
|
224 |
+
- type: map_at_10
|
225 |
+
value: 39.876
|
226 |
+
- type: map_at_100
|
227 |
+
value: 41.315000000000005
|
228 |
+
- type: map_at_1000
|
229 |
+
value: 41.451
|
230 |
+
- type: map_at_3
|
231 |
+
value: 37.194
|
232 |
+
- type: map_at_5
|
233 |
+
value: 38.728
|
234 |
+
- type: mrr_at_1
|
235 |
+
value: 37.053000000000004
|
236 |
+
- type: mrr_at_10
|
237 |
+
value: 45.281
|
238 |
+
- type: mrr_at_100
|
239 |
+
value: 46.188
|
240 |
+
- type: mrr_at_1000
|
241 |
+
value: 46.245999999999995
|
242 |
+
- type: mrr_at_3
|
243 |
+
value: 43.228
|
244 |
+
- type: mrr_at_5
|
245 |
+
value: 44.366
|
246 |
+
- type: ndcg_at_1
|
247 |
+
value: 37.053000000000004
|
248 |
+
- type: ndcg_at_10
|
249 |
+
value: 45.086
|
250 |
+
- type: ndcg_at_100
|
251 |
+
value: 50.756
|
252 |
+
- type: ndcg_at_1000
|
253 |
+
value: 53.123
|
254 |
+
- type: ndcg_at_3
|
255 |
+
value: 41.416
|
256 |
+
- type: ndcg_at_5
|
257 |
+
value: 43.098
|
258 |
+
- type: precision_at_1
|
259 |
+
value: 37.053000000000004
|
260 |
+
- type: precision_at_10
|
261 |
+
value: 8.34
|
262 |
+
- type: precision_at_100
|
263 |
+
value: 1.346
|
264 |
+
- type: precision_at_1000
|
265 |
+
value: 0.186
|
266 |
+
- type: precision_at_3
|
267 |
+
value: 19.647000000000002
|
268 |
+
- type: precision_at_5
|
269 |
+
value: 13.877
|
270 |
+
- type: recall_at_1
|
271 |
+
value: 30.293999999999997
|
272 |
+
- type: recall_at_10
|
273 |
+
value: 54.309
|
274 |
+
- type: recall_at_100
|
275 |
+
value: 78.59
|
276 |
+
- type: recall_at_1000
|
277 |
+
value: 93.82300000000001
|
278 |
+
- type: recall_at_3
|
279 |
+
value: 43.168
|
280 |
+
- type: recall_at_5
|
281 |
+
value: 48.192
|
282 |
+
- task:
|
283 |
+
type: Retrieval
|
284 |
+
dataset:
|
285 |
+
type: BeIR/cqadupstack
|
286 |
+
name: MTEB CQADupstackEnglishRetrieval
|
287 |
+
config: default
|
288 |
+
split: test
|
289 |
+
revision: None
|
290 |
+
metrics:
|
291 |
+
- type: map_at_1
|
292 |
+
value: 28.738000000000003
|
293 |
+
- type: map_at_10
|
294 |
+
value: 36.925999999999995
|
295 |
+
- type: map_at_100
|
296 |
+
value: 38.017
|
297 |
+
- type: map_at_1000
|
298 |
+
value: 38.144
|
299 |
+
- type: map_at_3
|
300 |
+
value: 34.446
|
301 |
+
- type: map_at_5
|
302 |
+
value: 35.704
|
303 |
+
- type: mrr_at_1
|
304 |
+
value: 35.478
|
305 |
+
- type: mrr_at_10
|
306 |
+
value: 42.786
|
307 |
+
- type: mrr_at_100
|
308 |
+
value: 43.458999999999996
|
309 |
+
- type: mrr_at_1000
|
310 |
+
value: 43.507
|
311 |
+
- type: mrr_at_3
|
312 |
+
value: 40.648
|
313 |
+
- type: mrr_at_5
|
314 |
+
value: 41.804
|
315 |
+
- type: ndcg_at_1
|
316 |
+
value: 35.478
|
317 |
+
- type: ndcg_at_10
|
318 |
+
value: 42.044
|
319 |
+
- type: ndcg_at_100
|
320 |
+
value: 46.249
|
321 |
+
- type: ndcg_at_1000
|
322 |
+
value: 48.44
|
323 |
+
- type: ndcg_at_3
|
324 |
+
value: 38.314
|
325 |
+
- type: ndcg_at_5
|
326 |
+
value: 39.798
|
327 |
+
- type: precision_at_1
|
328 |
+
value: 35.478
|
329 |
+
- type: precision_at_10
|
330 |
+
value: 7.764
|
331 |
+
- type: precision_at_100
|
332 |
+
value: 1.253
|
333 |
+
- type: precision_at_1000
|
334 |
+
value: 0.174
|
335 |
+
- type: precision_at_3
|
336 |
+
value: 18.047
|
337 |
+
- type: precision_at_5
|
338 |
+
value: 12.637
|
339 |
+
- type: recall_at_1
|
340 |
+
value: 28.738000000000003
|
341 |
+
- type: recall_at_10
|
342 |
+
value: 50.659
|
343 |
+
- type: recall_at_100
|
344 |
+
value: 68.76299999999999
|
345 |
+
- type: recall_at_1000
|
346 |
+
value: 82.811
|
347 |
+
- type: recall_at_3
|
348 |
+
value: 39.536
|
349 |
+
- type: recall_at_5
|
350 |
+
value: 43.763999999999996
|
351 |
+
- task:
|
352 |
+
type: Retrieval
|
353 |
+
dataset:
|
354 |
+
type: BeIR/cqadupstack
|
355 |
+
name: MTEB CQADupstackGamingRetrieval
|
356 |
+
config: default
|
357 |
+
split: test
|
358 |
+
revision: None
|
359 |
+
metrics:
|
360 |
+
- type: map_at_1
|
361 |
+
value: 38.565
|
362 |
+
- type: map_at_10
|
363 |
+
value: 50.168
|
364 |
+
- type: map_at_100
|
365 |
+
value: 51.11
|
366 |
+
- type: map_at_1000
|
367 |
+
value: 51.173
|
368 |
+
- type: map_at_3
|
369 |
+
value: 47.044000000000004
|
370 |
+
- type: map_at_5
|
371 |
+
value: 48.838
|
372 |
+
- type: mrr_at_1
|
373 |
+
value: 44.201
|
374 |
+
- type: mrr_at_10
|
375 |
+
value: 53.596999999999994
|
376 |
+
- type: mrr_at_100
|
377 |
+
value: 54.211
|
378 |
+
- type: mrr_at_1000
|
379 |
+
value: 54.247
|
380 |
+
- type: mrr_at_3
|
381 |
+
value: 51.202000000000005
|
382 |
+
- type: mrr_at_5
|
383 |
+
value: 52.608999999999995
|
384 |
+
- type: ndcg_at_1
|
385 |
+
value: 44.201
|
386 |
+
- type: ndcg_at_10
|
387 |
+
value: 55.694
|
388 |
+
- type: ndcg_at_100
|
389 |
+
value: 59.518
|
390 |
+
- type: ndcg_at_1000
|
391 |
+
value: 60.907
|
392 |
+
- type: ndcg_at_3
|
393 |
+
value: 50.395999999999994
|
394 |
+
- type: ndcg_at_5
|
395 |
+
value: 53.022999999999996
|
396 |
+
- type: precision_at_1
|
397 |
+
value: 44.201
|
398 |
+
- type: precision_at_10
|
399 |
+
value: 8.84
|
400 |
+
- type: precision_at_100
|
401 |
+
value: 1.162
|
402 |
+
- type: precision_at_1000
|
403 |
+
value: 0.133
|
404 |
+
- type: precision_at_3
|
405 |
+
value: 22.153
|
406 |
+
- type: precision_at_5
|
407 |
+
value: 15.260000000000002
|
408 |
+
- type: recall_at_1
|
409 |
+
value: 38.565
|
410 |
+
- type: recall_at_10
|
411 |
+
value: 68.65
|
412 |
+
- type: recall_at_100
|
413 |
+
value: 85.37400000000001
|
414 |
+
- type: recall_at_1000
|
415 |
+
value: 95.37400000000001
|
416 |
+
- type: recall_at_3
|
417 |
+
value: 54.645999999999994
|
418 |
+
- type: recall_at_5
|
419 |
+
value: 60.958
|
420 |
+
- task:
|
421 |
+
type: Retrieval
|
422 |
+
dataset:
|
423 |
+
type: BeIR/cqadupstack
|
424 |
+
name: MTEB CQADupstackGisRetrieval
|
425 |
+
config: default
|
426 |
+
split: test
|
427 |
+
revision: None
|
428 |
+
metrics:
|
429 |
+
- type: map_at_1
|
430 |
+
value: 23.945
|
431 |
+
- type: map_at_10
|
432 |
+
value: 30.641000000000002
|
433 |
+
- type: map_at_100
|
434 |
+
value: 31.599
|
435 |
+
- type: map_at_1000
|
436 |
+
value: 31.691000000000003
|
437 |
+
- type: map_at_3
|
438 |
+
value: 28.405
|
439 |
+
- type: map_at_5
|
440 |
+
value: 29.704000000000004
|
441 |
+
- type: mrr_at_1
|
442 |
+
value: 25.537
|
443 |
+
- type: mrr_at_10
|
444 |
+
value: 32.22
|
445 |
+
- type: mrr_at_100
|
446 |
+
value: 33.138
|
447 |
+
- type: mrr_at_1000
|
448 |
+
value: 33.214
|
449 |
+
- type: mrr_at_3
|
450 |
+
value: 30.151
|
451 |
+
- type: mrr_at_5
|
452 |
+
value: 31.298
|
453 |
+
- type: ndcg_at_1
|
454 |
+
value: 25.537
|
455 |
+
- type: ndcg_at_10
|
456 |
+
value: 34.638000000000005
|
457 |
+
- type: ndcg_at_100
|
458 |
+
value: 39.486
|
459 |
+
- type: ndcg_at_1000
|
460 |
+
value: 41.936
|
461 |
+
- type: ndcg_at_3
|
462 |
+
value: 30.333
|
463 |
+
- type: ndcg_at_5
|
464 |
+
value: 32.482
|
465 |
+
- type: precision_at_1
|
466 |
+
value: 25.537
|
467 |
+
- type: precision_at_10
|
468 |
+
value: 5.153
|
469 |
+
- type: precision_at_100
|
470 |
+
value: 0.7929999999999999
|
471 |
+
- type: precision_at_1000
|
472 |
+
value: 0.104
|
473 |
+
- type: precision_at_3
|
474 |
+
value: 12.429
|
475 |
+
- type: precision_at_5
|
476 |
+
value: 8.723
|
477 |
+
- type: recall_at_1
|
478 |
+
value: 23.945
|
479 |
+
- type: recall_at_10
|
480 |
+
value: 45.412
|
481 |
+
- type: recall_at_100
|
482 |
+
value: 67.836
|
483 |
+
- type: recall_at_1000
|
484 |
+
value: 86.467
|
485 |
+
- type: recall_at_3
|
486 |
+
value: 34.031
|
487 |
+
- type: recall_at_5
|
488 |
+
value: 39.039
|
489 |
+
- task:
|
490 |
+
type: Retrieval
|
491 |
+
dataset:
|
492 |
+
type: BeIR/cqadupstack
|
493 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
494 |
+
config: default
|
495 |
+
split: test
|
496 |
+
revision: None
|
497 |
+
metrics:
|
498 |
+
- type: map_at_1
|
499 |
+
value: 14.419
|
500 |
+
- type: map_at_10
|
501 |
+
value: 20.858999999999998
|
502 |
+
- type: map_at_100
|
503 |
+
value: 22.067999999999998
|
504 |
+
- type: map_at_1000
|
505 |
+
value: 22.192
|
506 |
+
- type: map_at_3
|
507 |
+
value: 18.673000000000002
|
508 |
+
- type: map_at_5
|
509 |
+
value: 19.968
|
510 |
+
- type: mrr_at_1
|
511 |
+
value: 17.785999999999998
|
512 |
+
- type: mrr_at_10
|
513 |
+
value: 24.878
|
514 |
+
- type: mrr_at_100
|
515 |
+
value: 26.021
|
516 |
+
- type: mrr_at_1000
|
517 |
+
value: 26.095000000000002
|
518 |
+
- type: mrr_at_3
|
519 |
+
value: 22.616
|
520 |
+
- type: mrr_at_5
|
521 |
+
value: 23.785
|
522 |
+
- type: ndcg_at_1
|
523 |
+
value: 17.785999999999998
|
524 |
+
- type: ndcg_at_10
|
525 |
+
value: 25.153
|
526 |
+
- type: ndcg_at_100
|
527 |
+
value: 31.05
|
528 |
+
- type: ndcg_at_1000
|
529 |
+
value: 34.052
|
530 |
+
- type: ndcg_at_3
|
531 |
+
value: 21.117
|
532 |
+
- type: ndcg_at_5
|
533 |
+
value: 23.048
|
534 |
+
- type: precision_at_1
|
535 |
+
value: 17.785999999999998
|
536 |
+
- type: precision_at_10
|
537 |
+
value: 4.590000000000001
|
538 |
+
- type: precision_at_100
|
539 |
+
value: 0.864
|
540 |
+
- type: precision_at_1000
|
541 |
+
value: 0.125
|
542 |
+
- type: precision_at_3
|
543 |
+
value: 9.908999999999999
|
544 |
+
- type: precision_at_5
|
545 |
+
value: 7.313
|
546 |
+
- type: recall_at_1
|
547 |
+
value: 14.419
|
548 |
+
- type: recall_at_10
|
549 |
+
value: 34.477999999999994
|
550 |
+
- type: recall_at_100
|
551 |
+
value: 60.02499999999999
|
552 |
+
- type: recall_at_1000
|
553 |
+
value: 81.646
|
554 |
+
- type: recall_at_3
|
555 |
+
value: 23.515
|
556 |
+
- type: recall_at_5
|
557 |
+
value: 28.266999999999996
|
558 |
+
- task:
|
559 |
+
type: Retrieval
|
560 |
+
dataset:
|
561 |
+
type: BeIR/cqadupstack
|
562 |
+
name: MTEB CQADupstackPhysicsRetrieval
|
563 |
+
config: default
|
564 |
+
split: test
|
565 |
+
revision: None
|
566 |
+
metrics:
|
567 |
+
- type: map_at_1
|
568 |
+
value: 26.268
|
569 |
+
- type: map_at_10
|
570 |
+
value: 35.114000000000004
|
571 |
+
- type: map_at_100
|
572 |
+
value: 36.212
|
573 |
+
- type: map_at_1000
|
574 |
+
value: 36.333
|
575 |
+
- type: map_at_3
|
576 |
+
value: 32.436
|
577 |
+
- type: map_at_5
|
578 |
+
value: 33.992
|
579 |
+
- type: mrr_at_1
|
580 |
+
value: 31.761
|
581 |
+
- type: mrr_at_10
|
582 |
+
value: 40.355999999999995
|
583 |
+
- type: mrr_at_100
|
584 |
+
value: 41.125
|
585 |
+
- type: mrr_at_1000
|
586 |
+
value: 41.186
|
587 |
+
- type: mrr_at_3
|
588 |
+
value: 37.937
|
589 |
+
- type: mrr_at_5
|
590 |
+
value: 39.463
|
591 |
+
- type: ndcg_at_1
|
592 |
+
value: 31.761
|
593 |
+
- type: ndcg_at_10
|
594 |
+
value: 40.422000000000004
|
595 |
+
- type: ndcg_at_100
|
596 |
+
value: 45.458999999999996
|
597 |
+
- type: ndcg_at_1000
|
598 |
+
value: 47.951
|
599 |
+
- type: ndcg_at_3
|
600 |
+
value: 35.972
|
601 |
+
- type: ndcg_at_5
|
602 |
+
value: 38.272
|
603 |
+
- type: precision_at_1
|
604 |
+
value: 31.761
|
605 |
+
- type: precision_at_10
|
606 |
+
value: 7.103
|
607 |
+
- type: precision_at_100
|
608 |
+
value: 1.133
|
609 |
+
- type: precision_at_1000
|
610 |
+
value: 0.152
|
611 |
+
- type: precision_at_3
|
612 |
+
value: 16.779
|
613 |
+
- type: precision_at_5
|
614 |
+
value: 11.877
|
615 |
+
- type: recall_at_1
|
616 |
+
value: 26.268
|
617 |
+
- type: recall_at_10
|
618 |
+
value: 51.053000000000004
|
619 |
+
- type: recall_at_100
|
620 |
+
value: 72.702
|
621 |
+
- type: recall_at_1000
|
622 |
+
value: 89.521
|
623 |
+
- type: recall_at_3
|
624 |
+
value: 38.619
|
625 |
+
- type: recall_at_5
|
626 |
+
value: 44.671
|
627 |
+
- task:
|
628 |
+
type: Retrieval
|
629 |
+
dataset:
|
630 |
+
type: BeIR/cqadupstack
|
631 |
+
name: MTEB CQADupstackProgrammersRetrieval
|
632 |
+
config: default
|
633 |
+
split: test
|
634 |
+
revision: None
|
635 |
+
metrics:
|
636 |
+
- type: map_at_1
|
637 |
+
value: 25.230999999999998
|
638 |
+
- type: map_at_10
|
639 |
+
value: 34.227000000000004
|
640 |
+
- type: map_at_100
|
641 |
+
value: 35.370000000000005
|
642 |
+
- type: map_at_1000
|
643 |
+
value: 35.488
|
644 |
+
- type: map_at_3
|
645 |
+
value: 31.496000000000002
|
646 |
+
- type: map_at_5
|
647 |
+
value: 33.034
|
648 |
+
- type: mrr_at_1
|
649 |
+
value: 30.822
|
650 |
+
- type: mrr_at_10
|
651 |
+
value: 39.045
|
652 |
+
- type: mrr_at_100
|
653 |
+
value: 39.809
|
654 |
+
- type: mrr_at_1000
|
655 |
+
value: 39.873
|
656 |
+
- type: mrr_at_3
|
657 |
+
value: 36.663000000000004
|
658 |
+
- type: mrr_at_5
|
659 |
+
value: 37.964
|
660 |
+
- type: ndcg_at_1
|
661 |
+
value: 30.822
|
662 |
+
- type: ndcg_at_10
|
663 |
+
value: 39.472
|
664 |
+
- type: ndcg_at_100
|
665 |
+
value: 44.574999999999996
|
666 |
+
- type: ndcg_at_1000
|
667 |
+
value: 47.162
|
668 |
+
- type: ndcg_at_3
|
669 |
+
value: 34.929
|
670 |
+
- type: ndcg_at_5
|
671 |
+
value: 37.002
|
672 |
+
- type: precision_at_1
|
673 |
+
value: 30.822
|
674 |
+
- type: precision_at_10
|
675 |
+
value: 7.055
|
676 |
+
- type: precision_at_100
|
677 |
+
value: 1.124
|
678 |
+
- type: precision_at_1000
|
679 |
+
value: 0.152
|
680 |
+
- type: precision_at_3
|
681 |
+
value: 16.591
|
682 |
+
- type: precision_at_5
|
683 |
+
value: 11.667
|
684 |
+
- type: recall_at_1
|
685 |
+
value: 25.230999999999998
|
686 |
+
- type: recall_at_10
|
687 |
+
value: 50.42100000000001
|
688 |
+
- type: recall_at_100
|
689 |
+
value: 72.685
|
690 |
+
- type: recall_at_1000
|
691 |
+
value: 90.469
|
692 |
+
- type: recall_at_3
|
693 |
+
value: 37.503
|
694 |
+
- type: recall_at_5
|
695 |
+
value: 43.123
|
696 |
+
- task:
|
697 |
+
type: Retrieval
|
698 |
+
dataset:
|
699 |
+
type: BeIR/cqadupstack
|
700 |
+
name: MTEB CQADupstackRetrieval
|
701 |
+
config: default
|
702 |
+
split: test
|
703 |
+
revision: None
|
704 |
+
metrics:
|
705 |
+
- type: map_at_1
|
706 |
+
value: 24.604166666666664
|
707 |
+
- type: map_at_10
|
708 |
+
value: 32.427166666666665
|
709 |
+
- type: map_at_100
|
710 |
+
value: 33.51474999999999
|
711 |
+
- type: map_at_1000
|
712 |
+
value: 33.6345
|
713 |
+
- type: map_at_3
|
714 |
+
value: 30.02366666666667
|
715 |
+
- type: map_at_5
|
716 |
+
value: 31.382333333333328
|
717 |
+
- type: mrr_at_1
|
718 |
+
value: 29.001166666666666
|
719 |
+
- type: mrr_at_10
|
720 |
+
value: 36.3315
|
721 |
+
- type: mrr_at_100
|
722 |
+
value: 37.16683333333333
|
723 |
+
- type: mrr_at_1000
|
724 |
+
value: 37.23341666666668
|
725 |
+
- type: mrr_at_3
|
726 |
+
value: 34.19916666666667
|
727 |
+
- type: mrr_at_5
|
728 |
+
value: 35.40458333333334
|
729 |
+
- type: ndcg_at_1
|
730 |
+
value: 29.001166666666666
|
731 |
+
- type: ndcg_at_10
|
732 |
+
value: 37.06883333333334
|
733 |
+
- type: ndcg_at_100
|
734 |
+
value: 41.95816666666666
|
735 |
+
- type: ndcg_at_1000
|
736 |
+
value: 44.501583333333336
|
737 |
+
- type: ndcg_at_3
|
738 |
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value: 32.973499999999994
|
739 |
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- type: ndcg_at_5
|
740 |
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value: 34.90833333333334
|
741 |
+
- type: precision_at_1
|
742 |
+
value: 29.001166666666666
|
743 |
+
- type: precision_at_10
|
744 |
+
value: 6.336
|
745 |
+
- type: precision_at_100
|
746 |
+
value: 1.0282499999999999
|
747 |
+
- type: precision_at_1000
|
748 |
+
value: 0.14391666666666664
|
749 |
+
- type: precision_at_3
|
750 |
+
value: 14.932499999999996
|
751 |
+
- type: precision_at_5
|
752 |
+
value: 10.50825
|
753 |
+
- type: recall_at_1
|
754 |
+
value: 24.604166666666664
|
755 |
+
- type: recall_at_10
|
756 |
+
value: 46.9525
|
757 |
+
- type: recall_at_100
|
758 |
+
value: 68.67816666666667
|
759 |
+
- type: recall_at_1000
|
760 |
+
value: 86.59783333333334
|
761 |
+
- type: recall_at_3
|
762 |
+
value: 35.49783333333333
|
763 |
+
- type: recall_at_5
|
764 |
+
value: 40.52525000000001
|
765 |
+
- task:
|
766 |
+
type: Retrieval
|
767 |
+
dataset:
|
768 |
+
type: BeIR/cqadupstack
|
769 |
+
name: MTEB CQADupstackStatsRetrieval
|
770 |
+
config: default
|
771 |
+
split: test
|
772 |
+
revision: None
|
773 |
+
metrics:
|
774 |
+
- type: map_at_1
|
775 |
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value: 23.559
|
776 |
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- type: map_at_10
|
777 |
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value: 29.023
|
778 |
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- type: map_at_100
|
779 |
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value: 29.818
