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2239
+ type: Retrieval
2240
+ dataset:
2241
+ type: trec-covid
2242
+ name: MTEB TRECCOVID
2243
+ config: default
2244
+ split: test
2245
+ revision: None
2246
+ metrics:
2247
+ - type: map_at_1
2248
+ value: 0.2
2249
+ - type: map_at_10
2250
+ value: 1.398
2251
+ - type: map_at_100
2252
+ value: 7.406
2253
+ - type: map_at_1000
2254
+ value: 18.401
2255
+ - type: map_at_3
2256
+ value: 0.479
2257
+ - type: map_at_5
2258
+ value: 0.772
2259
+ - type: mrr_at_1
2260
+ value: 70.0
2261
+ - type: mrr_at_10
2262
+ value: 79.25999999999999
2263
+ - type: mrr_at_100
2264
+ value: 79.25999999999999
2265
+ - type: mrr_at_1000
2266
+ value: 79.25999999999999
2267
+ - type: mrr_at_3
2268
+ value: 77.333
2269
+ - type: mrr_at_5
2270
+ value: 78.133
2271
+ - type: ndcg_at_1
2272
+ value: 63.0
2273
+ - type: ndcg_at_10
2274
+ value: 58.548
2275
+ - type: ndcg_at_100
2276
+ value: 45.216
2277
+ - type: ndcg_at_1000
2278
+ value: 41.149
2279
+ - type: ndcg_at_3
2280
+ value: 60.641999999999996
2281
+ - type: ndcg_at_5
2282
+ value: 61.135
2283
+ - type: precision_at_1
2284
+ value: 70.0
2285
+ - type: precision_at_10
2286
+ value: 64.0
2287
+ - type: precision_at_100
2288
+ value: 46.92
2289
+ - type: precision_at_1000
2290
+ value: 18.642
2291
+ - type: precision_at_3
2292
+ value: 64.667
2293
+ - type: precision_at_5
2294
+ value: 66.4
2295
+ - type: recall_at_1
2296
+ value: 0.2
2297
+ - type: recall_at_10
2298
+ value: 1.6729999999999998
2299
+ - type: recall_at_100
2300
+ value: 10.856
2301
+ - type: recall_at_1000
2302
+ value: 38.964999999999996
2303
+ - type: recall_at_3
2304
+ value: 0.504
2305
+ - type: recall_at_5
2306
+ value: 0.852
2307
+ - task:
2308
+ type: Retrieval
2309
+ dataset:
2310
+ type: webis-touche2020
2311
+ name: MTEB Touche2020
2312
+ config: default
2313
+ split: test
2314
+ revision: None
2315
+ metrics:
2316
+ - type: map_at_1
2317
+ value: 1.6629999999999998
2318
+ - type: map_at_10
2319
+ value: 8.601
2320
+ - type: map_at_100
2321
+ value: 14.354
2322
+ - type: map_at_1000
2323
+ value: 15.927
2324
+ - type: map_at_3
2325
+ value: 4.1930000000000005
2326
+ - type: map_at_5
2327
+ value: 5.655
2328
+ - type: mrr_at_1
2329
+ value: 18.367
2330
+ - type: mrr_at_10
2331
+ value: 34.466
2332
+ - type: mrr_at_100
2333
+ value: 35.235
2334
+ - type: mrr_at_1000
2335
+ value: 35.27
2336
+ - type: mrr_at_3
2337
+ value: 28.571
2338
+ - type: mrr_at_5
2339
+ value: 31.531
2340
+ - type: ndcg_at_1
2341
+ value: 14.285999999999998
2342
+ - type: ndcg_at_10
2343
+ value: 20.374
2344
+ - type: ndcg_at_100
2345
+ value: 33.532000000000004
2346
+ - type: ndcg_at_1000
2347
+ value: 45.561
2348
+ - type: ndcg_at_3
2349
+ value: 18.442
2350
+ - type: ndcg_at_5
2351
+ value: 18.076
2352
+ - type: precision_at_1
2353
+ value: 18.367
2354
+ - type: precision_at_10
2355
+ value: 20.204
2356
+ - type: precision_at_100
2357
+ value: 7.489999999999999
2358
+ - type: precision_at_1000
2359
+ value: 1.5630000000000002
2360
+ - type: precision_at_3
2361
+ value: 21.769
2362
+ - type: precision_at_5
2363
+ value: 20.408
2364
+ - type: recall_at_1
2365
+ value: 1.6629999999999998
2366
+ - type: recall_at_10
2367
+ value: 15.549
2368
+ - type: recall_at_100
2369
+ value: 47.497
2370
+ - type: recall_at_1000
2371
+ value: 84.524
2372
+ - type: recall_at_3
2373
+ value: 5.289
2374
+ - type: recall_at_5
2375
+ value: 8.035
2376
+ - task:
2377
+ type: Classification
2378
+ dataset:
2379
+ type: mteb/toxic_conversations_50k
2380
+ name: MTEB ToxicConversationsClassification
2381
+ config: default
2382
+ split: test
2383
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2384
+ metrics:
2385
+ - type: accuracy
2386
+ value: 71.8194
2387
+ - type: ap
2388
+ value: 14.447702451658554
2389
+ - type: f1
2390
+ value: 55.13659412856185
2391
+ - task:
2392
+ type: Classification
2393
+ dataset:
2394
+ type: mteb/tweet_sentiment_extraction
2395
