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+ - type: map_at_10
2153
+ value: 72.76899999999999
2154
+ - type: map_at_100
2155
+ value: 73.303
2156
+ - type: map_at_1000
2157
+ value: 73.32499999999999
2158
+ - type: map_at_3
2159
+ value: 70.514
2160
+ - type: map_at_5
2161
+ value: 71.929
2162
+ - type: mrr_at_1
2163
+ value: 66.333
2164
+ - type: mrr_at_10
2165
+ value: 73.75
2166
+ - type: mrr_at_100
2167
+ value: 74.119
2168
+ - type: mrr_at_1000
2169
+ value: 74.138
2170
+ - type: mrr_at_3
2171
+ value: 72.222
2172
+ - type: mrr_at_5
2173
+ value: 73.122
2174
+ - type: ndcg_at_1
2175
+ value: 66.333
2176
+ - type: ndcg_at_10
2177
+ value: 76.774
2178
+ - type: ndcg_at_100
2179
+ value: 78.78500000000001
2180
+ - type: ndcg_at_1000
2181
+ value: 79.254
2182
+ - type: ndcg_at_3
2183
+ value: 73.088
2184
+ - type: ndcg_at_5
2185
+ value: 75.002
2186
+ - type: precision_at_1
2187
+ value: 66.333
2188
+ - type: precision_at_10
2189
+ value: 9.833
2190
+ - type: precision_at_100
2191
+ value: 1.093
2192
+ - type: precision_at_1000
2193
+ value: 0.11299999999999999
2194
+ - type: precision_at_3
2195
+ value: 28.222
2196
+ - type: precision_at_5
2197
+ value: 18.333
2198
+ - type: recall_at_1
2199
+ value: 63.510999999999996
2200
+ - type: recall_at_10
2201
+ value: 87.98899999999999
2202
+ - type: recall_at_100
2203
+ value: 96.5
2204
+ - type: recall_at_1000
2205
+ value: 100.0
2206
+ - type: recall_at_3
2207
+ value: 77.86699999999999
2208
+ - type: recall_at_5
2209
+ value: 82.73899999999999
2210
+ - task:
2211
+ type: PairClassification
2212
+ dataset:
2213
+ type: mteb/sprintduplicatequestions-pairclassification
2214
+ name: MTEB SprintDuplicateQuestions
2215
+ config: default
2216
+ split: test
2217
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2218
+ metrics:
2219
+ - type: cos_sim_accuracy
2220
+ value: 99.78514851485149
2221
+ - type: cos_sim_ap
2222
+ value: 94.94214383862038
2223
+ - type: cos_sim_f1
2224
+ value: 89.02255639097744
2225
+ - type: cos_sim_precision
2226
+ value: 89.2462311557789
2227
+ - type: cos_sim_recall
2228
+ value: 88.8
2229
+ - type: dot_accuracy
2230
+ value: 99.78217821782178
2231
+ - type: dot_ap
2232
+ value: 94.69965247836805
2233
+ - type: dot_f1
2234
+ value: 88.78695208970439
2235
+ - type: dot_precision
2236
+ value: 90.54054054054053
2237
+ - type: dot_recall
2238
+ value: 87.1
2239
+ - type: euclidean_accuracy
2240
+ value: 99.78118811881188
2241
+ - type: euclidean_ap
2242
+ value: 94.9865187695411
2243
+ - type: euclidean_f1
2244
+ value: 88.99950223992036
2245
+ - type: euclidean_precision
2246
+ value: 88.60257680872151
2247
+ - type: euclidean_recall
2248
+ value: 89.4
2249
+ - type: manhattan_accuracy
2250
+ value: 99.78811881188119
2251
+ - type: manhattan_ap
2252
+ value: 95.0021236766459
2253
+ - type: manhattan_f1
2254
+ value: 89.12071535022356
2255
+ - type: manhattan_precision
2256
+ value: 88.54886475814413
2257
+ - type: manhattan_recall
2258
+ value: 89.7
2259
+ - type: max_accuracy
2260
+ value: 99.78811881188119
2261
+ - type: max_ap
2262
+ value: 95.0021236766459
2263
+ - type: max_f1
2264
+ value: 89.12071535022356
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: 68.93190546593995
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: 37.602808534760655
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: 52.29214480978073
2298
+ - type: mrr
2299
+ value: 53.123169722434426
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: 30.967800769650022
