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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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2214
+ name: MTEB SprintDuplicateQuestions
2215
+ config: default
2216
+ split: test
2217
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2218
+ metrics:
2219
+ - type: cos_sim_accuracy
2220
+ value: 99.72079207920792
2221
+ - type: cos_sim_ap
2222
+ value: 93.00265215525152
2223
+ - type: cos_sim_f1
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+ - type: cos_sim_precision
2226
+ value: 90.05586592178771
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+ - type: cos_sim_recall
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+ - type: dot_accuracy
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+ value: 99.66039603960397
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+ - type: dot_ap
2232
+ value: 91.22371407479089
2233
+ - type: dot_f1
2234
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2235
+ - type: dot_precision
2236
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2237
+ - type: dot_recall
2238
+ value: 80.7
2239
+ - type: euclidean_accuracy
2240
+ value: 99.71881188118812
2241
+ - type: euclidean_ap
2242
+ value: 92.88449963304728
2243
+ - type: euclidean_f1
2244
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+ - type: euclidean_precision
2246
+ value: 88.64864864864866
2247
+ - type: euclidean_recall
2248
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2249
+ - type: manhattan_accuracy
2250
+ value: 99.73267326732673
2251
+ - type: manhattan_ap
2252
+ value: 93.23055393056883
2253
+ - type: manhattan_f1
2254
+ value: 85.88957055214725
2255
+ - type: manhattan_precision
2256
+ value: 87.86610878661088
2257
+ - type: manhattan_recall
2258
+ value: 84.0
2259
+ - type: max_accuracy
2260
+ value: 99.73267326732673
2261
+ - type: max_ap
2262
+ value: 93.23055393056883
2263
+ - type: max_f1
2264
+ value: 85.88957055214725
2265
+ - task:
2266
+ type: Clustering
2267
+ dataset:
2268
+ type: mteb/stackexchange-clustering
2269
+ name: MTEB StackExchangeClustering
2270
+ config: default
2271
+ split: test
2272
+ revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
2273
+ metrics:
2274
+ - type: v_measure
2275
+ value: 77.3305735900358
2276
+ - task:
2277
+ type: Clustering
2278
+ dataset:
2279
+ type: mteb/stackexchange-clustering-p2p
2280
+ name: MTEB StackExchangeClusteringP2P
2281
+ config: default
2282
+ split: test
2283
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2284
+ metrics:
2285
+ - type: v_measure
2286
+ value: 41.32967136540674
2287
+ - task:
2288
+ type: Reranking
2289
+ dataset:
2290
+ type: mteb/stackoverflowdupquestions-reranking
2291
+ name: MTEB StackOverflowDupQuestions
2292
+ config: default
2293
+ split: test
2294
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2295
+ metrics:
2296
+ - type: map
2297
+ value: 55.95514866379359
2298
+ - type: mrr
2299
+ value: 56.95423245055598
2300
+ - task:
2301
+ type: Summarization
2302
+ dataset:
2303
+ type: mteb/summeval
2304
+ name: MTEB SummEval
2305
+ config: default
2306
+ split: test
2307
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2308
+ metrics:
2309
+ - type: cos_sim_pearson
2310
+ value: 30.783007208997144
2311
+ - type: cos_sim_spearman
2312
+ value: 30.373444721540533
2313
+ - type: dot_pearson
2314
+ value: 29.210604111143905
2315
+ - type: dot_spearman
2316
+ value: 29.98809758085659
2317
+ - task:
2318
+ type: Retrieval
2319
+ dataset:
2320
+ type: trec-covid
2321
+ name: MTEB TRECCOVID
2322
+ config: default
2323
+ split: test
2324
+ revision: None
2325
+ metrics:
2326
+ - type: map_at_1
2327
+ value: 0.234
2328
+ - type: map_at_10
2329
+ value: 1.894
2330
+ - type: map_at_100
2331
+ value: 1.894
2332
+ - type: map_at_1000
2333
+ value: 1.894
2334
+ - type: map_at_3
2335
+ value: 0.636
2336
+ - type: map_at_5
2337
+ value: 1.0
2338
+ - type: mrr_at_1
2339
+ value: 88.0
2340
+ - type: mrr_at_10
2341
