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Browse files- .gitattributes +1 -0
- local_models/embeddings-bge-large/1_Pooling/config.json +7 -0
- local_models/embeddings-bge-large/README.md +3011 -0
- local_models/embeddings-bge-large/added_tokens.json +7 -0
- local_models/embeddings-bge-large/config.json +32 -0
- local_models/embeddings-bge-large/config_sentence_transformers.json +7 -0
- local_models/embeddings-bge-large/modules.json +20 -0
- local_models/embeddings-bge-large/pytorch_model.bin +3 -0
- local_models/embeddings-bge-large/sentence_bert_config.json +4 -0
- local_models/embeddings-bge-large/special_tokens_map.json +7 -0
- local_models/embeddings-bge-large/tokenizer.json +0 -0
- local_models/embeddings-bge-large/tokenizer_config.json +58 -0
- local_models/embeddings-bge-large/vocab.txt +0 -0
- local_models/llama-2-7b-chat.Q4_K_M.gguf +3 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* 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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*.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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+
local_models/llama-2-7b-chat.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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local_models/embeddings-bge-large/1_Pooling/config.json
ADDED
@@ -0,0 +1,7 @@
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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local_models/embeddings-bge-large/README.md
ADDED
@@ -0,0 +1,3011 @@
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|
1 |
+
---
|
2 |
+
tags:
|
3 |
+
- sentence-transformers
|
4 |
+
- feature-extraction
|
5 |
+
- sentence-similarity
|
6 |
+
- transformers
|
7 |
+
- mteb
|
8 |
+
model-index:
|
9 |
+
- name: bge-large-en-v1.5
|
10 |
+
results:
|
11 |
+
- task:
|
12 |
+
type: Classification
|
13 |
+
dataset:
|
14 |
+
type: mteb/amazon_counterfactual
|
15 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
16 |
+
config: en
|
17 |
+
split: test
|
18 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
19 |
+
metrics:
|
20 |
+
- type: accuracy
|
21 |
+
value: 75.8507462686567
|
22 |
+
- type: ap
|
23 |
+
value: 38.566457320228245
|
24 |
+
- type: f1
|
25 |
+
value: 69.69386648043475
|
26 |
+
- task:
|
27 |
+
type: Classification
|
28 |
+
dataset:
|
29 |
+
type: mteb/amazon_polarity
|
30 |
+
name: MTEB AmazonPolarityClassification
|
31 |
+
config: default
|
32 |
+
split: test
|
33 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
34 |
+
metrics:
|
35 |
+
- type: accuracy
|
36 |
+
value: 92.416675
|
37 |
+
- type: ap
|
38 |
+
value: 89.1928861155922
|
39 |
+
- type: f1
|
40 |
+
value: 92.39477019574215
|
41 |
+
- task:
|
42 |
+
type: Classification
|
43 |
+
dataset:
|
44 |
+
type: mteb/amazon_reviews_multi
|
45 |
+
name: MTEB AmazonReviewsClassification (en)
|
46 |
+
config: en
|
47 |
+
split: test
|
48 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
49 |
+
metrics:
|
50 |
+
- type: accuracy
|
51 |
+
value: 48.175999999999995
|
52 |
+
- type: f1
|
53 |
+
value: 47.80712792870253
|
54 |
+
- task:
|
55 |
+
type: Retrieval
|
56 |
+
dataset:
|
57 |
+
type: arguana
|
58 |
+
name: MTEB ArguAna
|
59 |
+
config: default
|
60 |
+
split: test
|
61 |
+
revision: None
|
62 |
+
metrics:
|
63 |
+
- type: map_at_1
|
64 |
+
value: 40.184999999999995
|
65 |
+
- type: map_at_10
|
66 |
+
value: 55.654
|
67 |
+
- type: map_at_100
|
68 |
+
value: 56.25
|
69 |
+
- type: map_at_1000
|
70 |
+
value: 56.255
|
71 |
+
- type: map_at_3
|
72 |
+
value: 51.742999999999995
|
73 |
+
- type: map_at_5
|
74 |
+
value: 54.129000000000005
|
75 |
+
- type: mrr_at_1
|
76 |
+
value: 40.967
|
77 |
+
- type: mrr_at_10
|
78 |
+
value: 55.96
|
79 |
+
- type: mrr_at_100
|
80 |
+
value: 56.54900000000001
|
81 |
+
- type: mrr_at_1000
|
82 |
+
value: 56.554
|
83 |
+
- type: mrr_at_3
|
84 |
+
value: 51.980000000000004
|
85 |
+
- type: mrr_at_5
|
86 |
+
value: 54.44
|
87 |
+
- type: ndcg_at_1
|
88 |
+
value: 40.184999999999995
|
89 |
+
- type: ndcg_at_10
|
90 |
+
value: 63.542
|
91 |
+
- type: ndcg_at_100
|
92 |
+
value: 65.96499999999999
|
93 |
+
- type: ndcg_at_1000
|
94 |
+
value: 66.08699999999999
|
95 |
+
- type: ndcg_at_3
|
96 |
+
value: 55.582
|
97 |
+
- type: ndcg_at_5
|
98 |
+
value: 59.855000000000004
|
99 |
+
- type: precision_at_1
|
100 |
+
value: 40.184999999999995
|
101 |
+
- type: precision_at_10
|
102 |
+
value: 8.841000000000001
|
103 |
+
- type: precision_at_100
|
104 |
+
value: 0.987
|
105 |
+
- type: precision_at_1000
|
106 |
+
value: 0.1
|
107 |
+
- type: precision_at_3
|
108 |
+
value: 22.238
|
109 |
+
- type: precision_at_5
|
110 |
+
value: 15.405
|
111 |
+
- type: recall_at_1
|
112 |
+
value: 40.184999999999995
|
113 |
+
- type: recall_at_10
|
114 |
+
value: 88.407
|
115 |
+
- type: recall_at_100
|
116 |
+
value: 98.72
|
117 |
+
- type: recall_at_1000
|
118 |
+
value: 99.644
|
119 |
+
- type: recall_at_3
|
120 |
+
value: 66.714
|
121 |
+
- type: recall_at_5
|
122 |
+
value: 77.027
|
123 |
+
- task:
|
124 |
+
type: Clustering
|
125 |
+
dataset:
|
126 |
+
type: mteb/arxiv-clustering-p2p
|
127 |
+
name: MTEB ArxivClusteringP2P
|
128 |
+
config: default
|
129 |
+
split: test
|
130 |
+
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
131 |
+
metrics:
|
132 |
+
- type: v_measure
|
133 |
+
value: 48.567077926750066
|
134 |
+
- task:
|
135 |
+
type: Clustering
|
136 |
+
dataset:
|
137 |
+
type: mteb/arxiv-clustering-s2s
|
138 |
+
name: MTEB ArxivClusteringS2S
|
139 |
+
config: default
|
140 |
+
split: test
|
141 |
+
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
142 |
+
metrics:
|
143 |
+
- type: v_measure
|
144 |
+
value: 43.19453389182364
|
145 |
+
- task:
|
146 |
+
type: Reranking
|
147 |
+
dataset:
|
148 |
+
type: mteb/askubuntudupquestions-reranking
|
149 |
+
name: MTEB AskUbuntuDupQuestions
|
150 |
+
config: default
|
151 |
+
split: test
|
152 |
+
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
|
153 |
+
metrics:
|
154 |
+
- type: map
|
155 |
+
value: 64.46555939623092
|
156 |
+
- type: mrr
|
157 |
+
value: 77.82361605768807
|
158 |
+
- task:
|
159 |
+
type: STS
|
160 |
+
dataset:
|
161 |
+
type: mteb/biosses-sts
|
162 |
+
name: MTEB BIOSSES
|
163 |
+
config: default
|
164 |
+
split: test
|
165 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
166 |
+
metrics:
|
167 |
+
- type: cos_sim_pearson
|
168 |
+
value: 84.9554128814735
|
169 |
+
- type: cos_sim_spearman
|
170 |
+
value: 84.65373612172036
|
171 |
+
- type: euclidean_pearson
|
172 |
+
value: 83.2905059954138
|
173 |
+
- type: euclidean_spearman
|
174 |
+
value: 84.52240782811128
|
175 |
+
- type: manhattan_pearson
|
176 |
+
value: 82.99533802997436
|
177 |
+
- type: manhattan_spearman
|
178 |
+
value: 84.20673798475734
|
179 |
+
- task:
|
180 |
+
type: Classification
|
181 |
+
dataset:
|
182 |
+
type: mteb/banking77
|
183 |
+
name: MTEB Banking77Classification
|
184 |
+
config: default
|
185 |
+
split: test
|
186 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
187 |
+
metrics:
|
188 |
+
- type: accuracy
|
189 |
+
value: 87.78896103896103
|
190 |
+
- type: f1
|
191 |
+
value: 87.77189310964883
|
192 |
+
- task:
|
193 |
+
type: Clustering
|
194 |
+
dataset:
|
195 |
+
type: mteb/biorxiv-clustering-p2p
|
196 |
+
name: MTEB BiorxivClusteringP2P
|
197 |
+
config: default
|
198 |
+
split: test
|
199 |
+
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
200 |
+
metrics:
|
201 |
+
- type: v_measure
|
202 |
+
value: 39.714538337650495
|
203 |
+
- task:
|
204 |
+
type: Clustering
|
205 |
+
dataset:
|
206 |
+
type: mteb/biorxiv-clustering-s2s
|
207 |
+
name: MTEB BiorxivClusteringS2S
|
208 |
+
config: default
|
209 |
+
split: test
|
210 |
+
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
211 |
+
metrics:
|
212 |
+
- type: v_measure
|
213 |
+
value: 36.90108349284447
|
214 |
+
- task:
|
215 |
+
type: Retrieval
|
216 |
+
dataset:
|
217 |
+
type: BeIR/cqadupstack
|
218 |
+
name: MTEB CQADupstackAndroidRetrieval
|
219 |
+
config: default
|
220 |
+
split: test
|
221 |
+
revision: None
|
222 |
+
metrics:
|
223 |
+
- type: map_at_1
|
224 |
+
value: 32.795
|
225 |
+
- type: map_at_10
|
226 |
+
value: 43.669000000000004
|
227 |
+
- type: map_at_100
|
228 |
+
value: 45.151
|
229 |
+
- type: map_at_1000
|
230 |
+
value: 45.278
|
231 |
+
- type: map_at_3
|
232 |
+
value: 40.006
|
233 |
+
- type: map_at_5
|
234 |
+
value: 42.059999999999995
|
235 |
+
- type: mrr_at_1
|
236 |
+
value: 39.771
|
237 |
+
- type: mrr_at_10
|
238 |
+
value: 49.826
|
239 |
+
- type: mrr_at_100
|
240 |
+
value: 50.504000000000005
|
241 |
+
- type: mrr_at_1000
|
242 |
+
value: 50.549
|
243 |
+
- type: mrr_at_3
|
244 |
+
value: 47.115
|
245 |
+
- type: mrr_at_5
|
246 |
+
value: 48.832
|
247 |
+
- type: ndcg_at_1
|
248 |
+
value: 39.771
|
249 |
+
- type: ndcg_at_10
|
250 |
+
value: 50.217999999999996
|
251 |
+
- type: ndcg_at_100
|
252 |
+
value: 55.454
|
253 |
+
- type: ndcg_at_1000
|
254 |
+
value: 57.37
|
255 |
+
- type: ndcg_at_3
|
256 |
+
value: 44.885000000000005
|
257 |
+
- type: ndcg_at_5
|
258 |
+
value: 47.419
|
259 |
+
- type: precision_at_1
|
260 |
+
value: 39.771
|
261 |
+
- type: precision_at_10
|
262 |
+
value: 9.642000000000001
|
263 |
+
- type: precision_at_100
|
264 |
+
value: 1.538
|
265 |
+
- type: precision_at_1000
|
266 |
+
value: 0.198
|
267 |
+
- type: precision_at_3
|
268 |
+
value: 21.268
|
269 |
+
- type: precision_at_5
|
270 |
+
value: 15.536
|
271 |
+
- type: recall_at_1
|
272 |
+
value: 32.795
|
273 |
+
- type: recall_at_10
|
274 |
+
value: 62.580999999999996
|
275 |
+
- type: recall_at_100
|
276 |
+
value: 84.438
|
277 |
+
- type: recall_at_1000
|
278 |
+
value: 96.492
|
279 |
+
- type: recall_at_3
|
280 |
+
value: 47.071000000000005
|
281 |
+
- type: recall_at_5
|
282 |
+
value: 54.079
|
283 |
+
- task:
|
284 |
+
type: Retrieval
|
285 |
+
dataset:
|
286 |
+
type: BeIR/cqadupstack
|
287 |
+
name: MTEB CQADupstackEnglishRetrieval
|
288 |
+
config: default
|
289 |
+
split: test
|
290 |
+
revision: None
|
291 |
+
metrics:
|
292 |
+
- type: map_at_1
|
293 |
+
value: 32.671
|
294 |
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- type: map_at_10
|
295 |
+
value: 43.334
|
296 |
+
- type: map_at_100
|
297 |
+
value: 44.566
|
298 |
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- type: map_at_1000
|
299 |
+
value: 44.702999999999996
|
300 |
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- type: map_at_3
|
301 |
+
value: 40.343
|
302 |
+
- type: map_at_5
|
303 |
+
value: 41.983
|
304 |
+
- type: mrr_at_1
|
305 |
+
value: 40.764
|
306 |
+
- type: mrr_at_10
|
307 |
+
value: 49.382
|
308 |
+
- type: mrr_at_100
|
309 |
+
value: 49.988
|
310 |
+
- type: mrr_at_1000
|
311 |
+
value: 50.03300000000001
|
312 |
+
- type: mrr_at_3
|
313 |
+
value: 47.293
|
314 |
+
- type: mrr_at_5
|
315 |
+
value: 48.51
|
316 |
+
- type: ndcg_at_1
|
317 |
+
value: 40.764
|
318 |
+
- type: ndcg_at_10
|
319 |
+
value: 49.039
|
320 |
+
- type: ndcg_at_100
|
321 |
+
value: 53.259
|
322 |
+
- type: ndcg_at_1000
|
323 |
+
value: 55.253
|
324 |
+
- type: ndcg_at_3
|
325 |
+
value: 45.091
|
326 |
+
- type: ndcg_at_5
|
327 |
+
value: 46.839999999999996
|
328 |
+
- type: precision_at_1
|
329 |
+
value: 40.764
|
330 |
+
- type: precision_at_10
|
331 |
+
value: 9.191
|
332 |
+
- type: precision_at_100
|
333 |
+
value: 1.476
|
334 |
+
- type: precision_at_1000
|
335 |
+
value: 0.19499999999999998
|
336 |
+
- type: precision_at_3
|
337 |
+
value: 21.72
|
338 |
+
- type: precision_at_5
|
339 |
+
value: 15.299
|
340 |
+
- type: recall_at_1
|
341 |
+
value: 32.671
|
342 |
+
- type: recall_at_10
|
343 |
+
value: 58.816
|
344 |
+
- type: recall_at_100
|
345 |
+
value: 76.654
|
346 |
+
- type: recall_at_1000
|
347 |
+
value: 89.05999999999999
|
348 |
+
- type: recall_at_3
|
349 |
+
value: 46.743
|
350 |
+
- type: recall_at_5
|
351 |
+
value: 51.783
|
352 |
+
- task:
|
353 |
+
type: Retrieval
|
354 |
+
dataset:
|
355 |
+
type: BeIR/cqadupstack
|
356 |
+
name: MTEB CQADupstackGamingRetrieval
|
357 |
+
config: default
|
358 |
+
split: test
|
359 |
+
revision: None
|
360 |
+
metrics:
|
361 |
+
- type: map_at_1
|
362 |
+
value: 40.328
|
363 |
+
- type: map_at_10
|
364 |
+
value: 53.32599999999999
|
365 |
+
- type: map_at_100
|
366 |
+
value: 54.37499999999999
|
367 |
+
- type: map_at_1000
|
368 |
+
value: 54.429
|
369 |
+
- type: map_at_3
|
370 |
+
value: 49.902
|
371 |
+
- type: map_at_5
|
372 |
+
value: 52.002
|
373 |
+
- type: mrr_at_1
|
374 |
+
value: 46.332
|
375 |
+
- type: mrr_at_10
|
376 |
+
value: 56.858
|
377 |
+
- type: mrr_at_100
|
378 |
+
value: 57.522
|
379 |
+
- type: mrr_at_1000
|
380 |
+
value: 57.54899999999999
|
381 |
+
- type: mrr_at_3
|
382 |
+
value: 54.472
|
383 |
+
- type: mrr_at_5
|
384 |
+
value: 55.996
|
385 |
+
- type: ndcg_at_1
|
386 |
+
value: 46.332
|
387 |
+
- type: ndcg_at_10
|
388 |
+
value: 59.313
|
389 |
+
- type: ndcg_at_100
|
390 |
+
value: 63.266999999999996
|
391 |
+
- type: ndcg_at_1000
|
392 |
+
value: 64.36
|
393 |
+
- type: ndcg_at_3
|
394 |
+
value: 53.815000000000005
|
395 |
+
- type: ndcg_at_5
|
396 |
+
value: 56.814
|
397 |
+
- type: precision_at_1
|
398 |
+
value: 46.332
|
399 |
+
- type: precision_at_10
|
400 |
+
value: 9.53
|
401 |
+
- type: precision_at_100
|
402 |
+
value: 1.238
|
403 |
+
- type: precision_at_1000
|
404 |
+
value: 0.13699999999999998
|
405 |
+
- type: precision_at_3
|
406 |
+
value: 24.054000000000002
|
407 |
+
- type: precision_at_5
|
408 |
+
value: 16.589000000000002
|
409 |
+
- type: recall_at_1
|
410 |
+
value: 40.328
|
411 |
+
- type: recall_at_10
|
412 |
+
value: 73.421
|
413 |
+
- type: recall_at_100
|
414 |
+
value: 90.059
|
415 |
+
- type: recall_at_1000
|
416 |
+
value: 97.81
|
417 |
+
- type: recall_at_3
|
418 |
+
value: 59.009
|
419 |
+
- type: recall_at_5
|
420 |
+
value: 66.352
|
421 |
+
- task:
|
422 |
+
type: Retrieval
|
423 |
+
dataset:
|
424 |
+
type: BeIR/cqadupstack
|
425 |
+
name: MTEB CQADupstackGisRetrieval
|
426 |
+
config: default
|
427 |
+
split: test
|
428 |
+
revision: None
|
429 |
+
metrics:
|
430 |
+
- type: map_at_1
|
431 |
+
value: 27.424
|
432 |
+
- type: map_at_10
|
433 |
+
value: 36.332
|
434 |
+
- type: map_at_100
|
435 |
+
value: 37.347
|
436 |
+
- type: map_at_1000
|
437 |
+
value: 37.422
|
438 |
+
- type: map_at_3
|
439 |
+
value: 33.743
|
440 |
+
- type: map_at_5
|
441 |
+
value: 35.176
|
442 |
+
- type: mrr_at_1
|
443 |
+
value: 29.153000000000002
|
444 |
+
- type: mrr_at_10
|
445 |
+
value: 38.233
|
446 |
+
- type: mrr_at_100
|
447 |
+
value: 39.109
|
448 |
+
- type: mrr_at_1000
|
449 |
+
value: 39.164
|
450 |
+
- type: mrr_at_3
|
451 |
+
value: 35.876000000000005
|
452 |
+
- type: mrr_at_5
|
453 |
+
value: 37.169000000000004
|
454 |
+
- type: ndcg_at_1
|
455 |
+
value: 29.153000000000002
|
456 |
+
- type: ndcg_at_10
|
457 |
+
value: 41.439
|
458 |
+
- type: ndcg_at_100
|
459 |
+
value: 46.42
|
460 |
+
- type: ndcg_at_1000
|
461 |
+
value: 48.242000000000004
|
462 |
+
- type: ndcg_at_3
|
463 |
+
value: 36.362
|
464 |
+
- type: ndcg_at_5
|
465 |
+
value: 38.743
|
466 |
+
- type: precision_at_1
|
467 |
+
value: 29.153000000000002
|
