|
--- |
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pipeline_tag: sentence-similarity |
|
tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
|
- mteb |
|
model-index: |
|
- name: stella-large-zh-v2 |
|
results: |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/AFQMC |
|
name: MTEB AFQMC |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 47.34436411023816 |
|
- type: cos_sim_spearman |
|
value: 49.947084806624545 |
|
- type: euclidean_pearson |
|
value: 48.128834319004824 |
|
- type: euclidean_spearman |
|
value: 49.947064694876815 |
|
- type: manhattan_pearson |
|
value: 48.083561270166484 |
|
- type: manhattan_spearman |
|
value: 49.90207128584442 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/ATEC |
|
name: MTEB ATEC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 50.97998570817664 |
|
- type: cos_sim_spearman |
|
value: 53.11852606980578 |
|
- type: euclidean_pearson |
|
value: 55.12610520736481 |
|
- type: euclidean_spearman |
|
value: 53.11852832108405 |
|
- type: manhattan_pearson |
|
value: 55.10299116717361 |
|
- type: manhattan_spearman |
|
value: 53.11304196536268 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (zh) |
|
config: zh |
|
split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 40.81799999999999 |
|
- type: f1 |
|
value: 39.022194031906444 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/BQ |
|
name: MTEB BQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 62.83544115057508 |
|
- type: cos_sim_spearman |
|
value: 65.53509404838948 |
|
- type: euclidean_pearson |
|
value: 64.08198144850084 |
|
- type: euclidean_spearman |
|
value: 65.53509404760305 |
|
- type: manhattan_pearson |
|
value: 64.08808420747272 |
|
- type: manhattan_spearman |
|
value: 65.54907862648346 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/CLSClusteringP2P |
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name: MTEB CLSClusteringP2P |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 39.95428546140963 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/CLSClusteringS2S |
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name: MTEB CLSClusteringS2S |
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config: default |
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split: test |
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revision: None |
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metrics: |
|
- type: v_measure |
|
value: 38.18454393512963 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv1-reranking |
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name: MTEB CMedQAv1 |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map |
|
value: 85.4453602559479 |
|
- type: mrr |
|
value: 88.1418253968254 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv2-reranking |
|
name: MTEB CMedQAv2 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 85.82731720256984 |
|
- type: mrr |
|
value: 88.53230158730159 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/CmedqaRetrieval |
|
name: MTEB CmedqaRetrieval |
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config: default |
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split: dev |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.459 |
|
- type: map_at_10 |
|
value: 36.274 |
|
- type: map_at_100 |
|
value: 38.168 |
|
- type: map_at_1000 |
|
value: 38.292 |
|
- type: map_at_3 |
|
value: 32.356 |
|
- type: map_at_5 |
|
value: 34.499 |
|
- type: mrr_at_1 |
|
value: 37.584 |
|
- type: mrr_at_10 |
|
value: 45.323 |
|
- type: mrr_at_100 |
|
value: 46.361999999999995 |
|
- type: mrr_at_1000 |
|
value: 46.412 |
|
- type: mrr_at_3 |
|
value: 42.919000000000004 |
|
- type: mrr_at_5 |
|
value: 44.283 |
|
- type: ndcg_at_1 |
|
value: 37.584 |
|
- type: ndcg_at_10 |
|
value: 42.63 |
|
- type: ndcg_at_100 |
|
value: 50.114000000000004 |
|
- type: ndcg_at_1000 |
|
value: 52.312000000000005 |
|
- type: ndcg_at_3 |
|
value: 37.808 |
|
- type: ndcg_at_5 |
|
value: 39.711999999999996 |
|
- type: precision_at_1 |
|
value: 37.584 |
|
- type: precision_at_10 |
|
value: 9.51 |
|
- type: precision_at_100 |
|
value: 1.554 |
|
- type: precision_at_1000 |
|
value: 0.183 |
|
