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2
  pipeline_tag: sentence-similarity
3
  tags:
4
  - sentence-transformers
5
  - feature-extraction
6
  - sentence-similarity
7
  - transformers
 
8
 
9
  ---
10
 
11
- # {MODEL_NAME}
12
 
13
- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
14
 
15
  <!--- Describe your model here -->
16
 
 
1
  ---
2
+ model-index:
3
+ - name: gte_tiny
4
+ results:
5
+ - task:
6
+ type: Classification
7
+ dataset:
8
+ type: mteb/amazon_counterfactual
9
+ name: MTEB AmazonCounterfactualClassification (en)
10
+ config: en
11
+ split: test
12
+ revision: e8379541af4e31359cca9fbcf4b00f2671dba205
13
+ metrics:
14
+ - type: accuracy
15
+ value: 71.76119402985076
16
+ - type: ap
17
+ value: 34.63659287952359
18
+ - type: f1
19
+ value: 65.88939512571113
20
+ - task:
21
+ type: Classification
22
+ dataset:
23
+ type: mteb/amazon_polarity
24
+ name: MTEB AmazonPolarityClassification
25
+ config: default
26
+ split: test
27
+ revision: e2d317d38cd51312af73b3d32a06d1a08b442046
28
+ metrics:
29
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30
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31
+ - type: ap
32
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33
+ - type: f1
34
+ value: 86.5863470912001
35
+ - task:
36
+ type: Classification
37
+ dataset:
38
+ type: mteb/amazon_reviews_multi
39
+ name: MTEB AmazonReviewsClassification (en)
40
+ config: en
41
+ split: test
42
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
43
+ metrics:
44
+ - type: accuracy
45
+ value: 42.61000000000001
46
+ - type: f1
47
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48
+ - task:
49
+ type: Retrieval
50
+ dataset:
51
+ type: arguana
52
+ name: MTEB ArguAna
53
+ config: default
54
+ split: test
55
+ revision: None
56
+ metrics:
57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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69
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71
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74
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89
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95
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99
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100
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101
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105
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107
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109
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110
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111
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113
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114
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115
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116
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117
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118
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119
+ dataset:
120
+ type: mteb/arxiv-clustering-p2p
121
+ name: MTEB ArxivClusteringP2P
122
+ config: default
123
+ split: test
124
+ revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
125
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126
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127
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128
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129
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130
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131
+ type: mteb/arxiv-clustering-s2s
132
+ name: MTEB ArxivClusteringS2S
133
+ config: default
134
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135
+ revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
136
+ metrics:
137
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138
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139
+ - task:
140
+ type: Reranking
141
+ dataset:
142
+ type: mteb/askubuntudupquestions-reranking
143
+ name: MTEB AskUbuntuDupQuestions
144
+ config: default
145
+ split: test
146
+ revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
147
+ metrics:
148
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149
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150
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151
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152
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153
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154
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155
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156