468 |
+
- type: precision_at_10
|
469 |
+
value: 6.315999999999999
|
470 |
+
- type: precision_at_100
|
471 |
+
value: 0.927
|
472 |
+
- type: precision_at_1000
|
473 |
+
value: 0.11199999999999999
|
474 |
+
- type: precision_at_3
|
475 |
+
value: 15.443000000000001
|
476 |
+
- type: precision_at_5
|
477 |
+
value: 10.644
|
478 |
+
- type: recall_at_1
|
479 |
+
value: 27.424
|
480 |
+
- type: recall_at_10
|
481 |
+
value: 55.364000000000004
|
482 |
+
- type: recall_at_100
|
483 |
+
value: 78.211
|
484 |
+
- type: recall_at_1000
|
485 |
+
value: 91.74600000000001
|
486 |
+
- type: recall_at_3
|
487 |
+
value: 41.379
|
488 |
+
- type: recall_at_5
|
489 |
+
value: 47.14
|
490 |
+
- task:
|
491 |
+
type: Retrieval
|
492 |
+
dataset:
|
493 |
+
type: BeIR/cqadupstack
|
494 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
495 |
+
config: default
|
496 |
+
split: test
|
497 |
+
revision: None
|
498 |
+
metrics:
|
499 |
+
- type: map_at_1
|
500 |
+
value: 19.601
|
501 |
+
- type: map_at_10
|
502 |
+
value: 27.826
|
503 |
+
- type: map_at_100
|
504 |
+
value: 29.017
|
505 |
+
- type: map_at_1000
|
506 |
+
value: 29.137
|
507 |
+
- type: map_at_3
|
508 |
+
value: 25.125999999999998
|
509 |
+
- type: map_at_5
|
510 |
+
value: 26.765
|
511 |
+
- type: mrr_at_1
|
512 |
+
value: 24.005000000000003
|
513 |
+
- type: mrr_at_10
|
514 |
+
value: 32.716
|
515 |
+
- type: mrr_at_100
|
516 |
+
value: 33.631
|
517 |
+
- type: mrr_at_1000
|
518 |
+
value: 33.694
|
519 |
+
- type: mrr_at_3
|
520 |
+
value: 29.934
|
521 |
+
- type: mrr_at_5
|
522 |
+
value: 31.630999999999997
|
523 |
+
- type: ndcg_at_1
|
524 |
+
value: 24.005000000000003
|
525 |
+
- type: ndcg_at_10
|
526 |
+
value: 33.158
|
527 |
+
- type: ndcg_at_100
|
528 |
+
value: 38.739000000000004
|
529 |
+
- type: ndcg_at_1000
|
530 |
+
value: 41.495
|
531 |
+
- type: ndcg_at_3
|
532 |
+
value: 28.185
|
533 |
+
- type: ndcg_at_5
|
534 |
+
value: 30.796
|
535 |
+
- type: precision_at_1
|
536 |
+
value: 24.005000000000003
|
537 |
+
- type: precision_at_10
|
538 |
+
value: 5.908
|
539 |
+
- type: precision_at_100
|
540 |
+
value: 1.005
|
541 |
+
- type: precision_at_1000
|
542 |
+
value: 0.13899999999999998
|
543 |
+
- type: precision_at_3
|
544 |
+
value: 13.391
|
545 |
+
- type: precision_at_5
|
546 |
+
value: 9.876
|
547 |
+
- type: recall_at_1
|
548 |
+
value: 19.601
|
549 |
+
- type: recall_at_10
|
550 |
+
value: 44.746
|
551 |
+
- type: recall_at_100
|
552 |
+
value: 68.82300000000001
|
553 |
+
- type: recall_at_1000
|
554 |
+
value: 88.215
|
555 |
+
- type: recall_at_3
|
556 |
+
value: 31.239
|
557 |
+
- type: recall_at_5
|
558 |
+
value: 37.695
|
559 |
+
- task:
|
560 |
+
type: Retrieval
|
561 |
+
dataset:
|
562 |
+
type: BeIR/cqadupstack
|
563 |
+
name: MTEB CQADupstackPhysicsRetrieval
|
564 |
+
config: default
|
565 |
+
split: test
|
566 |
+
revision: None
|
567 |
+
metrics:
|
568 |
+
- type: map_at_1
|
569 |
+
value: 30.130000000000003
|
570 |
+
- type: map_at_10
|
571 |
+
value: 40.96
|
572 |
+
- type: map_at_100
|
573 |
+
value: 42.282
|
574 |
+
- type: map_at_1000
|
575 |
+
value: 42.392
|
576 |
+
- type: map_at_3
|
577 |
+
value: 37.889
|
578 |
+
- type: map_at_5
|
579 |
+
value: 39.661
|
580 |
+
- type: mrr_at_1
|
581 |
+
value: 36.958999999999996
|
582 |
+
- type: mrr_at_10
|
583 |
+
value: 46.835
|
584 |
+
- type: mrr_at_100
|
585 |
+
value: 47.644
|
586 |
+
- type: mrr_at_1000
|
587 |
+
value: 47.688
|
588 |
+
- type: mrr_at_3
|
589 |
+
value: 44.562000000000005
|
590 |
+
- type: mrr_at_5
|
591 |
+
value: 45.938
|
592 |
+
- type: ndcg_at_1
|
593 |
+
value: 36.958999999999996
|
594 |
+
- type: ndcg_at_10
|
595 |
+
value: 47.06
|
596 |
+
- type: ndcg_at_100
|
597 |
+
value: 52.345
|
598 |
+
- type: ndcg_at_1000
|
599 |
+
value: 54.35
|
600 |
+
- type: ndcg_at_3
|
601 |
+
value: 42.301
|
602 |
+
- type: ndcg_at_5
|
603 |
+
value: 44.635999999999996
|
604 |
+
- type: precision_at_1
|
605 |
+
value: 36.958999999999996
|
606 |
+
- type: precision_at_10
|
607 |
+
value: 8.479000000000001
|
608 |
+
- type: precision_at_100
|
609 |
+
value: 1.284
|
610 |
+
- type: precision_at_1000
|
611 |
+
value: 0.163
|
612 |
+
- type: precision_at_3
|
613 |
+
value: 20.244
|
614 |
+
- type: precision_at_5
|
615 |
+
value: 14.224999999999998
|
616 |
+
- type: recall_at_1
|
617 |
+
value: 30.130000000000003
|
618 |
+
- type: recall_at_10
|
619 |
+
value: 59.27
|
620 |
+
- type: recall_at_100
|
621 |
+
value: 81.195
|
622 |
+
- type: recall_at_1000
|
623 |
+
value: 94.21199999999999
|
624 |
+
- type: recall_at_3
|
625 |
+
value: 45.885
|
626 |
+
- type: recall_at_5
|
627 |
+
value: 52.016
|
628 |
+
- task:
|
629 |
+
type: Retrieval
|
630 |
+
dataset:
|
631 |
+
type: BeIR/cqadupstack
|
632 |
+
name: MTEB CQADupstackProgrammersRetrieval
|
633 |
+
config: default
|
634 |
+
split: test
|
635 |
+
revision: None
|
636 |
+
metrics:
|
637 |
+
- type: map_at_1
|
638 |
+
value: 26.169999999999998
|
639 |
+
- type: map_at_10
|
640 |
+
value: 36.451
|
641 |
+
- type: map_at_100
|
642 |
+
value: 37.791000000000004
|
643 |
+
- type: map_at_1000
|
644 |
+
value: 37.897
|
645 |
+
- type: map_at_3
|
646 |
+
value: 33.109
|
647 |
+
- type: map_at_5
|
648 |
+
value: 34.937000000000005
|
649 |
+
- type: mrr_at_1
|
650 |
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value: 32.877
|
651 |
+
- type: mrr_at_10
|
652 |
+
value: 42.368
|
653 |
+
- type: mrr_at_100
|
654 |
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value: 43.201
|
655 |
+
- type: mrr_at_1000
|
656 |
+
value: 43.259
|
657 |
+
- type: mrr_at_3
|
658 |
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value: 39.763999999999996
|
659 |
+
- type: mrr_at_5
|
660 |
+
value: 41.260000000000005
|
661 |
+
- type: ndcg_at_1
|
662 |
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value: 32.877
|
663 |
+
- type: ndcg_at_10
|
664 |
+
value: 42.659000000000006
|
665 |
+
- type: ndcg_at_100
|
666 |
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value: 48.161
|
667 |
+
- type: ndcg_at_1000
|
668 |
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value: 50.345
|
669 |
+
- type: ndcg_at_3
|
670 |
+
value: 37.302
|
671 |
+
- type: ndcg_at_5
|
672 |
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value: 39.722
|
673 |
+
- type: precision_at_1
|
674 |
+
value: 32.877
|
675 |
+
- type: precision_at_10
|
676 |
+
value: 7.9
|
677 |
+
- type: precision_at_100
|
678 |
+
value: 1.236
|
679 |
+
- type: precision_at_1000
|
680 |
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value: 0.158
|
681 |
+
- type: precision_at_3
|
682 |
+
value: 17.846
|
683 |
+
- type: precision_at_5
|
684 |
+
value: 12.9
|
685 |
+
- type: recall_at_1
|
686 |
+
value: 26.169999999999998
|
687 |
+
- type: recall_at_10
|
688 |
+
value: 55.35
|
689 |
+
- type: recall_at_100
|
690 |
+
value: 78.755
|
691 |
+
- type: recall_at_1000
|
692 |
+
value: 93.518
|
693 |
+
- type: recall_at_3
|
694 |
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value: 40.176
|
695 |
+
- type: recall_at_5
|
696 |
+
value: 46.589000000000006
|
697 |
+
- task:
|
698 |
+
type: Retrieval
|
699 |
+
dataset:
|
700 |
+
type: BeIR/cqadupstack
|
701 |
+
name: MTEB CQADupstackRetrieval
|
702 |
+
config: default
|
703 |
+
split: test
|
704 |
+
revision: None
|
705 |
+
metrics:
|
706 |
+
- type: map_at_1
|
707 |
+
value: 27.15516666666667
|
708 |
+
- type: map_at_10
|
709 |
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value: 36.65741666666667
|
710 |
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- type: map_at_100
|
711 |
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value: 37.84991666666666
|
712 |
+
- type: map_at_1000
|
713 |
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value: 37.96316666666667
|
714 |
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- type: map_at_3
|
715 |
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value: 33.74974999999999
|
716 |
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- type: map_at_5
|
717 |
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value: 35.3765
|
718 |
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- type: mrr_at_1
|
719 |
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value: 32.08233333333334
|
720 |
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- type: mrr_at_10
|
721 |
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value: 41.033833333333334
|
722 |
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- type: mrr_at_100
|
723 |
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value: 41.84524999999999
|
724 |
+
- type: mrr_at_1000
|
725 |
+
value: 41.89983333333333
|
726 |
+
- type: mrr_at_3
|
727 |
+
value: 38.62008333333333
|
728 |
+
- type: mrr_at_5
|
729 |
+
value: 40.03441666666666
|
730 |
+
- type: ndcg_at_1
|
731 |
+
value: 32.08233333333334
|
732 |
+
- type: ndcg_at_10
|
733 |
+
value: 42.229
|
734 |
+
- type: ndcg_at_100
|
735 |
+
value: 47.26716666666667
|
736 |
+
- type: ndcg_at_1000
|
737 |
+
value: 49.43466666666667
|
738 |
+
- type: ndcg_at_3
|
739 |
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value: 37.36408333333333
|
740 |
+
- type: ndcg_at_5
|
741 |
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value: 39.6715
|
742 |
+
- type: precision_at_1
|
743 |
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value: 32.08233333333334
|
744 |
+
- type: precision_at_10
|
745 |
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value: 7.382583333333334
|
746 |
+
- type: precision_at_100
|
747 |
+
value: 1.16625
|
748 |
+
- type: precision_at_1000
|
749 |
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value: 0.15408333333333332
|
750 |
+
- type: precision_at_3
|
751 |
+
value: 17.218
|
752 |
+
- type: precision_at_5
|
753 |
+
value: 12.21875
|
754 |
+
- type: recall_at_1
|
755 |
+
value: 27.15516666666667
|
756 |
+
- type: recall_at_10
|
757 |
+
value: 54.36683333333333
|
758 |
+
- type: recall_at_100
|
759 |
+
value: 76.37183333333333
|
760 |
+
- type: recall_at_1000
|
761 |
+
value: 91.26183333333333
|
762 |
+
- type: recall_at_3
|
763 |
+
value: 40.769916666666674
|
764 |
+
- type: recall_at_5
|
765 |
+
value: 46.702333333333335
|
766 |
+
- task:
|
767 |
+
type: Retrieval
|
768 |
+
dataset:
|
769 |
+
type: BeIR/cqadupstack
|
770 |
+
name: MTEB CQADupstackStatsRetrieval
|
771 |
+
config: default
|
772 |
+
split: test
|
773 |
+
revision: None
|
774 |
+
metrics:
|
775 |
+
- type: map_at_1
|
776 |
+
value: 25.749
|
777 |
+
- type: map_at_10
|
778 |
+
value: 33.001999999999995
|
779 |
+
- type: map_at_100
|
780 |
+
value: 33.891
|
781 |
+
- type: map_at_1000
|
782 |
+
value: 33.993
|
783 |
+
- type: map_at_3
|
784 |
+
value: 30.703999999999997
|
785 |
+
- type: map_at_5
|
786 |
+
value: 31.959
|
787 |
+
- type: mrr_at_1
|
788 |
+
value: 28.834
|
789 |
+
- type: mrr_at_10
|
790 |
+
value: 35.955
|
791 |
+
- type: mrr_at_100
|
792 |
+
value: 36.709
|
793 |
+
- type: mrr_at_1000
|
794 |
+
value: 36.779
|
795 |
+
- type: mrr_at_3
|
796 |
+
value: 33.947
|
797 |
+
- type: mrr_at_5
|
798 |
+
value: 35.089
|
799 |
+
- type: ndcg_at_1
|
800 |
+
value: 28.834
|
801 |
+
- type: ndcg_at_10
|
802 |
+
value: 37.329
|
803 |
+
- type: ndcg_at_100
|
804 |
+
value: 41.79
|
805 |
+
- type: ndcg_at_1000
|
806 |
+
value: 44.169000000000004
|
807 |
+
- type: ndcg_at_3
|
808 |
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value: 33.184999999999995
|
809 |
+
- type: ndcg_at_5
|
810 |
+
value: 35.107
|
811 |
+
- type: precision_at_1
|
812 |
+
value: 28.834
|
813 |
+
- type: precision_at_10
|
814 |
+
value: 5.7669999999999995
|
815 |
+
- type: precision_at_100
|
816 |
+
value: 0.876
|
817 |
+
- type: precision_at_1000
|
818 |
+
value: 0.11399999999999999
|
819 |
+
- type: precision_at_3
|
820 |
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value: 14.213000000000001
|
821 |
+
- type: precision_at_5
|
822 |
+
value: 9.754999999999999
|
823 |
+
- type: recall_at_1
|
824 |
+
value: 25.749
|
825 |
+
- type: recall_at_10
|
826 |
+
value: 47.791
|
827 |
+
- type: recall_at_100
|
828 |
+
value: 68.255
|
829 |
+
- type: recall_at_1000
|
830 |
+
value: 85.749
|
831 |
+
- type: recall_at_3
|
832 |
+
value: 36.199
|
833 |
+
- type: recall_at_5
|
834 |
+
value: 41.071999999999996
|
835 |
+
- task:
|
836 |
+
type: Retrieval
|
837 |
+
dataset:
|
838 |
+
type: BeIR/cqadupstack
|
839 |
+
name: MTEB CQADupstackTexRetrieval
|
840 |
+
config: default
|
841 |
+
split: test
|
842 |
+
revision: None
|
843 |
+
metrics:
|
844 |
+
- type: map_at_1
|
845 |
+
value: 17.777
|
846 |
+
- type: map_at_10
|
847 |
+
value: 25.201
|
848 |
+
- type: map_at_100
|
849 |
+
value: 26.423999999999996
|
850 |
+
- type: map_at_1000
|
851 |
+
value: 26.544
|
852 |
+
- type: map_at_3
|
853 |
+
value: 22.869
|
854 |
+
- type: map_at_5
|
855 |
+
value: 24.023
|
856 |
+
- type: mrr_at_1
|
857 |
+
value: 21.473
|
858 |
+
- type: mrr_at_10
|
859 |
+
value: 29.12
|
860 |
+
- type: mrr_at_100
|
861 |
+
value: 30.144
|
862 |
+
- type: mrr_at_1000
|
863 |
+
value: 30.215999999999998
|
864 |
+
- type: mrr_at_3
|
865 |
+
value: 26.933
|
866 |
+
- type: mrr_at_5
|
867 |
+
value: 28.051
|
868 |
+
- type: ndcg_at_1
|
869 |
+
value: 21.473
|
870 |
+
- type: ndcg_at_10
|
871 |
+
value: 30.003
|
872 |
+
- type: ndcg_at_100
|
873 |
+
value: 35.766
|
874 |
+
- type: ndcg_at_1000
|
875 |
+
value: 38.501000000000005
|
876 |
+
- type: ndcg_at_3
|
877 |
+
value: 25.773000000000003
|
878 |
+
- type: ndcg_at_5
|
879 |
+
value: 27.462999999999997
|
880 |
+
- type: precision_at_1
|
881 |
+
value: 21.473
|
882 |
+
- type: precision_at_10
|
883 |
+
value: 5.482
|
884 |
+
- type: precision_at_100
|
885 |
+
value: 0.975
|
886 |
+
- type: precision_at_1000
|
887 |
+
value: 0.13799999999999998
|
888 |
+
- type: precision_at_3
|
889 |
+
value: 12.205
|
890 |
+
- type: precision_at_5
|
891 |
+
value: 8.692
|
892 |
+
- type: recall_at_1
|
893 |
+
value: 17.777
|
894 |
+
- type: recall_at_10
|
895 |
+
value: 40.582
|
896 |
+
- type: recall_at_100
|
897 |
+
value: 66.305
|
898 |
+
- type: recall_at_1000
|
899 |
+
value: 85.636
|
900 |
+
- type: recall_at_3
|
901 |
+
value: 28.687
|
902 |
+
- type: recall_at_5
|
903 |
+
value: 33.089
|
904 |
+
- task:
|
905 |
+
type: Retrieval
|
906 |
+
dataset:
|
907 |
+
type: BeIR/cqadupstack
|
908 |
+
name: MTEB CQADupstackUnixRetrieval
|
909 |
+
config: default
|
910 |
+
split: test
|
911 |
+
revision: None
|
912 |
+
metrics:
|
913 |
+
- type: map_at_1
|
914 |
+
value: 26.677
|
915 |
+
- type: map_at_10
|
916 |
+
value: 36.309000000000005
|
917 |
+
- type: map_at_100
|
918 |
+
value: 37.403999999999996
|
919 |
+
- type: map_at_1000
|
920 |
+
value: 37.496
|
921 |
+
- type: map_at_3
|
922 |
+
value: 33.382
|
923 |
+
- type: map_at_5
|
924 |
+
value: 34.98
|
925 |
+
- type: mrr_at_1
|
926 |
+
value: 31.343
|
927 |
+
- type: mrr_at_10
|
928 |
+
value: 40.549
|
929 |
+
- type: mrr_at_100
|
930 |
+
value: 41.342
|
931 |
+
- type: mrr_at_1000
|
932 |
+
value: 41.397
|
933 |
+
- type: mrr_at_3
|
934 |
+
value: 38.029
|
935 |
+
- type: mrr_at_5
|
936 |
+
value: 39.451
|
937 |
+
- type: ndcg_at_1
|
938 |
+
value: 31.343
|
939 |
+
- type: ndcg_at_10
|
940 |
+
value: 42.1
