--- language: - zh pipeline_tag: sentence-similarity tags: - PEG - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: PEG results: - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 83.17439011737613 - type: mrr value: 85.62503968253968 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 85.97167310151039 - type: mrr value: 88.73103174603175 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 32.43532878646741 - type: mrr value: 31.374603174603173 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 24.528 - type: map_at_10 value: 35.662 - type: map_at_100 value: 37.464 - type: map_at_1000 value: 37.608000000000004 - type: map_at_3 value: 32.125 - type: map_at_5 value: 34.044000000000004 - type: mrr_at_1 value: 37.358999999999995 - type: mrr_at_10 value: 44.414 - type: mrr_at_100 value: 45.434000000000005 - type: mrr_at_1000 value: 45.495999999999995 - type: mrr_at_3 value: 42.352000000000004 - type: mrr_at_5 value: 43.45 - type: ndcg_at_1 value: 37.358999999999995 - type: ndcg_at_10 value: 41.533 - type: ndcg_at_100 value: 48.967 - type: ndcg_at_1000 value: 51.495999999999995 - type: ndcg_at_3 value: 37.297000000000004 - type: ndcg_at_5 value: 38.913 - type: precision_at_1 value: 37.358999999999995 - type: precision_at_10 value: 9.09 - type: precision_at_100 value: 1.522 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 20.979999999999997 - type: precision_at_5 value: 14.934 - type: recall_at_1 value: 24.528 - type: recall_at_10 value: 50.119 - type: recall_at_100 value: 81.443 - type: recall_at_1000 value: 98.38 - type: recall_at_3 value: 36.961 - type: recall_at_5 value: 42.392 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 70.179 - type: map_at_10 value: 78.52 - type: map_at_100 value: 78.77199999999999 - type: map_at_1000 value: 78.776 - type: map_at_3 value: 76.954 - type: map_at_5 value: 77.828 - type: mrr_at_1 value: 70.39 - type: mrr_at_10 value: 78.577 - type: mrr_at_100 value: 78.828 - type: mrr_at_1000 value: 78.83200000000001 - type: mrr_at_3 value: 77.151 - type: mrr_at_5 value: 77.947 - type: ndcg_at_1 value: 70.39 - type: ndcg_at_10 value: 82.223 - type: ndcg_at_100 value: 83.367 - type: ndcg_at_1000 value: 83.503 - type: ndcg_at_3 value: 79.142 - type: ndcg_at_5 value: 80.655 - type: precision_at_1 value: 70.39 - type: precision_at_10 value: 9.463000000000001 - type: precision_at_100 value: 0.9979999999999999 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 28.627000000000002 - type: precision_at_5 value: 17.935000000000002 - type: recall_at_1 value: 70.179 - type: recall_at_10 value: 93.572 - type: recall_at_100 value: 98.736 - type: recall_at_1000 value: 99.895 - type: recall_at_3 value: 85.274 - type: recall_at_5 value: 88.909 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.106 - type: map_at_10 value: 80.219 - type: map_at_100 value: 82.979 - type: map_at_1000 value: 83.021 - type: map_at_3 value: 55.348 - type: map_at_5 value: 70.025 - type: mrr_at_1 value: 89.75 - type: mrr_at_10 value: 93.208 - type: mrr_at_100 value: 93.24900000000001 - type: mrr_at_1000 value: 93.253 - type: mrr_at_3 value: 92.875 - type: mrr_at_5 value: 93.107 - type: ndcg_at_1 value: 89.75 - type: ndcg_at_10 value: 87.641 - type: ndcg_at_100 value: 90.14 - type: ndcg_at_1000 value: 90.514 - type: ndcg_at_3 value: 86.048 - type: ndcg_at_5 value: 85.25399999999999 - type: precision_at_1 value: 89.75 - type: precision_at_10 value: 42.105 - type: precision_at_100 value: 4.811 - type: precision_at_1000 value: 0.48900000000000005 - type: precision_at_3 value: 77.167 - type: precision_at_5 value: 65.41 - type: recall_at_1 value: 26.106 - type: recall_at_10 value: 89.209 - type: recall_at_100 