- type: precision_at_3 |
|
value: 21.505 |
|
- type: precision_at_5 |
|
value: 15.514 |
|
- type: recall_at_1 |
|
value: 24.459 |
|
- type: recall_at_10 |
|
value: 52.32 |
|
- type: recall_at_100 |
|
value: 83.423 |
|
- type: recall_at_1000 |
|
value: 98.247 |
|
- type: recall_at_3 |
|
value: 37.553 |
|
- type: recall_at_5 |
|
value: 43.712 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/CMNLI |
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name: MTEB Cmnli |
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config: default |
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split: validation |
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revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 77.7269993986771 |
|
- type: cos_sim_ap |
|
value: 86.8488070512359 |
|
- type: cos_sim_f1 |
|
value: 79.32095490716179 |
|
- type: cos_sim_precision |
|
value: 72.6107226107226 |
|
- type: cos_sim_recall |
|
value: 87.39770867430443 |
|
- type: dot_accuracy |
|
value: 77.7269993986771 |
|
- type: dot_ap |
|
value: 86.84218333157476 |
|
- type: dot_f1 |
|
value: 79.32095490716179 |
|
- type: dot_precision |
|
value: 72.6107226107226 |
|
- type: dot_recall |
|
value: 87.39770867430443 |
|
- type: euclidean_accuracy |
|
value: 77.7269993986771 |
|
- type: euclidean_ap |
|
value: 86.84880910178296 |
|
- type: euclidean_f1 |
|
value: 79.32095490716179 |
|
- type: euclidean_precision |
|
value: 72.6107226107226 |
|
- type: euclidean_recall |
|
value: 87.39770867430443 |
|
- type: manhattan_accuracy |
|
value: 77.82321106434155 |
|
- type: manhattan_ap |
|
value: 86.8152244713786 |
|
- type: manhattan_f1 |
|
value: 79.43262411347519 |
|
- type: manhattan_precision |
|
value: 72.5725338491296 |
|
- type: manhattan_recall |
|
value: 87.72504091653029 |
|
- type: max_accuracy |
|
value: 77.82321106434155 |
|
- type: max_ap |
|
value: 86.84880910178296 |
|
- type: max_f1 |
|
value: 79.43262411347519 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/CovidRetrieval |
|
name: MTEB CovidRetrieval |
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config: default |
|
split: dev |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 68.862 |
|
- type: map_at_10 |
|
value: 77.106 |
|
- type: map_at_100 |
|
value: 77.455 |
|
- type: map_at_1000 |
|
value: 77.459 |
|
- type: map_at_3 |
|
value: 75.457 |
|
- type: map_at_5 |
|
value: 76.254 |
|
- type: mrr_at_1 |
|
value: 69.125 |
|
- type: mrr_at_10 |
|
value: 77.13799999999999 |
|
- type: mrr_at_100 |
|
value: 77.488 |
|
- type: mrr_at_1000 |
|
value: 77.492 |
|
- type: mrr_at_3 |
|
value: 75.606 |
|
- type: mrr_at_5 |
|
value: 76.29599999999999 |
|
- type: ndcg_at_1 |
|
value: 69.02000000000001 |
|
- type: ndcg_at_10 |
|
value: 80.81099999999999 |
|
- type: ndcg_at_100 |
|
value: 82.298 |
|
- type: ndcg_at_1000 |
|
value: 82.403 |
|
- type: ndcg_at_3 |
|
value: 77.472 |
|
- type: ndcg_at_5 |
|
value: 78.892 |
|
- type: precision_at_1 |
|
value: 69.02000000000001 |
|
- type: precision_at_10 |
|
value: 9.336 |
|
- type: precision_at_100 |
|
value: 0.9990000000000001 |
|
- type: precision_at_1000 |
|
value: 0.101 |
|
- type: precision_at_3 |
|
value: 27.924 |
|
- type: precision_at_5 |
|
value: 17.492 |
|
- type: recall_at_1 |
|
value: 68.862 |
|
- type: recall_at_10 |
|
value: 92.308 |
|
- type: recall_at_100 |
|
value: 98.84100000000001 |
|
- type: recall_at_1000 |
|
value: 99.684 |
|
- type: recall_at_3 |
|
value: 83.193 |
|
- type: recall_at_5 |
|
value: 86.617 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/DuRetrieval |
|
name: MTEB DuRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.063999999999997 |
|
- type: map_at_10 |
|
value: 78.02 |
|
- type: map_at_100 |
|
value: 81.022 |
|
- type: map_at_1000 |
|
value: 81.06 |
|
- type: map_at_3 |
|
value: 53.613 |
|
- type: map_at_5 |
|
value: 68.008 |
|
- type: mrr_at_1 |
|
value: 87.8 |
|
- type: mrr_at_10 |
|
value: 91.827 |
|
- type: mrr_at_100 |
|
value: 91.913 |
|
- type: mrr_at_1000 |
|
value: 91.915 |
|
- type: mrr_at_3 |
|
value: 91.508 |
|
- type: mrr_at_5 |
|
value: 91.758 |
|
- type: ndcg_at_1 |
|
value: 87.8 |
|
- type: ndcg_at_10 |
|
value: 85.753 |
|
- type: ndcg_at_100 |
|