|
941 |
+
- type: ndcg_at_100
|
942 |
+
value: 47.089999999999996
|
943 |
+
- type: ndcg_at_1000
|
944 |
+
value: 49.222
|
945 |
+
- type: ndcg_at_3
|
946 |
+
value: 36.836999999999996
|
947 |
+
- type: ndcg_at_5
|
948 |
+
value: 39.21
|
949 |
+
- type: precision_at_1
|
950 |
+
value: 31.343
|
951 |
+
- type: precision_at_10
|
952 |
+
value: 7.164
|
953 |
+
- type: precision_at_100
|
954 |
+
value: 1.0959999999999999
|
955 |
+
- type: precision_at_1000
|
956 |
+
value: 0.13899999999999998
|
957 |
+
- type: precision_at_3
|
958 |
+
value: 16.915
|
959 |
+
- type: precision_at_5
|
960 |
+
value: 11.940000000000001
|
961 |
+
- type: recall_at_1
|
962 |
+
value: 26.677
|
963 |
+
- type: recall_at_10
|
964 |
+
value: 55.54599999999999
|
965 |
+
- type: recall_at_100
|
966 |
+
value: 77.094
|
967 |
+
- type: recall_at_1000
|
968 |
+
value: 92.01
|
969 |
+
- type: recall_at_3
|
970 |
+
value: 41.191
|
971 |
+
- type: recall_at_5
|
972 |
+
value: 47.006
|
973 |
+
- task:
|
974 |
+
type: Retrieval
|
975 |
+
dataset:
|
976 |
+
type: BeIR/cqadupstack
|
977 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
978 |
+
config: default
|
979 |
+
split: test
|
980 |
+
revision: None
|
981 |
+
metrics:
|
982 |
+
- type: map_at_1
|
983 |
+
value: 24.501
|
984 |
+
- type: map_at_10
|
985 |
+
value: 33.102
|
986 |
+
- type: map_at_100
|
987 |
+
value: 34.676
|
988 |
+
- type: map_at_1000
|
989 |
+
value: 34.888000000000005
|
990 |
+
- type: map_at_3
|
991 |
+
value: 29.944
|
992 |
+
- type: map_at_5
|
993 |
+
value: 31.613999999999997
|
994 |
+
- type: mrr_at_1
|
995 |
+
value: 29.447000000000003
|
996 |
+
- type: mrr_at_10
|
997 |
+
value: 37.996
|
998 |
+
- type: mrr_at_100
|
999 |
+
value: 38.946
|
1000 |
+
- type: mrr_at_1000
|
1001 |
+
value: 38.995000000000005
|
1002 |
+
- type: mrr_at_3
|
1003 |
+
value: 35.079
|
1004 |
+
- type: mrr_at_5
|
1005 |
+
value: 36.69
|
1006 |
+
- type: ndcg_at_1
|
1007 |
+
value: 29.447000000000003
|
1008 |
+
- type: ndcg_at_10
|
1009 |
+
value: 39.232
|
1010 |
+
- type: ndcg_at_100
|
1011 |
+
value: 45.247
|
1012 |
+
- type: ndcg_at_1000
|
1013 |
+
value: 47.613
|
1014 |
+
- type: ndcg_at_3
|
1015 |
+
value: 33.922999999999995
|
1016 |
+
- type: ndcg_at_5
|
1017 |
+
value: 36.284
|
1018 |
+
- type: precision_at_1
|
1019 |
+
value: 29.447000000000003
|
1020 |
+
- type: precision_at_10
|
1021 |
+
value: 7.648000000000001
|
1022 |
+
- type: precision_at_100
|
1023 |
+
value: 1.516
|
1024 |
+
- type: precision_at_1000
|
1025 |
+
value: 0.23900000000000002
|
1026 |
+
- type: precision_at_3
|
1027 |
+
value: 16.008
|
1028 |
+
- type: precision_at_5
|
1029 |
+
value: 11.779
|
1030 |
+
- type: recall_at_1
|
1031 |
+
value: 24.501
|
1032 |
+
- type: recall_at_10
|
1033 |
+
value: 51.18899999999999
|
1034 |
+
- type: recall_at_100
|
1035 |
+
value: 78.437
|
1036 |
+
- type: recall_at_1000
|
1037 |
+
value: 92.842
|
1038 |
+
- type: recall_at_3
|
1039 |
+
value: 35.808
|
1040 |
+
- type: recall_at_5
|
1041 |
+
value: 42.197
|
1042 |
+
- task:
|
1043 |
+
type: Retrieval
|
1044 |
+
dataset:
|
1045 |
+
type: BeIR/cqadupstack
|
1046 |
+
name: MTEB CQADupstackWordpressRetrieval
|
1047 |
+
config: default
|
1048 |
+
split: test
|
1049 |
+
revision: None
|
1050 |
+
metrics:
|
1051 |
+
- type: map_at_1
|
1052 |
+
value: 22.039
|
1053 |
+
- type: map_at_10
|
1054 |
+
value: 30.377
|
1055 |
+
- type: map_at_100
|
1056 |
+
value: 31.275
|
1057 |
+
- type: map_at_1000
|
1058 |
+
value: 31.379
|
1059 |
+
- type: map_at_3
|
1060 |
+
value: 27.98
|
1061 |
+
- type: map_at_5
|
1062 |
+
value: 29.358
|
1063 |
+
- type: mrr_at_1
|
1064 |
+
value: 24.03
|
1065 |
+
- type: mrr_at_10
|
1066 |
+
value: 32.568000000000005
|
1067 |
+
- type: mrr_at_100
|
1068 |
+
value: 33.403
|
1069 |
+
- type: mrr_at_1000
|
1070 |
+
value: 33.475
|
1071 |
+
- type: mrr_at_3
|
1072 |
+
value: 30.436999999999998
|
1073 |
+
- type: mrr_at_5
|
1074 |
+
value: 31.796000000000003
|
1075 |
+
- type: ndcg_at_1
|
1076 |
+
value: 24.03
|
1077 |
+
- type: ndcg_at_10
|
1078 |
+
value: 35.198
|
1079 |
+
- type: ndcg_at_100
|
1080 |
+
value: 39.668
|
1081 |
+
- type: ndcg_at_1000
|
1082 |
+
value: 42.296
|
1083 |
+
- type: ndcg_at_3
|
1084 |
+
value: 30.709999999999997
|
1085 |
+
- type: ndcg_at_5
|
1086 |
+
value: 33.024
|
1087 |
+
- type: precision_at_1
|
1088 |
+
value: 24.03
|
1089 |
+
- type: precision_at_10
|
1090 |
+
value: 5.564
|
1091 |
+
- type: precision_at_100
|
1092 |
+
value: 0.828
|
1093 |
+
- type: precision_at_1000
|
1094 |
+
value: 0.117
|
1095 |
+
- type: precision_at_3
|
1096 |
+
value: 13.309000000000001
|
1097 |
+
- type: precision_at_5
|
1098 |
+
value: 9.39
|
1099 |
+
- type: recall_at_1
|
1100 |
+
value: 22.039
|
1101 |
+
- type: recall_at_10
|
1102 |
+
value: 47.746
|
1103 |
+
- type: recall_at_100
|
1104 |
+
value: 68.23599999999999
|
1105 |
+
- type: recall_at_1000
|
1106 |
+
value: 87.852
|
1107 |
+
- type: recall_at_3
|
1108 |
+
value: 35.852000000000004
|
1109 |
+
- type: recall_at_5
|
1110 |
+
value: 41.410000000000004
|
1111 |
+
- task:
|
1112 |
+
type: Retrieval
|
1113 |
+
dataset:
|
1114 |
+
type: climate-fever
|
1115 |
+
name: MTEB ClimateFEVER
|
1116 |
+
config: default
|
1117 |
+
split: test
|
1118 |
+
revision: None
|
1119 |
+
metrics:
|
1120 |
+
- type: map_at_1
|
1121 |
+
value: 15.692999999999998
|
1122 |
+
- type: map_at_10
|
1123 |
+
value: 26.903
|
1124 |
+
- type: map_at_100
|
1125 |
+
value: 28.987000000000002
|
1126 |
+
- type: map_at_1000
|
1127 |
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value: 29.176999999999996
|
1128 |
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- type: map_at_3
|
1129 |
+
value: 22.137
|
1130 |
+
- type: map_at_5
|
1131 |
+
value: 24.758
|
1132 |
+
- type: mrr_at_1
|
1133 |
+
value: 35.57
|
1134 |
+
- type: mrr_at_10
|
1135 |
+
value: 47.821999999999996
|
1136 |
+
- type: mrr_at_100
|
1137 |
+
value: 48.608000000000004
|
1138 |
+
- type: mrr_at_1000
|
1139 |
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value: 48.638999999999996
|
1140 |
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- type: mrr_at_3
|
1141 |
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value: 44.452000000000005
|
1142 |
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- type: mrr_at_5
|
1143 |
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value: 46.546
|
1144 |
+
- type: ndcg_at_1
|
1145 |
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value: 35.57
|
1146 |
+
- type: ndcg_at_10
|
1147 |
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value: 36.567
|
1148 |
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- type: ndcg_at_100
|
1149 |
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value: 44.085
|
1150 |
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- type: ndcg_at_1000
|
1151 |
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value: 47.24
|
1152 |
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- type: ndcg_at_3
|
1153 |
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value: 29.964000000000002
|
1154 |
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- type: ndcg_at_5
|
1155 |
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value: 32.511
|
1156 |
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- type: precision_at_1
|
1157 |
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value: 35.57
|
1158 |
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- type: precision_at_10
|
1159 |
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value: 11.485
|
1160 |
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- type: precision_at_100
|
1161 |
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value: 1.9619999999999997
|
1162 |
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- type: precision_at_1000
|
1163 |
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value: 0.256
|
1164 |
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- type: precision_at_3
|
1165 |
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value: 22.237000000000002
|
1166 |
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- type: precision_at_5
|
1167 |
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value: 17.471999999999998
|
1168 |
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- type: recall_at_1
|
1169 |
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value: 15.692999999999998
|
1170 |
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- type: recall_at_10
|
1171 |
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value: 43.056
|
1172 |
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- type: recall_at_100
|
1173 |
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value: 68.628
|
1174 |
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- type: recall_at_1000
|
1175 |
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value: 86.075
|
1176 |
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- type: recall_at_3
|
1177 |
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value: 26.918999999999997
|
1178 |
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- type: recall_at_5
|
1179 |
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value: 34.14
|
1180 |
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- task:
|
1181 |
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type: Retrieval
|
1182 |
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dataset:
|
1183 |
+
type: dbpedia-entity
|
1184 |
+
name: MTEB DBPedia
|
1185 |
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config: default
|
1186 |
+
split: test
|
1187 |
+
revision: None
|
1188 |
+
metrics:
|
1189 |
+
- type: map_at_1
|
1190 |
+
value: 9.53
|
1191 |
+
- type: map_at_10
|
1192 |
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value: 20.951
|
1193 |
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- type: map_at_100
|
1194 |
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value: 30.136000000000003
|
1195 |
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- type: map_at_1000
|
1196 |
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value: 31.801000000000002
|
1197 |
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- type: map_at_3
|
1198 |
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value: 15.021
|
1199 |
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- type: map_at_5
|
1200 |
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value: 17.471999999999998
|
1201 |
+
- type: mrr_at_1
|
1202 |
+
value: 71.0
|
1203 |
+
- type: mrr_at_10
|
1204 |
+
value: 79.176
|
1205 |
+
- type: mrr_at_100
|
1206 |
+
value: 79.418
|
1207 |
+
- type: mrr_at_1000
|
1208 |
+
value: 79.426
|
1209 |
+
- type: mrr_at_3
|
1210 |
+
value: 78.125
|
1211 |
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- type: mrr_at_5
|
1212 |
+
value: 78.61200000000001
|
1213 |
+
- type: ndcg_at_1
|
1214 |
+
value: 58.5
|
1215 |
+
- type: ndcg_at_10
|
1216 |
+
value: 44.106
|
1217 |
+
- type: ndcg_at_100
|
1218 |
+
value: 49.268
|
1219 |
+
- type: ndcg_at_1000
|
1220 |
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value: 56.711999999999996
|
1221 |
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- type: ndcg_at_3
|
1222 |
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value: 48.934
|
1223 |
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- type: ndcg_at_5
|
1224 |
+
value: 45.826
|
1225 |
+
- type: precision_at_1
|
1226 |
+
value: 71.0
|
1227 |
+
- type: precision_at_10
|
1228 |
+
value: 35.0
|
1229 |
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- type: precision_at_100
|
1230 |
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value: 11.360000000000001
|
1231 |
+
- type: precision_at_1000
|
1232 |
+
value: 2.046
|
1233 |
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- type: precision_at_3
|
1234 |
+
value: 52.833
|
1235 |
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- type: precision_at_5
|
1236 |
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value: 44.15
|
1237 |
+
- type: recall_at_1
|
1238 |
+
value: 9.53
|
1239 |
+
- type: recall_at_10
|
1240 |
+
value: 26.811
|
1241 |
+
- type: recall_at_100
|
1242 |
+
value: 55.916999999999994
|
1243 |
+
- type: recall_at_1000
|
1244 |
+
value: 79.973
|
1245 |
+
- type: recall_at_3
|
1246 |
+
value: 16.413
|
1247 |
+
- type: recall_at_5
|
1248 |
+
value: 19.980999999999998
|
1249 |
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- task:
|
1250 |
+
type: Classification
|
1251 |
+
dataset:
|
1252 |
+
type: mteb/emotion
|
1253 |
+
name: MTEB EmotionClassification
|
1254 |
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config: default
|
1255 |
+
split: test
|
1256 |
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revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
1257 |
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metrics:
|
1258 |
+
- type: accuracy
|
1259 |
+
value: 51.519999999999996
|
1260 |
+
- type: f1
|
1261 |
+
value: 46.36601294761231
|
1262 |
+
- task:
|
1263 |
+
type: Retrieval
|
1264 |
+
dataset:
|
1265 |
+
type: fever
|
1266 |
+
name: MTEB FEVER
|
1267 |
+
config: default
|
1268 |
+
split: test
|
1269 |
+
revision: None
|
1270 |
+
metrics:
|
1271 |
+
- type: map_at_1
|
1272 |
+
value: 74.413
|
1273 |
+
- type: map_at_10
|
1274 |
+
value: 83.414
|
1275 |
+
- type: map_at_100
|
1276 |
+
value: 83.621
|
1277 |
+
- type: map_at_1000
|
1278 |
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value: 83.635
|
1279 |
+
- type: map_at_3
|
1280 |
+
value: 82.337
|
1281 |
+
- type: map_at_5
|
1282 |
+
value: 83.039
|
1283 |
+
- type: mrr_at_1
|
1284 |
+
value: 80.19800000000001
|
1285 |
+
- type: mrr_at_10
|
1286 |
+
value: 87.715
|
1287 |
+
- type: mrr_at_100
|
1288 |
+
value: 87.778
|
1289 |
+
- type: mrr_at_1000
|
1290 |
+
value: 87.779
|
1291 |
+
- type: mrr_at_3
|
1292 |
+
value: 87.106
|
1293 |
+
- type: mrr_at_5
|
1294 |
+
value: 87.555
|
1295 |
+
- type: ndcg_at_1
|
1296 |
+
value: 80.19800000000001
|
1297 |
+
- type: ndcg_at_10
|
1298 |
+
value: 87.182
|
1299 |
+
- type: ndcg_at_100
|
1300 |
+
value: 87.90299999999999
|
1301 |
+
- type: ndcg_at_1000
|
1302 |
+
value: 88.143
|
1303 |
+
- type: ndcg_at_3
|
1304 |
+
value: 85.60600000000001
|
1305 |
+
- type: ndcg_at_5
|
1306 |
+
value: 86.541
|
1307 |
+
- type: precision_at_1
|
1308 |
+
value: 80.19800000000001
|
1309 |
+
- type: precision_at_10
|
1310 |
+
value: 10.531
|
1311 |
+
- type: precision_at_100
|
1312 |
+
value: 1.113
|
1313 |
+
- type: precision_at_1000
|
1314 |
+
value: 0.11499999999999999
|
1315 |
+
- type: precision_at_3
|
1316 |
+
value: 32.933
|
1317 |
+
- type: precision_at_5
|
1318 |
+
value: 20.429
|
1319 |
+
- type: recall_at_1
|
1320 |
+
value: 74.413
|
1321 |
+
- type: recall_at_10
|
1322 |
+
value: 94.363
|
1323 |
+
- type: recall_at_100
|
1324 |
+
value: 97.165
|
1325 |
+
- type: recall_at_1000
|
1326 |
+
value: 98.668
|
1327 |
+
- type: recall_at_3
|
1328 |
+
value: 90.108
|
1329 |
+
- type: recall_at_5
|
1330 |
+
value: 92.52
|
1331 |
+
- task:
|
1332 |
+
type: Retrieval
|
1333 |
+
dataset:
|
1334 |
+
type: fiqa
|
1335 |
+
name: MTEB FiQA2018
|
1336 |
+
config: default
|