|
780 |
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- type: map_at_1000
|
781 |
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value: 29.909000000000002
|
782 |
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- type: map_at_3
|
783 |
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value: 27.037
|
784 |
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- type: map_at_5
|
785 |
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value: 28.225
|
786 |
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- type: mrr_at_1
|
787 |
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value: 26.994
|
788 |
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- type: mrr_at_10
|
789 |
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value: 31.962000000000003
|
790 |
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- type: mrr_at_100
|
791 |
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value: 32.726
|
792 |
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- type: mrr_at_1000
|
793 |
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value: 32.800000000000004
|
794 |
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- type: mrr_at_3
|
795 |
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value: 30.266
|
796 |
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- type: mrr_at_5
|
797 |
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value: 31.208999999999996
|
798 |
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- type: ndcg_at_1
|
799 |
+
value: 26.994
|
800 |
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- type: ndcg_at_10
|
801 |
+
value: 32.53
|
802 |
+
- type: ndcg_at_100
|
803 |
+
value: 36.758
|
804 |
+
- type: ndcg_at_1000
|
805 |
+
value: 39.362
|
806 |
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- type: ndcg_at_3
|
807 |
+
value: 28.985
|
808 |
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- type: ndcg_at_5
|
809 |
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value: 30.757
|
810 |
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- type: precision_at_1
|
811 |
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value: 26.994
|
812 |
+
- type: precision_at_10
|
813 |
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value: 4.968999999999999
|
814 |
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- type: precision_at_100
|
815 |
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value: 0.759
|
816 |
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- type: precision_at_1000
|
817 |
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value: 0.106
|
818 |
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- type: precision_at_3
|
819 |
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value: 12.219
|
820 |
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- type: precision_at_5
|
821 |
+
value: 8.527999999999999
|
822 |
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- type: recall_at_1
|
823 |
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value: 23.559
|
824 |
+
- type: recall_at_10
|
825 |
+
value: 40.585
|
826 |
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- type: recall_at_100
|
827 |
+
value: 60.306000000000004
|
828 |
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- type: recall_at_1000
|
829 |
+
value: 80.11
|
830 |
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- type: recall_at_3
|
831 |
+
value: 30.794
|
832 |
+
- type: recall_at_5
|
833 |
+
value: 35.186
|
834 |
+
- task:
|
835 |
+
type: Retrieval
|
836 |
+
dataset:
|
837 |
+
type: BeIR/cqadupstack
|
838 |
+
name: MTEB CQADupstackTexRetrieval
|
839 |
+
config: default
|
840 |
+
split: test
|
841 |
+
revision: None
|
842 |
+
metrics:
|
843 |
+
- type: map_at_1
|
844 |
+
value: 16.384999999999998
|
845 |
+
- type: map_at_10
|
846 |
+
value: 22.142
|
847 |
+
- type: map_at_100
|
848 |
+
value: 23.057
|
849 |
+
- type: map_at_1000
|
850 |
+
value: 23.177
|
851 |
+
- type: map_at_3
|
852 |
+
value: 20.29
|
853 |
+
- type: map_at_5
|
854 |
+
value: 21.332
|
855 |
+
- type: mrr_at_1
|
856 |
+
value: 19.89
|
857 |
+
- type: mrr_at_10
|
858 |
+
value: 25.771
|
859 |
+
- type: mrr_at_100
|
860 |
+
value: 26.599
|
861 |
+
- type: mrr_at_1000
|
862 |
+
value: 26.680999999999997
|
863 |
+
- type: mrr_at_3
|
864 |
+
value: 23.962
|
865 |
+
- type: mrr_at_5
|
866 |
+
value: 24.934
|
867 |
+
- type: ndcg_at_1
|
868 |
+
value: 19.89
|
869 |
+
- type: ndcg_at_10
|
870 |
+
value: 25.97
|
871 |
+
- type: ndcg_at_100
|
872 |
+
value: 30.605
|
873 |
+
- type: ndcg_at_1000
|
874 |
+
value: 33.619
|
875 |
+
- type: ndcg_at_3
|
876 |
+
value: 22.704
|
877 |
+
- type: ndcg_at_5
|
878 |
+
value: 24.199
|
879 |
+
- type: precision_at_1
|
880 |
+
value: 19.89
|
881 |
+
- type: precision_at_10
|
882 |
+
value: 4.553
|
883 |
+
- type: precision_at_100
|
884 |
+
value: 0.8049999999999999
|
885 |
+
- type: precision_at_1000
|
886 |
+
value: 0.122
|
887 |
+
- type: precision_at_3
|
888 |
+
value: 10.541
|
889 |
+
- type: precision_at_5
|
890 |
+
value: 7.46
|
891 |
+
- type: recall_at_1
|
892 |
+
value: 16.384999999999998
|
893 |
+
- type: recall_at_10
|
894 |
+
value: 34.001
|
895 |
+
- type: recall_at_100
|
896 |
+
value: 55.17100000000001
|
897 |
+
- type: recall_at_1000
|
898 |
+
value: 77.125
|
899 |
+
- type: recall_at_3
|
900 |
+
value: 24.618000000000002
|
901 |
+
- type: recall_at_5
|
902 |
+
value: 28.695999999999998
|
903 |
+
- task:
|
904 |
+
type: Retrieval
|
905 |
+
dataset:
|
906 |
+
type: BeIR/cqadupstack
|
907 |
+
name: MTEB CQADupstackUnixRetrieval
|
908 |
+
config: default
|
909 |
+
split: test
|
910 |
+
revision: None
|
911 |
+
metrics:
|
912 |
+
- type: map_at_1
|
913 |
+
value: 23.726
|
914 |
+
- type: map_at_10
|
915 |
+
value: 31.227
|
916 |
+
- type: map_at_100
|
917 |
+
value: 32.311
|
918 |
+
- type: map_at_1000
|
919 |
+
value: 32.419
|
920 |
+
- type: map_at_3
|
921 |
+
value: 28.765
|
922 |
+
- type: map_at_5
|
923 |
+
value: 30.229
|
924 |
+
- type: mrr_at_1
|
925 |
+
value: 27.705000000000002
|
926 |
+
- type: mrr_at_10
|
927 |
+
value: 35.085
|
928 |
+
- type: mrr_at_100
|
929 |
+
value: 35.931000000000004
|
930 |
+
- type: mrr_at_1000
|
931 |
+
value: 36
|
932 |
+
- type: mrr_at_3
|
933 |
+
value: 32.603
|
934 |
+
- type: mrr_at_5
|
935 |
+
value: 34.117999999999995
|
936 |
+
- type: ndcg_at_1
|
937 |
+
value: 27.705000000000002
|
938 |
+
- type: ndcg_at_10
|
939 |
+
value: 35.968
|
940 |
+
- type: ndcg_at_100
|
941 |
+
value: 41.197
|
942 |
+
- type: ndcg_at_1000
|
943 |
+
value: 43.76
|
944 |
+
- type: ndcg_at_3
|
945 |
+
value: 31.304
|
946 |
+
- type: ndcg_at_5
|
947 |
+
value: 33.661
|
948 |
+
- type: precision_at_1
|
949 |
+
value: 27.705000000000002
|
950 |
+
- type: precision_at_10
|
951 |
+
value: 5.942
|
952 |
+
- type: precision_at_100
|
953 |
+
value: 0.964
|
954 |
+
- type: precision_at_1000
|
955 |
+
value: 0.13
|
956 |
+
- type: precision_at_3
|
957 |
+
value: 13.868
|
958 |
+
- type: precision_at_5
|
959 |
+
value: 9.944
|
960 |
+
- type: recall_at_1
|
961 |
+
value: 23.726
|
962 |
+
- type: recall_at_10
|
963 |
+
value: 46.786
|
964 |
+
- type: recall_at_100
|
965 |
+
value: 70.072
|
966 |
+
- type: recall_at_1000
|
967 |
+
value: 88.2
|
968 |
+
- type: recall_at_3
|
969 |
+
value: 33.981
|
970 |
+
- type: recall_at_5
|
971 |
+
value: 39.893
|
972 |
+
- task:
|
973 |
+
type: Retrieval
|
974 |
+
dataset:
|
975 |
+
type: BeIR/cqadupstack
|
976 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
977 |
+
config: default
|
978 |
+
split: test
|
979 |
+
revision: None
|
980 |
+
metrics:
|
981 |
+
- type: map_at_1
|
982 |
+
value: 23.344
|
983 |
+
- type: map_at_10
|
984 |
+
value: 31.636999999999997
|
985 |
+
- type: map_at_100
|
986 |
+
value: 33.065
|
987 |
+
- type: map_at_1000
|
988 |
+
value: 33.300000000000004
|
989 |
+
- type: map_at_3
|
990 |
+
value: 29.351
|
991 |
+
- type: map_at_5
|
992 |
+
value: 30.432
|
993 |
+
- type: mrr_at_1
|
994 |
+
value: 27.866000000000003
|
995 |
+
- type: mrr_at_10
|
996 |
+
value: 35.587
|
997 |
+
- type: mrr_at_100
|
998 |
+
value: 36.52
|
999 |
+
- type: mrr_at_1000
|
1000 |
+
value: 36.597
|
1001 |
+
- type: mrr_at_3
|
1002 |
+
value: 33.696
|
1003 |
+
- type: mrr_at_5
|
1004 |
+
value: 34.713
|
1005 |
+
- type: ndcg_at_1
|
1006 |
+
value: 27.866000000000003
|
1007 |
+
- type: ndcg_at_10
|
1008 |
+
value: 36.61
|
1009 |
+
- type: ndcg_at_100
|
1010 |
+
value: 41.88
|
1011 |
+
- type: ndcg_at_1000
|
1012 |
+
value: 45.105000000000004
|
1013 |
+
- type: ndcg_at_3
|
1014 |
+
value: 33.038000000000004
|
1015 |
+
- type: ndcg_at_5
|
1016 |
+
value: 34.331
|
1017 |
+
- type: precision_at_1
|
1018 |
+
value: 27.866000000000003
|
1019 |
+
- type: precision_at_10
|
1020 |
+
value: 6.917
|
1021 |
+
- type: precision_at_100
|
1022 |
+
value: 1.3599999999999999
|
1023 |
+
- type: precision_at_1000
|
1024 |
+
value: 0.233
|
1025 |
+
- type: precision_at_3
|
1026 |
+
value: 15.547
|
1027 |
+
- type: precision_at_5
|
1028 |
+
value: 10.791
|
1029 |
+
- type: recall_at_1
|
1030 |
+
value: 23.344
|
1031 |
+
- type: recall_at_10
|
1032 |
+
value: 45.782000000000004
|
1033 |
+
- type: recall_at_100
|
1034 |
+
value: 69.503
|
1035 |
+
- type: recall_at_1000
|
1036 |
+
value: 90.742
|
1037 |
+
- type: recall_at_3
|
1038 |
+
value: 35.160000000000004
|
1039 |
+
- type: recall_at_5
|
1040 |
+
value: 39.058
|
1041 |
+
- task:
|
1042 |
+
type: Retrieval
|
1043 |
+
dataset:
|
1044 |
+
type: BeIR/cqadupstack
|
1045 |
+
name: MTEB CQADupstackWordpressRetrieval
|
1046 |
+
config: default
|
1047 |
+
split: test
|
1048 |
+
revision: None
|
1049 |
+
metrics:
|
1050 |
+
- type: map_at_1
|
1051 |
+
value: 20.776
|
1052 |
+
- type: map_at_10
|
1053 |
+
value: 27.285999999999998
|
1054 |
+
- type: map_at_100
|
1055 |
+
value: 28.235
|
1056 |
+
- type: map_at_1000
|
1057 |
+
value: 28.337
|
1058 |
+
- type: map_at_3
|
1059 |
+
value: 25.147000000000002
|
1060 |
+
- type: map_at_5
|
1061 |
+
value: 26.401999999999997
|
1062 |
+
- type: mrr_at_1
|
1063 |
+
value: 22.921
|
1064 |
+
- type: mrr_at_10
|
1065 |
+
value: 29.409999999999997
|
1066 |
+
- type: mrr_at_100
|
1067 |
+
value: 30.275000000000002
|
1068 |
+
- type: mrr_at_1000
|
1069 |
+
value: 30.354999999999997
|
1070 |
+
- type: mrr_at_3
|
1071 |
+
value: 27.418
|
1072 |
+
- type: mrr_at_5
|
1073 |
+
value: 28.592000000000002
|
1074 |
+
- type: ndcg_at_1
|
1075 |
+
value: 22.921
|
1076 |
+
- type: ndcg_at_10
|
1077 |
+
value: 31.239
|
1078 |
+
- type: ndcg_at_100
|
1079 |
+
value: 35.965
|
1080 |
+
- type: ndcg_at_1000
|
1081 |
+
value: 38.602
|
1082 |
+
- type: ndcg_at_3
|
1083 |
+
value: 27.174
|
1084 |
+
- type: ndcg_at_5
|
1085 |
+
value: 29.229
|
1086 |
+
- type: precision_at_1
|
1087 |
+
value: 22.921
|
1088 |
+
- type: precision_at_10
|
1089 |
+
value: 4.806
|
1090 |
+
- type: precision_at_100
|
1091 |
+
value: 0.776
|
1092 |
+
- type: precision_at_1000
|
1093 |
+
value: 0.11
|
1094 |
+
- type: precision_at_3
|
1095 |
+
value: 11.459999999999999
|
1096 |
+
- type: precision_at_5
|
1097 |
+
value: 8.022
|
1098 |
+
- type: recall_at_1
|
1099 |
+
value: 20.776
|
1100 |
+
- type: recall_at_10
|
1101 |
+
value: 41.294
|
1102 |
+
- type: recall_at_100
|
1103 |
+
value: 63.111
|
1104 |
+
- type: recall_at_1000
|
1105 |
+
value: 82.88600000000001
|
1106 |
+
- type: recall_at_3
|
1107 |
+
value: 30.403000000000002
|
1108 |
+
- type: recall_at_5
|
1109 |
+
value: 35.455999999999996
|
1110 |
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- task:
|
1111 |
+
type: Retrieval
|
1112 |
+
dataset:
|
1113 |
+
type: climate-fever
|
1114 |
+
name: MTEB ClimateFEVER
|
1115 |
+
config: default
|
1116 |
+
split: test
|
1117 |
+
revision: None
|
1118 |
+
metrics:
|
1119 |
+
- type: map_at_1
|
1120 |
+
value: 9.376
|
1121 |
+
- type: map_at_10
|
1122 |
+
value: 15.926000000000002
|
1123 |
+
- type: map_at_100
|
1124 |
+
value: 17.585
|
1125 |
+
- type: map_at_1000
|
1126 |
+
value: 17.776
|
1127 |
+
- type: map_at_3
|
1128 |
+
value: 13.014000000000001
|
1129 |
+
- type: map_at_5
|
1130 |
+
value: 14.417
|
1131 |
+
- type: mrr_at_1
|
1132 |
+
value: 20.195
|
1133 |
+
- type: mrr_at_10
|
1134 |
+
value: 29.95
|
1135 |
+
- type: mrr_at_100
|
1136 |
+
value: 31.052000000000003
|
1137 |
+
- type: mrr_at_1000
|
1138 |
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value: 31.108000000000004
|
1139 |
+
- type: mrr_at_3
|
1140 |
+
value: 26.667
|
1141 |
+
- type: mrr_at_5
|
1142 |
+
value: 28.458
|
1143 |
+
- type: ndcg_at_1
|
1144 |
+
value: 20.195
|
1145 |
+
- type: ndcg_at_10
|
1146 |
+
value: 22.871
|
1147 |
+
- type: ndcg_at_100
|
1148 |
+
value: 29.921999999999997
|
1149 |
+
- type: ndcg_at_1000
|
1150 |
+
value: 33.672999999999995
|
1151 |
+
- type: ndcg_at_3
|
1152 |
+
value: 17.782999999999998
|
1153 |
+
- type: ndcg_at_5
|
1154 |
+
value: 19.544
|
1155 |
+
- type: precision_at_1
|
1156 |
+
value: 20.195
|
1157 |
+
- type: precision_at_10
|
1158 |
+
value: 7.394
|
1159 |
+
- type: precision_at_100
|
1160 |
+
value: 1.493
|
1161 |
+
- type: precision_at_1000
|
1162 |
+
value: 0.218
|
1163 |
+
- type: precision_at_3
|
1164 |
+
value: 13.073
|
1165 |
+
- type: precision_at_5
|
1166 |
+
value: 10.436
|
1167 |
+
- type: recall_at_1
|
1168 |
+
value: 9.376
|
1169 |
+
- type: recall_at_10
|
1170 |
+
value: 28.544999999999998
|
1171 |
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- type: recall_at_100
|
1172 |
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value: 53.147999999999996
|
1173 |
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- type: recall_at_1000
|
1174 |
+
value: 74.62
|
1175 |
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- type: recall_at_3
|
1176 |
+
value: 16.464000000000002
|
1177 |
+
- type: recall_at_5
|
1178 |
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value: 21.004
|
1179 |
+
- task:
|
1180 |
+
type: Retrieval
|
1181 |
+
dataset:
|
1182 |
+
type: dbpedia-entity
|
1183 |
+
name: MTEB DBPedia
|
1184 |
+
config: default
|
1185 |
+
split: test
|
1186 |
+
revision: None
|
1187 |
+
metrics:
|
1188 |
+
- type: map_at_1
|
1189 |
+
value: 8.415000000000001
|
1190 |
+
- type: map_at_10
|
1191 |
+
value: 18.738
|
1192 |
+
- type: map_at_100
|
1193 |
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value: 27.291999999999998
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1196 |
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1197 |
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value: 13.196
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1198 |
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1199 |
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value: 15.539
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1200 |
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1201 |
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value: 66.5
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1202 |
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1203 |
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value: 74.518
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1204 |
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1205 |
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value: 74.86
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1206 |
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1207 |
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value: 74.87
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1208 |
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1209 |
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value: 72.375
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1210 |
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1211 |
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value: 73.86200000000001
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1212 |
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1213 |
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value: 54.37499999999999
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1214 |
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|
1215 |
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value: 41.317
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1216 |
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|
1217 |
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value: 45.845