value: 88.82900000000001 |
|
- type: ndcg_at_1000 |
|
value: 89.208 |
|
- type: ndcg_at_3 |
|
value: 84.191 |
|
- type: ndcg_at_5 |
|
value: 83.433 |
|
- type: precision_at_1 |
|
value: 87.8 |
|
- type: precision_at_10 |
|
value: 41.33 |
|
- type: precision_at_100 |
|
value: 4.8 |
|
- type: precision_at_1000 |
|
value: 0.48900000000000005 |
|
- type: precision_at_3 |
|
value: 75.767 |
|
- type: precision_at_5 |
|
value: 64.25999999999999 |
|
- type: recall_at_1 |
|
value: 25.063999999999997 |
|
- type: recall_at_10 |
|
value: 87.357 |
|
- type: recall_at_100 |
|
value: 97.261 |
|
- type: recall_at_1000 |
|
value: 99.309 |
|
- type: recall_at_3 |
|
value: 56.259 |
|
- type: recall_at_5 |
|
value: 73.505 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/EcomRetrieval |
|
name: MTEB EcomRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 46.800000000000004 |
|
- type: map_at_10 |
|
value: 56.898 |
|
- type: map_at_100 |
|
value: 57.567 |
|
- type: map_at_1000 |
|
value: 57.593 |
|
- type: map_at_3 |
|
value: 54.167 |
|
- type: map_at_5 |
|
value: 55.822 |
|
- type: mrr_at_1 |
|
value: 46.800000000000004 |
|
- type: mrr_at_10 |
|
value: 56.898 |
|
- type: mrr_at_100 |
|
value: 57.567 |
|
- type: mrr_at_1000 |
|
value: 57.593 |
|
- type: mrr_at_3 |
|
value: 54.167 |
|
- type: mrr_at_5 |
|
value: 55.822 |
|
- type: ndcg_at_1 |
|
value: 46.800000000000004 |
|
- type: ndcg_at_10 |
|
value: 62.07 |
|
- type: ndcg_at_100 |
|
value: 65.049 |
|
- type: ndcg_at_1000 |
|
value: 65.666 |
|
- type: ndcg_at_3 |
|
value: 56.54 |
|
- type: ndcg_at_5 |
|
value: 59.492999999999995 |
|
- type: precision_at_1 |
|
value: 46.800000000000004 |
|
- type: precision_at_10 |
|
value: 7.84 |
|
- type: precision_at_100 |
|
value: 0.9169999999999999 |
|
- type: precision_at_1000 |
|
value: 0.096 |
|
- type: precision_at_3 |
|
value: 21.133 |
|
- type: precision_at_5 |
|
value: 14.099999999999998 |
|
- type: recall_at_1 |
|
value: 46.800000000000004 |
|
- type: recall_at_10 |
|
value: 78.4 |
|
- type: recall_at_100 |
|
value: 91.7 |
|
- type: recall_at_1000 |
|
value: 96.39999999999999 |
|
- type: recall_at_3 |
|
value: 63.4 |
|
- type: recall_at_5 |
|
value: 70.5 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/IFlyTek-classification |
|
name: MTEB IFlyTek |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 47.98768757214313 |
|
- type: f1 |
|
value: 35.23884426992269 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/JDReview-classification |
|
name: MTEB JDReview |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 86.97936210131333 |
|
- type: ap |
|
value: 56.292679530375736 |
|
- type: f1 |
|
value: 81.87001614762136 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/LCQMC |
|
name: MTEB LCQMC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 71.17149643620844 |
|
- type: cos_sim_spearman |
|
value: 77.48040046337948 |
|
- type: euclidean_pearson |
|
value: 76.32337539923347 |
|
- type: euclidean_spearman |
|
value: 77.4804004621894 |
|
- type: manhattan_pearson |
|
value: 76.33275226275444 |
|
- type: manhattan_spearman |
|
value: 77.48979843086128 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/Mmarco-reranking |
|
name: MTEB MMarcoReranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 27.966807589556826 |
|
- type: mrr |
|
value: 26.92023809523809 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MMarcoRetrieval |
|
name: MTEB MMarcoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 66.15100000000001 |
|
- type: map_at_10 |
|
value: 75.048 |
|
- type: map_at_100 |
|
value: 75.374 |
|
- type: map_at_1000 |
|
value: 75.386 |
|
- type: map_at_3 |
|
value: 73.26700000000001 |
|
- type: map_at_5 |
|
value: 74.39 |
|
- type: mrr_at_1 |
|
value: 68.381 |
|
- type: mrr_at_10 |
|
value: 75.644 |
|
- type: mrr_at_100 |
|
value: 75.929 |
|
- type: mrr_at_1000 |
|
value: 75.93900000000001 |
|
- type: mrr_at_3 |
|
value: 74.1 |
|
- type: mrr_at_5 |
|
value: 75.053 |
|
- type: ndcg_at_1 |
|
value: 68.381 |
|
- type: ndcg_at_10 |
|
value: 78.669 |
|
- type: ndcg_at_100 |
|
value: 80.161 |
|
- type: ndcg_at_1000 |
|
value: 80.46799999999999 |
|
- type: ndcg_at_3 |
|
value: 75.3 |
|
- type: ndcg_at_5 |
|
value: 77.172 |
|