1337 |
+
split: test
|
1338 |
+
revision: None
|
1339 |
+
metrics:
|
1340 |
+
- type: map_at_1
|
1341 |
+
value: 22.701
|
1342 |
+
- type: map_at_10
|
1343 |
+
value: 37.122
|
1344 |
+
- type: map_at_100
|
1345 |
+
value: 39.178000000000004
|
1346 |
+
- type: map_at_1000
|
1347 |
+
value: 39.326
|
1348 |
+
- type: map_at_3
|
1349 |
+
value: 32.971000000000004
|
1350 |
+
- type: map_at_5
|
1351 |
+
value: 35.332
|
1352 |
+
- type: mrr_at_1
|
1353 |
+
value: 44.753
|
1354 |
+
- type: mrr_at_10
|
1355 |
+
value: 53.452
|
1356 |
+
- type: mrr_at_100
|
1357 |
+
value: 54.198
|
1358 |
+
- type: mrr_at_1000
|
1359 |
+
value: 54.225
|
1360 |
+
- type: mrr_at_3
|
1361 |
+
value: 50.952
|
1362 |
+
- type: mrr_at_5
|
1363 |
+
value: 52.464
|
1364 |
+
- type: ndcg_at_1
|
1365 |
+
value: 44.753
|
1366 |
+
- type: ndcg_at_10
|
1367 |
+
value: 45.021
|
1368 |
+
- type: ndcg_at_100
|
1369 |
+
value: 52.028
|
1370 |
+
- type: ndcg_at_1000
|
1371 |
+
value: 54.596000000000004
|
1372 |
+
- type: ndcg_at_3
|
1373 |
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value: 41.622
|
1374 |
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- type: ndcg_at_5
|
1375 |
+
value: 42.736000000000004
|
1376 |
+
- type: precision_at_1
|
1377 |
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value: 44.753
|
1378 |
+
- type: precision_at_10
|
1379 |
+
value: 12.284
|
1380 |
+
- type: precision_at_100
|
1381 |
+
value: 1.955
|
1382 |
+
- type: precision_at_1000
|
1383 |
+
value: 0.243
|
1384 |
+
- type: precision_at_3
|
1385 |
+
value: 27.828999999999997
|
1386 |
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- type: precision_at_5
|
1387 |
+
value: 20.061999999999998
|
1388 |
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- type: recall_at_1
|
1389 |
+
value: 22.701
|
1390 |
+
- type: recall_at_10
|
1391 |
+
value: 51.432
|
1392 |
+
- type: recall_at_100
|
1393 |
+
value: 77.009
|
1394 |
+
- type: recall_at_1000
|
1395 |
+
value: 92.511
|
1396 |
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- type: recall_at_3
|
1397 |
+
value: 37.919000000000004
|
1398 |
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- type: recall_at_5
|
1399 |
+
value: 44.131
|
1400 |
+
- task:
|
1401 |
+
type: Retrieval
|
1402 |
+
dataset:
|
1403 |
+
type: hotpotqa
|
1404 |
+
name: MTEB HotpotQA
|
1405 |
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config: default
|
1406 |
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split: test
|
1407 |
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revision: None
|
1408 |
+
metrics:
|
1409 |
+
- type: map_at_1
|
1410 |
+
value: 40.189
|
1411 |
+
- type: map_at_10
|
1412 |
+
value: 66.24600000000001
|
1413 |
+
- type: map_at_100
|
1414 |
+
value: 67.098
|
1415 |
+
- type: map_at_1000
|
1416 |
+
value: 67.149
|
1417 |
+
- type: map_at_3
|
1418 |
+
value: 62.684
|
1419 |
+
- type: map_at_5
|
1420 |
+
value: 64.974
|
1421 |
+
- type: mrr_at_1
|
1422 |
+
value: 80.378
|
1423 |
+
- type: mrr_at_10
|
1424 |
+
value: 86.127
|
1425 |
+
- type: mrr_at_100
|
1426 |
+
value: 86.29299999999999
|
1427 |
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- type: mrr_at_1000
|
1428 |
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value: 86.297
|
1429 |
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- type: mrr_at_3
|
1430 |
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value: 85.31400000000001
|
1431 |
+
- type: mrr_at_5
|
1432 |
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value: 85.858
|
1433 |
+
- type: ndcg_at_1
|
1434 |
+
value: 80.378
|
1435 |
+
- type: ndcg_at_10
|
1436 |
+
value: 74.101
|
1437 |
+
- type: ndcg_at_100
|
1438 |
+
value: 76.993
|
1439 |
+
- type: ndcg_at_1000
|
1440 |
+
value: 77.948
|
1441 |
+
- type: ndcg_at_3
|
1442 |
+
value: 69.232
|
1443 |
+
- type: ndcg_at_5
|
1444 |
+
value: 72.04599999999999
|
1445 |
+
- type: precision_at_1
|
1446 |
+
value: 80.378
|
1447 |
+
- type: precision_at_10
|
1448 |
+
value: 15.595999999999998
|
1449 |
+
- type: precision_at_100
|
1450 |
+
value: 1.7840000000000003
|
1451 |
+
- type: precision_at_1000
|
1452 |
+
value: 0.191
|
1453 |
+
- type: precision_at_3
|
1454 |
+
value: 44.884
|
1455 |
+
- type: precision_at_5
|
1456 |
+
value: 29.145
|
1457 |
+
- type: recall_at_1
|
1458 |
+
value: 40.189
|
1459 |
+
- type: recall_at_10
|
1460 |
+
value: 77.981
|
1461 |
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- type: recall_at_100
|
1462 |
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value: 89.21
|
1463 |
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- type: recall_at_1000
|
1464 |
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value: 95.48299999999999
|
1465 |
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- type: recall_at_3
|
1466 |
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value: 67.326
|
1467 |
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- type: recall_at_5
|
1468 |
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value: 72.863
|
1469 |
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- task:
|
1470 |
+
type: Classification
|
1471 |
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dataset:
|
1472 |
+
type: mteb/imdb
|
1473 |
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name: MTEB ImdbClassification
|
1474 |
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config: default
|
1475 |
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split: test
|
1476 |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
1477 |
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metrics:
|
1478 |
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- type: accuracy
|
1479 |
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value: 92.84599999999999
|
1480 |
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- type: ap
|
1481 |
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value: 89.4710787567357
|
1482 |
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- type: f1
|
1483 |
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value: 92.83752676932258
|
1484 |
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- task:
|
1485 |
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type: Retrieval
|
1486 |
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dataset:
|
1487 |
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type: msmarco
|
1488 |
+
name: MTEB MSMARCO
|
1489 |
+
config: default
|
1490 |
+
split: dev
|
1491 |
+
revision: None
|
1492 |
+
metrics:
|
1493 |
+
- type: map_at_1
|
1494 |
+
value: 23.132
|
1495 |
+
- type: map_at_10
|
1496 |
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value: 35.543
|
1497 |
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- type: map_at_100
|
1498 |
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value: 36.702
|
1499 |
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- type: map_at_1000
|
1500 |
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value: 36.748999999999995
|
1501 |
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- type: map_at_3
|
1502 |
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value: 31.737
|
1503 |
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- type: map_at_5
|
1504 |
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value: 33.927
|
1505 |
+
- type: mrr_at_1
|
1506 |
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value: 23.782
|
1507 |
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- type: mrr_at_10
|
1508 |
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value: 36.204
|
1509 |
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- type: mrr_at_100
|
1510 |
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value: 37.29
|
1511 |
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- type: mrr_at_1000
|
1512 |
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value: 37.330999999999996
|
1513 |
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- type: mrr_at_3
|
1514 |
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value: 32.458999999999996
|
1515 |
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- type: mrr_at_5
|
1516 |
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value: 34.631
|
1517 |
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- type: ndcg_at_1
|
1518 |
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value: 23.782
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1519 |
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- type: ndcg_at_10
|
1520 |
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value: 42.492999999999995
|
1521 |
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- type: ndcg_at_100
|
1522 |
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value: 47.985
|
1523 |
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- type: ndcg_at_1000
|
1524 |
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value: 49.141
|
1525 |
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- type: ndcg_at_3
|
1526 |
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value: 34.748000000000005
|
1527 |
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- type: ndcg_at_5
|
1528 |
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value: 38.651
|
1529 |
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- type: precision_at_1
|
1530 |
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value: 23.782
|
1531 |
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- type: precision_at_10
|
1532 |
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value: 6.665
|
1533 |
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- type: precision_at_100
|
1534 |
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value: 0.941
|
1535 |
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- type: precision_at_1000
|
1536 |
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value: 0.104
|
1537 |
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- type: precision_at_3
|
1538 |
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value: 14.776
|
1539 |
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- type: precision_at_5
|
1540 |
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value: 10.84
|
1541 |
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- type: recall_at_1
|
1542 |
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value: 23.132
|
1543 |
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- type: recall_at_10
|
1544 |
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value: 63.794
|
1545 |
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- type: recall_at_100
|
1546 |
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value: 89.027
|
1547 |
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- type: recall_at_1000
|
1548 |
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value: 97.807
|
1549 |
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- type: recall_at_3
|
1550 |
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value: 42.765
|
1551 |
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- type: recall_at_5
|
1552 |
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value: 52.11
|
1553 |
+
- task:
|
1554 |
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type: Classification
|
1555 |
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dataset:
|
1556 |
+
type: mteb/mtop_domain
|
1557 |
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name: MTEB MTOPDomainClassification (en)
|
1558 |
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config: en
|
1559 |
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split: test
|
1560 |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
1561 |
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metrics:
|
1562 |
+
- type: accuracy
|
1563 |
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value: 94.59188326493388
|
1564 |
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- type: f1
|
1565 |
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value: 94.3842594786827
|
1566 |
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- task:
|
1567 |
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type: Classification
|
1568 |
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dataset:
|
1569 |
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type: mteb/mtop_intent
|
1570 |
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name: MTEB MTOPIntentClassification (en)
|
1571 |
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config: en
|
1572 |
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split: test
|
1573 |
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
1574 |
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metrics:
|
1575 |
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- type: accuracy
|
1576 |
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value: 79.49384404924761
|
1577 |
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- type: f1
|
1578 |
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value: 59.7580539534629
|
1579 |
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- task:
|
1580 |
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type: Classification
|
1581 |
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dataset:
|
1582 |
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type: mteb/amazon_massive_intent
|
1583 |
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name: MTEB MassiveIntentClassification (en)
|
1584 |
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config: en
|
1585 |
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split: test
|
1586 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
1587 |
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metrics:
|
1588 |
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- type: accuracy
|
1589 |
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value: 77.56220578345663
|
1590 |
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- type: f1
|
1591 |
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value: 75.27228165561478
|
1592 |
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- task:
|
1593 |
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type: Classification
|
1594 |
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dataset:
|
1595 |
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type: mteb/amazon_massive_scenario
|
1596 |
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name: MTEB MassiveScenarioClassification (en)
|
1597 |
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config: en
|
1598 |
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split: test
|
1599 |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
1600 |
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metrics:
|
1601 |
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- type: accuracy
|
1602 |
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value: 80.53463349024884
|
1603 |
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- type: f1
|
1604 |
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value: 80.4893958236536
|
1605 |
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- task:
