|
--- |
|
language: |
|
- zh |
|
model-index: |
|
- name: Chuxin-Embedding |
|
results: |
|
- dataset: |
|
config: default |
|
name: MTEB CmedqaRetrieval (default) |
|
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 |
|
split: dev |
|
type: C-MTEB/CmedqaRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 33.391999999999996 |
|
- type: map_at_10 |
|
value: 48.715 |
|
- type: map_at_100 |
|
value: 50.381 |
|
- type: map_at_1000 |
|
value: 50.456 |
|
- type: map_at_3 |
|
value: 43.708999999999996 |
|
- type: map_at_5 |
|
value: 46.405 |
|
- type: mrr_at_1 |
|
value: 48.612 |
|
- type: mrr_at_10 |
|
value: 58.67099999999999 |
|
- type: mrr_at_100 |
|
value: 59.38 |
|
- type: mrr_at_1000 |
|
value: 59.396 |
|
- type: mrr_at_3 |
|
value: 55.906 |
|
- type: mrr_at_5 |
|
value: 57.421 |
|
- type: ndcg_at_1 |
|
value: 48.612 |
|
- type: ndcg_at_10 |
|
value: 56.581 |
|
- type: ndcg_at_100 |
|
value: 62.422999999999995 |
|
- type: ndcg_at_1000 |
|
value: 63.476 |
|
- type: ndcg_at_3 |
|
value: 50.271 |
|
- type: ndcg_at_5 |
|
value: 52.79899999999999 |
|
- type: precision_at_1 |
|
value: 48.612 |
|
- type: precision_at_10 |
|
value: 11.995000000000001 |
|
- type: precision_at_100 |
|
value: 1.696 |
|
- type: precision_at_1000 |
|
value: 0.185 |
|
- type: precision_at_3 |
|
value: 27.465 |
|
- type: precision_at_5 |
|
value: 19.675 |
|
- type: recall_at_1 |
|
value: 33.391999999999996 |
|
- type: recall_at_10 |
|
value: 69.87100000000001 |
|
- type: recall_at_100 |
|
value: 93.078 |
|
- type: recall_at_1000 |
|
value: 99.55199999999999 |
|
- type: recall_at_3 |
|
value: 50.939 |
|
- type: recall_at_5 |
|
value: 58.714 |
|
- type: main_score |
|
value: 56.581 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB CovidRetrieval (default) |
|
revision: 1271c7809071a13532e05f25fb53511ffce77117 |
|
split: dev |
|
type: C-MTEB/CovidRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 71.918 |
|
- type: map_at_10 |
|
value: 80.609 |
|
- type: map_at_100 |
|
value: 80.796 |
|
- type: map_at_1000 |
|
value: 80.798 |
|
- type: map_at_3 |
|
value: 79.224 |
|
- type: map_at_5 |
|
value: 79.96 |
|
- type: mrr_at_1 |
|
value: 72.076 |
|
- type: mrr_at_10 |
|
value: 80.61399999999999 |
|
- type: mrr_at_100 |
|
value: 80.801 |
|
- type: mrr_at_1000 |
|
value: 80.803 |
|
- type: mrr_at_3 |
|
value: 79.276 |
|
- type: mrr_at_5 |
|
value: 80.025 |
|
- type: ndcg_at_1 |
|
value: 72.076 |
|
- type: ndcg_at_10 |
|
value: 84.286 |
|
- type: ndcg_at_100 |
|
value: 85.14500000000001 |
|
- type: ndcg_at_1000 |
|
value: 85.21 |
|
- type: ndcg_at_3 |
|
value: 81.45400000000001 |
|
- type: ndcg_at_5 |
|
value: 82.781 |
|
- type: precision_at_1 |
|
value: 72.076 |
|
- type: precision_at_10 |
|
value: 9.663 |
|
- type: precision_at_100 |
|
value: 1.005 |
|
- type: precision_at_1000 |
|
value: 0.101 |
|
- type: precision_at_3 |
|
value: 29.398999999999997 |
|
- type: precision_at_5 |
|
value: 18.335 |
|
- type: recall_at_1 |
|
value: 71.918 |
|
- type: recall_at_10 |
|
value: 95.574 |
|
- type: recall_at_100 |
|
value: 99.473 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 87.82900000000001 |
|
- type: recall_at_5 |
|
value: 90.991 |
|
- type: main_score |
|
value: 84.286 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB DuRetrieval (default) |
|
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced |
|
split: dev |
|
type: C-MTEB/DuRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.019999999999996 |
|
- type: map_at_10 |
|
value: 77.744 |
|
- type: map_at_100 |
|
value: 80.562 |
|
- type: map_at_1000 |
|
value: 80.60300000000001 |
|
- type: map_at_3 |
|
value: 52.642999999999994 |
|
- type: map_at_5 |
|
value: 67.179 |
|
- type: mrr_at_1 |
|
value: 86.5 |
|
- type: mrr_at_10 |
|
value: 91.024 |
|
- type: mrr_at_100 |
|
value: 91.09 |
|
- type: mrr_at_1000 |
|
value: 91.093 |
|
- type: mrr_at_3 |
|
value: 90.558 |
|
- type: mrr_at_5 |
|
value: 90.913 |
|
- type: ndcg_at_1 |
|
value: 86.5 |
|
- type: ndcg_at_10 |
|
value: 85.651 |
|
- type: ndcg_at_100 |
|
value: 88.504 |
|
- type: ndcg_at_1000 |
|
value: 88.887 |
|
- type: ndcg_at_3 |
|
value: 82.707 |
|
- type: ndcg_at_5 |
|
value: 82.596 |
|
- type: precision_at_1 |
|
value: 86.5 |
|
- type: precision_at_10 |
|
value: 41.595 |
|
- type: precision_at_100 |
|
