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2194
+ - type: precision_at_5
2195
+ value: 18.4
2196
+ - type: recall_at_1
2197
+ value: 62.09400000000001
2198
+ - type: recall_at_10
2199
+ value: 89.022
2200
+ - type: recall_at_100
2201
+ value: 96.833
2202
+ - type: recall_at_1000
2203
+ value: 99.333
2204
+ - type: recall_at_3
2205
+ value: 75.922
2206
+ - type: recall_at_5
2207
+ value: 82.428
2208
+ - task:
2209
+ type: PairClassification
2210
+ dataset:
2211
+ type: mteb/sprintduplicatequestions-pairclassification
2212
+ name: MTEB SprintDuplicateQuestions
2213
+ config: default
2214
+ split: test
2215
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2216
+ metrics:
2217
+ - type: cos_sim_accuracy
2218
+ value: 99.82178217821782
2219
+ - type: cos_sim_ap
2220
+ value: 95.71282508220798
2221
+ - type: cos_sim_f1
2222
+ value: 90.73120494335737
2223
+ - type: cos_sim_precision
2224
+ value: 93.52441613588111
2225
+ - type: cos_sim_recall
2226
+ value: 88.1
2227
+ - type: dot_accuracy
2228
+ value: 99.73960396039604
2229
+ - type: dot_ap
2230
+ value: 92.98534606529098
2231
+ - type: dot_f1
2232
+ value: 86.83024536805209
2233
+ - type: dot_precision
2234
+ value: 86.96088264794383
2235
+ - type: dot_recall
2236
+ value: 86.7
2237
+ - type: euclidean_accuracy
2238
+ value: 99.82475247524752
2239
+ - type: euclidean_ap
2240
+ value: 95.72927039014849
2241
+ - type: euclidean_f1
2242
+ value: 90.89974293059126
2243
+ - type: euclidean_precision
2244
+ value: 93.54497354497354
2245
+ - type: euclidean_recall
2246
+ value: 88.4
2247
+ - type: manhattan_accuracy
2248
+ value: 99.82574257425742
2249
+ - type: manhattan_ap
2250
+ value: 95.72142177390405
2251
+ - type: manhattan_f1
2252
+ value: 91.00152516522625
2253
+ - type: manhattan_precision
2254
+ value: 92.55429162357808
2255
+ - type: manhattan_recall
2256
+ value: 89.5
2257
+ - type: max_accuracy
2258
+ value: 99.82574257425742
2259
+ - type: max_ap
2260
+ value: 95.72927039014849
2261
+ - type: max_f1
2262
+ value: 91.00152516522625
2263
+ - task:
2264
+ type: Clustering
2265
+ dataset:
2266
+ type: mteb/stackexchange-clustering
2267
+ name: MTEB StackExchangeClustering
2268
+ config: default
2269
+ split: test
2270
+ revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
2271
+ metrics:
2272
+ - type: v_measure
2273
+ value: 66.63957663468679
2274
+ - task:
2275
+ type: Clustering
2276
+ dataset:
2277
+ type: mteb/stackexchange-clustering-p2p
2278
+ name: MTEB StackExchangeClusteringP2P
2279
+ config: default
2280
+ split: test
2281
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2282
+ metrics:
2283
+ - type: v_measure
2284
+ value: 36.003307257923964
2285
+ - task:
2286
+ type: Reranking
2287
+ dataset:
2288
+ type: mteb/stackoverflowdupquestions-reranking
2289
+ name: MTEB StackOverflowDupQuestions
2290
+ config: default
2291
+ split: test
2292
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2293
+ metrics:
2294
+ - type: map
2295
+ value: 53.005825525863905
2296
+ - type: mrr
2297
+ value: 53.854683919022165
2298
+ - task:
2299
+ type: Summarization
2300
+ dataset:
2301
+ type: mteb/summeval
2302
+ name: MTEB SummEval
2303
+ config: default
2304
+ split: test
2305
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2306
+ metrics:
2307
+ - type: cos_sim_pearson
2308
+ value: 30.503611569974098
2309
+ - type: cos_sim_spearman
2310
+ value: 31.17155564248449
2311
+ - type: dot_pearson
2312
+ value: 26.740428413981306
2313
+ - type: dot_spearman
2314
+ value: 26.55727635469746
2315
+ - task:
2316
+ type: Retrieval
2317
+ dataset:
2318
+ type: trec-covid
2319
+ name: MTEB TRECCOVID
2320
+ config: default
2321
+ split: test
2322
+ revision: None
2323
+ metrics:
2324
+ - type: map_at_1
