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2212
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2214
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2216
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2267
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2269
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2271
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2289
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2291
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2292
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2301
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2302
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2303
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2304
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2305
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2306
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2308
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2319
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2320
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2321
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2322
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2323
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2324
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2325
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2379
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2381
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2383
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2384
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2385
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2386
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2387
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2388
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2389
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2390
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2391
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2392
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2393
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2394
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2404
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2413
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2415
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2416
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2417
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2418
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2419
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2420
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2421
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2422
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2423
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2424
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2425
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2426
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2430
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2432
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2435
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2436
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2437
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2438
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2439
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2440
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2441
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2442
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2443
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2444
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2445
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2447
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2449
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2450
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2451
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2453
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2454
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2455
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2456
+ dataset:
2457
+ type: mteb/toxic_conversations_50k
2458
+ name: MTEB ToxicConversationsClassification
2459
+ config: default
2460
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2461
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2462
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2463
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2464
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2465
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2471
+ dataset:
2472
+ type: mteb/tweet_sentiment_extraction
2473
+ name: MTEB TweetSentimentExtractionClassification
2474
+ config: default
2475
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2476
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2477
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2484
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2485
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2487
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2488
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2489
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2490
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2491
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2492
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2494
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2495
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2496
+ type: mteb/twittersemeval2015-pairclassification
2497
+ name: MTEB TwitterSemEval2015
2498
+ config: default
2499
+ split: test
2500
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2501
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2502
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2503
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2519
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2521
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2533
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2546
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2547
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2549
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2550
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2551
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2552
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2553
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2554
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2555
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2556
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2557
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2559
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2565
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2577
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2587
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2595
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+ - type: max_accuracy
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2599
+ - type: max_ap
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+ value: 85.6003454431979
2601
+ - type: max_f1
2602
+ value: 77.6957163958641
2603
+ license: mit
2604
+ language:
2605
+ - en
2606
+ ---
2607
+
2608
+
2609
+ <h1 align="center">FlagEmbedding</h1>
2610
+
2611
+
2612
+ <h4 align="center">
2613
+ <p>
2614
+ <a href=#model-list>Model List</a> |
2615
+ <a href=#frequently-asked-questions>FAQ</a> |
2616
+ <a href=#usage>Usage</a> |
2617
+ <a href="#evaluation">Evaluation</a> |
2618
+ <a href="#train">Train</a> |
2619
+ <a href="#contact">Contact</a> |
2620
+ <a href="#citation">Citation</a> |
2621
+ <a href="#license">License</a>
2622
+ <p>
2623
+ </h4>
2624
+
2625
+ For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
2626
+
2627
+ If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3).
2628
+
2629
+
2630
+ [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
2631
+
2632
+ FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
2633
+
2634
+ - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
2635
+ - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
2636
+ - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
2637
+ - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
2638
+ - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
2639
+
2640
+ ## News
2641
+ - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
2642
+ It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
2643
+ [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
2644
+ - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
2645
+ - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
2646
+ - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
2647
+ - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
2648
+ - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
2649
+ - 09/12/2023: New models:
2650
+ - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
2651
+ - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
2652
+
2653
+
2654
+ <details>
2655
+ <summary>More</summary>
2656
+ <!-- ### More -->
2657
+
2658
+ - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
2659
+ - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
2660
+ - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
2661
+ - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
2662
+ - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
2663
+
2664
+ </details>
2665
+
2666
+
2667
+ ## Model List
2668
+
2669
+ `bge` is short for `BAAI general embedding`.
2670
+
2671
+ | Model | Language | | Description | query instruction for retrieval [1] |
2672
+ |:-------------------------------|:--------:| :--------:| :--------:|:--------:|
2673
+ | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
2674
+ | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
2675
+ | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
2676
+ | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
2677
+ | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
2678
+ | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
2679
+ | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
2680
+ | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` |
2681
+ | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` |
2682
+ | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章:` |
2683
+ | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
2684
+ | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
2685
+ | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
2686
+ | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章:` |
2687
+ | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章:` |
2688
+ | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章:` |
2689
+
2690
+ [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
2691
+
2692
+ [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
2693
+ For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
2694
+
2695
+ All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
2696
+ If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
2697
+
2698
+
2699
+ ## Frequently asked questions
2700
+
2701
+ <details>
2702
+ <summary>1. How to fine-tune bge embedding model?</summary>
2703
+
2704
+ <!-- ### How to fine-tune bge embedding model? -->
2705
+ Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
2706
+ Some suggestions:
2707
+ - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
2708
+ - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
2709
+ - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
2710
+
2711
+
2712
+ </details>
2713
+
2714
+ <details>
2715
+ <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
2716
+
2717
+ <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
2718
+ **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
2719
+
2720
+ Since we finetune the models by contrastive learning with a temperature of 0.01,
2721
+ the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
2722
+ So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
2723
+
2724
+ For downstream tasks, such as passage retrieval or semantic similarity,
2725
+ **what matters is the relative order of the scores, not the absolute value.**
2726
+ If you need to filter similar sentences based on a similarity threshold,
2727
+ please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
2728
+
2729
+ </details>
2730
+
2731
+ <details>
2732
+ <summary>3. When does the query instruction need to be used</summary>
2733
+
2734
+ <!-- ### When does the query instruction need to be used -->
2735
+
2736
+ For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
2737
+ No instruction only has a slight degradation in retrieval performance compared with using instruction.
