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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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2203
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2206
+ type: PairClassification
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+ dataset:
2208
+ type: mteb/sprintduplicatequestions-pairclassification
2209
+ name: MTEB SprintDuplicateQuestions
2210
+ config: default
2211
+ split: test
2212
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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2214
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2265
+ config: default
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+ split: test
2267
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2269
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2270
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2274
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+ config: default
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2280
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2281
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+ name: MTEB StackOverflowDupQuestions
2287
+ config: default
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2289
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
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+ - type: mrr
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+ value: 50.916557911043206
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+ - task:
2296
+ type: Summarization
2297
+ dataset:
2298
+ type: mteb/summeval
2299
+ name: MTEB SummEval
2300
+ config: default
2301
+ split: test
2302
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2303
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2304
+ - type: cos_sim_pearson
2305
+ value: 31.562500894537145
2306
+ - type: cos_sim_spearman
2307
+ value: 31.162587976726307
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2309
+ value: 22.633662187735762
2310
+ - type: dot_spearman
2311
+ value: 22.723000282378962
2312
+ - 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: 0.219
2323
+ - type: map_at_10
2324
+ value: 1.871
2325
+ - type: map_at_100
2326
+ value: 10.487
2327
+ - type: map_at_1000
2328
+ value: 25.122
2329
+ - type: map_at_3
2330
+ value: 0.657
2331
+ - type: map_at_5
2332
+ value: 1.0699999999999998
2333
+ - type: mrr_at_1
2334
+ value: 84.0
2335
+ - type: mrr_at_10
2336
+ value: 89.567
2337
+ - type: mrr_at_100
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+ value: 89.748
2339
+ - type: mrr_at_1000
2340
+ value: 89.748
2341
+ - type: mrr_at_3
2342
+ value: 88.667
2343
+ - type: mrr_at_5
2344
+ value: 89.567
2345
+ - type: ndcg_at_1
2346
+ value: 80.0
2347
+ - type: ndcg_at_10
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+ value: 74.533
2349
+ - type: ndcg_at_100
2350
+ value: 55.839000000000006
2351
+ - type: ndcg_at_1000
2352
+ value: 49.748
2353
+ - type: ndcg_at_3
2354
+ value: 79.53099999999999
2355
+ - type: ndcg_at_5
2356
+ value: 78.245
2357
+ - type: precision_at_1
2358
+ value: 84.0
2359
+ - type: precision_at_10
2360
+ value: 78.4
2361
+ - type: precision_at_100
2362
+ value: 56.99999999999999
2363
+ - type: precision_at_1000
2364
+ value: 21.98
2365
+ - type: precision_at_3
2366
+ value: 85.333
2367
+ - type: precision_at_5
2368
+ value: 84.8
2369
+ - type: recall_at_1
2370
+ value: 0.219
2371
+ - type: recall_at_10
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2373
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+ value: 13.555
2375
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+ - type: recall_at_3
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+ value: 0.685
2379
+ - type: recall_at_5
2380
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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
2391
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_3
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2400
+ - type: map_at_5
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2414
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2433
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2447
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2448
+ - type: recall_at_5
2449
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2450
+ - 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
2458
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2459
+ - type: accuracy
2460
+ value: 71.0954
2461
+ - type: ap
2462
+ value: 14.216844153511959
2463
+ - type: f1
2464
+ value: 54.63687418565117
2465
+ - 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
+ metrics:
2474
+ - type: accuracy
2475
+ value: 61.46293152235427
2476
+ - type: f1
2477
+ value: 61.744177921638645
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
2486
+ metrics:
2487
+ - type: v_measure
2488
+ value: 41.12708617788644
2489
+ - task:
2490
+ type: PairClassification
2491
+ dataset:
2492
+ type: mteb/twittersemeval2015-pairclassification
2493
+ name: MTEB TwitterSemEval2015
2494
+ config: default
2495
+ split: test
2496
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2497
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2498
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2499
+ value: 85.75430649102938
2500
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2501
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2502
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2510
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2511
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2513
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2518
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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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2526
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2542
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2543
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+ - task:
2545
+ type: PairClassification
2546
+ dataset:
2547
+ type: mteb/twitterurlcorpus-pairclassification
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+ name: MTEB TwitterURLCorpus
2549
+ config: default
2550
+ split: test
2551
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
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+ metrics:
2553
+ - type: cos_sim_accuracy
2554
+ value: 88.90441262079403
2555
+ - type: cos_sim_ap
2556
+ value: 85.79331880741438
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+ - type: cos_sim_f1
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+ - type: cos_sim_precision
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+ value: 74.6683424102779
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+ - type: cos_sim_recall
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+ value: 82.33754234678165
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+ - type: dot_accuracy
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+ value: 84.89928978926534
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+ - type: dot_ap
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+ value: 75.25819218316
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+ - type: dot_f1
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+ value: 69.88730119720536
2569
+ - type: dot_precision
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+ value: 64.23362374959665
2571
+ - type: dot_recall
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+ value: 76.63227594702803
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+ - type: euclidean_accuracy
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+ value: 89.01695967710637
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+ - type: euclidean_ap
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+ value: 85.98986606038852
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+ - type: euclidean_f1
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+ value: 78.5277880014722
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+ - type: euclidean_precision
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+ value: 75.22211253701876
2581
+ - type: euclidean_recall
2582
+ value: 82.13735756082538
2583
+ - type: manhattan_accuracy
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+ value: 88.99561454573679
2585
+ - type: manhattan_ap
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+ value: 85.92262421793953
2587
+ - type: manhattan_f1
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+ value: 78.38866094740769
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+ - type: manhattan_precision
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+ value: 76.02373028505282
2591
+ - type: manhattan_recall
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+ value: 80.9054511857099
2593
+ - type: max_accuracy
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+ value: 89.01695967710637
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+ - type: max_ap
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+ value: 85.98986606038852
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+ - type: max_f1
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+ value: 78.5277880014722
2599
+ ---
2600
+
2601
+ # E5-small-v2
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
+
2618
+
2619
+ def average_pool(last_hidden_states: Tensor,
2620
+ attention_mask: Tensor) -> Tensor:
2621
+ last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
2622
+ return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
2623
+
2624
+
2625
+ # Each input text should start with "query: " or "passage: ".
2626
+ # For tasks other than retrieval, you can simply use the "query: " prefix.
2627
+ input_texts = ['query: how much protein should a female eat',
2628
+ 'query: summit define',
2629
+ "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.",
2630
+ "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."]
2631
+
2632
+ tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small-v2')
2633
+ model = AutoModel.from_pretrained('intfloat/e5-small-v2')
2634
+
2635
+ # Tokenize the input texts
2636
+ batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
2637
+
2638
+ outputs = model(**batch_dict)
2639
+ embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
2640
+
2641
+ # (Optionally) normalize embeddings
2642
+ embeddings = F.normalize(embeddings, p=2, dim=1)
2643
+ scores = (embeddings[:2] @ embeddings[2:].T) * 100
2644
+ print(scores.tolist())
2645
+ ```
2646
+
2647
+ ## Training Details
2648
+
2649
+ Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
2650
+
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+ }
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