+ value: 93.667
2342
+ - type: mrr_at_100
2343
+ value: 93.667
2344
+ - type: mrr_at_1000
2345
+ value: 93.667
2346
+ - type: mrr_at_3
2347
+ value: 93.667
2348
+ - type: mrr_at_5
2349
+ value: 93.667
2350
+ - type: ndcg_at_1
2351
+ value: 85.0
2352
+ - type: ndcg_at_10
2353
+ value: 74.798
2354
+ - type: ndcg_at_100
2355
+ value: 16.462
2356
+ - type: ndcg_at_1000
2357
+ value: 7.0889999999999995
2358
+ - type: ndcg_at_3
2359
+ value: 80.754
2360
+ - type: ndcg_at_5
2361
+ value: 77.319
2362
+ - type: precision_at_1
2363
+ value: 88.0
2364
+ - type: precision_at_10
2365
+ value: 78.0
2366
+ - type: precision_at_100
2367
+ value: 7.8
2368
+ - type: precision_at_1000
2369
+ value: 0.7799999999999999
2370
+ - type: precision_at_3
2371
+ value: 83.333
2372
+ - type: precision_at_5
2373
+ value: 80.80000000000001
2374
+ - type: recall_at_1
2375
+ value: 0.234
2376
+ - type: recall_at_10
2377
+ value: 2.093
2378
+ - type: recall_at_100
2379
+ value: 2.093
2380
+ - type: recall_at_1000
2381
+ value: 2.093
2382
+ - type: recall_at_3
2383
+ value: 0.662
2384
+ - type: recall_at_5
2385
+ value: 1.0739999999999998
2386
+ - task:
2387
+ type: Retrieval
2388
+ dataset:
2389
+ type: webis-touche2020
2390
+ name: MTEB Touche2020
2391
+ config: default
2392
+ split: test
2393
+ revision: None
2394
+ metrics:
2395
+ - type: map_at_1
2396
+ value: 2.703
2397
+ - type: map_at_10
2398
+ value: 10.866000000000001
2399
+ - type: map_at_100
2400
+ value: 10.866000000000001
2401
+ - type: map_at_1000
2402
+ value: 10.866000000000001
2403
+ - type: map_at_3
2404
+ value: 5.909
2405
+ - type: map_at_5
2406
+ value: 7.35
2407
+ - type: mrr_at_1
2408
+ value: 36.735
2409
+ - type: mrr_at_10
2410
+ value: 53.583000000000006
2411
+ - type: mrr_at_100
2412
+ value: 53.583000000000006
2413
+ - type: mrr_at_1000
2414
+ value: 53.583000000000006
2415
+ - type: mrr_at_3
2416
+ value: 49.32
2417
+ - type: mrr_at_5
2418
+ value: 51.769
2419
+ - type: ndcg_at_1
2420
+ value: 34.694
2421
+ - type: ndcg_at_10
2422
+ value: 27.926000000000002
2423
+ - type: ndcg_at_100
2424
+ value: 22.701
2425
+ - type: ndcg_at_1000
2426
+ value: 22.701
2427
+ - type: ndcg_at_3
2428
+ value: 32.073
2429
+ - type: ndcg_at_5
2430
+ value: 28.327999999999996
2431
+ - type: precision_at_1
2432
+ value: 36.735
2433
+ - type: precision_at_10
2434
+ value: 24.694
2435
+ - type: precision_at_100
2436
+ value: 2.469
2437
+ - type: precision_at_1000
2438
+ value: 0.247
2439
+ - type: precision_at_3
2440
+ value: 31.973000000000003
2441
+ - type: precision_at_5
2442
+ value: 26.939
2443
+ - type: recall_at_1
2444
+ value: 2.703
2445
+ - type: recall_at_10
2446
+ value: 17.702
2447
+ - type: recall_at_100
2448
+ value: 17.702
2449
+ - type: recall_at_1000
2450
+ value: 17.702
2451
+ - type: recall_at_3
2452
+ value: 7.208
2453
+ - type: recall_at_5
2454
+ value: 9.748999999999999
2455
+ - task:
2456
+ type: Classification
2457
+ dataset:
2458
+ type: mteb/toxic_conversations_50k
2459
+ name: MTEB ToxicConversationsClassification
2460
+ config: default
2461
+ split: test
2462
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2463
+ metrics:
2464
+ - type: accuracy
2465
+ value: 70.79960000000001
2466
+ - type: ap
2467
+ value: 15.467565415565815
2468
+ - type: f1
2469
+ value: 55.28639823443618
2470
+ - task:
2471
+ type: Classification
2472
+ dataset:
2473
+ type: mteb/tweet_sentiment_extraction
2474
+ name: MTEB TweetSentimentExtractionClassification
2475
+ config: default
2476
+ split: test
2477
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2478
+ metrics:
2479
+ - type: accuracy
2480
+ value: 64.7792869269949
2481
+ - type: f1
2482
+ value: 65.08597154774318
2483
+ - task:
2484
+ type: Clustering
2485
+ dataset:
2486
+ type: mteb/twentynewsgroups-clustering
2487
+ name: MTEB TwentyNewsgroupsClustering
2488
+ config: default
2489
+ split: test
2490
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2491
+ metrics:
2492
+ - type: v_measure
2493
+ value: 55.70352297774293
