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558
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2208
+ type: mteb/sprintduplicatequestions-pairclassification
2209
+ name: MTEB SprintDuplicateQuestions
2210
+ config: default
2211
+ split: test
2212
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2213
+ metrics:
2214
+ - type: cos_sim_accuracy
2215
+ value: 99.82178217821782
2216
+ - type: cos_sim_ap
2217
+ value: 95.71282508220798
2218
+ - type: cos_sim_f1
2219
+ value: 90.73120494335737
2220
+ - type: cos_sim_precision
2221
+ value: 93.52441613588111
2222
+ - type: cos_sim_recall
2223
+ value: 88.1
2224
+ - type: dot_accuracy
2225
+ value: 99.73960396039604
2226
+ - type: dot_ap
2227
+ value: 92.98534606529098
2228
+ - type: dot_f1
2229
+ value: 86.83024536805209
2230
+ - type: dot_precision
2231
+ value: 86.96088264794383
2232
+ - type: dot_recall
2233
+ value: 86.7
2234
+ - type: euclidean_accuracy
2235
+ value: 99.82475247524752
2236
+ - type: euclidean_ap
2237
+ value: 95.72927039014849
2238
+ - type: euclidean_f1
2239
+ value: 90.89974293059126
2240
+ - type: euclidean_precision
2241
+ value: 93.54497354497354
2242
+ - type: euclidean_recall
2243
+ value: 88.4
2244
+ - type: manhattan_accuracy
2245
+ value: 99.82574257425742
2246
+ - type: manhattan_ap
2247
+ value: 95.72142177390405
2248
+ - type: manhattan_f1
2249
+ value: 91.00152516522625
2250
+ - type: manhattan_precision
2251
+ value: 92.55429162357808
2252
+ - type: manhattan_recall
2253
+ value: 89.5
2254
+ - type: max_accuracy
2255
+ value: 99.82574257425742
2256
+ - type: max_ap
2257
+ value: 95.72927039014849
2258
+ - type: max_f1
2259
+ value: 91.00152516522625
2260
+ - task:
2261
+ type: Clustering
2262
+ dataset:
2263
+ type: mteb/stackexchange-clustering
2264
+ name: MTEB StackExchangeClustering
2265
+ config: default
2266
+ split: test
2267
+ revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
2268
+ metrics:
2269
+ - type: v_measure
2270
+ value: 66.63957663468679
2271
+ - task:
2272
+ type: Clustering
2273
+ dataset:
2274
+ type: mteb/stackexchange-clustering-p2p
2275
+ name: MTEB StackExchangeClusteringP2P
2276
+ config: default
2277
+ split: test
2278
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2279
+ metrics:
2280
+ - type: v_measure
2281
+ value: 36.003307257923964
2282
+ - task:
2283
+ type: Reranking
2284
+ dataset:
2285
+ type: mteb/stackoverflowdupquestions-reranking
2286
+ name: MTEB StackOverflowDupQuestions
2287
+ config: default
2288
+ split: test
2289
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2290
+ metrics:
2291
+ - type: map
2292
+ value: 53.005825525863905
2293
+ - type: mrr
2294
+ value: 53.854683919022165
2295
+ - task:
2296
+ type: Summarization
2297
+ dataset:
2298
+ type: mteb/summeval
2299
+ name: MTEB SummEval
2300
+ config: default
2301
+ split: test
2302
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2303
+ metrics:
2304
+ - type: cos_sim_pearson
2305
+ value: 30.503611569974098
2306
+ - type: cos_sim_spearman
2307
+ value: 31.17155564248449
2308
+ - type: dot_pearson
2309
+ value: 26.740428413981306
2310
+ - type: dot_spearman
2311
+ value: 26.55727635469746
2312
+ - task:
2313
+ type: Retrieval
2314
+ dataset:
2315
+ type: trec-covid
2316
+ name: MTEB TRECCOVID
2317
+ config: default
2318
+ split: test
2319
+ revision: None
2320
+ metrics:
2321
+ - type: map_at_1
2322
+ value: 0.23600000000000002
2323
+ - type: map_at_10
2324
+ value: 1.7670000000000001
2325
+ - type: map_at_100
2326
+ value: 10.208
2327
+ - type: map_at_1000
2328
+ value: 25.997999999999998
2329
+ - type: map_at_3
2330
+ value: 0.605
2331
+ - type: map_at_5
2332
+ value: 0.9560000000000001
2333
+ - type: mrr_at_1
2334
+ value: 84.0
2335
+ - type: mrr_at_10
2336