+ name: MTEB BIOSSES
157
+ config: default
158
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159
+ revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
160
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161
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162
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171
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172
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174
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175
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176
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177
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178
+ config: default
179
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180
+ revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
181
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182
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183
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184
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185
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186
+ - task:
187
+ type: Clustering
188
+ dataset:
189
+ type: mteb/biorxiv-clustering-p2p
190
+ name: MTEB BiorxivClusteringP2P
191
+ config: default
192
+ split: test
193
+ revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
194
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195
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196
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197
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198
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199
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200
+ type: mteb/biorxiv-clustering-s2s
201
+ name: MTEB BiorxivClusteringS2S
202
+ config: default
203
+ split: test
204
+ revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
205
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206
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207
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208
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209
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210
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211
+ type: BeIR/cqadupstack
212
+ name: MTEB CQADupstackAndroidRetrieval
213
+ config: default
214
+ split: test
215
+ revision: None
216
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217
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218
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280
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282
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284
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2198
+ value: 99.0
2199
+ - type: recall_at_3
2200
+ value: 66.717
2201
+ - type: recall_at_5
2202
+ value: 74.17200000000001
2203
+ - task:
2204
+ type: PairClassification
2205
+ dataset:
2206
+ type: mteb/sprintduplicatequestions-pairclassification
2207
+ name: MTEB SprintDuplicateQuestions
2208
+ config: default
2209
+ split: test
2210
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2211
+ metrics:
2212
+ - type: cos_sim_accuracy
2213
+ value: 99.82475247524752
2214
+ - type: cos_sim_ap
2215
+ value: 95.4781199603258
2216
+ - type: cos_sim_f1
2217
+ value: 91.16186693147964
2218
+ - type: cos_sim_precision
2219
+ value: 90.53254437869822
2220
+ - type: cos_sim_recall
2221
+ value: 91.8
2222
+ - type: dot_accuracy
2223
+ value: 99.75049504950495
2224
+ - type: dot_ap
2225
+ value: 93.05183539809457
2226
+ - type: dot_f1
2227
+ value: 87.31117824773412
2228
+ - type: dot_precision
2229
+ value: 87.93103448275862
2230
+ - type: dot_recall
2231
+ value: 86.7
2232
+ - type: euclidean_accuracy
2233
+ value: 99.82475247524752
2234
+ - type: euclidean_ap
2235
+ value: 95.38547978154382
2236
+ - type: euclidean_f1
2237
+ value: 91.16325511732403
2238
+ - type: euclidean_precision
2239
+ value: 91.02691924227318
2240
+ - type: euclidean_recall
2241
+ value: 91.3
2242
+ - type: manhattan_accuracy
2243
+ value: 99.82574257425742
2244
+ - type: manhattan_ap
2245
+ value: 95.47237521890308
2246
+ - type: manhattan_f1
2247
+ value: 91.27849355797821
2248
+ - type: manhattan_precision
2249
+ value: 90.47151277013754
2250
+ - type: manhattan_recall
2251
+ value: 92.10000000000001
2252
+ - type: max_accuracy
2253
+ value: 99.82574257425742
2254
+ - type: max_ap
2255
+ value: 95.4781199603258
2256
+ - type: max_f1
2257
+ value: 91.27849355797821
2258
+ - task:
2259
+ type: Clustering
2260
+ dataset:
2261
+ type: mteb/stackexchange-clustering
2262
+ name: MTEB StackExchangeClustering
2263