2738
+ So you can generate embedding without instruction in all cases for convenience.
2739
+
2740
+ For a retrieval task that uses short queries to find long related documents,
2741
+ it is recommended to add instructions for these short queries.
2742
+ **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
2743
+ In all cases, the documents/passages do not need to add the instruction.
2744
+
2745
+ </details>
2746
+
2747
+
2748
+ ## Usage
2749
+
2750
+ ### Usage for Embedding Model
2751
+
2752
+ Here are some examples for using `bge` models with
2753
+ [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
2754
+
2755
+ #### Using FlagEmbedding
2756
+ ```
2757
+ pip install -U FlagEmbedding
2758
+ ```
2759
+ If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
2760
+
2761
+ ```python
2762
+ from FlagEmbedding import FlagModel
2763
+ sentences_1 = ["样例数据-1", "样例数据-2"]
2764
+ sentences_2 = ["样例数据-3", "样例数据-4"]
2765
+ model = FlagModel('BAAI/bge-large-zh-v1.5',
2766
+ query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章:",
2767
+ use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
2768
+ embeddings_1 = model.encode(sentences_1)
2769
+ embeddings_2 = model.encode(sentences_2)
2770
+ similarity = embeddings_1 @ embeddings_2.T
2771
+ print(similarity)
2772
+
2773
+ # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
2774
+ # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
2775
+ queries = ['query_1', 'query_2']
2776
+ passages = ["样例文档-1", "样例文档-2"]
2777
+ q_embeddings = model.encode_queries(queries)
2778
+ p_embeddings = model.encode(passages)
2779
+ scores = q_embeddings @ p_embeddings.T
2780
+ ```
2781
+ For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
2782
+
2783
+ By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
2784
+ You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
2785
+
2786
+
2787
+ #### Using Sentence-Transformers
2788
+
2789
+ You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
2790
+
2791
+ ```
2792
+ pip install -U sentence-transformers
2793
+ ```
2794
+ ```python
2795
+ from sentence_transformers import SentenceTransformer
2796
+ sentences_1 = ["样例数据-1", "样例数据-2"]
2797
+ sentences_2 = ["样例数据-3", "样例数据-4"]
2798
+ model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
2799
+ embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
2800
+ embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
2801
+ similarity = embeddings_1 @ embeddings_2.T
2802
+ print(similarity)
2803
+ ```
2804
+ For s2p(short query to long passage) retrieval task,
2805
+ each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
2806
+ But the instruction is not needed for passages.
2807
+ ```python
2808
+ from sentence_transformers import SentenceTransformer
2809
+ queries = ['query_1', 'query_2']
2810
+ passages = ["样例文档-1", "样例文档-2"]
2811
+ instruction = "为这个句子生成表示以用于检索相关文章:"
2812
+
2813
+ model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
2814
+ q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
2815
+ p_embeddings = model.encode(passages, normalize_embeddings=True)
2816
+ scores = q_embeddings @ p_embeddings.T
2817
+ ```
2818
+
2819
+ #### Using Langchain
2820
+
2821
+ You can use `bge` in langchain like this:
2822
+ ```python
2823
+ from langchain.embeddings import HuggingFaceBgeEmbeddings
2824
+ model_name = "BAAI/bge-large-en-v1.5"
2825
+ model_kwargs = {'device': 'cuda'}
2826
+ encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
2827
+ model = HuggingFaceBgeEmbeddings(
2828
+ model_name=model_name,
2829
+ model_kwargs=model_kwargs,
2830
+ encode_kwargs=encode_kwargs,
2831
+ query_instruction="为这个句子生成表示以用于检索相关文章:"
2832
+ )
2833
+ model.query_instruction = "为这个句子生成表示以用于检索相关文章:"
2834
+ ```
2835
+
2836
+
2837
+ #### Using HuggingFace Transformers
2838
+
2839
+ With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
2840
+
2841
+ ```python
2842
+ from transformers import AutoTokenizer, AutoModel
2843
+ import torch
2844
+ # Sentences we want sentence embeddings for
2845
+ sentences = ["样例数据-1", "样例数据-2"]
2846
+
2847
+ # Load model from HuggingFace Hub
2848
+ tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
2849
+ model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
2850
+ model.eval()
2851
+
2852
+ # Tokenize sentences
2853
+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
2854
+ # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
2855
+ # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
2856
+
2857
+ # Compute token embeddings
2858
+ with torch.no_grad():
2859
+ model_output = model(**encoded_input)
2860
+ # Perform pooling. In this case, cls pooling.