|
1218 |
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|
1219 |
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value: 52.92
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1220 |
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1221 |
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value: 44.983000000000004
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1222 |
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|
1223 |
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value: 42.989
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1224 |
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- type: precision_at_1
|
1225 |
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value: 66.5
|
1226 |
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- type: precision_at_10
|
1227 |
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value: 33.6
|
1228 |
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- type: precision_at_100
|
1229 |
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value: 10.972999999999999
|
1230 |
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- type: precision_at_1000
|
1231 |
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value: 2.214
|
1232 |
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|
1233 |
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value: 48.583
|
1234 |
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- type: precision_at_5
|
1235 |
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value: 42.15
|
1236 |
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- type: recall_at_1
|
1237 |
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value: 8.415000000000001
|
1238 |
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- type: recall_at_10
|
1239 |
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value: 24.953
|
1240 |
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- type: recall_at_100
|
1241 |
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value: 52.48199999999999
|
1242 |
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- type: recall_at_1000
|
1243 |
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value: 75.093
|
1244 |
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- type: recall_at_3
|
1245 |
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value: 14.341000000000001
|
1246 |
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- type: recall_at_5
|
1247 |
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value: 18.468
|
1248 |
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- task:
|
1249 |
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type: Classification
|
1250 |
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dataset:
|
1251 |
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type: mteb/emotion
|
1252 |
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name: MTEB EmotionClassification
|
1253 |
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config: default
|
1254 |
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split: test
|
1255 |
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revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
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1256 |
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metrics:
|
1257 |
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- type: accuracy
|
1258 |
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value: 47.06499999999999
|
1259 |
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- type: f1
|
1260 |
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value: 41.439327599975385
|
1261 |
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- task:
|
1262 |
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type: Retrieval
|
1263 |
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dataset:
|
1264 |
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type: fever
|
1265 |
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name: MTEB FEVER
|
1266 |
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config: default
|
1267 |
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split: test
|
1268 |
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revision: None
|
1269 |
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metrics:
|
1270 |
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- type: map_at_1
|
1271 |
+
value: 66.02
|
1272 |
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- type: map_at_10
|
1273 |
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value: 76.68599999999999
|
1274 |
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- type: map_at_100
|
1275 |
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value: 76.959
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1276 |
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- type: map_at_1000
|
1277 |
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value: 76.972
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1278 |
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- type: map_at_3
|
1279 |
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value: 75.024
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1280 |
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- type: map_at_5
|
1281 |
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value: 76.153
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1282 |
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- type: mrr_at_1
|
1283 |
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value: 71.197
|
1284 |
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- type: mrr_at_10
|
1285 |
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value: 81.105
|
1286 |
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- type: mrr_at_100
|
1287 |
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value: 81.232
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1288 |
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- type: mrr_at_1000
|
1289 |
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value: 81.233
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1290 |
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- type: mrr_at_3
|
1291 |
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value: 79.758
|
1292 |
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- type: mrr_at_5
|
1293 |
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value: 80.69
|
1294 |
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- type: ndcg_at_1
|
1295 |
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value: 71.197
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1296 |
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- type: ndcg_at_10
|
1297 |
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value: 81.644
|
1298 |
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- type: ndcg_at_100
|
1299 |
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value: 82.645
|
1300 |
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- type: ndcg_at_1000
|
1301 |
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value: 82.879
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1302 |
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- type: ndcg_at_3
|
1303 |
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value: 78.792
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1304 |
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- type: ndcg_at_5
|
1305 |
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value: 80.528
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1306 |
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- type: precision_at_1
|
1307 |
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value: 71.197
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1308 |
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- type: precision_at_10
|
1309 |
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value: 10.206999999999999
|
1310 |
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- type: precision_at_100
|
1311 |
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value: 1.093
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1312 |
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- type: precision_at_1000
|
1313 |
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value: 0.11299999999999999
|
1314 |
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- type: precision_at_3
|
1315 |
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value: 30.868000000000002
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1316 |
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- type: precision_at_5
|
1317 |
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value: 19.559
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1318 |
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- type: recall_at_1
|
1319 |
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value: 66.02
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1320 |
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- type: recall_at_10
|
1321 |
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value: 92.50699999999999
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1322 |
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- type: recall_at_100
|
1323 |
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value: 96.497
|
1324 |
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- type: recall_at_1000
|
1325 |
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value: 97.956
|
1326 |
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- type: recall_at_3
|
1327 |
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value: 84.866
|
1328 |
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- type: recall_at_5
|
1329 |
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value: 89.16199999999999
|
1330 |
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- task:
|
1331 |
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type: Retrieval
|
1332 |
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dataset:
|
1333 |
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type: fiqa
|
1334 |
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name: MTEB FiQA2018
|
1335 |
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config: default
|
1336 |
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split: test
|
1337 |
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revision: None
|
1338 |
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metrics:
|
1339 |
+
- type: map_at_1
|
1340 |
+
value: 17.948
|
1341 |
+
- type: map_at_10
|
1342 |
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value: 29.833
|
1343 |
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- type: map_at_100
|
1344 |
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value: 31.487
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1345 |
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- type: map_at_1000
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1346 |
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value: 31.674000000000003
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1347 |
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- type: map_at_3
|
1348 |
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value: 26.029999999999998
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1349 |
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- type: map_at_5
|
1350 |
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value: 28.038999999999998
|
1351 |
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- type: mrr_at_1
|
1352 |
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value: 34.721999999999994
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1353 |
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- type: mrr_at_10
|
1354 |
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value: 44.214999999999996
|
1355 |
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- type: mrr_at_100
|
1356 |
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value: 44.994
|
1357 |
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- type: mrr_at_1000
|
1358 |
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value: 45.051
|
1359 |
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- type: mrr_at_3
|
1360 |
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value: 41.667
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1361 |
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- type: mrr_at_5
|
1362 |
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value: 43.032
|
1363 |
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- type: ndcg_at_1
|
1364 |
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value: 34.721999999999994
|
1365 |
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- type: ndcg_at_10
|
1366 |
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value: 37.434
|
1367 |
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- type: ndcg_at_100
|
1368 |
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value: 43.702000000000005
|
1369 |
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- type: ndcg_at_1000
|
1370 |
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value: 46.993
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1371 |
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- type: ndcg_at_3
|
1372 |
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value: 33.56
|
1373 |
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- type: ndcg_at_5
|
1374 |
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value: 34.687
|
1375 |
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- type: precision_at_1
|
1376 |
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value: 34.721999999999994
|
1377 |
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- type: precision_at_10
|
1378 |
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value: 10.401
|
1379 |
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- type: precision_at_100
|
1380 |
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value: 1.7049999999999998
|
1381 |
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- type: precision_at_1000
|
1382 |
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value: 0.22799999999999998
|
1383 |
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- type: precision_at_3
|
1384 |
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value: 22.531000000000002
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1385 |
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- type: precision_at_5
|
1386 |
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value: 16.42
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1387 |
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- type: recall_at_1
|
1388 |
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value: 17.948
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1389 |
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- type: recall_at_10
|
1390 |
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value: 45.062999999999995
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1391 |
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- type: recall_at_100
|
1392 |
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value: 68.191
|
1393 |
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- type: recall_at_1000
|
1394 |
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value: 87.954
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1395 |
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- type: recall_at_3
|
1396 |
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value: 31.112000000000002
|
1397 |
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- type: recall_at_5
|
1398 |
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value: 36.823
|
1399 |
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- task:
|
1400 |
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type: Retrieval
|
1401 |
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dataset:
|
1402 |
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type: hotpotqa
|
1403 |