+ config: default
2264
+ split: test
2265
+ revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
2266
+ metrics:
2267
+ - type: v_measure
2268
+ value: 57.542169376331245
2269
+ - task:
2270
+ type: Clustering
2271
+ dataset:
2272
+ type: mteb/stackexchange-clustering-p2p
2273
+ name: MTEB StackExchangeClusteringP2P
2274
+ config: default
2275
+ split: test
2276
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2277
+ metrics:
2278
+ - type: v_measure
2279
+ value: 35.74399302634387
2280
+ - task:
2281
+ type: Reranking
2282
+ dataset:
2283
+ type: mteb/stackoverflowdupquestions-reranking
2284
+ name: MTEB StackOverflowDupQuestions
2285
+ config: default
2286
+ split: test
2287
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2288
+ metrics:
2289
+ - type: map
2290
+ value: 49.65076347632749
2291
+ - type: mrr
2292
+ value: 50.418099057804945
2293
+ - task:
2294
+ type: Retrieval
2295
+ dataset:
2296
+ type: trec-covid
2297
+ name: MTEB TRECCOVID
2298
+ config: default
2299
+ split: test
2300
+ revision: None
2301
+ metrics:
2302
+ - type: map_at_1
2303
+ value: 0.208
2304
+ - type: map_at_10
2305
+ value: 1.434
2306
+ - type: map_at_100
2307
+ value: 7.829
2308
+ - type: map_at_1000
2309
+ value: 19.807
2310
+ - type: map_at_3
2311
+ value: 0.549
2312
+ - type: map_at_5
2313
+ value: 0.8330000000000001
2314
+ - type: mrr_at_1
2315
+ value: 78.0
2316
+ - type: mrr_at_10
2317
+ value: 85.35199999999999
2318
+ - type: mrr_at_100
2319
+ value: 85.673
2320
+ - type: mrr_at_1000
2321
+ value: 85.673
2322
+ - type: mrr_at_3
2323
+ value: 84.667
2324
+ - type: mrr_at_5
2325
+ value: 85.06700000000001
2326
+ - type: ndcg_at_1
2327
+ value: 72.0
2328
+ - type: ndcg_at_10
2329
+ value: 59.214999999999996
2330
+ - type: ndcg_at_100
2331
+ value: 44.681
2332
+ - type: ndcg_at_1000
2333
+ value: 43.035000000000004
2334
+ - type: ndcg_at_3
2335
+ value: 66.53099999999999
2336
+ - type: ndcg_at_5
2337
+ value: 63.23
2338
+ - type: precision_at_1
2339
+ value: 78.0
2340
+ - type: precision_at_10
2341
+ value: 62.4
2342
+ - type: precision_at_100
2343
+ value: 45.76
2344
+ - type: precision_at_1000
2345
+ value: 19.05
2346
+ - type: precision_at_3
2347
+ value: 71.333
2348
+ - type: precision_at_5
2349
+ value: 67.2
2350
+ - type: recall_at_1
2351
+ value: 0.208
2352
+ - type: recall_at_10
2353
+ value: 1.6580000000000001
2354
+ - type: recall_at_100
2355
+ value: 11.324
2356
+ - type: recall_at_1000
2357
+ value: 41.537
2358
+ - type: recall_at_3
2359
+ value: 0.579
2360
+ - type: recall_at_5
2361
+ value: 0.8959999999999999
2362
+ - task:
2363
+ type: Retrieval
2364
+ dataset:
2365
+ type: webis-touche2020
2366
+ name: MTEB Touche2020
2367
+ config: default
2368
+ split: test
2369
+ revision: None
2370
+ metrics:
2371
+ - type: map_at_1
2372
+ value: 2.442
2373
+ - type: map_at_10
2374
+ value: 8.863
2375
+ - type: map_at_100
2376
+ value: 14.606
2377
+ - type: map_at_1000
2378
+ value: 16.258
2379
+ - type: map_at_3
2380
+ value: 4.396
2381
+ - type: map_at_5
2382
+ value: 6.199000000000001
2383
+ - type: mrr_at_1
2384
+ value: 30.612000000000002
2385
+ - type: mrr_at_10
2386
+ value: 43.492
2387
+ - type: mrr_at_100
2388
+ value: 44.557
2389
+ - type: mrr_at_1000
2390
+ value: 44.557
2391
+ - type: mrr_at_3
2392
+ value: 40.816
2393
+ - type: mrr_at_5
2394
+ value: 42.143
2395
+ - type: ndcg_at_1
2396
+ value: 25.509999999999998
2397
+ - type: ndcg_at_10
2398
+ value: 22.076
2399
+ - type: ndcg_at_100
2400
+ value: 34.098
2401
+ - type: ndcg_at_1000
2402
+ value: 46.265
2403
+ - type: ndcg_at_3
2404
+ value: 24.19
2405
+ - type: ndcg_at_5
2406
+ value: 23.474
2407
+ - type: precision_at_1
2408
+ value: 30.612000000000002
2409
+ - type: precision_at_10
2410
+ value: 19.796
2411
+ - type: precision_at_100
2412
+ value: 7.286
2413
+ - type: precision_at_1000
2414
+ value: 1.5310000000000001
2415
+ - type: precision_at_3
2416
+ value: 25.85
2417
+ - type: precision_at_5
2418
+ value: 24.490000000000002
2419
+ - type: recall_at_1
2420
+ value: 2.442
2421
+ - type: recall_at_10
2422
+ value: 15.012
2423
+ - type: recall_at_100
2424
+ value: 45.865
2425
+ - type: recall_at_1000
2426
+ value: 82.958
2427
+ - type: recall_at_3
2428
+ value: 5.731
2429
+ - type: recall_at_5
2430
+ value: 9.301
2431
+ - task:
2432
+ type: Classification
2433
+ dataset:
2434
+ type: mteb/toxic_conversations_50k
2435
+ name: MTEB ToxicConversationsClassification
2436
+ config: default
2437
+ split: test
2438
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2439
+ metrics:
2440
+ - type: accuracy
2441
+ value: 70.974
2442
+ - type: ap
