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1
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17
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18
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19
+ name: MTEB AmazonCounterfactualClassification (en)
20
+ type: mteb/amazon_counterfactual
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22
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33
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34
+ name: MTEB AmazonPolarityClassification
35
+ type: mteb/amazon_polarity
36
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37
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38
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50
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67
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+ value: 56.17658995416349
2196
+ - task:
2197
+ type: Summarization
2198
+ dataset:
2199
+ name: MTEB SummEval
2200
+ type: mteb/summeval
2201
+ config: default
2202
+ split: test
2203
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2204
+ metrics:
2205
+ - type: cos_sim_pearson
2206
+ value: 30.954206018888453
2207
+ - type: cos_sim_spearman
2208
+ value: 32.71062599450096
2209
+ - type: dot_pearson
2210
+ value: 30.95420929056943
2211
+ - type: dot_spearman
2212
+ value: 32.71062599450096
2213
+ - task:
2214
+ type: Retrieval
2215
+ dataset:
2216
+ name: MTEB TRECCOVID
2217
+ type: trec-covid
2218
+ config: default
2219
+ split: test
2220
+ revision: None
2221
+ metrics:
2222
+ - type: map_at_1
2223
+ value: 0.22699999999999998
2224
+ - type: map_at_10
2225
+ value: 1.924
2226
+ - type: map_at_100
2227
+ value: 10.525
2228
+ - type: map_at_1000
2229
+ value: 24.973
2230
+ - type: map_at_3
2231
+ value: 0.638
2232
+ - type: map_at_5
2233
+ value: 1.0659999999999998
2234
+ - type: mrr_at_1
2235
+ value: 84
2236
+ - type: mrr_at_10
2237
+ value: 91.067
2238
+ - type: mrr_at_100
2239
+ value: 91.067
2240
+ - type: mrr_at_1000
2241
+ value: 91.067
2242
+ - type: mrr_at_3
2243
+ value: 90.667
2244
+ - type: mrr_at_5
2245
+ value: 91.067
2246
+ - type: ndcg_at_1
2247
+ value: 81
2248
+ - type: ndcg_at_10
2249
+ value: 75.566
2250
+ - type: ndcg_at_100
2251
+ value: 56.387
2252
+ - type: ndcg_at_1000
2253
+ value: 49.834
2254
+ - type: ndcg_at_3
2255
+ value: 80.899
2256
+ - type: ndcg_at_5
2257
+ value: 80.75099999999999
2258
+ - type: precision_at_1
2259
+ value: 84
2260
+ - type: precision_at_10
2261
+ value: 79
2262
+ - type: precision_at_100
2263
+ value: 57.56
2264
+ - type: precision_at_1000
2265
+ value: 21.8
2266
+ - type: precision_at_3
2267
+ value: 84.667
2268
+ - type: precision_at_5
2269
+ value: 85.2
2270
+ - type: recall_at_1
2271
+ value: 0.22699999999999998
2272
+ - type: recall_at_10
2273
+ value: 2.136
2274
+ - type: recall_at_100
2275
+ value: 13.861
2276
+ - type: recall_at_1000
2277
+ value: 46.299
2278
+ - type: recall_at_3
2279
+ value: 0.6649999999999999
2280
+ - type: recall_at_5
2281
+ value: 1.145
2282
+ - task:
2283
+ type: Retrieval
2284
+ dataset:
2285
+ name: MTEB Touche2020
2286
+ type: webis-touche2020
2287
+ config: default
2288
+ split: test
2289
+ revision: None
2290
+ metrics:
2291
+ - type: map_at_1
2292
+ value: 2.752
2293
+ - type: map_at_10
2294
+ value: 9.951
2295
+ - type: map_at_100
2296
+ value: 16.794999999999998
2297
+ - type: map_at_1000
2298
+ value: 18.251
2299
+ - type: map_at_3
2300
+ value: 5.288
2301
+ - type: map_at_5
2302