+ value: 90.167
2337
+ - type: mrr_at_100
2338
+ value: 90.167
2339
+ - type: mrr_at_1000
2340
+ value: 90.167
2341
+ - type: mrr_at_3
2342
+ value: 89.667
2343
+ - type: mrr_at_5
2344
+ value: 90.167
2345
+ - type: ndcg_at_1
2346
+ value: 77.0
2347
+ - type: ndcg_at_10
2348
+ value: 68.783
2349
+ - type: ndcg_at_100
2350
+ value: 54.196
2351
+ - type: ndcg_at_1000
2352
+ value: 52.077
2353
+ - type: ndcg_at_3
2354
+ value: 71.642
2355
+ - type: ndcg_at_5
2356
+ value: 70.45700000000001
2357
+ - type: precision_at_1
2358
+ value: 84.0
2359
+ - type: precision_at_10
2360
+ value: 73.0
2361
+ - type: precision_at_100
2362
+ value: 55.48
2363
+ - type: precision_at_1000
2364
+ value: 23.102
2365
+ - type: precision_at_3
2366
+ value: 76.0
2367
+ - type: precision_at_5
2368
+ value: 74.8
2369
+ - type: recall_at_1
2370
+ value: 0.23600000000000002
2371
+ - type: recall_at_10
2372
+ value: 1.9869999999999999
2373
+ - type: recall_at_100
2374
+ value: 13.749
2375
+ - type: recall_at_1000
2376
+ value: 50.157
2377
+ - type: recall_at_3
2378
+ value: 0.633
2379
+ - type: recall_at_5
2380
+ value: 1.0290000000000001
2381
+ - task:
2382
+ type: Retrieval
2383
+ dataset:
2384
+ type: webis-touche2020
2385
+ name: MTEB Touche2020
2386
+ config: default
2387
+ split: test
2388
+ revision: None
2389
+ metrics:
2390
+ - type: map_at_1
2391
+ value: 1.437
2392
+ - type: map_at_10
2393
+ value: 8.791
2394
+ - type: map_at_100
2395
+ value: 15.001999999999999
2396
+ - type: map_at_1000
2397
+ value: 16.549
2398
+ - type: map_at_3
2399
+ value: 3.8080000000000003
2400
+ - type: map_at_5
2401
+ value: 5.632000000000001
2402
+ - type: mrr_at_1
2403
+ value: 20.408
2404
+ - type: mrr_at_10
2405
+ value: 36.96
2406
+ - type: mrr_at_100
2407
+ value: 37.912
2408
+ - type: mrr_at_1000
2409
+ value: 37.912
2410
+ - type: mrr_at_3
2411
+ value: 29.592000000000002
2412
+ - type: mrr_at_5
2413
+ value: 34.489999999999995
2414
+ - type: ndcg_at_1
2415
+ value: 19.387999999999998
2416
+ - type: ndcg_at_10
2417
+ value: 22.554
2418
+ - type: ndcg_at_100
2419
+ value: 35.197
2420
+ - type: ndcg_at_1000
2421
+ value: 46.58
2422
+ - type: ndcg_at_3
2423
+ value: 20.285
2424
+ - type: ndcg_at_5
2425
+ value: 21.924
2426
+ - type: precision_at_1
2427
+ value: 20.408
2428
+ - type: precision_at_10
2429
+ value: 21.837
2430
+ - type: precision_at_100
2431
+ value: 7.754999999999999
2432
+ - type: precision_at_1000
2433
+ value: 1.537
2434
+ - type: precision_at_3
2435
+ value: 21.769
2436
+ - type: precision_at_5
2437
+ value: 23.673
2438
+ - type: recall_at_1
2439
+ value: 1.437
2440
+ - type: recall_at_10
2441
+ value: 16.314999999999998
2442
+ - type: recall_at_100
2443
+ value: 47.635
2444
+ - type: recall_at_1000
2445
+ value: 82.963
2446
+ - type: recall_at_3
2447
+ value: 4.955
2448
+ - type: recall_at_5
2449
+ value: 8.805
2450
+ - task:
2451
+ type: Classification
2452
+ dataset:
2453
+ type: mteb/toxic_conversations_50k
2454
+ name: MTEB ToxicConversationsClassification
2455
+ config: default
2456
+ split: test
2457
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2458
+ metrics:
2459
+ - type: accuracy
2460
+ value: 71.6128
2461
+ - type: ap
2462
+ value: 14.279639861175664
2463
+ - type: f1
2464
+ value: 54.922292491204274
2465
+ - task:
2466
+ type: Classification
2467
+ dataset:
2468
+ type: mteb/tweet_sentiment_extraction
2469
+ name: MTEB TweetSentimentExtractionClassification
2470
+ config: default
2471
+ split: test
2472
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2473
+ metrics:
2474
+ - type: accuracy
2475
+ value: 57.01188455008489
2476
+ - type: f1
2477
+ value: 57.377953019225515
2478
+ - task:
2479
+ type: Clustering
2480
+ dataset:
2481
+ type: mteb/twentynewsgroups-clustering
2482