value: 4.7940000000000005 |
|
- type: precision_at_1000 |
|
value: 0.48900000000000005 |
|
- type: precision_at_3 |
|
value: 74.233 |
|
- type: precision_at_5 |
|
value: 63.68000000000001 |
|
- type: recall_at_1 |
|
value: 25.019999999999996 |
|
- type: recall_at_10 |
|
value: 88.114 |
|
- type: recall_at_100 |
|
value: 97.442 |
|
- type: recall_at_1000 |
|
value: 99.39099999999999 |
|
- type: recall_at_3 |
|
value: 55.397 |
|
- type: recall_at_5 |
|
value: 73.095 |
|
- type: main_score |
|
value: 85.651 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB EcomRetrieval (default) |
|
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 |
|
split: dev |
|
type: C-MTEB/EcomRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 55.60000000000001 |
|
- type: map_at_10 |
|
value: 67.891 |
|
- type: map_at_100 |
|
value: 68.28699999999999 |
|
- type: map_at_1000 |
|
value: 68.28699999999999 |
|
- type: map_at_3 |
|
value: 64.86699999999999 |
|
- type: map_at_5 |
|
value: 66.652 |
|
- type: mrr_at_1 |
|
value: 55.60000000000001 |
|
- type: mrr_at_10 |
|
value: 67.891 |
|
- type: mrr_at_100 |
|
value: 68.28699999999999 |
|
- type: mrr_at_1000 |
|
value: 68.28699999999999 |
|
- type: mrr_at_3 |
|
value: 64.86699999999999 |
|
- type: mrr_at_5 |
|
value: 66.652 |
|
- type: ndcg_at_1 |
|
value: 55.60000000000001 |
|
- type: ndcg_at_10 |
|
value: 74.01100000000001 |
|
- type: ndcg_at_100 |
|
value: 75.602 |
|
- type: ndcg_at_1000 |
|
value: 75.602 |
|
- type: ndcg_at_3 |
|
value: 67.833 |
|
- type: ndcg_at_5 |
|
value: 71.005 |
|
- type: precision_at_1 |
|
value: 55.60000000000001 |
|
- type: precision_at_10 |
|
value: 9.33 |
|
- type: precision_at_100 |
|
value: 1.0 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 25.467000000000002 |
|
- type: precision_at_5 |
|
value: 16.8 |
|
- type: recall_at_1 |
|
value: 55.60000000000001 |
|
- type: recall_at_10 |
|
value: 93.30000000000001 |
|
- type: recall_at_100 |
|
value: 100.0 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 76.4 |
|
- type: recall_at_5 |
|
value: 84.0 |
|
- type: main_score |
|
value: 74.01100000000001 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB MMarcoRetrieval (default) |
|
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 |
|
split: dev |
|
type: C-MTEB/MMarcoRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 66.24799999999999 |
|
- type: map_at_10 |
|
value: 75.356 |
|
- type: map_at_100 |
|
value: 75.653 |
|
- type: map_at_1000 |
|
value: 75.664 |
|
- type: map_at_3 |
|
value: 73.515 |
|
- type: map_at_5 |
|
value: 74.67099999999999 |
|
- type: mrr_at_1 |
|
value: 68.496 |
|
- type: mrr_at_10 |
|
value: 75.91499999999999 |
|
- type: mrr_at_100 |
|
value: 76.17399999999999 |
|
- type: mrr_at_1000 |
|
value: 76.184 |
|
- type: mrr_at_3 |
|
value: 74.315 |
|
- type: mrr_at_5 |
|
value: 75.313 |
|
- type: ndcg_at_1 |
|
value: 68.496 |
|
- type: ndcg_at_10 |
|
value: 79.065 |
|
- type: ndcg_at_100 |
|
value: 80.417 |
|
- type: ndcg_at_1000 |
|
value: 80.72399999999999 |
|
- type: ndcg_at_3 |
|
value: 75.551 |
|
- type: ndcg_at_5 |
|
value: 77.505 |
|
- type: precision_at_1 |
|
value: 68.496 |
|
- type: precision_at_10 |
|
value: 9.563 |
|
- type: precision_at_100 |
|
value: 1.024 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 28.391 |
|
- type: precision_at_5 |
|
value: 18.086 |
|
- type: recall_at_1 |
|
value: 66.24799999999999 |
|
- type: recall_at_10 |
|
value: 89.97 |
|
- type: recall_at_100 |
|
value: 96.13199999999999 |
|
- type: recall_at_1000 |
|
value: 98.551 |
|
- type: recall_at_3 |
|
value: 80.624 |
|
- type: recall_at_5 |
|
value: 85.271 |
|
- type: main_score |
|
value: 79.065 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB MedicalRetrieval (default) |
|
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 |
|
split: dev |
|
type: C-MTEB/MedicalRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 61.8 |
|
- type: map_at_10 |
|
value: 71.101 |
|
- type: map_at_100 |
|
value: 71.576 |
|
- type: map_at_1000 |
|
value: 71.583 |
|
- type: map_at_3 |
|
value: 68.867 |
|
- type: map_at_5 |
|
value: 70.272 |
|
- type: mrr_at_1 |
|
value: 61.9 |
|
- type: mrr_at_10 |
|
value: 71.158 |
|
- type: mrr_at_100 |
|
value: 71.625 |
|
- type: mrr_at_1000 |
|
value: 71.631 |
|
- type: mrr_at_3 |
|
value: 68.917 |
|