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name: MTEB HotpotQA
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1404 |
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config: default
|
1405 |
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split: test
|
1406 |
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revision: None
|
1407 |
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metrics:
|
1408 |
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- type: map_at_1
|
1409 |
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value: 36.644
|
1410 |
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- type: map_at_10
|
1411 |
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value: 57.658
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1412 |
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- type: map_at_100
|
1413 |
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value: 58.562000000000005
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1414 |
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- type: map_at_1000
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1415 |
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value: 58.62500000000001
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1416 |
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1417 |
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value: 54.022999999999996
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1418 |
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1419 |
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value: 56.293000000000006
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1420 |
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1421 |
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value: 73.288
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1422 |
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1423 |
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value: 80.51700000000001
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1424 |
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- type: mrr_at_100
|
1425 |
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value: 80.72
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1426 |
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1427 |
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value: 80.728
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1428 |
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|
1429 |
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value: 79.33200000000001
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1430 |
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|
1431 |
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value: 80.085
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1432 |
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- type: ndcg_at_1
|
1433 |
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value: 73.288
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1434 |
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|
1435 |
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value: 66.61
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1436 |
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1437 |
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value: 69.723
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1438 |
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1439 |
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value: 70.96000000000001
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1440 |
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- type: ndcg_at_3
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1441 |
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value: 61.358999999999995
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1442 |
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|
1443 |
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value: 64.277
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1444 |
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- type: precision_at_1
|
1445 |
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value: 73.288
|
1446 |
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- type: precision_at_10
|
1447 |
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value: 14.17
|
1448 |
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- type: precision_at_100
|
1449 |
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value: 1.659
|
1450 |
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- type: precision_at_1000
|
1451 |
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value: 0.182
|
1452 |
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- type: precision_at_3
|
1453 |
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value: 39.487
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1454 |
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- type: precision_at_5
|
1455 |
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value: 25.999
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1456 |
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- type: recall_at_1
|
1457 |
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value: 36.644
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1458 |
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- type: recall_at_10
|
1459 |
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value: 70.851
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1460 |
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- type: recall_at_100
|
1461 |
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value: 82.94399999999999
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1462 |
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- type: recall_at_1000
|
1463 |
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value: 91.134
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1464 |
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- type: recall_at_3
|
1465 |
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value: 59.230000000000004
|
1466 |
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- type: recall_at_5
|
1467 |
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value: 64.997
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1468 |
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- task:
|
1469 |
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type: Classification
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1470 |
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dataset:
|
1471 |
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type: mteb/imdb
|
1472 |
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name: MTEB ImdbClassification
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1473 |
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config: default
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1474 |
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split: test
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1475 |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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1476 |
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metrics:
|
1477 |
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- type: accuracy
|
1478 |
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value: 86.00280000000001
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1479 |
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- type: ap
|
1480 |
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value: 80.46302061021223
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1481 |
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- type: f1
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1482 |
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1483 |
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- task:
|
1484 |
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1485 |
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dataset:
|
1486 |
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type: msmarco
|
1487 |
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name: MTEB MSMARCO
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1488 |
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config: default
|
1489 |
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split: dev
|
1490 |
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revision: None
|
1491 |
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metrics:
|
1492 |
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- type: map_at_1
|
1493 |
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value: 22.541
|
1494 |
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- type: map_at_10
|
1495 |
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value: 34.625
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1496 |
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1497 |
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value: 35.785
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1498 |
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- type: map_at_1000
|
1499 |
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value: 35.831
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1500 |
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1501 |
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value: 30.823
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1502 |
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|
1503 |
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value: 32.967999999999996
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1504 |
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1505 |
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value: 23.180999999999997
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1506 |
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|
1507 |
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value: 35.207
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1508 |
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1509 |
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value: 36.315
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1510 |
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1511 |
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value: 36.355
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1512 |
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1513 |
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value: 31.483
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1514 |
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1515 |
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value: 33.589999999999996
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1516 |
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1517 |
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value: 23.195
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1518 |
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1519 |
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value: 41.461
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1520 |
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|
1521 |
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value: 47.032000000000004
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1522 |
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1523 |
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1524 |
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1525 |
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value: 33.702
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1526 |
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1527 |
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value: 37.522
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1528 |
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|
1529 |
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value: 23.195
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1530 |
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- type: precision_at_10
|
1531 |
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value: 6.526999999999999
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1532 |
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- type: precision_at_100
|
1533 |
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value: 0.932
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1534 |
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1535 |
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value: 0.10300000000000001
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1536 |
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- type: precision_at_3
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1537 |
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value: 14.308000000000002
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1538 |
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- type: precision_at_5
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1539 |
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value: 10.507
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1540 |
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- type: recall_at_1
|
1541 |
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value: 22.541
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1542 |
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- type: recall_at_10
|
1543 |
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value: 62.524
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1544 |
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- type: recall_at_100
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1545 |
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value: 88.228
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1546 |
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- type: recall_at_1000
|
1547 |
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value: 97.243
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1548 |
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- type: recall_at_3
|
1549 |
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value: 41.38
|
1550 |
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- type: recall_at_5
|
1551 |
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value: 50.55
|
1552 |
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- task:
|
1553 |
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type: Classification
|
1554 |
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dataset:
|
1555 |
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type: mteb/mtop_domain
|
1556 |
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name: MTEB MTOPDomainClassification (en)
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1557 |
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config: en
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1558 |
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split: test
|
1559 |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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1560 |
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metrics:
|
1561 |
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- type: accuracy
|
1562 |
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value: 92.69949840401279
|
1563 |
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- type: f1
|
1564 |
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|
1565 |
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- task:
|
1566 |
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type: Classification
|
1567 |
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dataset:
|
1568 |
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type: mteb/mtop_intent
|
1569 |
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name: MTEB MTOPIntentClassification (en)
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1570 |
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1571 |
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1572 |