+ name: MTEB TwentyNewsgroupsClustering
2483
+ config: default
2484
+ split: test
2485
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2486
+ metrics:
2487
+ - type: v_measure
2488
+ value: 52.306769136544254
2489
+ - task:
2490
+ type: PairClassification
2491
+ dataset:
2492
+ type: mteb/twittersemeval2015-pairclassification
2493
+ name: MTEB TwitterSemEval2015
2494
+ config: default
2495
+ split: test
2496
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2497
+ metrics:
2498
+ - type: cos_sim_accuracy
2499
+ value: 85.64701674912082
2500
+ - type: cos_sim_ap
2501
+ value: 72.46600945328552
2502
+ - type: cos_sim_f1
2503
+ value: 67.96572367648784
2504
+ - type: cos_sim_precision
2505
+ value: 61.21801649397336
2506
+ - type: cos_sim_recall
2507
+ value: 76.38522427440633
2508
+ - type: dot_accuracy
2509
+ value: 82.33295583238957
2510
+ - type: dot_ap
2511
+ value: 62.54843443071716
2512
+ - type: dot_f1
2513
+ value: 60.38378562507096
2514
+ - type: dot_precision
2515
+ value: 52.99980067769583
2516
+ - type: dot_recall
2517
+ value: 70.15831134564644
2518
+ - type: euclidean_accuracy
2519
+ value: 85.7423854085951
2520
+ - type: euclidean_ap
2521
+ value: 72.76873850945174
2522
+ - type: euclidean_f1
2523
+ value: 68.23556960543262
2524
+ - type: euclidean_precision
2525
+ value: 61.3344559040202
2526
+ - type: euclidean_recall
2527
+ value: 76.88654353562005
2528
+ - type: manhattan_accuracy
2529
+ value: 85.74834594981225
2530
+ - type: manhattan_ap
2531
+ value: 72.66825372446462
2532
+ - type: manhattan_f1
2533
+ value: 68.21539194662853
2534
+ - type: manhattan_precision
2535
+ value: 62.185056472632496
2536
+ - type: manhattan_recall
2537
+ value: 75.54089709762533
2538
+ - type: max_accuracy
2539
+ value: 85.74834594981225
2540
+ - type: max_ap
2541
+ value: 72.76873850945174
2542
+ - type: max_f1
2543
+ value: 68.23556960543262
2544
+ - task:
2545
+ type: PairClassification
2546
+ dataset:
2547
+ type: mteb/twitterurlcorpus-pairclassification
2548
+ name: MTEB TwitterURLCorpus
2549
+ config: default
2550
+ split: test
2551
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2552
+ metrics:
2553
+ - type: cos_sim_accuracy
2554
+ value: 88.73171110334924
2555
+ - type: cos_sim_ap
2556
+ value: 85.51855542063649
2557
+ - type: cos_sim_f1
2558
+ value: 77.95706775700934
2559
+ - type: cos_sim_precision
2560
+ value: 74.12524298805887
2561
+ - type: cos_sim_recall
2562
+ value: 82.20665229442562
2563
+ - type: dot_accuracy
2564
+ value: 86.94842240074514
2565
+ - type: dot_ap
2566
+ value: 80.90995345771762
2567
+ - type: dot_f1
2568
+ value: 74.20765027322403
2569
+ - type: dot_precision
2570
+ value: 70.42594385285575
2571
+ - type: dot_recall
2572
+ value: 78.41854019094548
2573
+ - type: euclidean_accuracy
2574
+ value: 88.73753250281368
2575
+ - type: euclidean_ap
2576
+ value: 85.54712254033734
2577
+ - type: euclidean_f1
2578
+ value: 78.07565728654365
2579
+ - type: euclidean_precision
2580
+ value: 75.1120597652081
2581
+ - type: euclidean_recall
2582
+ value: 81.282722513089
2583
+ - type: manhattan_accuracy
2584
+ value: 88.72588970388482
2585
+ - type: manhattan_ap
2586
+ value: 85.52118291594071
2587
+ - type: manhattan_f1
2588
+ value: 78.04428724070593
2589
+ - type: manhattan_precision
2590
+ value: 74.83219105490002
2591
+ - type: manhattan_recall
2592
+ value: 81.54450261780106
2593
+ - type: max_accuracy
2594
+ value: 88.73753250281368
2595
+ - type: max_ap
2596
+ value: 85.54712254033734
2597
+ - type: max_f1
2598
+ value: 78.07565728654365
2599
+ language:
2600
+ - en
2601
  license: apache-2.0
2602
  ---
2603
+
2604
+ # gte-base
2605
+
2606
+ Gegeral Text Embeddings (GTE) model.
2607
+
2608
+ This model has 12 layers and the embedding size is 768.