- type: precision_at_1 |
|
value: 68.381 |
|
- type: precision_at_10 |
|
value: 9.48 |
|
- type: precision_at_100 |
|
value: 1.023 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 28.299999999999997 |
|
- type: precision_at_5 |
|
value: 17.98 |
|
- type: recall_at_1 |
|
value: 66.15100000000001 |
|
- type: recall_at_10 |
|
value: 89.238 |
|
- type: recall_at_100 |
|
value: 96.032 |
|
- type: recall_at_1000 |
|
value: 98.437 |
|
- type: recall_at_3 |
|
value: 80.318 |
|
- type: recall_at_5 |
|
value: 84.761 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (zh-CN) |
|
config: zh-CN |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 68.26160053799597 |
|
- type: f1 |
|
value: 65.96949453305112 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (zh-CN) |
|
config: zh-CN |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 73.12037659717554 |
|
- type: f1 |
|
value: 72.69052407105445 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MedicalRetrieval |
|
name: MTEB MedicalRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 50.1 |
|
- type: map_at_10 |
|
value: 56.489999999999995 |
|
- type: map_at_100 |
|
value: 57.007 |
|
- type: map_at_1000 |
|
value: 57.06400000000001 |
|
- type: map_at_3 |
|
value: 55.25 |
|
- type: map_at_5 |
|
value: 55.93 |
|
- type: mrr_at_1 |
|
value: 50.3 |
|
- type: mrr_at_10 |
|
value: 56.591 |
|
- type: mrr_at_100 |
|
value: 57.108000000000004 |
|
- type: mrr_at_1000 |
|
value: 57.165 |
|
- type: mrr_at_3 |
|
value: 55.35 |
|
- type: mrr_at_5 |
|
value: 56.03 |
|
- type: ndcg_at_1 |
|
value: 50.1 |
|
- type: ndcg_at_10 |
|
value: 59.419999999999995 |
|
- type: ndcg_at_100 |
|
value: 62.28900000000001 |
|
- type: ndcg_at_1000 |
|
value: 63.9 |
|
- type: ndcg_at_3 |
|
value: 56.813 |
|
- type: ndcg_at_5 |
|
value: 58.044 |
|
- type: precision_at_1 |
|
value: 50.1 |
|
- type: precision_at_10 |
|
value: 6.859999999999999 |
|
- type: precision_at_100 |
|
value: 0.828 |
|
- type: precision_at_1000 |
|
value: 0.096 |
|
- type: precision_at_3 |
|
value: 20.433 |
|
- type: precision_at_5 |
|
value: 12.86 |
|
- type: recall_at_1 |
|
value: 50.1 |
|
- type: recall_at_10 |
|
value: 68.60000000000001 |
|
- type: recall_at_100 |
|
value: 82.8 |
|
- type: recall_at_1000 |
|
value: 95.7 |
|
- type: recall_at_3 |
|
value: 61.3 |
|
- type: recall_at_5 |
|
value: 64.3 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/MultilingualSentiment-classification |
|
name: MTEB MultilingualSentiment |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 73.41000000000001 |
|
- type: f1 |
|
value: 72.87768282499509 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/OCNLI |
|
name: MTEB Ocnli |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 73.4163508391987 |
|
- type: cos_sim_ap |
|
value: 78.51058998215277 |
|
- type: cos_sim_f1 |
|
value: 75.3875968992248 |
|
- type: cos_sim_precision |
|
value: 69.65085049239033 |
|
- type: cos_sim_recall |
|
value: 82.15417106652588 |
|
- type: dot_accuracy |
|
value: 73.4163508391987 |
|
- type: dot_ap |
|
value: 78.51058998215277 |
|
- type: dot_f1 |
|
value: 75.3875968992248 |
|
- type: dot_precision |
|
value: 69.65085049239033 |
|
- type: dot_recall |
|
value: 82.15417106652588 |
|
- type: euclidean_accuracy |
|
value: 73.4163508391987 |
|
- type: euclidean_ap |
|
value: 78.51058998215277 |
|
- type: euclidean_f1 |
|
value: 75.3875968992248 |
|
- type: euclidean_precision |
|
value: 69.65085049239033 |
|
- type: euclidean_recall |
|
value: 82.15417106652588 |
|
- type: manhattan_accuracy |
|
value: 73.03735787763942 |
|
- type: manhattan_ap |
|
value: 78.4190891700083 |
|
- type: manhattan_f1 |
|
value: 75.32592950265573 |
|
- type: manhattan_precision |
|
value: 69.3950177935943 |
|
- type: manhattan_recall |
|
value: 82.36536430834214 |
|
- type: max_accuracy |
|
value: 73.4163508391987 |
|
- type: max_ap |
|
value: 78.51058998215277 |
|
- type: max_f1 |
|
value: 75.3875968992248 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/OnlineShopping-classification |
|
name: MTEB OnlineShopping |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 91.81000000000002 |
|
- type: ap |
|
value: 89.35809579688139 |
|
- type: f1 |
|