- type: mrr_at_5 |
|
value: 70.317 |
|
- type: ndcg_at_1 |
|
value: 61.8 |
|
- type: ndcg_at_10 |
|
value: 75.624 |
|
- type: ndcg_at_100 |
|
value: 77.702 |
|
- type: ndcg_at_1000 |
|
value: 77.836 |
|
- type: ndcg_at_3 |
|
value: 71.114 |
|
- type: ndcg_at_5 |
|
value: 73.636 |
|
- type: precision_at_1 |
|
value: 61.8 |
|
- type: precision_at_10 |
|
value: 8.98 |
|
- type: precision_at_100 |
|
value: 0.9900000000000001 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 25.867 |
|
- type: precision_at_5 |
|
value: 16.74 |
|
- type: recall_at_1 |
|
value: 61.8 |
|
- type: recall_at_10 |
|
value: 89.8 |
|
- type: recall_at_100 |
|
value: 99.0 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 77.60000000000001 |
|
- type: recall_at_5 |
|
value: 83.7 |
|
- type: main_score |
|
value: 75.624 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB T2Retrieval (default) |
|
revision: 8731a845f1bf500a4f111cf1070785c793d10e64 |
|
split: dev |
|
type: C-MTEB/T2Retrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.173000000000002 |
|
- type: map_at_10 |
|
value: 76.454 |
|
- type: map_at_100 |
|
value: 80.021 |
|
- type: map_at_1000 |
|
value: 80.092 |
|
- type: map_at_3 |
|
value: 53.876999999999995 |
|
- type: map_at_5 |
|
value: 66.122 |
|
- type: mrr_at_1 |
|
value: 89.519 |
|
- type: mrr_at_10 |
|
value: 92.091 |
|
- type: mrr_at_100 |
|
value: 92.179 |
|
- type: mrr_at_1000 |
|
value: 92.183 |
|
- type: mrr_at_3 |
|
value: 91.655 |
|
- type: mrr_at_5 |
|
value: 91.94 |
|
- type: ndcg_at_1 |
|
value: 89.519 |
|
- type: ndcg_at_10 |
|
value: 84.043 |
|
- type: ndcg_at_100 |
|
value: 87.60900000000001 |
|
- type: ndcg_at_1000 |
|
value: 88.32799999999999 |
|
- type: ndcg_at_3 |
|
value: 85.623 |
|
- type: ndcg_at_5 |
|
value: 84.111 |
|
- type: precision_at_1 |
|
value: 89.519 |
|
- type: precision_at_10 |
|
value: 41.760000000000005 |
|
- type: precision_at_100 |
|
value: 4.982 |
|
- type: precision_at_1000 |
|
value: 0.515 |
|
- type: precision_at_3 |
|
value: 74.944 |
|
- type: precision_at_5 |
|
value: 62.705999999999996 |
|
- type: recall_at_1 |
|
value: 27.173000000000002 |
|
- type: recall_at_10 |
|
value: 82.878 |
|
- type: recall_at_100 |
|
value: 94.527 |
|
- type: recall_at_1000 |
|
value: 98.24199999999999 |
|
- type: recall_at_3 |
|
value: 55.589 |
|
- type: recall_at_5 |
|
value: 69.476 |
|
- type: main_score |
|
value: 84.043 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB VideoRetrieval (default) |
|
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 |
|
split: dev |
|
type: C-MTEB/VideoRetrieval |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.1 |
|
- type: map_at_10 |
|
value: 79.62 |
|
- type: map_at_100 |
|
value: 79.804 |
|
- type: map_at_1000 |
|
value: 79.804 |
|
- type: map_at_3 |
|
value: 77.81700000000001 |
|
- type: map_at_5 |
|
value: 79.037 |
|
- type: mrr_at_1 |
|
value: 70.1 |
|
- type: mrr_at_10 |
|
value: 79.62 |
|
- type: mrr_at_100 |
|
value: 79.804 |
|
- type: mrr_at_1000 |
|
value: 79.804 |
|
- type: mrr_at_3 |
|
value: 77.81700000000001 |
|
- type: mrr_at_5 |
|
value: 79.037 |
|
- type: ndcg_at_1 |
|
value: 70.1 |
|
- type: ndcg_at_10 |
|
value: 83.83500000000001 |
|
- type: ndcg_at_100 |
|
value: 84.584 |
|
- type: ndcg_at_1000 |
|
value: 84.584 |
|
- type: ndcg_at_3 |
|
value: 80.282 |
|
- type: ndcg_at_5 |
|
value: 82.472 |
|
- type: precision_at_1 |
|
value: 70.1 |
|
- type: precision_at_10 |
|
value: 9.68 |
|
- type: precision_at_100 |
|
value: 1.0 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 29.133 |
|
- type: precision_at_5 |
|
value: 18.54 |
|
- type: recall_at_1 |
|
value: 70.1 |
|
- type: recall_at_10 |
|
value: 96.8 |
|
- type: recall_at_100 |
|
value: 100.0 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 87.4 |
|
- type: recall_at_5 |
|
value: 92.7 |
|
- type: main_score |
|
value: 83.83500000000001 |
|
task: |
|
type: Retrieval |
|
tags: |
|
- mteb |
|
--- |
|
|
|
# Chuxin-Embedding |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
Chuxin-Embedding 是专为增强中文文本检索能力而设计的嵌入模型。它基于bge-m3-retromae[1]，实现了预训练、微调、精调全流程。该模型在来自各个领域的大量语料库上进行训练，语料库的批量非常大。截至 2024 年 9 月 14 日, Chuxin-Embedding 在检索任务中表现出色，在 C-MTEB 中文检索排行榜上排名第一，领先的性能得分为 77.88，在AIR-Bench中文检索+重排序公开排行榜上排名第一，领先的性能得分为 64.78。 |
|
|
|