2861
+ sentence_embeddings = model_output[0][:, 0]
2862
+ # normalize embeddings
2863
+ sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
2864
+ print("Sentence embeddings:", sentence_embeddings)
2865
+ ```
2866
+
2867
+ #### Usage of the ONNX files
2868
+
2869
+ ```python
2870
+ from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore
2871
+
2872
+ import torch
2873
+ from transformers import AutoModel, AutoTokenizer
2874
+
2875
+ tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5')
2876
+ model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13")
2877
+ model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx")
2878
+
2879
+ # Sentences we want sentence embeddings for
2880
+ sentences = ["样例数据-1", "样例数据-2"]
2881
+
2882
+ # Tokenize sentences
2883
+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
2884
+ # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
2885
+ # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
2886
+
2887
+ model_output_ort = model_ort(**encoded_input)
2888
+ # Compute token embeddings
2889
+ with torch.no_grad():
2890
+ model_output = model(**encoded_input)
2891
+
2892
+ # model_output and model_output_ort are identical
2893
+
2894
+ ```
2895
+
2896
+ Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
2897
+ ```python
2898
+ import asyncio
2899
+ from infinity_emb import AsyncEmbeddingEngine, EngineArgs
2900
+
2901
+ sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
2902
+ engine = AsyncEmbeddingEngine.from_args(
2903
+ EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
2904
+ ))
2905
+
2906
+ async def main():
2907
+ async with engine:
2908
+ embeddings, usage = await engine.embed(sentences=sentences)
2909
+ asyncio.run(main())
2910
+ ```
2911
+
2912
+ ### Usage for Reranker
2913
+
2914
+ Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
2915
+ You can get a relevance score by inputting query and passage to the reranker.
2916
+ The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
2917
+
2918
+
2919
+ #### Using FlagEmbedding
2920
+ ```
2921
+ pip install -U FlagEmbedding
2922
+ ```
2923
+
2924
+ Get relevance scores (higher scores indicate more relevance):
2925
+ ```python
2926
+ from FlagEmbedding import FlagReranker
2927
+ reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
2928
+
2929
+ score = reranker.compute_score(['query', 'passage'])
2930
+ print(score)
2931
+
2932
+ scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
2933
+ print(scores)
2934
+ ```
2935
+
2936
+
2937
+ #### Using Huggingface transformers
2938
+
2939
+ ```python
2940
+ import torch
2941
+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
2942
+
2943
+ tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
2944
+ model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
2945
+ model.eval()
2946
+
2947
+ pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
2948
+ with torch.no_grad():
2949
+ inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
2950
+ scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
2951
+ print(scores)
2952
+ ```
2953
+
2954
+ ## Evaluation
2955
+
2956
+ `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
2957
+ For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
2958
+
2959
+ - **MTEB**:
2960
+
2961
+ | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
2962
+ |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
2963
+ | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
2964
+ | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
2965
+ | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
2966
+ | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
2967
+ | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
2968
+ | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
2969
+ | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
2970
+ | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
2971
+ | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
2972
+ | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
2973
+ | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
2974
+ | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
2975
+ | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
2976
+ | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
2977
+ | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
2978
+ | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
2979
+ | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
2980
+
2981
+
2982
+
2983
+ - **C-MTEB**:
2984
+ We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
2985
+ Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
2986
+
2987
+ | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
2988
+ |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
2989
+ | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
2990
+ | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
2991
+ | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
2992
+ | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
2993
+ | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
2994
+ | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
2995
+ | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
2996
+ | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
2997
+ | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
2998
+ | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
2999
+ | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
3000
+ | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
3001
+ | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
3002
+ | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
3003
+ | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
3004
+ | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
3005
+
3006
+
3007
+ - **Reranking**:
3008
+ See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
3009
+
3010
+ | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
3011
+ |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
3012
+ | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
3013
+ | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
3014
+ | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
3015
+ | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
3016
+ | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
3017
+ | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
3018
+ | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
3019
+ | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
3020
+ | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
3021
+ | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
3022
+
3023
+ \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
3024
+
3025
+ ## Train
3026
+
3027
+ ### BAAI Embedding
3028
+
3029
+ We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
3030
+ **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
3031
+ We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
3032
+ Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
3033
+ More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
3034
+
3035
+
3036
+
3037
+ ### BGE Reranker
3038
+
3039
+ Cross-encoder will perform full-attention over the input pair,
3040
+ which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
3041
+ Therefore, it can be used to re-rank the top-k documents returned by embedding model.
3042
+ We train the cross-encoder on a multilingual pair data,
3043
+ The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
3044
+ More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
3045
+
3046
+
3047
+ ## Contact
3048
+ If you have any question or suggestion related to this project, feel free to open an issue or pull request.
3049
+ You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
3050
+
3051
+
3052
+ ## Citation
3053
+
3054
+ If you find this repository useful, please consider giving a star :star: and citation
3055
+
3056
+ ```
3057
+ @misc{bge_embedding,
3058
+ title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
3059
+ author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
3060
+ year={2023},
3061
+ eprint={2309.07597},
3062
+ archivePrefix={arXiv},
3063
+ primaryClass={cs.CL}
3064
+ }
3065
+ ```
3066
+
3067
+ ## License
3068
+ FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
3069
+
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