value: 91.79220350456818 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/PAWSX |
|
name: MTEB PAWSX |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.10755999973859 |
|
- type: cos_sim_spearman |
|
value: 36.221732138848864 |
|
- type: euclidean_pearson |
|
value: 36.41120179336658 |
|
- type: euclidean_spearman |
|
value: 36.221731188009436 |
|
- type: manhattan_pearson |
|
value: 36.34865300346968 |
|
- type: manhattan_spearman |
|
value: 36.17696483080459 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/QBQTC |
|
name: MTEB QBQTC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 36.778975708100226 |
|
- type: cos_sim_spearman |
|
value: 38.733929926753724 |
|
- type: euclidean_pearson |
|
value: 37.13383498228113 |
|
- type: euclidean_spearman |
|
value: 38.73374886550868 |
|
- type: manhattan_pearson |
|
value: 37.175732896552404 |
|
- type: manhattan_spearman |
|
value: 38.74120541657908 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (zh) |
|
config: zh |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 65.97095922825076 |
|
- type: cos_sim_spearman |
|
value: 68.87452938308421 |
|
- type: euclidean_pearson |
|
value: 67.23101642424429 |
|
- type: euclidean_spearman |
|
value: 68.87452938308421 |
|
- type: manhattan_pearson |
|
value: 67.29909334410189 |
|
- type: manhattan_spearman |
|
value: 68.89807985930508 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/STSB |
|
name: MTEB STSB |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 78.98860630733722 |
|
- type: cos_sim_spearman |
|
value: 79.36601601355665 |
|
- type: euclidean_pearson |
|
value: 78.77295944956447 |
|
- type: euclidean_spearman |
|
value: 79.36585127278974 |
|
- type: manhattan_pearson |
|
value: 78.82060736131619 |
|
- type: manhattan_spearman |
|
value: 79.4395526421926 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/T2Reranking |
|
name: MTEB T2Reranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 66.40501824507894 |
|
- type: mrr |
|
value: 76.18463933756757 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/T2Retrieval |
|
name: MTEB T2Retrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.095000000000002 |
|
- type: map_at_10 |
|
value: 76.228 |
|
- type: map_at_100 |
|
value: 79.865 |
|
- type: map_at_1000 |
|
value: 79.935 |
|
- type: map_at_3 |
|
value: 53.491 |
|
- type: map_at_5 |
|
value: 65.815 |
|
- type: mrr_at_1 |
|
value: 89.554 |
|
- type: mrr_at_10 |
|
value: 92.037 |
|
- type: mrr_at_100 |
|
value: 92.133 |
|
- type: mrr_at_1000 |
|
value: 92.137 |
|
- type: mrr_at_3 |
|
value: 91.605 |
|
- type: mrr_at_5 |
|
value: 91.88 |
|
- type: ndcg_at_1 |
|
value: 89.554 |
|
- type: ndcg_at_10 |
|
value: 83.866 |
|
- type: ndcg_at_100 |
|
value: 87.566 |
|
- type: ndcg_at_1000 |
|
value: 88.249 |
|
- type: ndcg_at_3 |
|
value: 85.396 |
|
- type: ndcg_at_5 |
|
value: 83.919 |
|
- type: precision_at_1 |
|
value: 89.554 |
|
- type: precision_at_10 |
|
value: 41.792 |
|
- type: precision_at_100 |
|
value: 4.997 |
|
- type: precision_at_1000 |
|
value: 0.515 |
|
- type: precision_at_3 |
|
value: 74.795 |
|
- type: precision_at_5 |
|
value: 62.675000000000004 |
|
- type: recall_at_1 |
|
value: 27.095000000000002 |
|
- type: recall_at_10 |
|
value: 82.694 |
|
- type: recall_at_100 |
|
value: 94.808 |
|
- type: recall_at_1000 |
|
value: 98.30600000000001 |
|
- type: recall_at_3 |
|
value: 55.156000000000006 |
|
- type: recall_at_5 |
|
value: 69.19 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/TNews-classification |
|
name: MTEB TNews |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 51.929 |
|
- type: f1 |
|
value: 50.16876489927282 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringP2P |
|
name: MTEB ThuNewsClusteringP2P |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 61.404157724658894 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringS2S |
|
name: MTEB ThuNewsClusteringS2S |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 57.11418384351802 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/VideoRetrieval |
|
name: MTEB VideoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 52.1 |
|
- type: map_at_10 |
|
value: 62.956999999999994 |
|