Chuxin-Embedding is a specially designed embedding model aimed at enhancing the capability of Chinese text retrieval. It is based on bge-m3-retromae[1] and implements the entire process of pre-training, fine-tuning, and refining. This model has been trained on a vast amount of corpora from various fields. As of September 14, 2024, Chuxin-Embedding has shown outstanding performance in retrieval tasks. It ranks first on the C-MTEB Chinese Retrieval Leaderboard with a leading performance score of 77.88 and also ranks first on the AIR-Bench Chinese Retrieval + Re-ranking Public Leaderboard with a leading performance score of 64.78. |
|
|
|
## News |
|
- 2024/10/18: LLM生成及数据清洗 [Code](https://github.com/chuxin-llm/Chuxin-Embedding/blob/main/README_LLM.md) 。 |
|
- 2024/9/14: 团队的RAG框架欢迎试用 [ragnify](https://github.com/chuxin-llm/ragnify) 。 |
|
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- 2024/9/14: LLM generation and data clean [Code](https://github.com/chuxin-llm/Chuxin-Embedding) . |
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- 2024/9/14: The team's RAG framework is available for trial [ragnify](https://github.com/chuxin-llm/ragnify) . |
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## Training Details |
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基于bge-m3-retromae[1]，主要改动如下： |
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<!-- Provide a longer summary of what this model is. --> |
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- 基于bge-m3-retromae[1]在亿级数据上预训练。 |
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- 使用BGE pretrain [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain) 完成预训练。 |
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- 在收集的公开亿级检索数据集上实现了微调。 |
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- 使用BGE finetune [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) 完成微调。 |
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- 在收集的公开百万级检索数据集和百万级LLM合成数据集上实现了精调。 |
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- 使用BGE finetune [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) 和 BGE unified_finetune [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune) 完成精调。 |
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- 通过 LLM (QWEN-72B) 进行数据生成，使用 LLM 为message生成新query |
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- 数据清洗： |
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- 简单的基于规则清洗 |
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- LLM判断是否可作为搜索引擎查询的query |
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- rerank模型对(query,message)评分，舍弃pos中的负例，neg中的正例 |
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Based on bge-m3-retromae[1], the main modifications are as follows: |
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- Pre-trained on a billion-level dataset based on bge-m3-retromae[1]. |
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- Pre-training is completed using BGE pretrain [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain) . |
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- Fine-tuned on a publicly collected billion-level retrieval dataset. |
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- Fine-tuning is completed using BGE finetune [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune). |
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- Refined on a publicly collected million-level retrieval dataset and a million-level LLM synthetic dataset. |
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- Refining is completed using BGE finetune [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) and BGE unified_finetune [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune). |
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- Data generation is performed through LLM (QWEN-72B), using LLM to generate new query for messages. |
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- Data cleaning: |
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- Simple rule-based cleaning |
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- LLM to determine whether a query can be used as a search engine query |
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- The rerank model scores (query, message) pairs, discarding negative examples in the positive set and positive examples in the negative set. |