2311
+ - type: cos_sim_spearman
2312
+ value: 31.168490040206926
2313
+ - type: dot_pearson
2314
+ value: 30.888603021128553
2315
+ - type: dot_spearman
2316
+ value: 31.028241262520385
2317
+ - task:
2318
+ type: Retrieval
2319
+ dataset:
2320
+ type: mteb/trec-covid
2321
+ name: MTEB TRECCOVID
2322
+ config: default
2323
+ split: test
2324
+ revision: None
2325
+ metrics:
2326
+ - type: map_at_1
2327
+ value: 0.22300000000000003
2328
+ - type: map_at_10
2329
+ value: 1.781
2330
+ - type: map_at_100
2331
+ value: 9.905999999999999
2332
+ - type: map_at_1000
2333
+ value: 23.455000000000002
2334
+ - type: map_at_3
2335
+ value: 0.569
2336
+ - type: map_at_5
2337
+ value: 0.918
2338
+ - type: mrr_at_1
2339
+ value: 84.0
2340
+ - type: mrr_at_10
2341
+ value: 91.067
2342
+ - type: mrr_at_100
2343
+ value: 91.067
2344
+ - type: mrr_at_1000
2345
+ value: 91.067
2346
+ - type: mrr_at_3
2347
+ value: 90.667
2348
+ - type: mrr_at_5
2349
+ value: 91.067
2350
+ - type: ndcg_at_1
2351
+ value: 78.0
2352
+ - type: ndcg_at_10
2353
+ value: 73.13499999999999
2354
+ - type: ndcg_at_100
2355
+ value: 55.32
2356
+ - type: ndcg_at_1000
2357
+ value: 49.532
2358
+ - type: ndcg_at_3
2359
+ value: 73.715
2360
+ - type: ndcg_at_5
2361
+ value: 72.74199999999999
2362
+ - type: precision_at_1
2363
+ value: 84.0
2364
+ - type: precision_at_10
2365
+ value: 78.8
2366
+ - type: precision_at_100
2367
+ value: 56.32
2368
+ - type: precision_at_1000
2369
+ value: 21.504
2370
+ - type: precision_at_3
2371
+ value: 77.333
2372
+ - type: precision_at_5
2373
+ value: 78.0
2374
+ - type: recall_at_1
2375
+ value: 0.22300000000000003
2376
+ - type: recall_at_10
2377
+ value: 2.049
2378
+ - type: recall_at_100
2379
+ value: 13.553
2380
+ - type: recall_at_1000
2381
+ value: 46.367999999999995
2382
+ - type: recall_at_3
2383
+ value: 0.604
2384
+ - type: recall_at_5
2385
+ value: 1.015
2386
+ - task:
2387
+ type: Retrieval
2388
+ dataset:
2389
+ type: mteb/touche2020
2390
+ name: MTEB Touche2020
2391
+ config: default
2392
+ split: test
2393
+ revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
2394
+ metrics:
2395
+ - type: map_at_1
2396
+ value: 3.0380000000000003
2397
+ - type: map_at_10
2398
+ value: 10.188
2399
+ - type: map_at_100
2400
+ value: 16.395
2401
+ - type: map_at_1000
2402
+ value: 18.024
2403
+ - type: map_at_3
2404
+ value: 6.236
2405
+ - type: map_at_5
2406
+ value: 7.276000000000001
2407
+ - type: mrr_at_1
2408
+ value: 34.694
2409
+ - type: mrr_at_10
2410
+ value: 46.292
2411
+ - type: mrr_at_100
2412
+ value: 47.446
2413
+ - type: mrr_at_1000
2414
+ value: 47.446
2415
+ - type: mrr_at_3
2416
+ value: 41.156
2417
+ - type: mrr_at_5
2418
+ value: 44.32
2419
+ - type: ndcg_at_1
2420
+ value: 32.653
2421
+ - type: ndcg_at_10
2422
+ value: 25.219
2423
+ - type: ndcg_at_100
2424
+ value: 37.802
2425
+ - type: ndcg_at_1000
2426
+ value: 49.274
2427
+ - type: ndcg_at_3
2428
+ value: 28.605999999999998
2429
+ - type: ndcg_at_5
2430
+ value: 26.21
2431
+ - type: precision_at_1
2432
+ value: 34.694
2433
+ - type: precision_at_10
2434
+ value: 21.837
2435
+ - type: precision_at_100
2436
+ value: 7.776
2437
+ - type: precision_at_1000
2438
+ value: 1.522
2439
+ - type: precision_at_3
2440
+ value: 28.571
2441
+ - type: precision_at_5
2442
+ value: 25.306
2443
+ - type: recall_at_1
2444
+ value: 3.0380000000000003
2445
+ - type: recall_at_10
2446
+ value: 16.298000000000002
2447
+ - type: recall_at_100
2448
+ value: 48.712
2449