- type: map_at_100 |
|
value: 63.502 |
|
- type: map_at_1000 |
|
value: 63.51599999999999 |
|
- type: map_at_3 |
|
value: 60.75000000000001 |
|
- type: map_at_5 |
|
value: 62.195 |
|
- type: mrr_at_1 |
|
value: 52.0 |
|
- type: mrr_at_10 |
|
value: 62.907000000000004 |
|
- type: mrr_at_100 |
|
value: 63.452 |
|
- type: mrr_at_1000 |
|
value: 63.466 |
|
- type: mrr_at_3 |
|
value: 60.699999999999996 |
|
- type: mrr_at_5 |
|
value: 62.144999999999996 |
|
- type: ndcg_at_1 |
|
value: 52.1 |
|
- type: ndcg_at_10 |
|
value: 67.93299999999999 |
|
- type: ndcg_at_100 |
|
value: 70.541 |
|
- type: ndcg_at_1000 |
|
value: 70.91300000000001 |
|
- type: ndcg_at_3 |
|
value: 63.468 |
|
- type: ndcg_at_5 |
|
value: 66.08800000000001 |
|
- type: precision_at_1 |
|
value: 52.1 |
|
- type: precision_at_10 |
|
value: 8.34 |
|
- type: precision_at_100 |
|
value: 0.955 |
|
- type: precision_at_1000 |
|
value: 0.098 |
|
- type: precision_at_3 |
|
value: 23.767 |
|
- type: precision_at_5 |
|
value: 15.540000000000001 |
|
- type: recall_at_1 |
|
value: 52.1 |
|
- type: recall_at_10 |
|
value: 83.39999999999999 |
|
- type: recall_at_100 |
|
value: 95.5 |
|
- type: recall_at_1000 |
|
value: 98.4 |
|
- type: recall_at_3 |
|
value: 71.3 |
|
- type: recall_at_5 |
|
value: 77.7 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/waimai-classification |
|
name: MTEB Waimai |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 87.12 |
|
- type: ap |
|
value: 70.85284793227382 |
|
- type: f1 |
|
value: 85.55420883566512 |
|
--- |
|
|
|
**新闻 | News** |
|
|
|
**[2024-04-06]** 开源[puff](https://huggingface.co/infgrad/puff-base-v1)系列模型，**专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语**。 |
|
|
|
**[2024-02-27]** 开源stella-mrl-large-zh-v3.5-1792d模型，支持**向量可变维度**。 |
|
|
|
**[2024-02-17]** 开源stella v3系列、dialogue编码模型和相关训练数据。 |
|
|
|
**[2023-10-19]** 开源stella-base-en-v2 使用简单，**不需要任何前缀文本**。 |
|
|
|
**[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，**不需要任何前缀文本**。 |
|
|
|
**[2023-09-11]** 开源stella-base-zh和stella-large-zh |
|
|
|
欢迎去[本人主页](https://huggingface.co/infgrad)查看最新模型，并提出您的宝贵意见！ |
|
|
|
## stella model |
|
|
|
|
|
stella是一个通用的文本编码模型，主要有以下模型： |
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? | |
|
|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:| |
|
| stella-base-en-v2 | 0.2 | 768 | 512 | English | No | |
|
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No | |
|
| stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No | |
|
| stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes | |
|
| stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes | |
|
|
|
完整的训练思路和训练过程已记录在[博客1](https://zhuanlan.zhihu.com/p/655322183)和[博客2](https://zhuanlan.zhihu.com/p/662209559)，欢迎阅读讨论。 |
|
|
|
**训练数据：** |
|
|
|
1. 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本 |
|
2. 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据 |
|
|
|
**训练方法：** |
|
|
|
1. 对比学习损失函数 |
|
2. 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例) |
|
3. EWC(Elastic Weights Consolidation)[4] |
|
4. cosent loss[5] |
|
5. 每一种类型的数据一个迭代器，分别计算loss进行更新 |
|
|
|
stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction( |
|
比如piccolo的`查询:`, `结果:`, e5的`query:`和`passage:`)。 |
|
|
|
**初始权重：**\ |
|
stella-base-zh和stella-large-zh分别以piccolo-base-zh[6]和piccolo-large-zh作为基础模型，512-1024的position |
|
embedding使用层次分解位置编码[7]进行初始化。\ |
|
感谢商汤科技研究院开源的[piccolo系列模型](https://huggingface.co/sensenova)。 |
|
|
|
stella is a general-purpose text encoder, which mainly includes the following models: |
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? | |
|
|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:| |
|
| stella-base-en-v2 | 0.2 | 768 | 512 | English | No | |
|
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No | |
|
| stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No | |
|
| stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes | |
|
| stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes | |
|
|
|
The training data mainly includes: |
|
|
|
1. Open-source training data (wudao_base_200GB, m3e, and simclue), with a focus on selecting texts with lengths greater |
|
than 512. |
|
2. A batch of (question, paragraph) and (sentence, paragraph) data constructed on a general corpus using LLM. |
