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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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24
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26
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27
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28
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39
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42
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58
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488
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489
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494
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558
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+ config: default
2211
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2212
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2214
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2266
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2270
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2274
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2280
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2281
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2283
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2284
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+ name: MTEB StackOverflowDupQuestions
2287
+ config: default
2288
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2289
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
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+ value: 52.33861726508785
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2298
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2299
+ name: MTEB SummEval
2300
+ config: default
2301
+ split: test
2302
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2303
+ metrics:
2304
+ - type: cos_sim_pearson
2305
+ value: 25.658532855940212
2306
+ - type: cos_sim_spearman
2307
+ value: 25.202702076359323
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+ - type: dot_spearman
2311
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+ - task:
2313
+ type: Retrieval
2314
+ dataset:
2315
+ type: trec-covid
2316
+ name: MTEB TRECCOVID
2317
+ config: default
2318
+ split: test
2319
+ revision: None
2320
+ metrics:
2321
+ - type: map_at_1
2322
+ value: 0.22
2323
+ - type: map_at_10
2324
+ value: 1.9539999999999997
2325
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+ value: 27.861000000000004
2329
+ - type: map_at_3
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2336
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2341
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2342
+ value: 90.333
2343
+ - type: mrr_at_5
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+ value: 90.333
2345
+ - type: ndcg_at_1
2346
+ value: 80.0
2347
+ - type: ndcg_at_10
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+ value: 78.31700000000001
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+ - type: ndcg_at_100
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+ - type: ndcg_at_1000
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+ value: 52.733
2353
+ - type: ndcg_at_3
2354
+ value: 81.46900000000001
2355
+ - type: ndcg_at_5
2356
+ value: 80.74
2357
+ - type: precision_at_1
2358
+ value: 84.0
2359
+ - type: precision_at_10
2360
+ value: 84.0
2361
+ - type: precision_at_100
2362
+ value: 60.980000000000004
2363
+ - type: precision_at_1000
2364
+ value: 23.432
2365
+ - type: precision_at_3
2366
+ value: 87.333
2367
+ - type: precision_at_5
2368
+ value: 86.8
2369
+ - type: recall_at_1
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+ value: 0.22
2371
+ - type: recall_at_10
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+ value: 14.557999999999998
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2377
+ - type: recall_at_3
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+ value: 0.685
2379
+ - type: recall_at_5
2380
+ value: 1.121
2381
+ - task:
2382
+ type: Retrieval
2383
+ dataset:
2384
+ type: webis-touche2020
2385
+ name: MTEB Touche2020
2386
+ config: default
2387
+ split: test
2388
+ revision: None
2389
+ metrics:
2390
+ - type: map_at_1
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+ - type: map_at_10
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+ value: 11.701
2394
+ - type: map_at_100
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2398
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2400
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2402
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2449
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+ - task:
2451
+ type: Classification
2452
+ dataset:
2453
+ type: mteb/toxic_conversations_50k
2454
+ name: MTEB ToxicConversationsClassification
2455
+ config: default
2456
+ split: test
2457
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
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2459
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2460
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+ - type: ap
2462
+ value: 14.330910591410134
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+ - type: f1
2464
+ value: 54.45745186286521
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+ - task:
2466
+ type: Classification
2467
+ dataset:
2468
+ type: mteb/tweet_sentiment_extraction
2469
+ name: MTEB TweetSentimentExtractionClassification
2470
+ config: default
2471
+ split: test
2472
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2473
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2474
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2475
+ value: 61.20543293718167
2476
+ - type: f1
2477
+ value: 61.45365480309872
2478
+ - task:
2479
+ type: Clustering
2480
+ dataset:
2481
+ type: mteb/twentynewsgroups-clustering
2482
+ name: MTEB TwentyNewsgroupsClustering
2483
+ config: default
2484
+ split: test
2485
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
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+ metrics:
2487
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2488
+ value: 43.81162998944145
2489
+ - task:
2490
+ type: PairClassification
2491
+ dataset:
2492
+ type: mteb/twittersemeval2015-pairclassification
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+ name: MTEB TwitterSemEval2015
2494
+ config: default
2495
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2496
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2497
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2498
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2499
+ value: 86.69011146212075
2500
+ - type: cos_sim_ap
2501
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2502
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2504