2325
+ value: 0.23600000000000002
2326
+ - type: map_at_10
2327
+ value: 1.7670000000000001
2328
+ - type: map_at_100
2329
+ value: 10.208
2330
+ - type: map_at_1000
2331
+ value: 25.997999999999998
2332
+ - type: map_at_3
2333
+ value: 0.605
2334
+ - type: map_at_5
2335
+ value: 0.9560000000000001
2336
+ - type: mrr_at_1
2337
+ value: 84.0
2338
+ - type: mrr_at_10
2339
+ value: 90.167
2340
+ - type: mrr_at_100
2341
+ value: 90.167
2342
+ - type: mrr_at_1000
2343
+ value: 90.167
2344
+ - type: mrr_at_3
2345
+ value: 89.667
2346
+ - type: mrr_at_5
2347
+ value: 90.167
2348
+ - type: ndcg_at_1
2349
+ value: 77.0
2350
+ - type: ndcg_at_10
2351
+ value: 68.783
2352
+ - type: ndcg_at_100
2353
+ value: 54.196
2354
+ - type: ndcg_at_1000
2355
+ value: 52.077
2356
+ - type: ndcg_at_3
2357
+ value: 71.642
2358
+ - type: ndcg_at_5
2359
+ value: 70.45700000000001
2360
+ - type: precision_at_1
2361
+ value: 84.0
2362
+ - type: precision_at_10
2363
+ value: 73.0
2364
+ - type: precision_at_100
2365
+ value: 55.48
2366
+ - type: precision_at_1000
2367
+ value: 23.102
2368
+ - type: precision_at_3
2369
+ value: 76.0
2370
+ - type: precision_at_5
2371
+ value: 74.8
2372
+ - type: recall_at_1
2373
+ value: 0.23600000000000002
2374
+ - type: recall_at_10
2375
+ value: 1.9869999999999999
2376
+ - type: recall_at_100
2377
+ value: 13.749
2378
+ - type: recall_at_1000
2379
+ value: 50.157
2380
+ - type: recall_at_3
2381
+ value: 0.633
2382
+ - type: recall_at_5
2383
+ value: 1.0290000000000001
2384
+ - task:
2385
+ type: Retrieval
2386
+ dataset:
2387
+ type: webis-touche2020
2388
+ name: MTEB Touche2020
2389
+ config: default
2390
+ split: test
2391
+ revision: None
2392
+ metrics:
2393
+ - type: map_at_1
2394
+ value: 1.437
2395
+ - type: map_at_10
2396
+ value: 8.791
2397
+ - type: map_at_100
2398
+ value: 15.001999999999999
2399
+ - type: map_at_1000
2400
+ value: 16.549
2401
+ - type: map_at_3
2402
+ value: 3.8080000000000003
2403
+ - type: map_at_5
2404
+ value: 5.632000000000001
2405
+ - type: mrr_at_1
2406
+ value: 20.408
2407
+ - type: mrr_at_10
2408
+ value: 36.96
2409
+ - type: mrr_at_100
2410
+ value: 37.912
2411
+ - type: mrr_at_1000
2412
+ value: 37.912
2413
+ - type: mrr_at_3
2414
+ value: 29.592000000000002
2415
+ - type: mrr_at_5
2416
+ value: 34.489999999999995
2417
+ - type: ndcg_at_1
2418
+ value: 19.387999999999998
2419
+ - type: ndcg_at_10
2420
+ value: 22.554
2421
+ - type: ndcg_at_100
2422
+ value: 35.197
2423
+ - type: ndcg_at_1000
2424
+ value: 46.58
2425
+ - type: ndcg_at_3
2426
+ value: 20.285
2427
+ - type: ndcg_at_5
2428
+ value: 21.924
2429
+ - type: precision_at_1
2430
+ value: 20.408
2431
+ - type: precision_at_10
2432
+ value: 21.837
2433
+ - type: precision_at_100
2434
+ value: 7.754999999999999
2435
+ - type: precision_at_1000
2436
+ value: 1.537
2437
+ - type: precision_at_3
2438
+ value: 21.769
2439
+ - type: precision_at_5
2440
+ value: 23.673
2441
+ - type: recall_at_1
2442
+ value: 1.437
2443
+ - type: recall_at_10
2444
+ value: 16.314999999999998
2445
+ - type: recall_at_100
2446
+ value: 47.635
2447
+ - type: recall_at_1000
2448
+ value: 82.963
2449
+ - type: recall_at_3
2450
+ value: 4.955
2451
+ - type: recall_at_5
2452
+ value: 8.805
2453
+ - task:
2454
+ type: Classification
2455
+ dataset:
2456
+ type: mteb/toxic_conversations_50k
2457
+ name: MTEB ToxicConversationsClassification
2458
+ config: default
2459
+ split: test
2460
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2461
+ metrics:
2462
+ - type: accuracy
2463
+ value: 71.6128
2464
+ - type: ap
2465
+ value: 14.279639861175664
2466
+ - type: f1
2467
+ value: 54.922292491204274
2468
+ - task:
2469
+ type: Classification
2470
+ dataset:
2471
+ type: mteb/tweet_sentiment_extraction