|
|
|
The loss functions mainly include: |
|
|
|
1. Contrastive learning loss function |
|
2. Contrastive learning loss function with hard negative examples (based on bm25 and vector hard negatives) |
|
3. EWC (Elastic Weights Consolidation) |
|
4. cosent loss |
|
|
|
Model weight initialization:\ |
|
stella-base-zh and stella-large-zh use piccolo-base-zh and piccolo-large-zh as the base models, respectively, and the |
|
512-1024 position embedding uses the initialization strategy of hierarchical decomposed position encoding. |
|
|
|
Training strategy:\ |
|
One iterator for each type of data, separately calculating the loss. |
|
|
|
Based on stella models, stella-v2 use more training data and remove instruction by Knowledge Distillation. |
|
|
|
## Metric |
|
|
|
#### C-MTEB leaderboard (Chinese) |
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) | |
|
|:------------------:|:---------------:|:---------:|:---------------:|:------------:|:------------------:|:--------------:|:-----------------------:|:-------------:|:-------------:|:-------:| |
|
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 | |
|
| stella-base-zh-v2 | 0.2 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.95 | 66.1 | 70.08 | 56.92 | |
|
| stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 | |
|
| stella-base-zh | 0.2 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 | |
|
|
|
#### MTEB leaderboard (English) |
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | |
|
|:-----------------:|:---------------:|:---------:|:---------------:|:------------:|:-------------------:|:---------------:|:-----------------------:|:-------------:|:--------------:|:--------:|:------------------:| |
|
| stella-base-en-v2 | 0.2 | 768 | 512 | 62.61 | 75.28 | 44.9 | 86.45 | 58.77 | 50.1 | 83.02 | 32.52 | |
|
|
|
#### Reproduce our results |
|
|
|
**C-MTEB:** |
|
|
|
```python |
|
import torch |
|
import numpy as np |
|
from typing import List |
|
from mteb import MTEB |
|
from sentence_transformers import SentenceTransformer |
|
|
|
|
|
class FastTextEncoder(): |
|
def __init__(self, model_name): |
|
self.model = SentenceTransformer(model_name).cuda().half().eval() |
|
self.model.max_seq_length = 512 |
|
|
|
def encode( |
|
self, |
|
input_texts: List[str], |
|
*args, |
|
**kwargs |
|
): |
|
new_sens = list(set(input_texts)) |
|
new_sens.sort(key=lambda x: len(x), reverse=True) |
|
vecs = self.model.encode( |
|
new_sens, normalize_embeddings=True, convert_to_numpy=True, batch_size=256 |
|
).astype(np.float32) |
|
sen2arrid = {sen: idx for idx, sen in enumerate(new_sens)} |
|
vecs = vecs[[sen2arrid[sen] for sen in input_texts]] |
|
torch.cuda.empty_cache() |
|
return vecs |
|
|
|
|
|
if __name__ == '__main__': |
|
model_name = "infgrad/stella-base-zh-v2" |
|
output_folder = "zh_mteb_results/stella-base-zh-v2" |
|
task_names = [t.description["name"] for t in MTEB(task_langs=['zh', 'zh-CN']).tasks] |
|
model = FastTextEncoder(model_name) |
|
for task in task_names: |
|
MTEB(tasks=[task], task_langs=['zh', 'zh-CN']).run(model, output_folder=output_folder) |
|
|
|
``` |
|
|
|
**MTEB:** |
|
|
|
You can use official script to reproduce our result. [scripts/run_mteb_english.py](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/run_mteb_english.py) |
|
|
|
#### Evaluation for long text |
|
|
|
经过实际观察发现，C-MTEB的评测数据长度基本都是小于512的， |
|
更致命的是那些长度大于512的文本，其重点都在前半部分 |
|
这里以CMRC2018的数据为例说明这个问题： |
|
|
|
``` |
|
question: 《无双大蛇z》是谁旗下ω-force开发的动作游戏？ |
|
|
|
passage：《无双大蛇z》是光荣旗下ω-force开发的动作游戏，于2009年3月12日登陆索尼playstation3，并于2009年11月27日推...... |
|
``` |
|
|
|
passage长度为800多，大于512，但是对于这个question而言只需要前面40个字就足以检索，多的内容对于模型而言是一种噪声，反而降低了效果。\ |
|
简言之，现有数据集的2个问题：\ |
|
1）长度大于512的过少\ |
|
2）即便大于512，对于检索而言也只需要前512的文本内容\ |
|
导致**无法准确评估模型的长文本编码能力。** |
|
|
|
为了解决这个问题，搜集了相关开源数据并使用规则进行过滤，最终整理了6份长文本测试集,他们分别是： |
|
|
|
- CMRC2018，通用百科 |
|
- CAIL，法律阅读理解 |
|
- DRCD，繁体百科，已转简体 |
|
- Military，军工问答 |
|
- Squad，英文阅读理解，已转中文 |
|
- Multifieldqa_zh，清华的大模型长文本理解能力评测数据[9] |
|
|
|
处理规则是选取答案在512长度之后的文本，短的测试数据会欠采样一下，长短文本占比约为1:2，所以模型既得理解短文本也得理解长文本。 |
|
除了Military数据集，我们提供了其他5个测试数据的下载地址：https://drive.google.com/file/d/1WC6EWaCbVgz-vPMDFH4TwAMkLyh5WNcN/view?usp=sharing |
