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  4. special_tokens_map.json +7 -0
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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2208
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2267
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2269
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2300
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2304
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+ type: trec-covid
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+ name: MTEB TRECCOVID
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+ revision: None
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2321
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+ - type: ndcg_at_5
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+ - type: precision_at_1
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+ value: 84.0
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+ - type: precision_at_10
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+ value: 74.0
2361
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+ value: 53.2
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+ - type: precision_at_1000
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+ value: 21.836
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+ value: 79.333
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+ value: 78.4
2369
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2381
+ - task:
2382
+ type: Retrieval
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2384
+ type: webis-touche2020
2385
+ name: MTEB Touche2020
2386
+ config: default
2387
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2388
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2389
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2390
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2449
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2451
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2453
+ type: mteb/toxic_conversations_50k
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+ name: MTEB ToxicConversationsClassification
2455
+ config: default
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+ split: test
2457
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
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2459
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2460
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2461
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2462
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+ - type: f1
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2466
+ type: Classification
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+ dataset:
2468
+ type: mteb/tweet_sentiment_extraction
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+ name: MTEB TweetSentimentExtractionClassification
2470
+ config: default
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+ split: test
2472
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2473
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2474
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2475
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+ - type: f1
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+ value: 60.69810537250234
2478
+ - task:
2479
+ type: Clustering
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2481
+ type: mteb/twentynewsgroups-clustering
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+ name: MTEB TwentyNewsgroupsClustering
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+ config: default
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2485
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
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2487
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2488
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2489
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2490
+ type: PairClassification
2491
+ dataset:
2492
+ type: mteb/twittersemeval2015-pairclassification
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+ name: MTEB TwitterSemEval2015
2494
+ config: default
2495
+ split: test
2496
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2497
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2498
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2499
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2500
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2501
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2542
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2543
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2544
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2545
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2546
+ dataset:
2547
+ type: mteb/twitterurlcorpus-pairclassification
2548
+ name: MTEB TwitterURLCorpus
2549
+ config: default
2550
+ split: test
2551
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
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+ metrics:
2553
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+ - type: cos_sim_ap
2556
+ value: 86.63028865482376
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+ - type: cos_sim_precision
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+ value: 82.99199260856174
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+ - type: dot_accuracy
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+ value: 85.23305002522606
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+ value: 76.0482687263196
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+ - type: dot_precision
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+ value: 65.86933474688577
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+ - type: dot_recall
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+ value: 76.53988296889437
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+ value: 89.26145845461248
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+ - type: euclidean_ap
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+ value: 86.54073288416006
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+ - type: euclidean_f1
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+ value: 78.9721371479794
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+ - type: euclidean_precision
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+ value: 76.68649354417525
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+ - type: euclidean_recall
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+ value: 81.39821373575609
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+ - type: manhattan_accuracy
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+ value: 89.22847052431405
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+ - type: manhattan_ap
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+ value: 75.32694594027555
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+ - type: manhattan_recall
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+ - type: max_accuracy
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+ value: 89.29056545193464
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+ - type: max_ap
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+ value: 86.63028865482376
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+ - type: max_f1
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+ value: 79.18166458532285
2599
+ ---
2600
+
2601
+ # E5-base-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 768.
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-base-v2')
2633
+ model = AutoModel.from_pretrained('intfloat/e5-base-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
+
2651
+ ## Benchmark Evaluation
2652
+
2653
+ Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
2654
+ on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
2655
+
2656
+ ## Citation
2657
+
2658
+ If you find our paper or models helpful, please consider cite as follows:
2659
+
2660
+ ```
2661
+ @article{wang2022text,
2662
+ title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
2663
+ author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
2664
+ journal={arXiv preprint arXiv:2212.03533},
2665
+ year={2022}
2666
+ }
2667
+ ```
2668
+
2669
+ ## Limitations
2670
+
2671
+ This model only works for English texts. Long texts will be truncated to at most 512 tokens.
2672
+
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+ }
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