2472
+ name: MTEB TweetSentimentExtractionClassification
2473
+ config: default
2474
+ split: test
2475
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2476
+ metrics:
2477
+ - type: accuracy
2478
+ value: 57.01188455008489
2479
+ - type: f1
2480
+ value: 57.377953019225515
2481
+ - task:
2482
+ type: Clustering
2483
+ dataset:
2484
+ type: mteb/twentynewsgroups-clustering
2485
+ name: MTEB TwentyNewsgroupsClustering
2486
+ config: default
2487
+ split: test
2488
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2489
+ metrics:
2490
+ - type: v_measure
2491
+ value: 52.306769136544254
2492
+ - task:
2493
+ type: PairClassification
2494
+ dataset:
2495
+ type: mteb/twittersemeval2015-pairclassification
2496
+ name: MTEB TwitterSemEval2015
2497
+ config: default
2498
+ split: test
2499
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2500
+ metrics:
2501
+ - type: cos_sim_accuracy
2502
+ value: 85.64701674912082
2503
+ - type: cos_sim_ap
2504
+ value: 72.46600945328552
2505
+ - type: cos_sim_f1
2506
+ value: 67.96572367648784
2507
+ - type: cos_sim_precision
2508
+ value: 61.21801649397336
2509
+ - type: cos_sim_recall
2510
+ value: 76.38522427440633
2511
+ - type: dot_accuracy
2512
+ value: 82.33295583238957
2513
+ - type: dot_ap
2514
+ value: 62.54843443071716
2515
+ - type: dot_f1
2516
+ value: 60.38378562507096
2517
+ - type: dot_precision
2518
+ value: 52.99980067769583
2519
+ - type: dot_recall
2520
+ value: 70.15831134564644
2521
+ - type: euclidean_accuracy
2522
+ value: 85.7423854085951
2523
+ - type: euclidean_ap
2524
+ value: 72.76873850945174
2525
+ - type: euclidean_f1
2526
+ value: 68.23556960543262
2527
+ - type: euclidean_precision
2528
+ value: 61.3344559040202
2529
+ - type: euclidean_recall
2530
+ value: 76.88654353562005
2531
+ - type: manhattan_accuracy
2532
+ value: 85.74834594981225
2533
+ - type: manhattan_ap
2534
+ value: 72.66825372446462
2535
+ - type: manhattan_f1
2536
+ value: 68.21539194662853
2537
+ - type: manhattan_precision
2538
+ value: 62.185056472632496
2539
+ - type: manhattan_recall
2540
+ value: 75.54089709762533
2541
+ - type: max_accuracy
2542
+ value: 85.74834594981225
2543
+ - type: max_ap
2544
+ value: 72.76873850945174
2545
+ - type: max_f1
2546
+ value: 68.23556960543262
2547
+ - task:
2548
+ type: PairClassification
2549
+ dataset:
2550
+ type: mteb/twitterurlcorpus-pairclassification
2551
+ name: MTEB TwitterURLCorpus
2552
+ config: default
2553
+ split: test
2554
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2555
+ metrics:
2556
+ - type: cos_sim_accuracy
2557
+ value: 88.73171110334924
2558
+ - type: cos_sim_ap
2559
+ value: 85.51855542063649
2560
+ - type: cos_sim_f1
2561
+ value: 77.95706775700934
2562
+ - type: cos_sim_precision
2563
+ value: 74.12524298805887
2564
+ - type: cos_sim_recall
2565
+ value: 82.20665229442562
2566
+ - type: dot_accuracy
2567
+ value: 86.94842240074514
2568
+ - type: dot_ap
2569
+ value: 80.90995345771762
2570
+ - type: dot_f1
2571
+ value: 74.20765027322403
2572
+ - type: dot_precision
2573
+ value: 70.42594385285575
2574
+ - type: dot_recall
2575
+ value: 78.41854019094548
2576
+ - type: euclidean_accuracy
2577
+ value: 88.73753250281368
2578
+ - type: euclidean_ap
2579
+ value: 85.54712254033734
2580
+ - type: euclidean_f1
2581
+ value: 78.07565728654365
2582
+ - type: euclidean_precision
2583
+ value: 75.1120597652081
2584
+ - type: euclidean_recall
2585
+ value: 81.282722513089
2586
+ - type: manhattan_accuracy
2587
+ value: 88.72588970388482
2588
+ - type: manhattan_ap
2589
+ value: 85.52118291594071
2590
+ - type: manhattan_f1
2591
+ value: 78.04428724070593
2592
+ - type: manhattan_precision
2593
+ value: 74.83219105490002
2594
+ - type: manhattan_recall