+ name: MTEB TweetSentimentExtractionClassification
2396
+ config: default
2397
+ split: test
2398
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2399
+ metrics:
2400
+ - type: accuracy
2401
+ value: 63.310696095076416
2402
+ - type: f1
2403
+ value: 63.360434851097814
2404
+ - task:
2405
+ type: Clustering
2406
+ dataset:
2407
+ type: mteb/twentynewsgroups-clustering
2408
+ name: MTEB TwentyNewsgroupsClustering
2409
+ config: default
2410
+ split: test
2411
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2412
+ metrics:
2413
+ - type: v_measure
2414
+ value: 51.30677907335145
2415
+ - task:
2416
+ type: PairClassification
2417
+ dataset:
2418
+ type: mteb/twittersemeval2015-pairclassification
2419
+ name: MTEB TwitterSemEval2015
2420
+ config: default
2421
+ split: test
2422
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2423
+ metrics:
2424
+ - type: cos_sim_accuracy
2425
+ value: 86.12386004649221
2426
+ - type: cos_sim_ap
2427
+ value: 73.99096426215495
2428
+ - type: cos_sim_f1
2429
+ value: 68.18416968442834
2430
+ - type: cos_sim_precision
2431
+ value: 66.86960933536275
2432
+ - type: cos_sim_recall
2433
+ value: 69.55145118733509
2434
+ - type: dot_accuracy
2435
+ value: 86.12386004649221
2436
+ - type: dot_ap
2437
+ value: 73.99096813038672
2438
+ - type: dot_f1
2439
+ value: 68.18416968442834
2440
+ - type: dot_precision
2441
+ value: 66.86960933536275
2442
+ - type: dot_recall
2443
+ value: 69.55145118733509
2444
+ - type: euclidean_accuracy
2445
+ value: 86.12386004649221
2446
+ - type: euclidean_ap
2447
+ value: 73.99095984980165
2448
+ - type: euclidean_f1
2449
+ value: 68.18416968442834
2450
+ - type: euclidean_precision
2451
+ value: 66.86960933536275
2452
+ - type: euclidean_recall
2453
+ value: 69.55145118733509
2454
+ - type: manhattan_accuracy
2455
+ value: 86.09405734040651
2456
+ - type: manhattan_ap
2457
+ value: 73.96825745608601
2458
+ - type: manhattan_f1
2459
+ value: 68.13888179729383
2460
+ - type: manhattan_precision
2461
+ value: 65.99901088031652
2462
+ - type: manhattan_recall
2463
+ value: 70.42216358839049
2464
+ - type: max_accuracy
2465
+ value: 86.12386004649221
2466
+ - type: max_ap
2467
+ value: 73.99096813038672
2468
+ - type: max_f1
2469
+ value: 68.18416968442834
2470
+ - task:
2471
+ type: PairClassification
2472
+ dataset:
2473
+ type: mteb/twitterurlcorpus-pairclassification
2474
+ name: MTEB TwitterURLCorpus
2475
+ config: default
2476
+ split: test
2477
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2478
+ metrics:
2479
+ - type: cos_sim_accuracy
2480
+ value: 88.99367407924865
2481
+ - type: cos_sim_ap
2482
+ value: 86.19720829843081
2483
+ - type: cos_sim_f1
2484
+ value: 78.39889075384951
2485
+ - type: cos_sim_precision
2486
+ value: 74.5110278818144
2487
+ - type: cos_sim_recall
2488
+ value: 82.71481367416075
2489
+ - type: dot_accuracy
2490
+ value: 88.99367407924865
2491
+ - type: dot_ap
2492
+ value: 86.19718471454047
2493
+ - type: dot_f1
2494
+ value: 78.39889075384951
2495
+ - type: dot_precision
2496
+ value: 74.5110278818144
2497
+ - type: dot_recall
2498
+ value: 82.71481367416075
2499
+ - type: euclidean_accuracy
2500
+ value: 88.99367407924865
2501
+ - type: euclidean_ap
2502
+ value: 86.1972021422436
2503
+ - type: euclidean_f1
2504
+ value: 78.39889075384951
2505
+ - type: euclidean_precision
2506
+ value: 74.5110278818144
2507
+ - type: euclidean_recall
2508
+ value: 82.71481367416075
2509
+ - type: manhattan_accuracy
2510
+ value: 88.95680521597392
2511
+ - type: manhattan_ap
2512
+ value: 86.16659921351506
2513
+ - type: manhattan_f1
2514
+ value: 78.39125971550081
2515
+ - type: manhattan_precision
2516
+ value: 74.82502799552073
2517
+ - type: manhattan_recall
2518
+ value: 82.31444410224823
2519
+ - type: max_accuracy
2520
+ value: 88.99367407924865
2521
+ - type: max_ap
2522
+ value: 86.19720829843081
2523
+ - type: max_f1
2524
+ value: 78.39889075384951
2525
  ---
2526
 
2527
  # hkunlp/instructor-base