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## Collect more data for retrieval-type tasks |
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1. 预训练数据 |
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- ChineseWebText、 oasis、 oscar、 SkyPile、 wudao |
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2. 微调数据 |
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- MTP 、webqa、nlpcc、csl、bq、atec、ccks |
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3. 精调数据 |
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- BGE-M3 、Huatuo26M-Lite 、covid ... |
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- LLM 合成（BGE-M3 、Huatuo26M-Lite 、covid、wudao、wanjuan_news、mnbvc_news_wiki、mldr、medical QA...） |
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## Performance |
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**C_MTEB RETRIEVAL** |
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| Model | **Average** | **CmedqaRetrieval** | **CovidRetrieval** | **DuRetrieval** | **EcomRetrieval** | **MedicalRetrieval** | **MMarcoRetrieval** | **T2Retrieval** | **VideoRetrieval** | |
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| :-------------------: | :---------: | :-------: | :------------: | :-----------: | :-----------: | :-------: | :----------: | :-------: | :----------: | |
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| Zhihui_LLM_Embedding | 76.74 | 48.69 | 84.39 | 91.34 | 71.96 | 65.19 | 84.77 |88.3 | 79.31 | |
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| zpoint_large_embedding_zh | 76.36 | 47.16 | 89.14 | 89.23 | 70.74 | 68.14 | 82.38 | 83.81 | 80.26 | |
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| **Chuxin-Embedding** | **77.88** | 56.58 | 84.28 | 85.65 | 74.01 | 75.62 | 79.06 | 84.04 | 83.84 | |
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**AIR-Bench** |
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| Retrieval Method | Reranking Model | **Average** | **wiki_zh** | **web_zh** | **news_zh** | **healthcare_zh** | **finance_zh** | |
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| :-------------------: | :---------:| :---------: | :-------: | :------------: | :-----------: | :-----------: | :----------: | |
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| bge-m3 | bge-reranker-large | 64.53 | 76.11 | 67.8 | 63.25 | 62.9 | 52.61 | |
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| gte-Qwen2-7B-instruct |bge-reranker-large | 63.39 | 78.09 | 67.56 | 63.14 | 61.12 | 47.02 | |
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| **Chuxin-Embedding** | bge-reranker-large | **64.78** |76.23 | 68.44 | 64.2 | 62.93 | 52.11 | |
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## Generate Embedding for text |
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```python |
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#pip install -U FlagEmbedding |
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from FlagEmbedding import FlagModel |
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model = FlagModel('chuxin-llm/Chuxin-Embedding', |
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query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", |
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use_fp16=True) |
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sentences_1 = ["样例数据-1", "样例数据-2"] |
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sentences_2 = ["样例数据-3", "样例数据-1"] |
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embeddings_1 = model.encode(sentences_1) |
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embeddings_2 = model.encode(sentences_2) |
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similarity = embeddings_1 @ embeddings_2.T |
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print(similarity) |
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``` |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Reference |
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1. https://huggingface.co/BAAI/bge-m3-retromae |
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2. https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3 |
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3. https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