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1573 |
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metrics:
|
1574 |
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1575 |
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1576 |
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|
1577 |
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value: 55.88499977508287
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1578 |
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- task:
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1579 |
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|
1580 |
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dataset:
|
1581 |
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type: mteb/amazon_massive_intent
|
1582 |
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name: MTEB MassiveIntentClassification (en)
|
1583 |
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config: en
|
1584 |
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split: test
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1585 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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1586 |
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metrics:
|
1587 |
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- type: accuracy
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1588 |
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value: 71.62071284465365
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1589 |
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- type: f1
|
1590 |
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value: 69.36717546572152
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1591 |
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- task:
|
1592 |
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type: Classification
|
1593 |
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dataset:
|
1594 |
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type: mteb/amazon_massive_scenario
|
1595 |
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name: MTEB MassiveScenarioClassification (en)
|
1596 |
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config: en
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1597 |
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1598 |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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1599 |
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metrics:
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1600 |
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- type: accuracy
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1601 |
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value: 76.35843981170142
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1602 |
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- type: f1
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1603 |
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value: 76.15496453538884
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1604 |
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- task:
|
1605 |
+
type: Clustering
|
1606 |
+
dataset:
|
1607 |
+
type: mteb/medrxiv-clustering-p2p
|
1608 |
+
name: MTEB MedrxivClusteringP2P
|
1609 |
+
config: default
|
1610 |
+
split: test
|
1611 |
+
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
1612 |
+
metrics:
|
1613 |
+
- type: v_measure
|
1614 |
+
value: 31.33664956793118
|
1615 |
+
- task:
|
1616 |
+
type: Clustering
|
1617 |
+
dataset:
|
1618 |
+
type: mteb/medrxiv-clustering-s2s
|
1619 |
+
name: MTEB MedrxivClusteringS2S
|
1620 |
+
config: default
|
1621 |
+
split: test
|
1622 |
+
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
1623 |
+
metrics:
|
1624 |
+
- type: v_measure
|
1625 |
+
value: 27.883839621715524
|
1626 |
+
- task:
|
1627 |
+
type: Reranking
|
1628 |
+
dataset:
|
1629 |
+
type: mteb/mind_small
|
1630 |
+
name: MTEB MindSmallReranking
|
1631 |
+
config: default
|
1632 |
+
split: test
|
1633 |
+
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
1634 |
+
metrics:
|
1635 |
+
- type: map
|
1636 |
+
value: 30.096874986740758
|
1637 |
+
- type: mrr
|
1638 |
+
value: 30.97300481932132
|
1639 |
+
- task:
|
1640 |
+
type: Retrieval
|
1641 |
+
dataset:
|
1642 |
+
type: nfcorpus
|
1643 |
+
name: MTEB NFCorpus
|
1644 |
+
config: default
|
1645 |
+
split: test
|
1646 |
+
revision: None
|
1647 |
+
metrics:
|
1648 |
+
- type: map_at_1
|
1649 |
+
value: 5.4
|
1650 |
+
- type: map_at_10
|
1651 |
+
value: 11.852
|
1652 |
+
- type: map_at_100
|
1653 |
+
value: 14.758
|
1654 |
+
- type: map_at_1000
|
1655 |
+
value: 16.134
|
1656 |
+
- type: map_at_3
|
1657 |
+
value: 8.558
|
1658 |
+
- type: map_at_5
|
1659 |
+
value: 10.087
|
1660 |
+
- type: mrr_at_1
|
1661 |
+
value: 44.272
|
1662 |
+
- type: mrr_at_10
|
1663 |
+
value: 52.05800000000001
|
1664 |
+
- type: mrr_at_100
|
1665 |
+
value: 52.689
|
1666 |
+
- type: mrr_at_1000
|
1667 |
+
value: 52.742999999999995
|
1668 |
+
- type: mrr_at_3
|
1669 |
+
value: 50.205999999999996
|
1670 |
+
- type: mrr_at_5
|
1671 |
+
value: 51.367
|
1672 |
+
- type: ndcg_at_1
|
1673 |
+
value: 42.57
|
1674 |
+
- type: ndcg_at_10
|
1675 |
+
value: 32.449
|
1676 |
+
- type: ndcg_at_100
|
1677 |
+
value: 29.596
|
1678 |
+
- type: ndcg_at_1000
|
1679 |
+
value: 38.351
|
1680 |
+
- type: ndcg_at_3
|
1681 |
+
value: 37.044
|
1682 |
+
- type: ndcg_at_5
|
1683 |
+
value: 35.275
|
1684 |
+
- type: precision_at_1
|
1685 |
+
value: 44.272
|
1686 |
+
- type: precision_at_10
|
1687 |
+
value: 23.87
|
1688 |
+
- type: precision_at_100
|
1689 |
+
value: 7.625
|
1690 |
+
- type: precision_at_1000
|
1691 |
+
value: 2.045
|
1692 |
+
- type: precision_at_3
|
1693 |
+
value: 34.365
|
1694 |
+
- type: precision_at_5
|
1695 |
+
value: 30.341
|
1696 |
+
- type: recall_at_1
|
1697 |
+
value: 5.4
|
1698 |
+
- type: recall_at_10
|
1699 |
+
value: 15.943999999999999
|
1700 |
+
- type: recall_at_100
|
1701 |
+
value: 29.805
|
1702 |
+
- type: recall_at_1000
|
1703 |
+
value: 61.695
|
1704 |
+
- type: recall_at_3
|
1705 |
+
value: 9.539
|
1706 |
+
- type: recall_at_5
|
1707 |
+
value: 12.127
|
1708 |
+
- task:
|
1709 |
+
type: Retrieval
|
1710 |
+
dataset:
|
1711 |
+
type: nq
|
1712 |
+
name: MTEB NQ
|
1713 |
+
config: default
|
1714 |
+
split: test
|
1715 |
+
revision: None
|
1716 |
+
metrics:
|
1717 |
+
- type: map_at_1
|
1718 |
+
value: 36.047000000000004
|
1719 |
+
- type: map_at_10
|
1720 |
+
value: 51.6
|
1721 |
+
- type: map_at_100
|
1722 |
+
value: 52.449999999999996
|
1723 |
+
- type: map_at_1000
|
1724 |
+
value: 52.476
|
1725 |
+
- type: map_at_3
|
1726 |
+
value: 47.452
|
1727 |
+
- type: map_at_5
|
1728 |
+
value: 49.964
|
1729 |
+
- type: mrr_at_1
|
1730 |
+
value: 40.382
|
1731 |
+
- type: mrr_at_10
|
1732 |
+
value: 54.273
|
1733 |
+
- type: mrr_at_100
|
1734 |
+
value: 54.859
|
1735 |
+
- type: mrr_at_1000
|
1736 |
+
value: 54.876000000000005
|
1737 |
+
- type: mrr_at_3
|
1738 |
+
value: 51.014
|
1739 |
+
- type: mrr_at_5
|
1740 |
+
value: 52.983999999999995
|
1741 |
+
- type: ndcg_at_1
|
1742 |
+
value: 40.353
|
1743 |
+
- type: ndcg_at_10
|
1744 |
+
value: 59.11300000000001
|
1745 |
+
- type: ndcg_at_100
|
1746 |
+
value: 62.604000000000006
|
1747 |
+
- type: ndcg_at_1000
|
1748 |
+
value: 63.187000000000005
|
1749 |
+
- type: ndcg_at_3
|
1750 |
+
value: 51.513
|
1751 |
+
- type: ndcg_at_5
|
1752 |
+
value: 55.576
|
1753 |
+
- type: precision_at_1
|
1754 |
+
value: 40.353
|
1755 |
+
- type: precision_at_10
|
1756 |
+
value: 9.418
|
1757 |
+
- type: precision_at_100
|
1758 |
+
value: 1.1440000000000001
|
1759 |
+
- type: precision_at_1000
|
1760 |
+
value: 0.12
|
1761 |
+
- type: precision_at_3
|
1762 |
+
value: 23.078000000000003
|
1763 |
+
- type: precision_at_5
|
1764 |
+
value: 16.250999999999998
|
1765 |
+
- type: recall_at_1
|
1766 |
+
value: 36.047000000000004
|
1767 |
+
- type: recall_at_10
|
1768 |
+
value: 79.22200000000001
|
1769 |
+
- type: recall_at_100
|
1770 |
+
value: 94.23
|
1771 |
+
- type: recall_at_1000
|
1772 |
+
value: 98.51100000000001
|
1773 |
+
- type: recall_at_3
|
1774 |
+
value: 59.678
|
1775 |
+
- type: recall_at_5
|
1776 |
+
value: 68.967
|
1777 |
+
- task:
|
1778 |
+
type: Retrieval
|
1779 |
+
dataset:
|
1780 |
+
type: quora
|
1781 |
+
name: MTEB QuoraRetrieval
|
1782 |
+
config: default
|
1783 |
+
split: test
|
1784 |
+
revision: None
|
1785 |
+
metrics:
|
1786 |
+
- type: map_at_1
|
1787 |
+
value: 68.232
|
1788 |
+
- type: map_at_10
|
1789 |
+
value: 81.674
|
1790 |
+
- type: map_at_100
|
1791 |
+
value: 82.338
|
1792 |
+
- type: map_at_1000
|
1793 |
+
value: 82.36099999999999
|
1794 |
+
- type: map_at_3
|
1795 |
+
value: 78.833
|
1796 |
+
- type: map_at_5
|
1797 |
+
value: 80.58
|
1798 |
+
- type: mrr_at_1
|
1799 |
+
value: 78.64
|
1800 |
+
- type: mrr_at_10
|
1801 |
+
value: 85.164
|
1802 |
+
- type: mrr_at_100
|
1803 |
+
value: 85.317
|
1804 |
+
- type: mrr_at_1000
|
1805 |
+
value: 85.319
|
1806 |
+
- type: mrr_at_3
|
1807 |
+
value: 84.127
|
1808 |
+
- type: mrr_at_5
|
1809 |
+
value: 84.789
|
1810 |
+
- type: ndcg_at_1
|
1811 |
+
value: 78.63
|
1812 |
+
- type: ndcg_at_10
|
1813 |
+
value: 85.711
|
1814 |
+
- type: ndcg_at_100
|
1815 |
+
value: 87.238
|
1816 |
+
- type: ndcg_at_1000
|
1817 |
+
value: 87.444
|
1818 |
+
- type: ndcg_at_3
|
1819 |
+
value: 82.788
|
1820 |
+
- type: ndcg_at_5
|
1821 |
+
value: 84.313
|
1822 |
+
- type: precision_at_1
|
1823 |
+
value: 78.63
|
1824 |
+
- type: precision_at_10
|
1825 |
+
value: 12.977
|
1826 |
+
- type: precision_at_100
|
1827 |
+
value: 1.503
|
1828 |
+
- type: precision_at_1000
|
1829 |
+
value: 0.156
|
1830 |
+
- type: precision_at_3
|
1831 |
+
value: 36.113
|
1832 |
+
- type: precision_at_5
|
1833 |
+
value: 23.71
|
1834 |
+
- type: recall_at_1
|
1835 |
+
value: 68.232
|
1836 |
+
- type: recall_at_10
|
1837 |
+
value: 93.30199999999999
|
1838 |
+
- type: recall_at_100
|
1839 |
+
value: 98.799
|
1840 |
+
- type: recall_at_1000
|
1841 |
+
value: 99.885
|
1842 |
+
- type: recall_at_3
|
1843 |
+
value: 84.827
|
1844 |
+
- type: recall_at_5
|
1845 |
+
value: 89.188
|
1846 |
+
- task:
|
1847 |
+
type: Clustering
|
1848 |
+
dataset:
|
1849 |
+
type: mteb/reddit-clustering
|
1850 |
+
name: MTEB RedditClustering
|
1851 |
+
config: default
|
1852 |
+
split: test
|
1853 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
1854 |
+
metrics:
|
1855 |
+
- type: v_measure
|
1856 |
+
value: 45.71879170816294
|
1857 |
+
- task:
|
1858 |
+
type: Clustering
|
1859 |
+
dataset:
|
1860 |
+
type: mteb/reddit-clustering-p2p
|
1861 |
+
name: MTEB RedditClusteringP2P
|
1862 |
+
config: default
|
1863 |
+
split: test
|
1864 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
1865 |
+
metrics:
|
1866 |
+
- type: v_measure
|
1867 |
+
value: 59.65866311751794
|
1868 |
+
- task:
|
1869 |
+
type: Retrieval
|
1870 |
+
dataset:
|
1871 |
+
type: scidocs
|
1872 |
+
name: MTEB SCIDOCS
|
1873 |
+
config: default
|
1874 |
+
split: test
|
1875 |
+
revision: None
|
1876 |
+
metrics:
|
1877 |
+
- type: map_at_1
|
1878 |
+
value: 4.218
|
1879 |
+
- type: map_at_10
|
1880 |
+
value: 10.337
|
1881 |
+
- type: map_at_100
|
1882 |
+
value: 12.131
|
1883 |
+
- type: map_at_1000
|
1884 |
+
value: 12.411
|
1885 |
+
- type: map_at_3
|
1886 |
+
value: 7.4270000000000005
|
1887 |
+
- type: map_at_5
|
1888 |
+
value: 8.913
|
1889 |
+
- type: mrr_at_1
|
1890 |
+
value: 20.8
|
1891 |
+
- type: mrr_at_10
|
1892 |
+
value: 30.868000000000002
|
1893 |
+
- type: mrr_at_100
|
1894 |
+
value: 31.903
|
1895 |
+
- type: mrr_at_1000
|
1896 |
+
value: 31.972
|
1897 |
+
- type: mrr_at_3
|
1898 |
+
value: 27.367
|
1899 |
+
- type: mrr_at_5
|
1900 |
+
value: 29.372
|
1901 |
+
- type: ndcg_at_1
|
1902 |
+
value: 20.8
|
1903 |
+
- type: ndcg_at_10
|
1904 |
+
value: 17.765
|
1905 |
+
- type: ndcg_at_100
|
1906 |
+
value: 24.914
|
1907 |
+
- type: ndcg_at_1000
|
1908 |
+
value: 30.206
|
1909 |
+
- type: ndcg_at_3
|
1910 |
+
value: 16.64
|
1911 |
+
- type: ndcg_at_5
|
1912 |
+
value: 14.712
|
1913 |
+
- type: precision_at_1
|
1914 |
+
value: 20.8
|
1915 |
+
- type: precision_at_10
|
1916 |
+
value: 9.24
|
1917 |
+
- type: precision_at_100
|
1918 |
+
value: 1.9560000000000002
|
1919 |
+
- type: precision_at_1000
|
1920 |
+
value: 0.32299999999999995
|
1921 |
+
- type: precision_at_3
|
1922 |
+
value: 15.467
|
1923 |
+
- type: precision_at_5
|
1924 |
+
value: 12.94
|
1925 |
+
- type: recall_at_1
|
1926 |
+
value: 4.218
|
1927 |
+
- type: recall_at_10
|
1928 |
+
value: 18.752
|
1929 |
+
- type: recall_at_100
|
1930 |
+
value: 39.7
|
1931 |
+
- type: recall_at_1000
|
1932 |
+
value: 65.57300000000001
|
1933 |
+
- type: recall_at_3
|
1934 |
+
value: 9.428
|
1935 |
+
- type: recall_at_5
|
1936 |
+
value: 13.133000000000001
|
1937 |
+
- task:
|
1938 |
+
type: STS
|
1939 |
+
dataset:
|
1940 |
+
type: mteb/sickr-sts
|
1941 |
+
name: MTEB SICK-R
|
1942 |
+
config: default
|
1943 |
+
split: test
|
1944 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
1945 |
+
metrics:
|
1946 |
+
- type: cos_sim_pearson
|
1947 |
+
value: 83.04338850207233
|
1948 |
+
- type: cos_sim_spearman
|
1949 |
+
value: 78.5054651430423
|
1950 |
+
- type: euclidean_pearson
|
1951 |
+
value: 80.30739451228612
|
1952 |
+
- type: euclidean_spearman
|
1953 |
+
value: 78.48377464299097
|
1954 |
+
- type: manhattan_pearson
|
1955 |
+
value: 80.40795049052781
|
1956 |
+
- type: manhattan_spearman
|
1957 |
+
value: 78.49506205443114
|
1958 |
+
- task:
|
1959 |
+
type: STS
|
1960 |
+
dataset:
|
1961 |
+
type: mteb/sts12-sts
|
1962 |
+
name: MTEB STS12
|
1963 |
+
config: default
|
1964 |
+
split: test
|
1965 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
1966 |
+
metrics:
|
1967 |
+
- type: cos_sim_pearson
|
1968 |
+
value: 84.11596224442962
|
1969 |
+
- type: cos_sim_spearman
|
1970 |
+
value: 76.20997388935461
|
1971 |
+
- type: euclidean_pearson
|
1972 |
+
value: 80.56858451349109
|
1973 |
+
- type: euclidean_spearman
|
1974 |
+
value: 75.92659183871186
|
1975 |
+
- type: manhattan_pearson
|
1976 |
+
value: 80.60246102203844
|
1977 |
+
- type: manhattan_spearman
|
1978 |
+
value: 76.03018971432664
|
1979 |
+
- task:
|
1980 |
+
type: STS
|
1981 |
+
dataset:
|
1982 |
+
type: mteb/sts13-sts
|
1983 |
+
name: MTEB STS13
|
1984 |
+
config: default
|
1985 |
+
split: test
|
1986 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
1987 |
+
metrics:
|
1988 |
+
- type: cos_sim_pearson
|
1989 |
+
value: 81.34691640755737
|
1990 |
+
- type: cos_sim_spearman
|
1991 |
+
value: 82.4018369631579
|
1992 |
+
- type: euclidean_pearson
|
1993 |
+
value: 81.87673092245366
|
1994 |
+
- type: euclidean_spearman
|
1995 |
+
value: 82.3671489960678
|
1996 |
+
- type: manhattan_pearson
|
1997 |
+
value: 81.88222387719948
|
1998 |
+
- type: manhattan_spearman
|
1999 |
+
value: 82.3816590344736
|
2000 |
+
- task:
|
2001 |
+
type: STS
|
2002 |
+
dataset:
|
2003 |
+
type: mteb/sts14-sts
|
2004 |
+
name: MTEB STS14
|
2005 |
+
config: default
|
2006 |
+
split: test
|
2007 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
2008 |
+
metrics:
|
2009 |
+
- type: cos_sim_pearson
|
2010 |
+
value: 81.2836092579524
|
2011 |
+
- type: cos_sim_spearman
|
2012 |
+
value: 78.99982781772064
|
2013 |
+
- type: euclidean_pearson
|
2014 |
+
value: 80.5184271010527
|
2015 |
+
- type: euclidean_spearman
|
2016 |
+
value: 78.89777392101904
|
2017 |
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- type: manhattan_pearson
|
2018 |
+
value: 80.53585705018664
|
2019 |
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- type: manhattan_spearman
|
2020 |
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value: 78.92898405472994
|
2021 |
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- task:
|
2022 |
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type: STS
|
2023 |
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dataset:
|
2024 |
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type: mteb/sts15-sts
|
2025 |
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name: MTEB STS15
|
2026 |
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config: default
|
2027 |
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split: test
|
2028 |