|
1606 |
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type: Clustering
|
1607 |
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dataset:
|
1608 |
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type: mteb/medrxiv-clustering-p2p
|
1609 |
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name: MTEB MedrxivClusteringP2P
|
1610 |
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config: default
|
1611 |
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split: test
|
1612 |
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
1613 |
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metrics:
|
1614 |
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- type: v_measure
|
1615 |
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value: 32.56100273484962
|
1616 |
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- task:
|
1617 |
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type: Clustering
|
1618 |
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dataset:
|
1619 |
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type: mteb/medrxiv-clustering-s2s
|
1620 |
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name: MTEB MedrxivClusteringS2S
|
1621 |
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config: default
|
1622 |
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split: test
|
1623 |
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
1624 |
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metrics:
|
1625 |
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- type: v_measure
|
1626 |
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value: 31.470380028839607
|
1627 |
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- task:
|
1628 |
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type: Reranking
|
1629 |
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dataset:
|
1630 |
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type: mteb/mind_small
|
1631 |
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name: MTEB MindSmallReranking
|
1632 |
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config: default
|
1633 |
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split: test
|
1634 |
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revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
1635 |
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metrics:
|
1636 |
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- type: map
|
1637 |
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value: 32.06102792457849
|
1638 |
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- type: mrr
|
1639 |
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value: 33.30709199672238
|
1640 |
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- task:
|
1641 |
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type: Retrieval
|
1642 |
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dataset:
|
1643 |
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type: nfcorpus
|
1644 |
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name: MTEB NFCorpus
|
1645 |
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config: default
|
1646 |
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split: test
|
1647 |
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revision: None
|
1648 |
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metrics:
|
1649 |
+
- type: map_at_1
|
1650 |
+
value: 6.776999999999999
|
1651 |
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- type: map_at_10
|
1652 |
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value: 14.924000000000001
|
1653 |
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- type: map_at_100
|
1654 |
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value: 18.955
|
1655 |
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- type: map_at_1000
|
1656 |
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value: 20.538999999999998
|
1657 |
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- type: map_at_3
|
1658 |
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value: 10.982
|
1659 |
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- type: map_at_5
|
1660 |
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value: 12.679000000000002
|
1661 |
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- type: mrr_at_1
|
1662 |
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value: 47.988
|
1663 |
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- type: mrr_at_10
|
1664 |
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value: 57.232000000000006
|
1665 |
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- type: mrr_at_100
|
1666 |
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value: 57.818999999999996
|
1667 |
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- type: mrr_at_1000
|
1668 |
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value: 57.847
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1669 |
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- type: mrr_at_3
|
1670 |
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value: 54.901999999999994
|
1671 |
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- type: mrr_at_5
|
1672 |
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value: 56.481
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1673 |
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- type: ndcg_at_1
|
1674 |
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value: 46.594
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1675 |
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- type: ndcg_at_10
|
1676 |
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value: 38.129000000000005
|
1677 |
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- type: ndcg_at_100
|
1678 |
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value: 35.54
|
1679 |
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- type: ndcg_at_1000
|
1680 |
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value: 44.172
|
1681 |
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- type: ndcg_at_3
|
1682 |
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value: 43.025999999999996
|
1683 |
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- type: ndcg_at_5
|
1684 |
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value: 41.052
|
1685 |
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- type: precision_at_1
|
1686 |
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value: 47.988
|
1687 |
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- type: precision_at_10
|
1688 |
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value: 28.111000000000004
|
1689 |
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- type: precision_at_100
|
1690 |
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value: 8.929
|
1691 |
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- type: precision_at_1000
|
1692 |
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value: 2.185
|
1693 |
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- type: precision_at_3
|
1694 |
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value: 40.144000000000005
|
1695 |
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- type: precision_at_5
|
1696 |
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value: 35.232
|
1697 |
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- type: recall_at_1
|
1698 |
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value: 6.776999999999999
|
1699 |
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- type: recall_at_10
|
1700 |
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value: 19.289
|
1701 |
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- type: recall_at_100
|
1702 |
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value: 36.359
|
1703 |
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- type: recall_at_1000
|
1704 |
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value: 67.54
|
1705 |
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- type: recall_at_3
|
1706 |
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value: 11.869
|
1707 |
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- type: recall_at_5
|
1708 |
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value: 14.999
|
1709 |
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- task:
|
1710 |
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type: Retrieval
|
1711 |
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dataset:
|
1712 |
+
type: nq
|
1713 |
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name: MTEB NQ
|
1714 |
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config: default
|
1715 |
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split: test
|
1716 |
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revision: None
|
1717 |
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metrics:
|
1718 |
+
- type: map_at_1
|
1719 |
+
value: 31.108000000000004
|
1720 |
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- type: map_at_10
|
1721 |
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value: 47.126000000000005
|
1722 |
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- type: map_at_100
|
1723 |
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value: 48.171
|
1724 |
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- type: map_at_1000
|
1725 |
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value: 48.199
|
1726 |
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- type: map_at_3
|
1727 |
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value: 42.734
|
1728 |
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- type: map_at_5
|
1729 |
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value: 45.362
|
1730 |
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- type: mrr_at_1
|
1731 |
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value: 34.936
|
1732 |
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- type: mrr_at_10
|
1733 |
+
value: 49.571
|
1734 |
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- type: mrr_at_100
|
1735 |
+
value: 50.345
|
1736 |
+
- type: mrr_at_1000
|
1737 |
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value: 50.363
|
1738 |
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- type: mrr_at_3
|
1739 |
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value: 45.959
|
1740 |
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- type: mrr_at_5
|
1741 |
+
value: 48.165
|
1742 |
+
- type: ndcg_at_1
|
1743 |
+
value: 34.936
|
1744 |
+
- type: ndcg_at_10
|
1745 |
+
value: 55.028999999999996
|
1746 |
+
- type: ndcg_at_100
|
1747 |
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value: 59.244
|
1748 |
+
- type: ndcg_at_1000
|
1749 |
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value: 59.861
|
1750 |
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- type: ndcg_at_3
|
1751 |
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value: 46.872
|
1752 |
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- type: ndcg_at_5
|
1753 |
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value: 51.217999999999996
|
1754 |
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- type: precision_at_1
|
1755 |
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value: 34.936
|
1756 |
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- type: precision_at_10
|
1757 |
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value: 9.099
|
1758 |
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- type: precision_at_100
|
1759 |
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value: 1.145
|
1760 |
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- type: precision_at_1000
|
1761 |
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value: 0.12
|
1762 |
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- type: precision_at_3
|
1763 |
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value: 21.456
|
1764 |
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- type: precision_at_5
|
1765 |
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value: 15.411
|
1766 |
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- type: recall_at_1
|
1767 |
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value: 31.108000000000004
|
1768 |
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- type: recall_at_10
|
1769 |
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value: 76.53999999999999
|
1770 |
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- type: recall_at_100
|
1771 |
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value: 94.39
|
1772 |
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- type: recall_at_1000
|
1773 |
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value: 98.947
|
1774 |
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- type: recall_at_3
|
1775 |
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value: 55.572
|
1776 |
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- type: recall_at_5
|
1777 |
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value: 65.525
|
1778 |
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- task:
|
1779 |
+
type: Retrieval
|
1780 |
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dataset:
|
1781 |
+
type: quora
|
1782 |
+
name: MTEB QuoraRetrieval
|
1783 |
+
config: default
|
1784 |
+
split: test
|
1785 |
+
revision: None
|
1786 |
+
metrics:
|
1787 |
+
- type: map_at_1
|
1788 |
+
value: 71.56400000000001
|
1789 |
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- type: map_at_10
|
1790 |
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value: 85.482
|
1791 |
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- type: map_at_100
|
1792 |
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value: 86.114
|
1793 |
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- type: map_at_1000
|
1794 |
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value: 86.13
|
1795 |
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- type: map_at_3
|
1796 |
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value: 82.607
|
1797 |
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- type: map_at_5
|
1798 |
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value: 84.405
|
1799 |
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- type: mrr_at_1
|
1800 |
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value: 82.42
|
1801 |
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- type: mrr_at_10
|
1802 |
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value: 88.304
|
1803 |
+
- type: mrr_at_100
|
1804 |
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value: 88.399
|
1805 |
+
- type: mrr_at_1000
|
1806 |
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value: 88.399
|
1807 |
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- type: mrr_at_3
|
1808 |
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value: 87.37
|
1809 |
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- type: mrr_at_5