2494
+ - task:
2495
+ type: PairClassification
2496
+ dataset:
2497
+ type: mteb/twittersemeval2015-pairclassification
2498
+ name: MTEB TwitterSemEval2015
2499
+ config: default
2500
+ split: test
2501
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2502
+ metrics:
2503
+ - type: cos_sim_accuracy
2504
+ value: 88.27561542588067
2505
+ - type: cos_sim_ap
2506
+ value: 81.08262141256193
2507
+ - type: cos_sim_f1
2508
+ value: 73.82341501361338
2509
+ - type: cos_sim_precision
2510
+ value: 72.5720112159062
2511
+ - type: cos_sim_recall
2512
+ value: 75.11873350923483
2513
+ - type: dot_accuracy
2514
+ value: 86.66030875603504
2515
+ - type: dot_ap
2516
+ value: 76.6052349228621
2517
+ - type: dot_f1
2518
+ value: 70.13897280966768
2519
+ - type: dot_precision
2520
+ value: 64.70457079152732
2521
+ - type: dot_recall
2522
+ value: 76.56992084432717
2523
+ - type: euclidean_accuracy
2524
+ value: 88.37098408535495
2525
+ - type: euclidean_ap
2526
+ value: 81.12515230092113
2527
+ - type: euclidean_f1
2528
+ value: 74.10338225909379
2529
+ - type: euclidean_precision
2530
+ value: 71.76761433868974
2531
+ - type: euclidean_recall
2532
+ value: 76.59630606860158
2533
+ - type: manhattan_accuracy
2534
+ value: 88.34118137926924
2535
+ - type: manhattan_ap
2536
+ value: 80.95751834536561
2537
+ - type: manhattan_f1
2538
+ value: 73.9119496855346
2539
+ - type: manhattan_precision
2540
+ value: 70.625
2541
+ - type: manhattan_recall
2542
+ value: 77.5197889182058
2543
+ - type: max_accuracy
2544
+ value: 88.37098408535495
2545
+ - type: max_ap
2546
+ value: 81.12515230092113
2547
+ - type: max_f1
2548
+ value: 74.10338225909379
2549
+ - task:
2550
+ type: PairClassification
2551
+ dataset:
2552
+ type: mteb/twitterurlcorpus-pairclassification
2553
+ name: MTEB TwitterURLCorpus
2554
+ config: default
2555
+ split: test
2556
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2557
+ metrics:
2558
+ - type: cos_sim_accuracy
2559
+ value: 89.79896767182831
2560
+ - type: cos_sim_ap
2561
+ value: 87.40071784061065
2562
+ - type: cos_sim_f1
2563
+ value: 79.87753144712087
2564
+ - type: cos_sim_precision
2565
+ value: 76.67304015296367
2566
+ - type: cos_sim_recall
2567
+ value: 83.3615645210964
2568
+ - type: dot_accuracy
2569
+ value: 88.95486474948578
2570
+ - type: dot_ap
2571
+ value: 86.00227979119943
2572
+ - type: dot_f1
2573
+ value: 78.54601474525914
2574
+ - type: dot_precision
2575
+ value: 75.00525394045535
2576
+ - type: dot_recall
2577
+ value: 82.43763473975977
2578
+ - type: euclidean_accuracy
2579
+ value: 89.7892653393876
2580
+ - type: euclidean_ap
2581
+ value: 87.42174706480819
2582
+ - type: euclidean_f1
2583
+ value: 80.07283321194465
2584
+ - type: euclidean_precision
2585
+ value: 75.96738529574351
2586
+ - type: euclidean_recall
2587
+ value: 84.6473668001232
2588
+ - type: manhattan_accuracy
2589
+ value: 89.8474793340319
2590
+ - type: manhattan_ap
2591
+ value: 87.47814292587448
2592
+ - type: manhattan_f1
2593
+ value: 80.15461150280949
2594
+ - type: manhattan_precision
2595
+ value: 74.88798234468
2596
+ - type: manhattan_recall
2597
+ value: 86.21804742839544
2598
+ - type: max_accuracy
2599
+ value: 89.8474793340319
2600
+ - type: max_ap
2601
+ value: 87.47814292587448
2602
+ - type: max_f1
2603
+ value: 80.15461150280949
2604
+ ---
2605
+
2606
+ # Model Summary
2607
+
2608
+ > GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.
2609
+
2610
+ - **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm)
2611
+ - **Paper:** [TODO](https://arxiv.org/abs/2308.07124)
2612
+
2613
+ | Model | Description |
2614
+ |-------|-------------|
2615
+ | [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT |
2616
+ | [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT |
2617
+
2618
+ # Use
2619
+
2620
+ The model usage is documented [here](TODO). It supports GritLM, Transformers, Sentence Transformers.
2621
+
2622
+ # Citation
2623
+
2624
+ ```bibtex
2625
+ TODO
2626
+ ```