+ - type: recall_at_1000
2450
+ value: 83.16799999999999
2451
+ - type: recall_at_3
2452
+ value: 7.265000000000001
2453
+ - type: recall_at_5
2454
+ value: 9.551
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: 83.978
2466
+ - type: ap
2467
+ value: 24.751887949330015
2468
+ - type: f1
2469
+ value: 66.8685134049279
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: 61.573288058856825
2481
+ - type: f1
2482
+ value: 61.973261751726604
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: 48.75483298792469
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: 86.36824223639506
2505
+ - type: cos_sim_ap
2506
+ value: 75.53126388573047
2507
+ - type: cos_sim_f1
2508
+ value: 67.9912831688245
2509
+ - type: cos_sim_precision
2510
+ value: 66.11817501869858
2511
+ - type: cos_sim_recall
2512
+ value: 69.9736147757256
2513
+ - type: dot_accuracy
2514
+ value: 86.39804494248078
2515
+ - type: dot_ap
2516
+ value: 75.27598891718046
2517
+ - type: dot_f1
2518
+ value: 67.91146284159763
2519
+ - type: dot_precision
2520
+ value: 63.90505003490807
2521
+ - type: dot_recall
2522
+ value: 72.45382585751979
2523
+ - type: euclidean_accuracy
2524
+ value: 86.36228169517793
2525
+ - type: euclidean_ap
2526
+ value: 75.51438087434647
2527
+ - type: euclidean_f1
2528
+ value: 68.02370523061066
2529
+ - type: euclidean_precision
2530
+ value: 66.46525679758308
2531
+ - type: euclidean_recall
2532
+ value: 69.65699208443272
2533
+ - type: manhattan_accuracy
2534
+ value: 86.46361089586935
2535
+ - type: manhattan_ap
2536
+ value: 75.50800785730111
2537
+ - type: manhattan_f1
2538
+ value: 67.9220437187253
2539
+ - type: manhattan_precision
2540
+ value: 67.79705573080967
2541
+ - type: manhattan_recall
2542
+ value: 68.04749340369392
2543
+ - type: max_accuracy
2544
+ value: 86.46361089586935
2545
+ - type: max_ap
2546
+ value: 75.53126388573047
2547
+ - type: max_f1
2548
+ value: 68.02370523061066
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: 88.80350836341057
2560
+ - type: cos_sim_ap
2561
+ value: 85.51101933260743
2562
+ - type: cos_sim_f1
2563
+ value: 77.9152271629704
2564
+ - type: cos_sim_precision
2565
+ value: 75.27815662910056
2566
+ - type: cos_sim_recall
2567
+ value: 80.74376347397599
2568
+ - type: dot_accuracy
2569
+ value: 88.84425815966158
2570
+ - type: dot_ap
2571
+ value: 85.49726945962519
2572
+ - type: dot_f1
2573
+ value: 77.94445269567801
2574
+ - type: dot_precision
2575
+ value: 75.27251864601261
2576
+ - type: dot_recall
2577
+ value: 80.81305820757623
2578
+ - type: euclidean_accuracy
2579
+ value: 88.80350836341057
2580
+ - type: euclidean_ap
2581
+ value: 85.4882880790211
2582
+ - type: euclidean_f1
2583
+ value: 77.87063284615103
2584
+ - type: euclidean_precision
2585
+ value: 74.61022927689595
2586
+ - type: euclidean_recall
2587
+ value: 81.42901139513397
2588
+ - type: manhattan_accuracy
2589
+ value: 88.7161873714441
2590
+ - type: manhattan_ap
2591
+ value: 85.45753871906821
2592
+ - type: manhattan_f1
2593
+ value: 77.8686401480111
2594
+ - type: manhattan_precision
2595
+ value: 74.95903683123174
2596
+ - type: manhattan_recall
2597
+ value: 81.01324299353249
2598
+ - type: max_accuracy
2599
+ value: 88.84425815966158
2600
+ - type: max_ap
2601
+ value: 85.51101933260743
2602
+ - type: max_f1
2603
+ value: 77.94445269567801
2604
  ---
2605
+
2606
+ <!-- **English** | [中文](./README_zh.md) -->
2607
+
2608
+ # gte-base-en-v1.5
2609
+
2610
+ We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**.