|
1810 |
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value: 88.024
|
1811 |
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- type: ndcg_at_1
|
1812 |
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value: 82.45
|
1813 |
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- type: ndcg_at_10
|
1814 |
+
value: 89.06500000000001
|
1815 |
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- type: ndcg_at_100
|
1816 |
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value: 90.232
|
1817 |
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- type: ndcg_at_1000
|
1818 |
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value: 90.305
|
1819 |
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- type: ndcg_at_3
|
1820 |
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value: 86.375
|
1821 |
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- type: ndcg_at_5
|
1822 |
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value: 87.85300000000001
|
1823 |
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- type: precision_at_1
|
1824 |
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value: 82.45
|
1825 |
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- type: precision_at_10
|
1826 |
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value: 13.486999999999998
|
1827 |
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- type: precision_at_100
|
1828 |
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value: 1.534
|
1829 |
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- type: precision_at_1000
|
1830 |
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value: 0.157
|
1831 |
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- type: precision_at_3
|
1832 |
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value: 37.813
|
1833 |
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- type: precision_at_5
|
1834 |
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value: 24.773999999999997
|
1835 |
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- type: recall_at_1
|
1836 |
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value: 71.56400000000001
|
1837 |
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- type: recall_at_10
|
1838 |
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value: 95.812
|
1839 |
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- type: recall_at_100
|
1840 |
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value: 99.7
|
1841 |
+
- type: recall_at_1000
|
1842 |
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value: 99.979
|
1843 |
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- type: recall_at_3
|
1844 |
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value: 87.966
|
1845 |
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- type: recall_at_5
|
1846 |
+
value: 92.268
|
1847 |
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- task:
|
1848 |
+
type: Clustering
|
1849 |
+
dataset:
|
1850 |
+
type: mteb/reddit-clustering
|
1851 |
+
name: MTEB RedditClustering
|
1852 |
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config: default
|
1853 |
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split: test
|
1854 |
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revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
1855 |
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metrics:
|
1856 |
+
- type: v_measure
|
1857 |
+
value: 57.241876648614145
|
1858 |
+
- task:
|
1859 |
+
type: Clustering
|
1860 |
+
dataset:
|
1861 |
+
type: mteb/reddit-clustering-p2p
|
1862 |
+
name: MTEB RedditClusteringP2P
|
1863 |
+
config: default
|
1864 |
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split: test
|
1865 |
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revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
1866 |
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metrics:
|
1867 |
+
- type: v_measure
|
1868 |
+
value: 64.66212576446223
|
1869 |
+
- task:
|
1870 |
+
type: Retrieval
|
1871 |
+
dataset:
|
1872 |
+
type: scidocs
|
1873 |
+
name: MTEB SCIDOCS
|
1874 |
+
config: default
|
1875 |
+
split: test
|
1876 |
+
revision: None
|
1877 |
+
metrics:
|
1878 |
+
- type: map_at_1
|
1879 |
+
value: 5.308
|
1880 |
+
- type: map_at_10
|
1881 |
+
value: 13.803
|
1882 |
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- type: map_at_100
|
1883 |
+
value: 16.176
|
1884 |
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- type: map_at_1000
|
1885 |
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value: 16.561
|
1886 |
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- type: map_at_3
|
1887 |
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value: 9.761000000000001
|
1888 |
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- type: map_at_5
|
1889 |
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value: 11.802
|
1890 |
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- type: mrr_at_1
|
1891 |
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value: 26.200000000000003
|
1892 |
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- type: mrr_at_10
|
1893 |
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value: 37.621
|
1894 |
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- type: mrr_at_100
|
1895 |
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value: 38.767
|
1896 |
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- type: mrr_at_1000
|
1897 |
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value: 38.815
|
1898 |
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- type: mrr_at_3
|
1899 |
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value: 34.117
|
1900 |
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- type: mrr_at_5
|
1901 |
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value: 36.107
|
1902 |
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- type: ndcg_at_1
|
1903 |
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value: 26.200000000000003
|
1904 |
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- type: ndcg_at_10
|
1905 |
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value: 22.64
|
1906 |
+
- type: ndcg_at_100
|
1907 |
+
value: 31.567
|
1908 |
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- type: ndcg_at_1000
|
1909 |
+
value: 37.623
|
1910 |
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- type: ndcg_at_3
|
1911 |
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value: 21.435000000000002
|
1912 |
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- type: ndcg_at_5
|
1913 |
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value: 18.87
|
1914 |
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- type: precision_at_1
|
1915 |
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value: 26.200000000000003
|
1916 |
+
- type: precision_at_10
|
1917 |
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value: 11.74
|
1918 |
+
- type: precision_at_100
|
1919 |
+
value: 2.465
|
1920 |
+
- type: precision_at_1000
|
1921 |
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value: 0.391
|
1922 |
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- type: precision_at_3
|
1923 |
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value: 20.033
|
1924 |
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- type: precision_at_5
|
1925 |
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value: 16.64
|
1926 |
+
- type: recall_at_1
|
1927 |
+
value: 5.308
|
1928 |
+
- type: recall_at_10
|
1929 |
+
value: 23.794999999999998
|
1930 |
+
- type: recall_at_100
|
1931 |
+
value: 50.015
|
1932 |
+
- type: recall_at_1000
|
1933 |
+
value: 79.283
|
1934 |
+
- type: recall_at_3
|
1935 |
+
value: 12.178
|
1936 |
+
- type: recall_at_5
|
1937 |
+
value: 16.882
|
1938 |
+
- task:
|
1939 |
+
type: STS
|
1940 |
+
dataset:
|
1941 |
+
type: mteb/sickr-sts
|
1942 |
+
name: MTEB SICK-R
|
1943 |
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config: default
|
1944 |
+
split: test
|
1945 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
1946 |
+
metrics:
|
1947 |
+
- type: cos_sim_pearson
|
1948 |
+
value: 84.93231134675553
|
1949 |
+
- type: cos_sim_spearman
|
1950 |
+
value: 81.68319292603205
|
1951 |
+
- type: euclidean_pearson
|
1952 |
+
value: 81.8396814380367
|
1953 |
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- type: euclidean_spearman
|
1954 |
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value: 81.24641903349945
|
1955 |
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- type: manhattan_pearson
|
1956 |
+
value: 81.84698799204274
|
1957 |
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- type: manhattan_spearman
|
1958 |
+
value: 81.24269997904105
|
1959 |
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- task:
|
1960 |
+
type: STS
|
1961 |
+
dataset:
|
1962 |
+
type: mteb/sts12-sts
|
1963 |
+
name: MTEB STS12
|
1964 |
+
config: default
|
1965 |
+
split: test
|
1966 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
1967 |
+
metrics:
|
1968 |
+
- type: cos_sim_pearson
|
1969 |
+
value: 86.73241671587446
|
1970 |
+
- type: cos_sim_spearman
|
1971 |
+
value: 79.05091082971826
|
1972 |
+
- type: euclidean_pearson
|
1973 |
+
value: 83.91146869578044
|
1974 |
+
- type: euclidean_spearman
|
1975 |
+
value: 79.87978465370936
|
1976 |
+
- type: manhattan_pearson
|
1977 |
+
value: 83.90888338917678
|
1978 |
+
- type: manhattan_spearman
|
1979 |
+
value: 79.87482848584241
|
1980 |
+
- task:
|
1981 |
+
type: STS
|
1982 |
+
dataset:
|
1983 |
+
type: mteb/sts13-sts
|
1984 |
+
name: MTEB STS13
|
1985 |
+
config: default
|
1986 |
+
split: test
|
1987 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
1988 |
+
metrics:
|
1989 |
+
- type: cos_sim_pearson
|
1990 |
+
value: 85.14970731146177
|
1991 |
+
- type: cos_sim_spearman
|
1992 |
+
value: 86.37363490084627
|
1993 |
+
- type: euclidean_pearson
|
1994 |
+
value: 83.02154218530433
|
1995 |
+
- type: euclidean_spearman
|
1996 |
+
value: 83.80258761957367
|
1997 |
+
- type: manhattan_pearson
|
1998 |
+
value: 83.01664495119347
|
1999 |
+
- type: manhattan_spearman
|
2000 |
+
value: 83.77567458007952
|
2001 |
+
- task:
|
2002 |
+
type: STS
|
2003 |
+
dataset:
|
2004 |
+
type: mteb/sts14-sts
|
2005 |
+
name: MTEB STS14
|
2006 |
+
config: default
|
2007 |
+
split: test
|
2008 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
2009 |
+
metrics:
|
2010 |
+
- type: cos_sim_pearson
|
2011 |
+
value: 83.40474139886784
|
2012 |
+
- type: cos_sim_spearman
|
2013 |
+
value: 82.77768789165984
|
2014 |
+
- type: euclidean_pearson
|
2015 |
+
value: 80.7065877443695
|
2016 |
+
- type: euclidean_spearman
|
2017 |
+
value: 81.375940662505
|
2018 |
+
- type: manhattan_pearson
|
2019 |
+
value: 80.6507552270278
|
2020 |
+
- type: manhattan_spearman
|
2021 |
+
value: 81.32782179098741
|
2022 |
+
- task:
|
2023 |
+
type: STS
|
2024 |
+
dataset:
|
2025 |
+
type: mteb/sts15-sts
|
2026 |
+
name: MTEB STS15
|
2027 |
+
config: default
|
2028 |
+
split: test
|
2029 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
2030 |
+
metrics:
|
2031 |
+
- type: cos_sim_pearson
|
2032 |
+
value: 87.08585968722274
|
2033 |
+
- type: cos_sim_spearman
|
2034 |
+
value: 88.03110031451399
|
2035 |
+
- type: euclidean_pearson
|
2036 |
+
value: 85.74012019602384
|
2037 |
+
- type: euclidean_spearman
|
2038 |
+
value: 86.13592849438209
|
2039 |
+
- type: manhattan_pearson
|
2040 |
+
value: 85.74404842369206
|
2041 |
+
- type: manhattan_spearman
|
2042 |
+
value: 86.14492318960154
|
2043 |
+
- task:
|
2044 |
+
type: STS
|
2045 |
+
dataset:
|
2046 |
+
type: mteb/sts16-sts
|
2047 |
+
name: MTEB STS16
|
2048 |
+
config: default
|
2049 |
+
split: test
|
2050 |
+
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
2051 |
+
metrics:
|
2052 |
+
- type: cos_sim_pearson
|
2053 |
+
value: 84.95069052788875
|
2054 |
+
- type: cos_sim_spearman
|
2055 |
+
value: 86.4867991595147
|
2056 |
+
- type: euclidean_pearson
|
2057 |
+
value: 84.31013325754635
|
2058 |
+
- type: euclidean_spearman
|
2059 |
+
value: 85.01529258006482
|
2060 |
+
- type: manhattan_pearson
|
2061 |
+
value: 84.26995570085374
|
2062 |
+
- type: manhattan_spearman
|
2063 |
+
value: 84.96982104986162
|
2064 |
+
- task:
|
2065 |
+
type: STS
|
2066 |
+
dataset:
|
2067 |
+
type: mteb/sts17-crosslingual-sts
|
2068 |
+
name: MTEB STS17 (en-en)
|
2069 |
+
config: en-en
|
2070 |
+
split: test
|
2071 |
+
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
2072 |
+
metrics:
|
2073 |
+
- type: cos_sim_pearson
|
2074 |
+
value: 87.54617647971897
|
2075 |
+
- type: cos_sim_spearman
|
2076 |
+
value: 87.49834181751034
|
2077 |
+
- type: euclidean_pearson
|
2078 |
+
value: 86.01015322577122
|
2079 |
+
- type: euclidean_spearman
|
2080 |
+
value: 84.63362652063199
|
2081 |
+
- type: manhattan_pearson
|
2082 |
+
value: 86.13807574475706
|
2083 |
+
- type: manhattan_spearman
|
2084 |
+
value: 84.7772370721132
|
2085 |
+
- task:
|
2086 |
+
type: STS
|
2087 |
+
dataset:
|
2088 |
+
type: mteb/sts22-crosslingual-sts
|
2089 |
+
name: MTEB STS22 (en)
|
2090 |
+
config: en
|
2091 |
+
split: test
|
2092 |
+
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
2093 |
+
metrics:
|
2094 |
+
- type: cos_sim_pearson
|
2095 |
+
value: 67.20047755786615
|
2096 |
+
- type: cos_sim_spearman
|
2097 |
+
value: 67.05324077987636
|
2098 |
+
- type: euclidean_pearson
|
2099 |
+
value: 66.91930642976601
|
2100 |
+
- type: euclidean_spearman
|
2101 |
+
value: 65.21491856099105
|
2102 |
+
- type: manhattan_pearson
|
2103 |
+
value: 66.78756851976624
|
2104 |
+
- type: manhattan_spearman
|
2105 |
+
value: 65.12356257740728
|
2106 |
+
- task:
|
2107 |
+
type: STS
|
2108 |
+
dataset:
|
2109 |
+
type: mteb/stsbenchmark-sts
|
2110 |
+
name: MTEB STSBenchmark
|
2111 |
+
config: default
|
2112 |
+
split: test
|
2113 |
+
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
2114 |
+
metrics:
|
2115 |
+
- type: cos_sim_pearson
|
2116 |
+
value: 86.19852871539686
|
2117 |
+
- type: cos_sim_spearman
|
2118 |
+
value: 87.5161895296395
|
2119 |
+
- type: euclidean_pearson
|
2120 |
+
value: 84.59848645207485
|
2121 |
+
- type: euclidean_spearman
|
2122 |
+
value: 85.26427328757919
|
2123 |
+
- type: manhattan_pearson
|
2124 |
+
value: 84.59747366996524
|
2125 |
+
- type: manhattan_spearman
|
2126 |
+
value: 85.24045855146915
|
2127 |
+
- task:
|
2128 |
+
type: Reranking
|
2129 |
+
dataset:
|
2130 |
+
type: mteb/scidocs-reranking
|
2131 |
+
name: MTEB SciDocsRR
|
2132 |
+
config: default
|
2133 |
+
split: test
|
2134 |
+
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
2135 |
+
metrics:
|
2136 |
+
- type: map
|
2137 |
+
value: 87.63320317811032
|
2138 |
+
- type: mrr
|
2139 |
+
value: 96.26242947321379
|
2140 |
+
- task:
|
2141 |
+
type: Retrieval
|
2142 |
+
dataset:
|
2143 |
+
type: scifact
|
2144 |
+
name: MTEB SciFact
|
2145 |
+
config: default
|
2146 |
+
split: test
|
2147 |
+
revision: None
|
2148 |
+
metrics:
|
2149 |
+
- type: map_at_1
|
2150 |
+
value: 60.928000000000004
|
2151 |
+
- type: map_at_10
|
2152 |
+
value: 70.112
|
2153 |
+
- type: map_at_100
|
2154 |
+
value: 70.59299999999999