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2506
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2510
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2511
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2512
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2513
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2515
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2519
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2520
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2521
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2522
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2523
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2524
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2526
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2538
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2539
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2540
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2541
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2542
+ - type: max_f1
2543
+ value: 70.10202763786646
2544
+ - task:
2545
+ type: PairClassification
2546
+ dataset:
2547
+ type: mteb/twitterurlcorpus-pairclassification
2548
+ name: MTEB TwitterURLCorpus
2549
+ config: default
2550
+ split: test
2551
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2552
+ metrics:
2553
+ - type: cos_sim_accuracy
2554
+ value: 89.25951798812434
2555
+ - type: cos_sim_ap
2556
+ value: 86.31476416599727
2557
+ - type: cos_sim_f1
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+ value: 78.52709971038477
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+ - type: cos_sim_precision
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+ value: 76.7629972792117
2561
+ - type: cos_sim_recall
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+ value: 80.37419156144134
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+ - type: dot_accuracy
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+ value: 88.03896456708192
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+ - type: dot_ap
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+ value: 83.26963599196237
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+ - type: dot_f1
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+ value: 76.72696459492317
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+ - type: dot_precision
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+ value: 73.56411162133521
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+ - type: dot_recall
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+ value: 80.17400677548507
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+ - type: euclidean_accuracy
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+ value: 89.21682772538519
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+ - type: euclidean_ap
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+ value: 86.29306071289969
2577
+ - type: euclidean_f1
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+ value: 78.40827030519554
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+ - type: euclidean_precision
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+ value: 77.42250243939053
2581
+ - type: euclidean_recall
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+ value: 79.41946412072683
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+ - type: manhattan_accuracy
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+ value: 89.22458959133776
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+ - type: manhattan_ap
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+ value: 86.2901934710645
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+ - type: manhattan_f1
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+ value: 78.54211378440453
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+ - type: manhattan_precision
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+ value: 76.85505858079729
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+ - type: manhattan_recall
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+ value: 80.30489682784109
2593
+ - type: max_accuracy
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+ value: 89.25951798812434
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+ - type: max_ap
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+ value: 86.31476416599727
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+ - type: max_f1
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+ value: 78.54211378440453
2599
+ ---
2600
+
2601
+ ## E5-large
2602
+
2603
+ [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
2604
+ Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
2605
+
2606
+ This model has 12 layers and the embedding size is 384.
2607
+
2608
+ ## Usage
2609
+
2610
+ Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
2611
+
2612
+ ```python
2613
+ import torch.nn.functional as F
2614
+
2615
+ from torch import Tensor
2616
+ from transformers import AutoTokenizer, AutoModel
2617
+ from transformers.modeling_outputs import BaseModelOutput
2618
+
2619
+
2620
+ def average_pool(last_hidden_states: Tensor,
2621
+ attention_mask: Tensor) -> Tensor:
2622
+ last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
2623
+ return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
2624
+
2625
+
2626
+ # Each input text should start with "query: " or "passage: ".
2627
+ # For tasks other than retrieval, you can simply use the "query: " prefix.
2628
+ input_texts = ['query: how much protein should a female eat',
2629
+ 'query: summit define',
2630
+ "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
2631
+ "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
2632
+
2633
+ tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-large')
2634
+ model = AutoModel.from_pretrained('intfloat/e5-large')
2635
+
2636
+ # Tokenize the input texts
2637
+ batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
2638
+
2639
+ outputs: BaseModelOutput = model(**batch_dict)
2640
+ embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
2641
+
2642
+ # (Optionally) normalize embeddings
2643
+ embeddings = F.normalize(embeddings, p=2, dim=1)
2644
+ scores = (embeddings[:2] @ embeddings[2:].T) * 100
2645
+ print(scores.tolist())
2646
+ ```
2647
+
2648
+ ## Training Details
2649
+
2650
+ Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
2651
+
2652
+ ## Benchmark Evaluation
2653
+
2654
+ Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
2655
+ on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
2656
+
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+ }
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tokenizer_config.json ADDED
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