2611
+ The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU).
2612
+
2613
+ The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)).
2614
+
2615
+ We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct).
2616
+
2617
+ <!-- Provide a longer summary of what this model is. -->
2618
+
2619
+ - **Developed by:** Institute for Intelligent Computing, Alibaba Group
2620
+ - **Model type:** Text Embeddings
2621
+ - **Paper:** Coming soon.
2622
+
2623
+ <!-- - **Demo [optional]:** [More Information Needed] -->
2624
+
2625
+ ### Model list
2626
+
2627
+ | Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
2628
+ |:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
2629
+ |[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| English | 7720 | 32768 | 4096 | 67.34 | 87.57 |
2630
+ |[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 409 | 8192 | 1024 | 65.39 | 86.71 |
2631
+ |[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 |
2632
+
2633
+
2634
+ ## How to Get Started with the Model
2635
+
2636
+ Use the code below to get started with the model.
2637
+
2638
+ ```python
2639
+ import torch.nn.functional as F
2640
+ from transformers import AutoModel, AutoTokenizer
2641
+
2642
+ input_texts = [
2643
+ "what is the capital of China?",
2644
+ "how to implement quick sort in python?",
2645
+ "Beijing",
2646
+ "sorting algorithms"
2647
+ ]
2648
+
2649
+ model_path = 'Alibaba-NLP/gte-base-en-v1.5'
2650
+ tokenizer = AutoTokenizer.from_pretrained(model_path)
2651
+ model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
2652
+
2653
+ # Tokenize the input texts
2654
+ batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
2655
+
2656
+ outputs = model(**batch_dict)
2657
+ embeddings = outputs.last_hidden_state[:, 0]
2658
+
2659
+ # (Optionally) normalize embeddings
2660
+ embeddings = F.normalize(embeddings, p=2, dim=1)
2661
+ scores = (embeddings[:1] @ embeddings[1:].T) * 100
2662
+ print(scores.tolist())
2663
+ ```
2664
+
2665
+ **It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/test-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
2666
+
2667
+
2668
+ Use with sentence-transformers:
2669
+
2670
+ ```python
2671
+ from sentence_transformers import SentenceTransformer
2672
+ from sentence_transformers.util import cos_sim
2673
+
2674
+ sentences = ['That is a happy person', 'That is a very happy person']
2675
+
2676
+ model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5')
2677
+ embeddings = model.encode(sentences)
2678
+ print(cos_sim(embeddings[0], embeddings[1]))
2679
+ ```
2680
+
2681
+ ## Training Details
2682
+
2683
+ ### Training Data
2684
+
2685
+ - Masked language modeling (MLM): `c4-en`
2686
+ - Weak-supervised contrastive (WSC) pre-training: GTE pre-training data
2687
+ - Supervised contrastive fine-tuning: GTE fine-tuning data
2688
+
2689
+ ### Training Procedure
2690
+
2691
+ - MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000
2692
+ - MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000
2693
+ - WSC: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000
2694
+ - Fine-tuning: TODO
2695
+
2696
+
2697
+ ## Evaluation
2698
+
2699
+
2700
+ ### MTEB
2701
+
2702
+ The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
2703
+
2704
+ The gte results setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2).
2705
+
2706
+ | Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
2707
+ |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
2708
+ | [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 409 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
2709
+ | [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
2710
+ | [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
2711
+ | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
2712
+ | [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
2713
+ | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
2714
+
2715
+
2716
+ ### LoCo
2717
+
2718
+ | Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
2719
+ |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
2720
+ | [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
2721
+ | [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
2722
+ | [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
2723
+
2724
+
2725
+
2726
+ ## Citation [TODO]
2727
+
2728
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
2729
+
2730
+ **BibTeX:**
2731
+
2732
+ [More Information Needed]
2733
+
2734
+ **APA:**
2735
+
2736
+ [More Information Needed]
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