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revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
2029 |
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metrics:
|
2030 |
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- type: cos_sim_pearson
|
2031 |
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value: 86.7349907750784
|
2032 |
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- type: cos_sim_spearman
|
2033 |
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value: 87.7611234446225
|
2034 |
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- type: euclidean_pearson
|
2035 |
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value: 86.98759326731624
|
2036 |
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- type: euclidean_spearman
|
2037 |
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|
2038 |
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- type: manhattan_pearson
|
2039 |
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|
2040 |
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- type: manhattan_spearman
|
2041 |
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|
2042 |
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- task:
|
2043 |
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type: STS
|
2044 |
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dataset:
|
2045 |
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type: mteb/sts16-sts
|
2046 |
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name: MTEB STS16
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2047 |
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config: default
|
2048 |
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split: test
|
2049 |
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revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
2050 |
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metrics:
|
2051 |
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- type: cos_sim_pearson
|
2052 |
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value: 81.75873694924825
|
2053 |
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- type: cos_sim_spearman
|
2054 |
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value: 83.80237999094724
|
2055 |
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- type: euclidean_pearson
|
2056 |
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value: 83.55023725861537
|
2057 |
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- type: euclidean_spearman
|
2058 |
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value: 84.12744338577744
|
2059 |
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- type: manhattan_pearson
|
2060 |
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value: 83.58816983036232
|
2061 |
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- type: manhattan_spearman
|
2062 |
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|
2063 |
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- task:
|
2064 |
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type: STS
|
2065 |
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dataset:
|
2066 |
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type: mteb/sts17-crosslingual-sts
|
2067 |
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name: MTEB STS17 (en-en)
|
2068 |
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config: en-en
|
2069 |
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split: test
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2070 |
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
2071 |
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metrics:
|
2072 |
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- type: cos_sim_pearson
|
2073 |
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value: 87.21630882940174
|
2074 |
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- type: cos_sim_spearman
|
2075 |
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|
2076 |
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- type: euclidean_pearson
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2077 |
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|
2078 |
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- type: euclidean_spearman
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2079 |
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|
2080 |
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- type: manhattan_pearson
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2081 |
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2082 |
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- type: manhattan_spearman
|
2083 |
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|
2084 |
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- task:
|
2085 |
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type: STS
|
2086 |
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dataset:
|
2087 |
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type: mteb/sts22-crosslingual-sts
|
2088 |
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name: MTEB STS22 (en)
|
2089 |
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config: en
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2090 |
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split: test
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2091 |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
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2092 |
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metrics:
|
2093 |
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|
2094 |
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value: 61.11854063060489
|
2095 |
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- type: cos_sim_spearman
|
2096 |
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|
2097 |
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- type: euclidean_pearson
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2098 |
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value: 61.679090067000864
|
2099 |
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- type: euclidean_spearman
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2100 |
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|
2101 |
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- type: manhattan_pearson
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2102 |
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|
2103 |
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- type: manhattan_spearman
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2104 |
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|
2105 |
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- task:
|
2106 |
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type: STS
|
2107 |
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dataset:
|
2108 |
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type: mteb/stsbenchmark-sts
|
2109 |
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name: MTEB STSBenchmark
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2110 |
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config: default
|
2111 |
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split: test
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2112 |
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revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
2113 |
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metrics:
|
2114 |
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- type: cos_sim_pearson
|
2115 |
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value: 84.00319882832645
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2116 |
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- type: cos_sim_spearman
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2117 |
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value: 85.94529772647257
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2118 |
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- type: euclidean_pearson
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2119 |
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2120 |
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- type: euclidean_spearman
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2121 |
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2122 |
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- type: manhattan_pearson
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2123 |
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value: 85.58422770541893
|
2124 |
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- type: manhattan_spearman
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2125 |
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2126 |
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- task:
|
2127 |
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type: Reranking
|
2128 |
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dataset:
|
2129 |
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type: mteb/scidocs-reranking
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2130 |
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name: MTEB SciDocsRR
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2131 |
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config: default
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2132 |
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split: test
|
2133 |
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revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
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2134 |
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metrics:
|
2135 |
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- type: map
|
2136 |
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value: 79.16198731863916
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2137 |
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- type: mrr
|
2138 |
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|
2139 |
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- task:
|
2140 |
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type: Retrieval
|
2141 |
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dataset:
|
2142 |
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type: scifact
|
2143 |
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name: MTEB SciFact
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2144 |
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config: default
|
2145 |
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split: test
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2146 |
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revision: None
|
2147 |
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metrics:
|
2148 |
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- type: map_at_1
|
2149 |
+
value: 54.761
|
2150 |
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- type: map_at_10
|
2151 |
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value: 64.396
|
2152 |
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- type: map_at_100
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2153 |
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value: 65.07
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2154 |
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- type: map_at_1000
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2155 |
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value: 65.09899999999999
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2156 |
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- type: map_at_3
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2157 |
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value: 61.846000000000004
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2158 |
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- type: map_at_5
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2159 |
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value: 63.284
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2160 |
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- type: mrr_at_1
|
2161 |
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value: 57.667
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2162 |
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- type: mrr_at_10
|
2163 |
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value: 65.83099999999999
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2164 |
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- type: mrr_at_100
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2165 |
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value: 66.36800000000001
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2166 |
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- type: mrr_at_1000
|
2167 |
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value: 66.39399999999999
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2168 |
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- type: mrr_at_3
|
2169 |
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value: 64.056
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2170 |
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- type: mrr_at_5
|
2171 |
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value: 65.206
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2172 |
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- type: ndcg_at_1
|
2173 |
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value: 57.667
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2174 |
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- type: ndcg_at_10
|
2175 |
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value: 68.854
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2176 |
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- type: ndcg_at_100
|
2177 |
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value: 71.59100000000001
|
2178 |
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- type: ndcg_at_1000
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2179 |
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value: 72.383
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2180 |
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- type: ndcg_at_3
|
2181 |
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value: 64.671
|
2182 |
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- type: ndcg_at_5
|
2183 |
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value: 66.796
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2184 |
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- type: precision_at_1
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2185 |
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value: 57.667
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2186 |
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- type: precision_at_10
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2187 |
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value: 9.167
|
2188 |
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- type: precision_at_100
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2189 |
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value: 1.053
|
2190 |
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- type: precision_at_1000
|
2191 |
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value: 0.11199999999999999
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2192 |
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- type: precision_at_3
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2193 |
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value: 25.444
|
2194 |
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- type: precision_at_5
|
2195 |
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value: 16.667
|
2196 |
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- type: recall_at_1
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2197 |
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value: 54.761
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2198 |