+ value: 6.954000000000001
2303
+ - type: mrr_at_1
2304
+ value: 38.775999999999996
2305
+ - type: mrr_at_10
2306
+ value: 50.458000000000006
2307
+ - type: mrr_at_100
2308
+ value: 51.324999999999996
2309
+ - type: mrr_at_1000
2310
+ value: 51.339999999999996
2311
+ - type: mrr_at_3
2312
+ value: 46.939
2313
+ - type: mrr_at_5
2314
+ value: 47.857
2315
+ - type: ndcg_at_1
2316
+ value: 36.735
2317
+ - type: ndcg_at_10
2318
+ value: 25.198999999999998
2319
+ - type: ndcg_at_100
2320
+ value: 37.938
2321
+ - type: ndcg_at_1000
2322
+ value: 49.145
2323
+ - type: ndcg_at_3
2324
+ value: 29.348000000000003
2325
+ - type: ndcg_at_5
2326
+ value: 25.804
2327
+ - type: precision_at_1
2328
+ value: 38.775999999999996
2329
+ - type: precision_at_10
2330
+ value: 22.041
2331
+ - type: precision_at_100
2332
+ value: 7.939
2333
+ - type: precision_at_1000
2334
+ value: 1.555
2335
+ - type: precision_at_3
2336
+ value: 29.932
2337
+ - type: precision_at_5
2338
+ value: 24.490000000000002
2339
+ - type: recall_at_1
2340
+ value: 2.752
2341
+ - type: recall_at_10
2342
+ value: 16.197
2343
+ - type: recall_at_100
2344
+ value: 49.166
2345
+ - type: recall_at_1000
2346
+ value: 84.18900000000001
2347
+ - type: recall_at_3
2348
+ value: 6.438000000000001
2349
+ - type: recall_at_5
2350
+ value: 9.093
2351
+ - task:
2352
+ type: Classification
2353
+ dataset:
2354
+ name: MTEB ToxicConversationsClassification
2355
+ type: mteb/toxic_conversations_50k
2356
+ config: default
2357
+ split: test
2358
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2359
+ metrics:
2360
+ - type: accuracy
2361
+ value: 71.47980000000001
2362
+ - type: ap
2363
+ value: 14.605194452178754
2364
+ - type: f1
2365
+ value: 55.07362924988948
2366
+ - task:
2367
+ type: Classification
2368
+ dataset:
2369
+ name: MTEB TweetSentimentExtractionClassification
2370
+ type: mteb/tweet_sentiment_extraction
2371
+ config: default
2372
+ split: test
2373
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2374
+ metrics:
2375
+ - type: accuracy
2376
+ value: 59.708545557441994
2377
+ - type: f1
2378
+ value: 60.04751270975683
2379
+ - task:
2380
+ type: Clustering
2381
+ dataset:
2382
+ name: MTEB TwentyNewsgroupsClustering
2383
+ type: mteb/twentynewsgroups-clustering
2384
+ config: default
2385
+ split: test
2386
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2387
+ metrics:
2388
+ - type: v_measure
2389
+ value: 53.21105960597211
2390
+ - task:
2391
+ type: PairClassification
2392
+ dataset:
2393
+ name: MTEB TwitterSemEval2015
2394
+ type: mteb/twittersemeval2015-pairclassification
2395
+ config: default
2396
+ split: test
2397
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2398
+ metrics:
2399
+ - type: cos_sim_accuracy
2400
+ value: 87.58419264469214
2401
+ - type: cos_sim_ap
2402
+ value: 78.55300004517404
2403
+ - type: cos_sim_f1
2404
+ value: 71.49673530889001
2405
+ - type: cos_sim_precision
2406
+ value: 68.20795400095831
2407
+ - type: cos_sim_recall
2408
+ value: 75.11873350923483
2409
+ - type: dot_accuracy
2410
+ value: 87.58419264469214
2411
+ - type: dot_ap
2412
+ value: 78.55297659559511
2413
+ - type: dot_f1
2414
+ value: 71.49673530889001
2415
+ - type: dot_precision
2416
+ value: 68.20795400095831
2417
+ - type: dot_recall
2418
+ value: 75.11873350923483
2419
+ - type: euclidean_accuracy
2420
+ value: 87.58419264469214
2421
+ - type: euclidean_ap
2422
+ value: 78.55300477331477
2423
+ - type: euclidean_f1
2424