value: 97.475 - type: recall_at_1000 value: 99.459 - type: recall_at_3 value: 57.793000000000006 - type: recall_at_5 value: 75.03999999999999 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 53.300000000000004 - type: map_at_10 value: 62.663999999999994 - type: map_at_100 value: 63.222 - type: map_at_1000 value: 63.235 - type: map_at_3 value: 60.06700000000001 - type: map_at_5 value: 61.687000000000005 - type: mrr_at_1 value: 53.300000000000004 - type: mrr_at_10 value: 62.663999999999994 - type: mrr_at_100 value: 63.222 - type: mrr_at_1000 value: 63.235 - type: mrr_at_3 value: 60.06700000000001 - type: mrr_at_5 value: 61.687000000000005 - type: ndcg_at_1 value: 53.300000000000004 - type: ndcg_at_10 value: 67.431 - type: ndcg_at_100 value: 70.01899999999999 - type: ndcg_at_1000 value: 70.35 - type: ndcg_at_3 value: 62.188 - type: ndcg_at_5 value: 65.067 - type: precision_at_1 value: 53.300000000000004 - type: precision_at_10 value: 8.25 - type: precision_at_100 value: 0.943 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 22.767 - type: precision_at_5 value: 15.040000000000001 - type: recall_at_1 value: 53.300000000000004 - type: recall_at_10 value: 82.5 - type: recall_at_100 value: 94.3 - type: recall_at_1000 value: 96.89999999999999 - type: recall_at_3 value: 68.30000000000001 - type: recall_at_5 value: 75.2 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 70.616 - type: map_at_10 value: 79.404 - type: map_at_100 value: 79.642 - type: map_at_1000 value: 79.649 - type: map_at_3 value: 77.717 - type: map_at_5 value: 78.88 - type: mrr_at_1 value: 72.951 - type: mrr_at_10 value: 79.893 - type: mrr_at_100 value: 80.10199999999999 - type: mrr_at_1000 value: 80.108 - type: mrr_at_3 value: 78.479 - type: mrr_at_5 value: 79.452 - type: ndcg_at_1 value: 72.951 - type: ndcg_at_10 value: 82.80499999999999 - type: ndcg_at_100 value: 83.857 - type: ndcg_at_1000 value: 84.054 - type: ndcg_at_3 value: 79.69800000000001 - type: ndcg_at_5 value: 81.636 - type: precision_at_1 value: 72.951 - type: precision_at_10 value: 9.868 - type: precision_at_100 value: 1.039 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 29.857 - type: precision_at_5 value: 18.948 - type: recall_at_1 value: 70.616 - type: recall_at_10 value: 92.729 - type: recall_at_100 value: 97.452 - type: recall_at_1000 value: 99.035 - type: recall_at_3 value: 84.63300000000001 - type: recall_at_5 value: 89.242 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 52.5 - type: map_at_10 value: 58.121 - type: map_at_100 value: 58.648999999999994 - type: map_at_1000 value: 58.704 - type: map_at_3 value: 56.95 - type: map_at_5 value: 57.605 - type: mrr_at_1 value: 52.5 - type: mrr_at_10 value: 58.11300000000001 - type: mrr_at_100 value: 58.64 - type: mrr_at_1000 value: 58.695 - type: mrr_at_3 value: 56.916999999999994 - type: mrr_at_5 value: 57.597 - type: ndcg_at_1 value: 52.5 - type: ndcg_at_10 value: 60.79900000000001 - type: ndcg_at_100 value: 63.63799999999999 - type: ndcg_at_1000 value: 65.168 - type: ndcg_at_3 value: 58.353 - type: ndcg_at_5 value: 59.541 - type: precision_at_1 value: 52.5 - type: precision_at_10 value: 6.92 - type: precision_at_100 value: 0.831 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 20.8 - type: precision_at_5 value: 13.059999999999999 - type: recall_at_1 value: 52.5 - type: recall_at_10 value: 69.19999999999999 - type: recall_at_100 value: 83.1 - type: recall_at_1000 value: 95.3 - type: recall_at_3 value: 62.4 - type: recall_at_5 value: 65.3 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 68.84678245613873 - type: mrr value: 79.8603319684904 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 27.750000000000004 - type: map_at_10 value: 78.97 - type: map_at_100 value: 82.527 - type: map_at_1000 value: 82.583 - type: map_at_3 value: 55.312 - type: map_at_5 value: 68.125 - type: mrr_at_1 value: 90.904 - type: mrr_at_10 value: 93.229 - type: mrr_at_100 value: 93.305 - type: mrr_at_1000 value: 93.309 - type: mrr_at_3 value: 92.85799999999999 - type: mrr_at_5 value: 93.095 - type: ndcg_at_1 value: 90.904 - type: ndcg_at_10 value: 86.143 - type: ndcg_at_100 value: 89.434 - type: ndcg_at_1000 value: 89.973 - type: ndcg_at_3 value: 87.356 - type: ndcg_at_5 value: 86.071 - type: precision_at_1 value: 90.904 - type: precision_at_10 value: 42.897999999999996 - type: precision_at_100 value: 5.054 - type: precision_at_1000 value: 0.517 - type: precision_at_3 value: 76.512 - type: precision_at_5 value: 64.267 - type: recall_at_1 value: 27.750000000000004 - type: recall_at_10 value: 85.153 - type: recall_at_100 value: 96.009 - type: recall_at_1000 value: 98.807 - type: recall_at_3 value: 56.979 - type: recall_at_5 value: 71.377 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 64.4 - type: map_at_10 value: 73.506 - type: map_at_100 value: 73.87700000000001 - type: map_at_1000 value: 73.884 - type: map_at_3 value: 72.033 - type: map_at_5 value: 73.068 - type: mrr_at_1 value: 64.4 - type: mrr_at_10 value: 73.506 - type: mrr_at_100 value: 73.87700000000001 - type: mrr_at_1000 value: 73.884 - type: mrr_at_3 value: 72.033 - type: mrr_at_5 value: 73.068 - type: ndcg_at_1 value: 64.4 - type: ndcg_at_10 value: 77.325 - type: ndcg_at_100 value: 79.171 - type: ndcg_at_1000 value: 79.351 - type: ndcg_at_3 value: 74.421 - type: ndcg_at_5 value: 76.28 - type: precision_at_1 value: 64.4 - type: precision_at_10 value: 8.9 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 27.1 - type: precision_at_5 value: 17.16 - type: recall_at_1 value: 64.4 - type: recall_at_10 value: 89.0 - type: recall_at_100 value: 97.7 - type: recall_at_1000 value: 99.1 - type: recall_at_3 value: 81.3 - type: recall_at_5 value: 85.8 --- license: apache-2.0 library_name: transformers ---

PEG: Towards Robust Text Retrieval with Progressive Learning

## Model Details We propose the PEG model (a Progressively Learned Textual Embedding), which progressively adjusts the weights of samples contributing to the loss within an extremely large batch, based on the difficulty levels of negative samples. we have amassed an extensive collection of over 110 million data, spanning a wide range of fields such as general knowledge, finance, tourism, medicine, and more. Our technical report is available at [Paper](https://arxiv.org/pdf/2311.11691.pdf) ## Usage (HuggingFace Transformers) Install transformers: ``` pip install transformers ``` Then load model and predict: ```python from transformers import AutoModel, AutoTokenizer import torch # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('TownsWu/PEG') model = AutoModel.from_pretrained('TownsWu/PEG') sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡'] # Tokenize sentences inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): last_hidden_state = model(**inputs, return_dict=True).last_hidden_state embeddings = last_hidden_state[:, 0] print("embeddings:") print(embeddings) ``` ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Tong Wu(townswu@tencent.com). ## Citation If you find our work helpful for your research, please consider citing the following BibTeX entry: ``` @article{wu2023towards, title={Towards Robust Text Retrieval with Progressive Learning}, author={Wu, Tong and Qin, Yulei and Zhang, Enwei and Xu, Zihan and Gao, Yuting and Li, Ke and Sun, Xing}, journal={arXiv preprint arXiv:2311.11691}, year={2023} } ```