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- type: recall_at_10
|
2199 |
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value: 80.9
|
2200 |
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- type: recall_at_100
|
2201 |
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value: 92.767
|
2202 |
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- type: recall_at_1000
|
2203 |
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value: 99
|
2204 |
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- type: recall_at_3
|
2205 |
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value: 69.672
|
2206 |
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- type: recall_at_5
|
2207 |
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value: 75.083
|
2208 |
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- task:
|
2209 |
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type: PairClassification
|
2210 |
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dataset:
|
2211 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
2212 |
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name: MTEB SprintDuplicateQuestions
|
2213 |
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config: default
|
2214 |
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split: test
|
2215 |
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revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
2216 |
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metrics:
|
2217 |
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- type: cos_sim_accuracy
|
2218 |
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value: 99.8079207920792
|
2219 |
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- type: cos_sim_ap
|
2220 |
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value: 94.88470927617445
|
2221 |
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- type: cos_sim_f1
|
2222 |
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value: 90.08179959100204
|
2223 |
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- type: cos_sim_precision
|
2224 |
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value: 92.15481171548117
|
2225 |
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- type: cos_sim_recall
|
2226 |
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value: 88.1
|
2227 |
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- type: dot_accuracy
|
2228 |
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value: 99.58613861386138
|
2229 |
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- type: dot_ap
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2230 |
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value: 82.94822578881316
|
2231 |
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- type: dot_f1
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2232 |
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value: 77.33333333333333
|
2233 |
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- type: dot_precision
|
2234 |
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value: 79.36842105263158
|
2235 |
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- type: dot_recall
|
2236 |
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value: 75.4
|
2237 |
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- type: euclidean_accuracy
|
2238 |
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value: 99.8069306930693
|
2239 |
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- type: euclidean_ap
|
2240 |
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value: 94.81367858031837
|
2241 |
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- type: euclidean_f1
|
2242 |
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value: 90.01009081735621
|
2243 |
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- type: euclidean_precision
|
2244 |
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value: 90.83503054989816
|
2245 |
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- type: euclidean_recall
|
2246 |
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value: 89.2
|
2247 |
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- type: manhattan_accuracy
|
2248 |
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value: 99.81188118811882
|
2249 |
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- type: manhattan_ap
|
2250 |
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value: 94.91405337220161
|
2251 |
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- type: manhattan_f1
|
2252 |
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value: 90.2763561924258
|
2253 |
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- type: manhattan_precision
|
2254 |
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value: 92.45283018867924
|
2255 |
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- type: manhattan_recall
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2256 |
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value: 88.2
|
2257 |
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- type: max_accuracy
|
2258 |
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value: 99.81188118811882
|
2259 |
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- type: max_ap
|
2260 |
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value: 94.91405337220161
|
2261 |
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- type: max_f1
|
2262 |
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value: 90.2763561924258
|
2263 |
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- task:
|
2264 |
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type: Clustering
|
2265 |
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dataset:
|
2266 |
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type: mteb/stackexchange-clustering
|
2267 |
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name: MTEB StackExchangeClustering
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2268 |
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config: default
|
2269 |
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split: test
|
2270 |
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revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
2271 |
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metrics:
|
2272 |
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- type: v_measure
|
2273 |
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value: 58.511599500053094
|
2274 |
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- task:
|
2275 |
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type: Clustering
|
2276 |
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dataset:
|
2277 |
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type: mteb/stackexchange-clustering-p2p
|
2278 |
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name: MTEB StackExchangeClusteringP2P
|
2279 |
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config: default
|
2280 |
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split: test
|
2281 |
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revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
2282 |
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metrics:
|
2283 |
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- type: v_measure
|
2284 |
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value: 31.984728147814707
|
2285 |
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- task:
|
2286 |
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type: Reranking
|
2287 |
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dataset:
|
2288 |
+
type: mteb/stackoverflowdupquestions-reranking
|
2289 |
+
name: MTEB StackOverflowDupQuestions
|
2290 |
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config: default
|
2291 |
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split: test
|
2292 |
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revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
2293 |
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metrics:
|
2294 |
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- type: map
|
2295 |
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value: 49.93428193939015
|
2296 |
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- type: mrr
|
2297 |
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value: 50.916557911043206
|
2298 |
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- task:
|
2299 |
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type: Summarization
|
2300 |
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dataset:
|
2301 |
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type: mteb/summeval
|
2302 |
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name: MTEB SummEval
|
2303 |
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config: default
|
2304 |
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split: test
|
2305 |
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revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
2306 |
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metrics:
|
2307 |
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- type: cos_sim_pearson
|
2308 |
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value: 31.562500894537145
|
2309 |
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- type: cos_sim_spearman
|
2310 |
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value: 31.162587976726307
|
2311 |
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- type: dot_pearson
|
2312 |
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value: 22.633662187735762
|
2313 |
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- type: dot_spearman
|
2314 |
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value: 22.723000282378962
|
2315 |
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- task:
|
2316 |
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type: Retrieval
|
2317 |
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dataset:
|
2318 |
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type: trec-covid
|
2319 |
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name: MTEB TRECCOVID
|
2320 |
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config: default
|
2321 |
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split: test
|
2322 |
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revision: None
|
2323 |
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metrics:
|
2324 |
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- type: map_at_1
|
2325 |
+
value: 0.219
|
2326 |
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- type: map_at_10
|
2327 |
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value: 1.871
|
2328 |
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- type: map_at_100
|
2329 |
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value: 10.487
|
2330 |
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- type: map_at_1000
|
2331 |
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value: 25.122
|
2332 |
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- type: map_at_3
|
2333 |
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value: 0.657
|
2334 |
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- type: map_at_5
|
2335 |
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value: 1.0699999999999998
|
2336 |
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- type: mrr_at_1
|
2337 |
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value: 84
|
2338 |
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|
2339 |
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value: 89.567
|
2340 |
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- type: mrr_at_100
|
2341 |
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value: 89.748
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2342 |
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- type: mrr_at_1000
|
2343 |
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value: 89.748
|
2344 |
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- type: mrr_at_3
|
2345 |
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value: 88.667
|
2346 |
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- type: mrr_at_5
|
2347 |
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value: 89.567
|
2348 |
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- type: ndcg_at_1
|
2349 |
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value: 80
|
2350 |
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- type: ndcg_at_10
|
2351 |
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value: 74.533
|
2352 |
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- type: ndcg_at_100
|
2353 |
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value: 55.839000000000006
|
2354 |
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- type: ndcg_at_1000
|
2355 |
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value: 49.748
|
2356 |
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- type: ndcg_at_3
|
2357 |
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value: 79.53099999999999
|
2358 |
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- type: ndcg_at_5
|
2359 |
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value: 78.245
|
2360 |
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- type: precision_at_1
|
2361 |
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value: 84
|
2362 |
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- type: precision_at_10
|
2363 |
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value: 78.4
|
2364 |
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- type: precision_at_100
|
2365 |
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value: 56.99999999999999
|
2366 |
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- type: precision_at_1000
|
2367 |
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value: 21.98
|
2368 |
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- type: precision_at_3
|
2369 |
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value: 85.333
|
2370 |
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- type: precision_at_5
|
2371 |
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value: 84.8
|
2372 |
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- type: recall_at_1
|
2373 |
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value: 0.219
|
2374 |
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- type: recall_at_10
|
2375 |
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value: 2.02
|
2376 |
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- type: recall_at_100
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2377 |
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value: 13.555
|
2378 |
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- type: recall_at_1000
|
2379 |
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value: 46.739999999999995
|
2380 |
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- type: recall_at_3
|
2381 |
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value: 0.685
|
2382 |
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- type: recall_at_5
|
2383 |
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value: 1.13
|
2384 |
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- task:
|
2385 |
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type: Retrieval
|
2386 |
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dataset:
|
2387 |
+
type: webis-touche2020
|
2388 |
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name: MTEB Touche2020
|
2389 |
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config: default
|
2390 |
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split: test
|
2391 |
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revision: None
|
2392 |
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metrics:
|
2393 |
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- type: map_at_1
|
2394 |
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value: 3.5029999999999997
|
2395 |
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- type: map_at_10
|
2396 |