|
2155 |
+
- type: map_at_1000
|
2156 |
+
value: 70.623
|
2157 |
+
- type: map_at_3
|
2158 |
+
value: 66.846
|
2159 |
+
- type: map_at_5
|
2160 |
+
value: 68.447
|
2161 |
+
- type: mrr_at_1
|
2162 |
+
value: 64.0
|
2163 |
+
- type: mrr_at_10
|
2164 |
+
value: 71.212
|
2165 |
+
- type: mrr_at_100
|
2166 |
+
value: 71.616
|
2167 |
+
- type: mrr_at_1000
|
2168 |
+
value: 71.64500000000001
|
2169 |
+
- type: mrr_at_3
|
2170 |
+
value: 68.77799999999999
|
2171 |
+
- type: mrr_at_5
|
2172 |
+
value: 70.094
|
2173 |
+
- type: ndcg_at_1
|
2174 |
+
value: 64.0
|
2175 |
+
- type: ndcg_at_10
|
2176 |
+
value: 74.607
|
2177 |
+
- type: ndcg_at_100
|
2178 |
+
value: 76.416
|
2179 |
+
- type: ndcg_at_1000
|
2180 |
+
value: 77.102
|
2181 |
+
- type: ndcg_at_3
|
2182 |
+
value: 69.126
|
2183 |
+
- type: ndcg_at_5
|
2184 |
+
value: 71.41300000000001
|
2185 |
+
- type: precision_at_1
|
2186 |
+
value: 64.0
|
2187 |
+
- type: precision_at_10
|
2188 |
+
value: 9.933
|
2189 |
+
- type: precision_at_100
|
2190 |
+
value: 1.077
|
2191 |
+
- type: precision_at_1000
|
2192 |
+
value: 0.11299999999999999
|
2193 |
+
- type: precision_at_3
|
2194 |
+
value: 26.556
|
2195 |
+
- type: precision_at_5
|
2196 |
+
value: 17.467
|
2197 |
+
- type: recall_at_1
|
2198 |
+
value: 60.928000000000004
|
2199 |
+
- type: recall_at_10
|
2200 |
+
value: 87.322
|
2201 |
+
- type: recall_at_100
|
2202 |
+
value: 94.833
|
2203 |
+
- type: recall_at_1000
|
2204 |
+
value: 100.0
|
2205 |
+
- type: recall_at_3
|
2206 |
+
value: 72.628
|
2207 |
+
- type: recall_at_5
|
2208 |
+
value: 78.428
|
2209 |
+
- task:
|
2210 |
+
type: PairClassification
|
2211 |
+
dataset:
|
2212 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
2213 |
+
name: MTEB SprintDuplicateQuestions
|
2214 |
+
config: default
|
2215 |
+
split: test
|
2216 |
+
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
2217 |
+
metrics:
|
2218 |
+
- type: cos_sim_accuracy
|
2219 |
+
value: 99.86237623762376
|
2220 |
+
- type: cos_sim_ap
|
2221 |
+
value: 96.72586477206649
|
2222 |
+
- type: cos_sim_f1
|
2223 |
+
value: 93.01858362631845
|
2224 |
+
- type: cos_sim_precision
|
2225 |
+
value: 93.4409687184662
|
2226 |
+
- type: cos_sim_recall
|
2227 |
+
value: 92.60000000000001
|
2228 |
+
- type: dot_accuracy
|
2229 |
+
value: 99.78019801980199
|
2230 |
+
- type: dot_ap
|
2231 |
+
value: 93.72748205246228
|
2232 |
+
- type: dot_f1
|
2233 |
+
value: 89.04109589041096
|
2234 |
+
- type: dot_precision
|
2235 |
+
value: 87.16475095785441
|
2236 |
+
- type: dot_recall
|
2237 |
+
value: 91.0
|
2238 |
+
- type: euclidean_accuracy
|
2239 |
+
value: 99.85445544554456
|
2240 |
+
- type: euclidean_ap
|
2241 |
+
value: 96.6661459876145
|
2242 |
+
- type: euclidean_f1
|
2243 |
+
value: 92.58337481333997
|
2244 |
+
- type: euclidean_precision
|
2245 |
+
value: 92.17046580773042
|
2246 |
+
- type: euclidean_recall
|
2247 |
+
value: 93.0
|
2248 |
+
- type: manhattan_accuracy
|
2249 |
+
value: 99.85445544554456
|
2250 |
+
- type: manhattan_ap
|
2251 |
+
value: 96.6883549244056
|
2252 |
+
- type: manhattan_f1
|
2253 |
+
value: 92.57598405580468
|
2254 |
+
- type: manhattan_precision
|
2255 |
+
value: 92.25422045680239
|
2256 |
+
- type: manhattan_recall
|
2257 |
+
value: 92.9
|
2258 |
+
- type: max_accuracy
|
2259 |
+
value: 99.86237623762376
|
2260 |
+
- type: max_ap
|
2261 |
+
value: 96.72586477206649
|
2262 |
+
- type: max_f1
|
2263 |
+
value: 93.01858362631845
|
2264 |
+
- task:
|
2265 |
+
type: Clustering
|
2266 |
+
dataset:
|
2267 |
+
type: mteb/stackexchange-clustering
|
2268 |
+
name: MTEB StackExchangeClustering
|
2269 |
+
config: default
|
2270 |
+
split: test
|
2271 |
+
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
2272 |
+
metrics:
|
2273 |
+
- type: v_measure
|
2274 |
+
value: 66.39930057069995
|
2275 |
+
- task:
|
2276 |
+
type: Clustering
|
2277 |
+
dataset:
|
2278 |
+
type: mteb/stackexchange-clustering-p2p
|
2279 |
+
name: MTEB StackExchangeClusteringP2P
|
2280 |
+
config: default
|
2281 |
+
split: test
|
2282 |
+
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
2283 |
+
metrics:
|
2284 |
+
- type: v_measure
|
2285 |
+
value: 34.96398659903402
|
2286 |
+
- task:
|
2287 |
+
type: Reranking
|
2288 |
+
dataset:
|
2289 |
+
type: mteb/stackoverflowdupquestions-reranking
|
2290 |
+
name: MTEB StackOverflowDupQuestions
|
2291 |
+
config: default
|
2292 |
+
split: test
|
2293 |
+
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
2294 |
+
metrics:
|
2295 |
+
- type: map
|
2296 |
+
value: 55.946944700355395
|
2297 |
+
- type: mrr
|
2298 |
+
value: 56.97151398438164
|
2299 |
+
- task:
|
2300 |
+
type: Summarization
|
2301 |
+
dataset:
|
2302 |
+
type: mteb/summeval
|
2303 |
+
name: MTEB SummEval
|
2304 |
+
config: default
|
2305 |
+
split: test
|
2306 |
+
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
2307 |
+
metrics:
|
2308 |
+
- type: cos_sim_pearson
|
2309 |
+
value: 31.541657650692905
|
2310 |
+
- type: cos_sim_spearman
|
2311 |
+
value: 31.605804192286303
|
2312 |
+
- type: dot_pearson
|
2313 |
+
value: 28.26905996736398
|
2314 |
+
- type: dot_spearman
|
2315 |
+
value: 27.864801765851187
|
2316 |
+
- task:
|
2317 |
+
type: Retrieval
|
2318 |
+
dataset:
|
2319 |
+
type: trec-covid
|
2320 |
+
name: MTEB TRECCOVID
|
2321 |
+
config: default
|
2322 |
+
split: test
|
2323 |
+
revision: None
|
2324 |
+
metrics:
|
2325 |
+
- type: map_at_1
|
2326 |
+
value: 0.22599999999999998
|
2327 |
+
- type: map_at_10
|
2328 |
+
value: 1.8870000000000002
|
2329 |
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- type: map_at_100
|
2330 |
+
value: 9.78
|
2331 |
+
- type: map_at_1000
|
2332 |
+
value: 22.514
|
2333 |
+
- type: map_at_3
|
2334 |
+
value: 0.6669999999999999
|
2335 |
+
- type: map_at_5
|
2336 |
+
value: 1.077
|
2337 |
+
- type: mrr_at_1
|
2338 |
+
value: 82.0
|
2339 |
+
- type: mrr_at_10
|
2340 |
+
value: 89.86699999999999
|
2341 |
+
- type: mrr_at_100
|
2342 |
+
value: 89.86699999999999
|
2343 |
+
- type: mrr_at_1000
|
2344 |
+
value: 89.86699999999999
|
2345 |
+
- type: mrr_at_3
|
2346 |
+
value: 89.667
|
2347 |
+
- type: mrr_at_5
|
2348 |
+
value: 89.667
|
2349 |
+
- type: ndcg_at_1
|
2350 |
+
value: 79.0
|
2351 |
+
- type: ndcg_at_10
|
2352 |
+
value: 74.818
|
2353 |
+
- type: ndcg_at_100
|
2354 |
+
value: 53.715999999999994
|
2355 |
+
- type: ndcg_at_1000
|
2356 |
+
value: 47.082
|
2357 |
+
- type: ndcg_at_3
|
2358 |
+
value: 82.134
|
2359 |
+
- type: ndcg_at_5
|
2360 |
+
value: 79.81899999999999
|
2361 |
+
- type: precision_at_1
|
2362 |
+
value: 82.0
|
2363 |
+
- type: precision_at_10
|
2364 |
+
value: 78.0
|
2365 |
+
- type: precision_at_100
|
2366 |
+
value: 54.48
|
2367 |
+
- type: precision_at_1000
|
2368 |
+
value: 20.518
|
2369 |
+
- type: precision_at_3
|
2370 |
+
value: 87.333
|
2371 |
+
- type: precision_at_5
|
2372 |
+
value: 85.2
|
2373 |
+
- type: recall_at_1
|
2374 |
+
value: 0.22599999999999998
|
2375 |
+
- type: recall_at_10
|
2376 |
+
value: 2.072
|
2377 |
+
- type: recall_at_100
|
2378 |
+
value: 13.013
|
2379 |
+
- type: recall_at_1000
|
2380 |
+
value: 43.462
|
2381 |
+
- type: recall_at_3
|
2382 |
+
value: 0.695
|
2383 |
+
- type: recall_at_5
|
2384 |
+
value: 1.139
|
2385 |
+
- task:
|
2386 |
+
type: Retrieval
|
2387 |
+
dataset:
|
2388 |
+
type: webis-touche2020
|
2389 |
+
name: MTEB Touche2020
|
2390 |
+
config: default
|
2391 |
+
split: test
|
2392 |
+
revision: None
|
2393 |
+
metrics:
|
2394 |
+
- type: map_at_1
|
2395 |
+
value: 2.328
|
2396 |
+
- type: map_at_10
|
2397 |
+
value: 9.795
|
2398 |
+
- type: map_at_100
|
2399 |
+
value: 15.801000000000002
|
2400 |
+
- type: map_at_1000
|
2401 |
+
value: 17.23
|
2402 |
+
- type: map_at_3
|
2403 |
+
value: 4.734
|
2404 |
+
- type: map_at_5
|
2405 |
+
value: 6.644
|
2406 |
+
- type: mrr_at_1
|
2407 |
+
value: 30.612000000000002
|
2408 |
+
- type: mrr_at_10
|
2409 |
+
value: 46.902
|
2410 |
+
- type: mrr_at_100
|
2411 |
+
value: 47.495
|
2412 |
+
- type: mrr_at_1000
|
2413 |
+
value: 47.495
|
2414 |
+
- type: mrr_at_3
|
2415 |
+
value: 41.156
|
2416 |
+
- type: mrr_at_5
|
2417 |
+
value: 44.218
|
2418 |
+
- type: ndcg_at_1
|
2419 |
+
value: 28.571
|
2420 |
+
- type: ndcg_at_10
|
2421 |
+
value: 24.806
|
2422 |
+
- type: ndcg_at_100
|
2423 |
+
value: 36.419000000000004
|
2424 |
+
- type: ndcg_at_1000
|
2425 |
+
value: 47.272999999999996
|
2426 |
+
- type: ndcg_at_3
|
2427 |
+
value: 25.666
|
2428 |
+
- type: ndcg_at_5
|
2429 |
+
value: 25.448999999999998
|
2430 |
+
- type: precision_at_1
|
2431 |
+
value: 30.612000000000002
|
2432 |
+
- type: precision_at_10
|
2433 |
+
value: 23.061
|
2434 |
+
- type: precision_at_100
|
2435 |
+
value: 7.714
|
2436 |
+
- type: precision_at_1000
|
2437 |
+
value: 1.484
|
2438 |
+
- type: precision_at_3
|
2439 |
+
value: 26.531
|
2440 |
+
- type: precision_at_5
|
2441 |
+
value: 26.122
|
2442 |
+
- type: recall_at_1
|
2443 |
+
value: 2.328
|
2444 |
+
- type: recall_at_10
|
2445 |
+
value: 16.524
|
2446 |
+
- type: recall_at_100
|
2447 |
+
value: 47.179
|
2448 |
+
- type: recall_at_1000
|
2449 |
+
value: 81.22200000000001
|
2450 |
+
- type: recall_at_3
|
2451 |
+
value: 5.745
|
2452 |
+
- type: recall_at_5
|
2453 |
+
value: 9.339
|
2454 |
+
- task:
|
2455 |
+
type: Classification
|
2456 |
+
dataset:
|
2457 |
+
type: mteb/toxic_conversations_50k
|
2458 |
+
name: MTEB ToxicConversationsClassification
|
2459 |
+
config: default
|
2460 |
+
split: test
|
2461 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
2462 |
+
metrics:
|
2463 |
+
- type: accuracy
|
2464 |
+
value: 70.9142
|
2465 |
+
- type: ap
|
2466 |
+
value: 14.335574772555415
|
2467 |
+
- type: f1
|
2468 |
+
value: 54.62839595194111
|
2469 |
+
- task:
|
2470 |
+
type: Classification
|
2471 |
+
dataset:
|
2472 |
+
type: mteb/tweet_sentiment_extraction
|
2473 |
+
name: MTEB TweetSentimentExtractionClassification
|
2474 |
+
config: default
|
2475 |
+
split: test
|
2476 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
2477 |
+
metrics:
|
2478 |
+
- type: accuracy
|
2479 |
+
value: 59.94340690435768
|
2480 |
+
- type: f1
|
2481 |
+
value: 60.286487936731916
|
2482 |
+
- task:
|
2483 |
+
type: Clustering
|
2484 |
+
dataset:
|
2485 |
+
type: mteb/twentynewsgroups-clustering
|
2486 |
+
name: MTEB TwentyNewsgroupsClustering
|
2487 |
+
config: default
|
2488 |
+
split: test
|
2489 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
2490 |
+
metrics:
|
2491 |
+
- type: v_measure
|
2492 |
+
value: 51.26597708987974
|
2493 |
+
- task:
|
2494 |
+
type: PairClassification
|
2495 |
+
dataset:
|
2496 |
+
type: mteb/twittersemeval2015-pairclassification
|
2497 |
+
name: MTEB TwitterSemEval2015
|
2498 |
+
config: default
|
2499 |
+
split: test
|
2500 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
2501 |
+
metrics:
|
2502 |
+
- type: cos_sim_accuracy
|
2503 |
+
value: 87.48882398521786
|
2504 |
+
- type: cos_sim_ap
|
2505 |
+
value: 79.04326607602204
|
2506 |
+
- type: cos_sim_f1
|
2507 |
+
value: 71.64566826860633
|
2508 |
+
- type: cos_sim_precision
|
2509 |
+
value: 70.55512918905092
|
2510 |
+
- type: cos_sim_recall
|
2511 |
+
value: 72.77044854881267
|
2512 |
+
- type: dot_accuracy
|
2513 |
+
value: 84.19264469213805
|
2514 |
+
- type: dot_ap
|
2515 |
+
value: 67.96360043562528
|
2516 |
+
- type: dot_f1
|
2517 |
+
value: 64.06418393006827
|
2518 |
+
- type: dot_precision
|
2519 |
+
value: 58.64941898706424
|
2520 |
+
- type: dot_recall
|
2521 |
+
value: 70.58047493403694
|
2522 |
+
- type: euclidean_accuracy
|
2523 |
+
value: 87.45902127913214
|
2524 |
+
- type: euclidean_ap
|
2525 |
+
value: 78.9742237648272
|
2526 |
+
- type: euclidean_f1
|
2527 |
+
value: 71.5553235908142
|
2528 |
+
- type: euclidean_precision
|
2529 |
+
value: 70.77955601445535
|
2530 |
+
- type: euclidean_recall
|
2531 |
+
value: 72.34828496042216
|
2532 |
+
- type: manhattan_accuracy
|
2533 |
+
value: 87.41729749061214
|
2534 |
+
- type: manhattan_ap
|
2535 |
+
value: 78.90073137580596
|
2536 |
+
- type: manhattan_f1
|
2537 |
+
value: 71.3942611553533
|
2538 |
+
- type: manhattan_precision
|
2539 |
+
value: 68.52705653967483
|
2540 |
+
- type: manhattan_recall
|
2541 |
+
value: 74.51187335092348
|
2542 |
+
- type: max_accuracy
|
2543 |
+
value: 87.48882398521786
|
2544 |
+
- type: max_ap
|
2545 |
+
value: 79.04326607602204
|
2546 |
+
- type: max_f1
|
2547 |
+
value: 71.64566826860633
|
2548 |
+
- task:
|
2549 |
+
type: PairClassification
|
2550 |
+
dataset:
|
2551 |
+
type: mteb/twitterurlcorpus-pairclassification
|
2552 |
+
name: MTEB TwitterURLCorpus
|
2553 |
+
config: default
|
2554 |
+
split: test
|
2555 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
2556 |
+
metrics:
|
2557 |
+
- type: cos_sim_accuracy
|
2558 |
+
value: 88.68125897465751
|
2559 |
+
- type: cos_sim_ap
|
2560 |
+
value: 85.6003454431979
|
2561 |
+
- type: cos_sim_f1
|
2562 |
+
value: 77.6957163958641
|
2563 |
+
- type: cos_sim_precision
|
2564 |
+
value: 73.0110366307807
|
2565 |
+
- type: cos_sim_recall
|
2566 |
+
value: 83.02279026793964
|
2567 |
+
- type: dot_accuracy
|
2568 |
+
value: 87.7672992587418
|
2569 |
+
- type: dot_ap
|
2570 |
+
value: 82.4971301112899
|
2571 |
+
- type: dot_f1
|
2572 |
+
value: 75.90528233151184
|
2573 |
+
- type: dot_precision
|
2574 |
+
value: 72.0370626469368
|
2575 |
+
- type: dot_recall
|
2576 |
+
value: 80.21250384970742
|
2577 |
+
- type: euclidean_accuracy
|
2578 |
+
value: 88.4503434625684
|
2579 |
+
- type: euclidean_ap
|
2580 |
+
value: 84.91949884748384
|
2581 |
+
- type: euclidean_f1
|
2582 |
+
value: 76.92365018444684
|
2583 |
+
- type: euclidean_precision
|
2584 |
+
value: 74.53245721712759
|
2585 |
+
- type: euclidean_recall
|
2586 |
+
value: 79.47336002463813
|
2587 |
+
- type: manhattan_accuracy
|
2588 |
+
value: 88.47556952691427
|
2589 |
+
- type: manhattan_ap
|
2590 |
+
value: 84.8963689101517
|
2591 |
+
- type: manhattan_f1
|
2592 |
+
value: 76.85901249256395
|
2593 |
+
- type: manhattan_precision
|
2594 |
+
value: 74.31693989071039
|
2595 |
+
- type: manhattan_recall
|
2596 |
+
value: 79.58115183246073
|
2597 |
+
- type: max_accuracy
|
2598 |
+
value: 88.68125897465751
|
2599 |
+
- type: max_ap
|
2600 |
+
value: 85.6003454431979
|
2601 |
+
- type: max_f1
|
2602 |
+
value: 77.6957163958641
|
2603 |
+
license: mit
|
2604 |
+
language:
|
2605 |
+
- en
|
2606 |
+
---
|
2607 |
+
|
2608 |
+
|
2609 |
+
<h1 align="center">FlagEmbedding</h1>
|
2610 |
+
|
2611 |
+
|
2612 |
+
<h4 align="center">
|
2613 |
+
<p>
|
2614 |
+
<a href=#model-list>Model List</a> |
|
2615 |
+
<a href=#frequently-asked-questions>FAQ</a> |
|
2616 |
+
<a href=#usage>Usage</a> |
|
2617 |
+
<a href="#evaluation">Evaluation</a> |
|
2618 |
+
<a href="#train">Train</a> |
|
2619 |
+
<a href="#contact">Contact</a> |
|
2620 |
+
<a href="#citation">Citation</a> |
|
2621 |
+
<a href="#license">License</a>
|
2622 |
+
<p>
|
2623 |
+
</h4>
|
2624 |
+
|
2625 |
+
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
|
2626 |
+
|
2627 |
+
|
2628 |
+
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
|
2629 |
+
|
2630 |
+
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
|
2631 |
+
And it also can be used in vector databases for LLMs.