+ value: 71.49673530889001
2425
+ - type: euclidean_precision
2426
+ value: 68.20795400095831
2427
+ - type: euclidean_recall
2428
+ value: 75.11873350923483
2429
+ - type: manhattan_accuracy
2430
+ value: 87.5663110210407
2431
+ - type: manhattan_ap
2432
+ value: 78.49982050876562
2433
+ - type: manhattan_f1
2434
+ value: 71.35488740722104
2435
+ - type: manhattan_precision
2436
+ value: 68.18946862226497
2437
+ - type: manhattan_recall
2438
+ value: 74.82849604221636
2439
+ - type: max_accuracy
2440
+ value: 87.58419264469214
2441
+ - type: max_ap
2442
+ value: 78.55300477331477
2443
+ - type: max_f1
2444
+ value: 71.49673530889001
2445
+ - task:
2446
+ type: PairClassification
2447
+ dataset:
2448
+ name: MTEB TwitterURLCorpus
2449
+ type: mteb/twitterurlcorpus-pairclassification
2450
+ config: default
2451
+ split: test
2452
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2453
+ metrics:
2454
+ - type: cos_sim_accuracy
2455
+ value: 89.09069740365584
2456
+ - type: cos_sim_ap
2457
+ value: 86.22749303724757
2458
+ - type: cos_sim_f1
2459
+ value: 78.36863452005407
2460
+ - type: cos_sim_precision
2461
+ value: 76.49560117302053
2462
+ - type: cos_sim_recall
2463
+ value: 80.33569448721897
2464
+ - type: dot_accuracy
2465
+ value: 89.09069740365584
2466
+ - type: dot_ap
2467
+ value: 86.22750233655673
2468
+ - type: dot_f1
2469
+ value: 78.36863452005407
2470
+ - type: dot_precision
2471
+ value: 76.49560117302053
2472
+ - type: dot_recall
2473
+ value: 80.33569448721897
2474
+ - type: euclidean_accuracy
2475
+ value: 89.09069740365584
2476
+ - type: euclidean_ap
2477
+ value: 86.22749355597347
2478
+ - type: euclidean_f1
2479
+ value: 78.36863452005407
2480
+ - type: euclidean_precision
2481
+ value: 76.49560117302053
2482
+ - type: euclidean_recall
2483
+ value: 80.33569448721897
2484
+ - type: manhattan_accuracy
2485
+ value: 89.08293553770326
2486
+ - type: manhattan_ap
2487
+ value: 86.21913616084771
2488
+ - type: manhattan_f1
2489
+ value: 78.3907031479847
2490
+ - type: manhattan_precision
2491
+ value: 75.0352013517319
2492
+ - type: manhattan_recall
2493
+ value: 82.06036341238065
2494
+ - type: max_accuracy
2495
+ value: 89.09069740365584
2496
+ - type: max_ap
2497
+ value: 86.22750233655673
2498
+ - type: max_f1
2499
+ value: 78.3907031479847
2500
+ ---
2501
+
2502
+ # nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF
2503
+ This model was converted to GGUF format from [`mixedbread-ai/mxbai-embed-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
2504
+ Refer to the [original model card](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) for more details on the model.
2505
+ ## Use with llama.cpp
2506
+
2507
+ Install llama.cpp through brew.
2508
+
2509
+ ```bash
2510
+ brew install ggerganov/ggerganov/llama.cpp
2511
+ ```
2512
+ Invoke the llama.cpp server or the CLI.
2513
+
2514
+ CLI:
2515
+
2516
+ ```bash
2517
+ llama-cli --hf-repo nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF --model mxbai-embed-large-v1.Q4_K_M.gguf -p "The meaning to life and the universe is"
2518
+ ```
2519
+
2520
+ Server:
2521
+
2522
+ ```bash
2523
+ llama-server --hf-repo nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF --model mxbai-embed-large-v1.Q4_K_M.gguf -c 2048
2524
+ ```
2525
+
2526
+ Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
2527
+
2528
+ ```
2529
+ git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mxbai-embed-large-v1.Q4_K_M.gguf -n 128
2530
+ ```