2443
+ value: 14.534996211286682
2444
+ - type: f1
2445
+ value: 54.785946183399005
2446
+ - task:
2447
+ type: Classification
2448
+ dataset:
2449
+ type: mteb/tweet_sentiment_extraction
2450
+ name: MTEB TweetSentimentExtractionClassification
2451
+ config: default
2452
+ split: test
2453
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2454
+ metrics:
2455
+ - type: accuracy
2456
+ value: 58.56819468024901
2457
+ - type: f1
2458
+ value: 58.92391487111204
2459
+ - task:
2460
+ type: Clustering
2461
+ dataset:
2462
+ type: mteb/twentynewsgroups-clustering
2463
+ name: MTEB TwentyNewsgroupsClustering
2464
+ config: default
2465
+ split: test
2466
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2467
+ metrics:
2468
+ - type: v_measure
2469
+ value: 43.273202335218194
2470
+ - task:
2471
+ type: PairClassification
2472
+ dataset:
2473
+ type: mteb/twittersemeval2015-pairclassification
2474
+ name: MTEB TwitterSemEval2015
2475
+ config: default
2476
+ split: test
2477
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2478
+ metrics:
2479
+ - type: cos_sim_accuracy
2480
+ value: 84.37742146986946
2481
+ - type: cos_sim_ap
2482
+ value: 68.1684129575579
2483
+ - type: cos_sim_f1
2484
+ value: 64.93475108748189
2485
+ - type: cos_sim_precision
2486
+ value: 59.89745876058849
2487
+ - type: cos_sim_recall
2488
+ value: 70.89709762532982
2489
+ - type: dot_accuracy
2490
+ value: 80.49710913750968
2491
+ - type: dot_ap
2492
+ value: 54.699790073944186
2493
+ - type: dot_f1
2494
+ value: 54.45130013221684
2495
+ - type: dot_precision
2496
+ value: 46.74612183125236
2497
+ - type: dot_recall
2498
+ value: 65.19788918205805
2499
+ - type: euclidean_accuracy
2500
+ value: 84.5085533766466
2501
+ - type: euclidean_ap
2502
+ value: 68.38835695236224
2503
+ - type: euclidean_f1
2504
+ value: 65.3391121002694
2505
+ - type: euclidean_precision
2506
+ value: 58.75289656625237
2507
+ - type: euclidean_recall
2508
+ value: 73.58839050131925
2509
+ - type: manhattan_accuracy
2510
+ value: 84.40126363473803
2511
+ - type: manhattan_ap
2512
+ value: 68.09539181555348
2513
+ - type: manhattan_f1
2514
+ value: 64.99028182701653
2515
+ - type: manhattan_precision
2516
+ value: 60.22062134173795
2517
+ - type: manhattan_recall
2518
+ value: 70.58047493403694
2519
+ - type: max_accuracy
2520
+ value: 84.5085533766466
2521
+ - type: max_ap
2522
+ value: 68.38835695236224
2523
+ - type: max_f1
2524
+ value: 65.3391121002694
2525
+ - task:
2526
+ type: PairClassification
2527
+ dataset:
2528
+ type: mteb/twitterurlcorpus-pairclassification
2529
+ name: MTEB TwitterURLCorpus
2530
+ config: default
2531
+ split: test
2532
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2533
+ metrics:
2534
+ - type: cos_sim_accuracy
2535
+ value: 88.34167733923235
2536
+ - type: cos_sim_ap
2537
+ value: 84.84136381147736
2538
+ - type: cos_sim_f1
2539
+ value: 77.01434980904001
2540
+ - type: cos_sim_precision
2541
+ value: 74.27937915742794
2542
+ - type: cos_sim_recall
2543
+ value: 79.95842315983985
2544
+ - type: dot_accuracy
2545
+ value: 85.06422944075756
2546
+ - type: dot_ap
2547
+ value: 76.49446747522325
2548
+ - type: dot_f1
2549
+ value: 71.11606520830432
2550
+ - type: dot_precision
2551
+ value: 64.93638676844785
2552
+ - type: dot_recall
2553
+ value: 78.59562673236834
2554
+ - type: euclidean_accuracy
2555
+ value: 88.45810532852097
2556
+ - type: euclidean_ap
2557
+ value: 84.91526721863501
2558
+ - type: euclidean_f1
2559
+ value: 77.04399001750662
2560
+ - type: euclidean_precision
2561
+ value: 74.62298867162133
2562
+ - type: euclidean_recall
2563
+ value: 79.62734832152756
2564
+ - type: manhattan_accuracy
2565
+ value: 88.46004579500912
2566
+ - type: manhattan_ap
2567
+ value: 84.81590026238194
2568
+ - type: manhattan_f1
2569
+ value: 76.97804626491822
2570
+ - type: manhattan_precision
2571
+ value: 73.79237288135593
2572
+ - type: manhattan_recall
2573
+ value: 80.45118570988605
2574
+ - type: max_accuracy
2575
+ value: 88.46004579500912
2576
+ - type: max_ap
2577
+ value: 84.91526721863501
2578
+ - type: max_f1
2579
+ value: 77.04399001750662
2580
+
2581
  pipeline_tag: sentence-similarity
2582
  tags:
2583
  - sentence-transformers
2584
  - feature-extraction
2585
  - sentence-similarity
2586
  - transformers
2587
+ - mteb
2588
 
2589
  ---
2590
 
2591
+ # {gte-tiny}
2592
 
2593
+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
2594
+ It is distilled from `thenlper/gte-small`, with comparable (slightly worse) performance at around half the size.
2595
 
2596
  <!--- Describe your model here -->
2597