|
2632 |
+
|
2633 |
+
************* 🌟**Updates**🌟 *************
|
2634 |
+
- 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire:
|
2635 |
+
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
|
2636 |
+
- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
|
2637 |
+
- 09/12/2023: New models:
|
2638 |
+
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
|
2639 |
+
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
|
2640 |
+
|
2641 |
+
|
2642 |
+
<details>
|
2643 |
+
<summary>More</summary>
|
2644 |
+
<!-- ### More -->
|
2645 |
+
|
2646 |
+
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
|
2647 |
+
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
|
2648 |
+
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
|
2649 |
+
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
|
2650 |
+
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
|
2651 |
+
|
2652 |
+
</details>
|
2653 |
+
|
2654 |
+
|
2655 |
+
## Model List
|
2656 |
+
|
2657 |
+
`bge` is short for `BAAI general embedding`.
|
2658 |
+
|
2659 |
+
| Model | Language | | Description | query instruction for retrieval [1] |
|
2660 |
+
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
|
2661 |
+
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
|
2662 |
+
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
|
2663 |
+
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
|
2664 |
+
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
|
2665 |
+
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
|
2666 |
+
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
|
2667 |
+
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` |
|
2668 |
+
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` |
|
2669 |
+
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` |
|
2670 |
+
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
|
2671 |
+
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
|
2672 |
+
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
|
2673 |
+
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章:` |
|
2674 |
+
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章:` |
|
2675 |
+
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章:` |
|
2676 |
+
|
2677 |
+
|
2678 |
+
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
|
2679 |
+
|
2680 |
+
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
|
2681 |
+
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
|
2682 |
+
|
2683 |
+
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
|
2684 |
+
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
|
2685 |
+
|
2686 |
+
|
2687 |
+
## Frequently asked questions
|
2688 |
+
|
2689 |
+
<details>
|
2690 |
+
<summary>1. How to fine-tune bge embedding model?</summary>
|
2691 |
+
|
2692 |
+
<!-- ### How to fine-tune bge embedding model? -->
|
2693 |
+
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
|
2694 |
+
Some suggestions:
|
2695 |
+
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
|
2696 |
+
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
|
2697 |
+
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
|
2698 |
+
|
2699 |
+
|
2700 |
+
</details>
|
2701 |
+
|
2702 |
+
<details>
|
2703 |
+
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
|
2704 |
+
|
2705 |
+
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
|
2706 |
+
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
|
2707 |
+
|
2708 |
+
Since we finetune the models by contrastive learning with a temperature of 0.01,
|
2709 |
+
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
|
2710 |
+
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
|
2711 |
+
|
2712 |
+
For downstream tasks, such as passage retrieval or semantic similarity,
|
2713 |
+
**what matters is the relative order of the scores, not the absolute value.**
|
2714 |
+
If you need to filter similar sentences based on a similarity threshold,
|
2715 |
+
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
|
2716 |
+
|
2717 |
+
</details>
|
2718 |
+
|
2719 |
+
<details>
|
2720 |
+
<summary>3. When does the query instruction need to be used</summary>
|
2721 |
+
|
2722 |
+
<!-- ### When does the query instruction need to be used -->
|
2723 |
+
|
2724 |
+
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
|
2725 |
+
No instruction only has a slight degradation in retrieval performance compared with using instruction.
|
2726 |
+
So you can generate embedding without instruction in all cases for convenience.
|
2727 |
+
|
2728 |
+
For a retrieval task that uses short queries to find long related documents,
|
2729 |
+
it is recommended to add instructions for these short queries.
|
2730 |
+
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
|
2731 |
+
In all cases, the documents/passages do not need to add the instruction.
|
2732 |
+
|
2733 |
+
</details>
|
2734 |
+
|
2735 |
+
|
2736 |
+
## Usage
|
2737 |
+
|
2738 |
+
### Usage for Embedding Model
|
2739 |
+
|
2740 |
+
Here are some examples for using `bge` models with
|
2741 |
+
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
|
2742 |
+
|
2743 |
+
#### Using FlagEmbedding
|
2744 |
+
```
|
2745 |
+
pip install -U FlagEmbedding
|
2746 |
+
```
|
2747 |
+
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
|
2748 |
+
|
2749 |
+
```python
|
2750 |
+
from FlagEmbedding import FlagModel
|
2751 |
+
sentences_1 = ["样例数据-1", "样例数据-2"]
|
2752 |
+
sentences_2 = ["样例数据-3", "样例数据-4"]
|
2753 |
+
model = FlagModel('BAAI/bge-large-zh-v1.5',
|
2754 |
+
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章:",
|
2755 |
+
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
|
2756 |
+
embeddings_1 = model.encode(sentences_1)
|
2757 |
+
embeddings_2 = model.encode(sentences_2)
|
2758 |
+
similarity = embeddings_1 @ embeddings_2.T
|
2759 |
+
print(similarity)
|
2760 |
+
|
2761 |
+
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
|
2762 |
+
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
|
2763 |
+
queries = ['query_1', 'query_2']
|
2764 |
+
passages = ["样例文档-1", "样例文档-2"]
|
2765 |
+
q_embeddings = model.encode_queries(queries)
|
2766 |
+
p_embeddings = model.encode(passages)
|
2767 |
+
scores = q_embeddings @ p_embeddings.T
|
2768 |
+
```
|
2769 |
+
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
|
2770 |
+
|
2771 |
+
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
|
2772 |
+
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
|
2773 |
+
|
2774 |
+
|
2775 |
+
#### Using Sentence-Transformers
|
2776 |
+
|
2777 |
+
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
|
2778 |
+
|
2779 |
+
```
|
2780 |
+
pip install -U sentence-transformers
|
2781 |
+
```
|
2782 |
+
```python
|
2783 |
+
from sentence_transformers import SentenceTransformer
|
2784 |
+
sentences_1 = ["样例数据-1", "样例数据-2"]
|
2785 |
+
sentences_2 = ["样例数据-3", "样例数据-4"]
|
2786 |
+
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
|
2787 |
+
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
|
2788 |
+
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
|
2789 |
+
similarity = embeddings_1 @ embeddings_2.T
|
2790 |
+
print(similarity)
|
2791 |
+
```
|
2792 |
+
For s2p(short query to long passage) retrieval task,
|
2793 |
+
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
|
2794 |
+
But the instruction is not needed for passages.
|
2795 |
+
```python
|
2796 |
+
from sentence_transformers import SentenceTransformer
|
2797 |
+
queries = ['query_1', 'query_2']
|
2798 |
+
passages = ["样例文档-1", "样例文档-2"]
|
2799 |
+
instruction = "为这个句子生成表示以用于检索相关文章:"
|
2800 |
+
|
2801 |
+
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
|
2802 |
+
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
|
2803 |
+
p_embeddings = model.encode(passages, normalize_embeddings=True)
|
2804 |
+
scores = q_embeddings @ p_embeddings.T
|
2805 |
+
```
|
2806 |
+
|
2807 |
+
#### Using Langchain
|
2808 |
+
|
2809 |
+
You can use `bge` in langchain like this:
|
2810 |
+
```python
|
2811 |
+
from langchain.embeddings import HuggingFaceBgeEmbeddings
|
2812 |
+
model_name = "BAAI/bge-large-en-v1.5"
|
2813 |
+
model_kwargs = {'device': 'cuda'}
|
2814 |
+
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
|
2815 |
+
model = HuggingFaceBgeEmbeddings(
|
2816 |
+
model_name=model_name,
|
2817 |
+
model_kwargs=model_kwargs,
|
2818 |
+
encode_kwargs=encode_kwargs,
|
2819 |
+
query_instruction="为这个句子生成表示以用于检索相关文章:"
|
2820 |
+
)
|
2821 |
+
model.query_instruction = "为这个句子生成表示以用于检索相关文章:"
|
2822 |
+
```
|
2823 |
+
|
2824 |
+
|
2825 |
+
#### Using HuggingFace Transformers
|
2826 |
+
|
2827 |
+
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
|
2828 |
+
|
2829 |
+
```python
|
2830 |
+
from transformers import AutoTokenizer, AutoModel
|
2831 |
+
import torch
|
2832 |
+
# Sentences we want sentence embeddings for
|
2833 |
+
sentences = ["样例数据-1", "样例数据-2"]
|
2834 |
+
|
2835 |
+
# Load model from HuggingFace Hub
|
2836 |
+
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
|
2837 |
+
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
|
2838 |
+
model.eval()
|
2839 |
+
|
2840 |
+
# Tokenize sentences
|
2841 |
+
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
2842 |
+
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
|
2843 |
+
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
|
2844 |
+
|
2845 |
+
# Compute token embeddings
|
2846 |
+
with torch.no_grad():
|
2847 |
+
model_output = model(**encoded_input)
|
2848 |
+
# Perform pooling. In this case, cls pooling.
|
2849 |
+
sentence_embeddings = model_output[0][:, 0]
|
2850 |
+
# normalize embeddings
|
2851 |
+
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
|
2852 |
+
print("Sentence embeddings:", sentence_embeddings)
|
2853 |
+
```
|
2854 |
+
|
2855 |
+
### Usage for Reranker
|
2856 |
+
|
2857 |
+
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
|
2858 |
+
You can get a relevance score by inputting query and passage to the reranker.
|
2859 |
+
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
|
2860 |
+
|
2861 |
+
|
2862 |
+
#### Using FlagEmbedding
|
2863 |
+
```
|
2864 |
+
pip install -U FlagEmbedding
|
2865 |
+
```
|
2866 |
+
|
2867 |
+
Get relevance scores (higher scores indicate more relevance):
|
2868 |
+
```python
|
2869 |
+
from FlagEmbedding import FlagReranker
|
2870 |
+
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
|
2871 |
+
|
2872 |
+
score = reranker.compute_score(['query', 'passage'])
|
2873 |
+
print(score)
|
2874 |
+
|
2875 |
+
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
|
2876 |
+
print(scores)
|
2877 |
+
```
|
2878 |
+
|
2879 |
+
|
2880 |
+
#### Using Huggingface transformers
|
2881 |
+
|
2882 |
+
```python
|
2883 |
+
import torch
|
2884 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
2885 |
+
|
2886 |
+
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
|
2887 |
+
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
|
2888 |
+
model.eval()
|
2889 |
+
|
2890 |
+
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
|
2891 |
+
with torch.no_grad():
|
2892 |
+
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
|
2893 |
+
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
|
2894 |
+
print(scores)
|
2895 |
+
```
|
2896 |
+
|
2897 |
+
## Evaluation
|
2898 |
+
|
2899 |
+
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
|
2900 |
+
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
|
2901 |
+
|
2902 |
+
- **MTEB**:
|
2903 |
+
|
2904 |
+
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|
2905 |
+
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
2906 |
+
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
|
2907 |
+
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
|
2908 |
+
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
|
2909 |
+
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
|
2910 |
+
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
|
2911 |
+
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
|
2912 |
+
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
|
2913 |
+
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
|
2914 |
+
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
|
2915 |
+
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
|
2916 |
+
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
|
2917 |
+
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
|
2918 |
+
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
|
2919 |
+
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
|
2920 |
+
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
|
2921 |
+
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
|
2922 |
+
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
|
2923 |
+
|
2924 |
+
|
2925 |
+
|
2926 |
+
- **C-MTEB**:
|
2927 |
+
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
|
2928 |
+
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
|
2929 |
+
|
2930 |
+
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|
2931 |
+
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
|
2932 |
+
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
|
2933 |
+
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
|
2934 |
+
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
|
2935 |
+
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
|
2936 |
+
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
|
2937 |
+
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
|
2938 |
+
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
|
2939 |
+
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
|
2940 |
+
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
|
2941 |
+
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
|
2942 |
+
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
|
2943 |
+
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
|
2944 |
+
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
|
2945 |
+
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
|
2946 |
+
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
|
2947 |
+
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
|
2948 |
+
|
2949 |
+
|
2950 |
+
- **Reranking**:
|
2951 |
+
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
|
2952 |
+
|
2953 |
+
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|
2954 |
+
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
|
2955 |
+
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
|
2956 |
+
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
|
2957 |
+
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
|
2958 |
+
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
|
2959 |
+
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
|
2960 |
+
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
|
2961 |
+
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
|
2962 |
+
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
|
2963 |
+
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
|
2964 |
+
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
|
2965 |
+
|
2966 |
+
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
|
2967 |
+
|
2968 |
+
## Train
|
2969 |
+
|
2970 |
+
### BAAI Embedding
|
2971 |
+
|
2972 |
+
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
|
2973 |
+
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
|
2974 |
+
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
|
2975 |
+
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
|
2976 |
+
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
|
2977 |
+
|
2978 |
+
|
2979 |
+
|
2980 |
+
### BGE Reranker
|
2981 |
+
|
2982 |
+
Cross-encoder will perform full-attention over the input pair,
|
2983 |
+
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
|
2984 |
+
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
|
2985 |
+
We train the cross-encoder on a multilingual pair data,
|
2986 |
+
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
|
2987 |
+
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
|
2988 |
+
|
2989 |
+
|
2990 |
+
## Contact
|
2991 |
+
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
|
2992 |
+
You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn).
|
2993 |
+
|
2994 |
+
|
2995 |
+
## Citation
|
2996 |
+
|
2997 |
+
If you find this repository useful, please consider giving a star :star: and citation
|
2998 |
+
|
2999 |
+
```
|
3000 |
+
@misc{bge_embedding,
|
3001 |
+
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
|
3002 |
+
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
|
3003 |
+
year={2023},
|
3004 |
+
eprint={2309.07597},
|
3005 |
+
archivePrefix={arXiv},
|
3006 |
+
primaryClass={cs.CL}
|
3007 |
+
}
|
3008 |
+
```
|
3009 |
+
|
3010 |
+
## License
|
3011 |
+
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
|
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local_models/embeddings-bge-large/special_tokens_map.json
ADDED
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local_models/embeddings-bge-large/tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
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|
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|
local_models/embeddings-bge-large/vocab.txt
ADDED
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See raw diff
|
|
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