2595
+ value: 81.54450261780106
2596
+ - type: max_accuracy
2597
+ value: 88.73753250281368
2598
+ - type: max_ap
2599
+ value: 85.54712254033734
2600
+ - type: max_f1
2601
+ value: 78.07565728654365
2602
+ language:
2603
+ - en
2604
  license: mit
2605
  ---
2606
+
2607
+ # gte-base
2608
+
2609
+ General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
2610
+
2611
+ The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
2612
+
2613
+ ## Metrics
2614
+
2615
+ We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
2616
+
2617
+
2618
+
2619
+ | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
2620
+ |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
2621
+ | [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
2622
+ | [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
2623
+ | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
2624
+ | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
2625
+ | [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
2626
+ | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
2627
+ | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
2628
+ | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
2629
+ | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
2630
+ | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
2631
+ | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
2632
+ | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
2633
+ | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
2634
+ | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
2635
+
2636
+
2637
+ ## Usage
2638
+
2639
+ Code example
2640
+
2641
+ ```python
2642
+ import torch.nn.functional as F
2643
+ from torch import Tensor
2644
+ from transformers import AutoTokenizer, AutoModel
2645
+
2646
+ def average_pool(last_hidden_states: Tensor,
2647
+ attention_mask: Tensor) -> Tensor:
2648
+ last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
2649
+ return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
2650
+
2651
+ input_texts = [
2652
+ "what is the capital of China?",
2653
+ "how to implement quick sort in python?",
2654
+ "Beijing",
2655
+ "sorting algorithms"
2656
+ ]
2657
+
2658
+ tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base")
2659
+ model = AutoModel.from_pretrained("thenlper/gte-base")
2660
+
2661
+ # Tokenize the input texts
2662
+ batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
2663
+
2664
+ outputs = model(**batch_dict)
2665
+ embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
2666
+
2667
+ # (Optionally) normalize embeddings
2668
+ embeddings = F.normalize(embeddings, p=2, dim=1)
2669
+ scores = (embeddings[:1] @ embeddings[1:].T) * 100
2670
+ print(scores.tolist())
2671
+ ```
2672
+
2673
+ Use with sentence-transformers:
2674
+ ```python
2675
+ from sentence_transformers import SentenceTransformer
2676
+ from sentence_transformers.util import cos_sim
2677
+
2678
+ sentences = ['That is a happy person', 'That is a very happy person']
2679
+
2680
+ model = SentenceTransformer('thenlper/gte-base')
2681
+ embeddings = model.encode(sentences)
2682
+ print(cos_sim(embeddings[0], embeddings[1]))
2683
+ ```
2684
+
2685
+ ### Limitation
2686
+
2687
+ This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
2688
+
2689
+ ### Citation
2690
+
2691
+ If you find our paper or models helpful, please consider citing them as follows:
2692
+
2693
+ ```
2694
+ @misc{li2023general,
2695
+ title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
2696
+ author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
2697
+ year={2023},
2698
+ eprint={2308.03281},
2699
+ archivePrefix={arXiv},
2700
+ primaryClass={cs.CL}
2701
+ }
2702
+ ```
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