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value: 11.042
|
2397 |
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2398 |
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value: 16.326999999999998
|
2399 |
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- type: map_at_1000
|
2400 |
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value: 17.836
|
2401 |
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- type: map_at_3
|
2402 |
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value: 6.174
|
2403 |
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- type: map_at_5
|
2404 |
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value: 7.979
|
2405 |
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- type: mrr_at_1
|
2406 |
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value: 42.857
|
2407 |
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- type: mrr_at_10
|
2408 |
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value: 52.617000000000004
|
2409 |
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- type: mrr_at_100
|
2410 |
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value: 53.351000000000006
|
2411 |
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- type: mrr_at_1000
|
2412 |
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value: 53.351000000000006
|
2413 |
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- type: mrr_at_3
|
2414 |
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value: 46.939
|
2415 |
+
- type: mrr_at_5
|
2416 |
+
value: 50.714000000000006
|
2417 |
+
- type: ndcg_at_1
|
2418 |
+
value: 38.775999999999996
|
2419 |
+
- type: ndcg_at_10
|
2420 |
+
value: 27.125
|
2421 |
+
- type: ndcg_at_100
|
2422 |
+
value: 35.845
|
2423 |
+
- type: ndcg_at_1000
|
2424 |
+
value: 47.377
|
2425 |
+
- type: ndcg_at_3
|
2426 |
+
value: 29.633
|
2427 |
+
- type: ndcg_at_5
|
2428 |
+
value: 28.378999999999998
|
2429 |
+
- type: precision_at_1
|
2430 |
+
value: 42.857
|
2431 |
+
- type: precision_at_10
|
2432 |
+
value: 24.082
|
2433 |
+
- type: precision_at_100
|
2434 |
+
value: 6.877999999999999
|
2435 |
+
- type: precision_at_1000
|
2436 |
+
value: 1.463
|
2437 |
+
- type: precision_at_3
|
2438 |
+
value: 29.932
|
2439 |
+
- type: precision_at_5
|
2440 |
+
value: 28.571
|
2441 |
+
- type: recall_at_1
|
2442 |
+
value: 3.5029999999999997
|
2443 |
+
- type: recall_at_10
|
2444 |
+
value: 17.068
|
2445 |
+
- type: recall_at_100
|
2446 |
+
value: 43.361
|
2447 |
+
- type: recall_at_1000
|
2448 |
+
value: 78.835
|
2449 |
+
- type: recall_at_3
|
2450 |
+
value: 6.821000000000001
|
2451 |
+
- type: recall_at_5
|
2452 |
+
value: 10.357
|
2453 |
+
- task:
|
2454 |
+
type: Classification
|
2455 |
+
dataset:
|
2456 |
+
type: mteb/toxic_conversations_50k
|
2457 |
+
name: MTEB ToxicConversationsClassification
|
2458 |
+
config: default
|
2459 |
+
split: test
|
2460 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
2461 |
+
metrics:
|
2462 |
+
- type: accuracy
|
2463 |
+
value: 71.0954
|
2464 |
+
- type: ap
|
2465 |
+
value: 14.216844153511959
|
2466 |
+
- type: f1
|
2467 |
+
value: 54.63687418565117
|
2468 |
+
- task:
|
2469 |
+
type: Classification
|
2470 |
+
dataset:
|
2471 |
+
type: mteb/tweet_sentiment_extraction
|
2472 |
+
name: MTEB TweetSentimentExtractionClassification
|
2473 |
+
config: default
|
2474 |
+
split: test
|
2475 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
2476 |
+
metrics:
|
2477 |
+
- type: accuracy
|
2478 |
+
value: 61.46293152235427
|
2479 |
+
- type: f1
|
2480 |
+
value: 61.744177921638645
|
2481 |
+
- task:
|
2482 |
+
type: Clustering
|
2483 |
+
dataset:
|
2484 |
+
type: mteb/twentynewsgroups-clustering
|
2485 |
+
name: MTEB TwentyNewsgroupsClustering
|
2486 |
+
config: default
|
2487 |
+
split: test
|
2488 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
2489 |
+
metrics:
|
2490 |
+
- type: v_measure
|
2491 |
+
value: 41.12708617788644
|
2492 |
+
- task:
|
2493 |
+
type: PairClassification
|
2494 |
+
dataset:
|
2495 |
+
type: mteb/twittersemeval2015-pairclassification
|
2496 |
+
name: MTEB TwitterSemEval2015
|
2497 |
+
config: default
|
2498 |
+
split: test
|
2499 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
2500 |
+
metrics:
|
2501 |
+
- type: cos_sim_accuracy
|
2502 |
+
value: 85.75430649102938
|
2503 |
+
- type: cos_sim_ap
|
2504 |
+
value: 73.34252536948081
|
2505 |
+
- type: cos_sim_f1
|
2506 |
+
value: 67.53758935173774
|
2507 |
+
- type: cos_sim_precision
|
2508 |
+
value: 63.3672525439408
|
2509 |
+
- type: cos_sim_recall
|
2510 |
+
value: 72.29551451187335
|
2511 |
+
- type: dot_accuracy
|
2512 |
+
value: 81.71305954580676
|
2513 |
+
- type: dot_ap
|
2514 |
+
value: 59.5532209082386
|
2515 |
+
- type: dot_f1
|
2516 |
+
value: 56.18466898954705
|
2517 |
+
- type: dot_precision
|
2518 |
+
value: 47.830923248053395
|
2519 |
+
- type: dot_recall
|
2520 |
+
value: 68.07387862796834
|
2521 |
+
- type: euclidean_accuracy
|
2522 |
+
value: 85.81987244441795
|
2523 |
+
- type: euclidean_ap
|
2524 |
+
value: 73.34325409809446
|
2525 |
+
- type: euclidean_f1
|
2526 |
+
value: 67.83451360417443
|
2527 |
+
- type: euclidean_precision
|
2528 |
+
value: 64.09955388588871
|
2529 |
+
- type: euclidean_recall
|
2530 |
+
value: 72.0316622691293
|
2531 |
+
- type: manhattan_accuracy
|
2532 |
+
value: 85.68277999642368
|
2533 |
+
- type: manhattan_ap
|
2534 |
+
value: 73.1535450121903
|
2535 |
+
- type: manhattan_f1
|
2536 |
+
value: 67.928237896289
|
2537 |
+
- type: manhattan_precision
|
2538 |
+
value: 63.56945722171113
|
2539 |
+
- type: manhattan_recall
|
2540 |
+
value: 72.9287598944591
|
2541 |
+
- type: max_accuracy
|
2542 |
+
value: 85.81987244441795
|
2543 |
+
- type: max_ap
|
2544 |
+
value: 73.34325409809446
|
2545 |
+
- type: max_f1
|
2546 |
+
value: 67.928237896289
|
2547 |
+
- task:
|
2548 |
+
type: PairClassification
|
2549 |
+
dataset:
|
2550 |
+
type: mteb/twitterurlcorpus-pairclassification
|
2551 |
+
name: MTEB TwitterURLCorpus
|
2552 |
+
config: default
|
2553 |
+
split: test
|
2554 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
2555 |
+
metrics:
|
2556 |
+
- type: cos_sim_accuracy
|
2557 |
+
value: 88.90441262079403
|
2558 |
+
- type: cos_sim_ap
|
2559 |
+
value: 85.79331880741438
|
2560 |
+
- type: cos_sim_f1
|
2561 |
+
value: 78.31563529842548
|
2562 |
+
- type: cos_sim_precision
|
2563 |
+
value: 74.6683424102779
|
2564 |
+
- type: cos_sim_recall
|
2565 |
+
value: 82.33754234678165
|
2566 |
+
- type: dot_accuracy
|
2567 |
+
value: 84.89928978926534
|
2568 |
+
- type: dot_ap
|
2569 |
+
value: 75.25819218316
|
2570 |
+
- type: dot_f1
|
2571 |
+
value: 69.88730119720536
|
2572 |
+
- type: dot_precision
|
2573 |
+
value: 64.23362374959665
|
2574 |
+
- type: dot_recall
|
2575 |
+
value: 76.63227594702803
|
2576 |
+
- type: euclidean_accuracy
|
2577 |
+
value: 89.01695967710637
|
2578 |
+
- type: euclidean_ap
|
2579 |
+
value: 85.98986606038852
|
2580 |
+
- type: euclidean_f1
|
2581 |
+
value: 78.5277880014722
|
2582 |
+
- type: euclidean_precision
|
2583 |
+
value: 75.22211253701876
|
2584 |
+
- type: euclidean_recall
|
2585 |
+
value: 82.13735756082538
|
2586 |
+
- type: manhattan_accuracy
|
2587 |
+
value: 88.99561454573679
|
2588 |
+
- type: manhattan_ap
|
2589 |
+
value: 85.92262421793953
|
2590 |
+
- type: manhattan_f1
|
2591 |
+
value: 78.38866094740769
|
2592 |
+
- type: manhattan_precision
|
2593 |
+
value: 76.02373028505282
|
2594 |
+
- type: manhattan_recall
|
2595 |
+
value: 80.9054511857099
|
2596 |
+
- type: max_accuracy
|
2597 |
+
value: 89.01695967710637
|
2598 |
+
- type: max_ap
|
2599 |
+
value: 85.98986606038852
|
2600 |
+
- type: max_f1
|
2601 |
+
value: 78.5277880014722
|
2602 |
+
language:
|
2603 |
+
- en
|
2604 |
+
license: mit
|
2605 |
+
duplicated_from: michaelfeil/ct2fast-e5-small-v2
|
2606 |
+
---
|
2607 |
+
# # Fast-Inference with Ctranslate2
|
2608 |
+
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
|
2609 |
+
|
2610 |
+
quantized version of [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2)
|
2611 |
+
```bash
|
2612 |
+
pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.16.0
|
2613 |
+
```
|
2614 |
+
|
2615 |
+
```python
|
2616 |
+
# from transformers import AutoTokenizer
|
2617 |
+
model_name = "michaelfeil/ct2fast-e5-small-v2"
|
2618 |
+
model_name_orig="intfloat/e5-small-v2"
|
2619 |
+
|
2620 |
+
from hf_hub_ctranslate2 import EncoderCT2fromHfHub
|
2621 |
+
model = EncoderCT2fromHfHub(
|
2622 |
+
# load in int8 on CUDA
|
2623 |
+
model_name_or_path=model_name,
|
2624 |
+
device="cuda",
|
2625 |
+
compute_type="int8_float16"
|
2626 |
+
)
|
2627 |
+
outputs = model.generate(
|
2628 |
+
text=["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
|
2629 |
+
max_length=64,
|
2630 |
+
) # perform downstream tasks on outputs
|
2631 |
+
outputs["pooler_output"]
|
2632 |
+
outputs["last_hidden_state"]
|
2633 |
+
outputs["attention_mask"]
|
2634 |
+
|
2635 |
+
# alternative, use SentenceTransformer Mix-In
|
2636 |
+
# for end-to-end Sentence embeddings generation
|
2637 |
+
# (not pulling from this CT2fast-HF repo)
|
2638 |
+
|
2639 |
+
from hf_hub_ctranslate2 import CT2SentenceTransformer
|
2640 |
+
model = CT2SentenceTransformer(
|
2641 |
+
model_name_orig, compute_type="int8_float16", device="cuda"
|
2642 |
+
)
|
2643 |
+
embeddings = model.encode(
|
2644 |
+
["I like soccer", "I like tennis", "The eiffel tower is in Paris"],
|
2645 |
+
batch_size=32,
|
2646 |
+
convert_to_numpy=True,
|
2647 |
+
normalize_embeddings=True,
|
2648 |
+
)
|
2649 |
+
print(embeddings.shape, embeddings)
|
2650 |
+
scores = (embeddings @ embeddings.T) * 100
|
2651 |
+
|
2652 |
+
```
|
2653 |
+
|
2654 |
+
Checkpoint compatible to [ctranslate2>=3.16.0](https://github.com/OpenNMT/CTranslate2)
|
2655 |
+
and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2)
|
2656 |
+
- `compute_type=int8_float16` for `device="cuda"`
|
2657 |
+
- `compute_type=int8` for `device="cpu"`
|
2658 |
+
|
2659 |
+
Converted on 2023-06-19 using
|
2660 |
+
```
|
2661 |
+
ct2-transformers-converter --model intfloat/e5-small-v2 --output_dir ~/tmp-ct2fast-e5-small-v2 --force --copy_files tokenizer.json modules.json README.md tokenizer_config.json sentence_bert_config.json vocab.txt special_tokens_map.json .gitattributes --trust_remote_code
|
2662 |
+
```
|
2663 |
+
|
2664 |
+
# Licence and other remarks:
|
2665 |
+
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
|
2666 |
+
|
2667 |
+
# Original description
|
2668 |
+
|
2669 |
+
|
2670 |
+
# E5-small-v2
|
2671 |
+
|
2672 |
+
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
|
2673 |
+
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
|
2674 |
+
|
2675 |
+
This model has 12 layers and the embedding size is 384.
|
2676 |
+
|
2677 |
+
## Usage
|
2678 |
+
|
2679 |
+
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
|
2680 |
+
|
2681 |
+
```python
|
2682 |
+
import torch.nn.functional as F
|
2683 |
+
|
2684 |
+
from torch import Tensor
|
2685 |
+
from transformers import AutoTokenizer, AutoModel
|
2686 |
+
|
2687 |
+
|
2688 |
+
def average_pool(last_hidden_states: Tensor,
|
2689 |
+
attention_mask: Tensor) -> Tensor:
|
2690 |
+
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
|
2691 |
+
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
|
2692 |
+
|
2693 |
+
|
2694 |
+
# Each input text should start with "query: " or "passage: ".
|
2695 |
+
# For tasks other than retrieval, you can simply use the "query: " prefix.
|
2696 |
+
input_texts = ['query: how much protein should a female eat',
|
2697 |
+
'query: summit define',
|
2698 |
+
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
|
2699 |
+
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
|
2700 |
+
|
2701 |
+
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small-v2')
|
2702 |
+
model = AutoModel.from_pretrained('intfloat/e5-small-v2')
|
2703 |
+
|
2704 |
+
# Tokenize the input texts
|
2705 |
+
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
|
2706 |
+
|
2707 |
+
outputs = model(**batch_dict)
|
2708 |
+
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
|
2709 |
+
|
2710 |
+
# (Optionally) normalize embeddings
|
2711 |
+
embeddings = F.normalize(embeddings, p=2, dim=1)
|
2712 |
+
scores = (embeddings[:2] @ embeddings[2:].T) * 100
|
2713 |
+
print(scores.tolist())
|
2714 |
+
```
|
2715 |
+
|
2716 |
+
## Training Details
|
2717 |
+
|
2718 |
+
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
|
2719 |
+
|
2720 |
+
## Benchmark Evaluation
|
2721 |
+
|
2722 |
+
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
|
2723 |
+
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
|
2724 |
+
|
2725 |
+
## Citation
|
2726 |
+
|
2727 |
+
If you find our paper or models helpful, please consider cite as follows:
|
2728 |
+
|
2729 |
+
```
|
2730 |
+
@article{wang2022text,
|
2731 |
+
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
|
2732 |
+
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
|
2733 |
+
journal={arXiv preprint arXiv:2212.03533},
|
2734 |
+
year={2022}
|
2735 |
+
}
|
2736 |
+
```
|
2737 |
+
|
2738 |
+
## Limitations
|
2739 |
+
|
2740 |
+
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
|
2741 |
+
|
2742 |
+
## Sentence Transformers
|
2743 |
+
|
2744 |
+
Below is an example for usage with sentence_transformers. `pip install sentence_transformers~=2.2.2`
|
2745 |
+
This is community contributed, and results may vary up to numerical precision.
|
2746 |
+
```python
|
2747 |
+
from sentence_transformers import SentenceTransformer
|
2748 |
+
model = SentenceTransformer('intfloat/e5-small-v2')
|
2749 |
+
embeddings = model.encode(input_texts, normalize_embeddings=True)
|
2750 |
+
```
|
config.json
ADDED
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "tmp/",
|
3 |
+
"architectures": [
|
4 |
+
"BertModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"classifier_dropout": null,
|
8 |
+
"hidden_act": "gelu",
|
9 |
+
"hidden_dropout_prob": 0.1,
|
10 |
+
"hidden_size": 384,
|
11 |
+
"initializer_range": 0.02,
|
12 |
+
"intermediate_size": 1536,
|
13 |
+
"layer_norm_eps": 1e-12,
|
14 |
+
"max_position_embeddings": 512,
|
15 |
+
"model_type": "bert",
|
16 |
+
"num_attention_heads": 12,
|
17 |
+
"num_hidden_layers": 12,
|
18 |
+
"pad_token_id": 0,
|
19 |
+
"position_embedding_type": "absolute",
|
20 |
+
"torch_dtype": "float32",
|
21 |
+
"transformers_version": "4.29.0.dev0",
|
22 |
+
"type_vocab_size": 2,
|
23 |
+
"use_cache": true,
|
24 |
+
"vocab_size": 30522,
|
25 |
+
"bos_token": "<s>",
|
26 |
+
"eos_token": "</s>",
|
27 |
+
"layer_norm_epsilon": 1e-12,
|
28 |
+
"unk_token": "[UNK]"
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29 |
+
}
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model.bin
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a201dec3f480d71769e0126bc25d0da451185a75f654d1ece99a26be83dd02aa
|
3 |
+
size 133448364
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modules.json
ADDED
@@ -0,0 +1,20 @@
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|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,7 @@
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cls_token": "[CLS]",
|
3 |
+
"mask_token": "[MASK]",
|
4 |
+
"pad_token": "[PAD]",
|
5 |
+
"sep_token": "[SEP]",
|
6 |
+
"unk_token": "[UNK]"
|
7 |
+
}
|
tokenizer.json
ADDED
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tokenizer_config.json
ADDED
@@ -0,0 +1,15 @@
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|
|
|
1 |
+
{
|
2 |
+
"clean_up_tokenization_spaces": true,
|
3 |
+
"cls_token": "[CLS]",
|
4 |
+
"do_basic_tokenize": true,
|
5 |
+
"do_lower_case": true,
|
6 |
+
"mask_token": "[MASK]",
|
7 |
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"model_max_length": 1000000000000000019884624838656,
|
8 |
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"never_split": null,
|
9 |
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"pad_token": "[PAD]",
|
10 |
+
"sep_token": "[SEP]",
|
11 |
+
"strip_accents": null,
|
12 |
+
"tokenize_chinese_chars": true,
|
13 |
+
"tokenizer_class": "BertTokenizer",
|
14 |
+
"unk_token": "[UNK]"
|
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+
}
|
vocab.txt
ADDED
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vocabulary.json
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vocabulary.txt
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