|
|
|
评测指标为Recall@5, 结果如下： |
|
|
|
| Dataset | piccolo-base-zh | piccolo-large-zh | bge-base-zh | bge-large-zh | stella-base-zh | stella-large-zh | |
|
|:---------------:|:---------------:|:----------------:|:-----------:|:------------:|:--------------:|:---------------:| |
|
| CMRC2018 | 94.34 | 93.82 | 91.56 | 93.12 | 96.08 | 95.56 | |
|
| CAIL | 28.04 | 33.64 | 31.22 | 33.94 | 34.62 | 37.18 | |
|
| DRCD | 78.25 | 77.9 | 78.34 | 80.26 | 86.14 | 84.58 | |
|
| Military | 76.61 | 73.06 | 75.65 | 75.81 | 83.71 | 80.48 | |
|
| Squad | 91.21 | 86.61 | 87.87 | 90.38 | 93.31 | 91.21 | |
|
| Multifieldqa_zh | 81.41 | 83.92 | 83.92 | 83.42 | 79.9 | 80.4 | |
|
| **Average** | 74.98 | 74.83 | 74.76 | 76.15 | **78.96** | **78.24** | |
|
|
|
**注意：** 因为长文本评测数据数量稀少，所以构造时也使用了train部分，如果自行评测，请注意模型的训练数据以免数据泄露。 |
|
|
|
## Usage |
|
|
|
#### stella 中文系列模型 |
|
|
|
stella-base-zh 和 stella-large-zh: 本模型是在piccolo基础上训练的，因此**用法和piccolo完全一致** |
|
，即在检索重排任务上给query和passage加上`查询: `和`结果: `。对于短短匹配不需要做任何操作。 |
|
|
|
stella-base-zh-v2 和 stella-large-zh-v2: 本模型使用简单，**任何使用场景中都不需要加前缀文本**。 |
|
|
|
stella中文系列模型均使用mean pooling做为文本向量。 |
|
|
|
在sentence-transformer库中的使用方法： |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
sentences = ["数据1", "数据2"] |
|
model = SentenceTransformer('infgrad/stella-base-zh-v2') |
|
print(model.max_seq_length) |
|
embeddings_1 = model.encode(sentences, normalize_embeddings=True) |
|
embeddings_2 = model.encode(sentences, normalize_embeddings=True) |
|
similarity = embeddings_1 @ embeddings_2.T |
|
print(similarity) |
|
``` |
|
|
|
直接使用transformers库： |
|
|
|
```python |
|
from transformers import AutoModel, AutoTokenizer |
|
from sklearn.preprocessing import normalize |
|
|
|
model = AutoModel.from_pretrained('infgrad/stella-base-zh-v2') |
|
tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-zh-v2') |
|
sentences = ["数据1", "数据ABCDEFGH"] |
|
batch_data = tokenizer( |
|
batch_text_or_text_pairs=sentences, |
|
padding="longest", |
|
return_tensors="pt", |
|
max_length=1024, |
|
truncation=True, |
|
) |
|
attention_mask = batch_data["attention_mask"] |
|
model_output = model(**batch_data) |
|
last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) |
|
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
vectors = normalize(vectors, norm="l2", axis=1, ) |
|
print(vectors.shape) # 2,768 |
|
``` |
|
|
|
#### stella models for English |
|
|
|
**Using Sentence-Transformers:** |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
sentences = ["one car come", "one car go"] |
|
model = SentenceTransformer('infgrad/stella-base-en-v2') |
|
print(model.max_seq_length) |
|
embeddings_1 = model.encode(sentences, normalize_embeddings=True) |
|
embeddings_2 = model.encode(sentences, normalize_embeddings=True) |
|
similarity = embeddings_1 @ embeddings_2.T |
|
print(similarity) |
|
``` |
|
|
|
**Using HuggingFace Transformers:** |
|
|
|
```python |
|
from transformers import AutoModel, AutoTokenizer |
|
from sklearn.preprocessing import normalize |
|
|
|
model = AutoModel.from_pretrained('infgrad/stella-base-en-v2') |
|
tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-en-v2') |
|
sentences = ["one car come", "one car go"] |
|
batch_data = tokenizer( |
|
batch_text_or_text_pairs=sentences, |
|
padding="longest", |
|
return_tensors="pt", |
|
max_length=512, |
|
truncation=True, |
|
) |
|
attention_mask = batch_data["attention_mask"] |
|
model_output = model(**batch_data) |
|
last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) |
|
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
vectors = normalize(vectors, norm="l2", axis=1, ) |
|
print(vectors.shape) # 2,768 |
|
``` |
|
|
|
## Training Detail |
|
|
|
**硬件：** 单卡A100-80GB |
|
|
|
**环境：** torch1.13.*; transformers-trainer + deepspeed + gradient-checkpointing |
|
|
|
**学习率：** 1e-6 |
|
|
|
**batch_size：** base模型为1024，额外增加20%的难负例；large模型为768，额外增加20%的难负例 |
|
|
|
**数据量：** 第一版模型约100万，其中用LLM构造的数据约有200K. LLM模型大小为13b。v2系列模型到了2000万训练数据。 |
|
|
|
## ToDoList |
|
|
|
**评测的稳定性：** |
|
评测过程中发现Clustering任务会和官方的结果不一致，大约有±0.0x的小差距，原因是聚类代码没有设置random_seed，差距可以忽略不计，不影响评测结论。 |
|
|
|
**更高质量的长文本训练和测试数据：** 训练数据多是用13b模型构造的，肯定会存在噪声。 |
|
测试数据基本都是从mrc数据整理来的，所以问题都是factoid类型，不符合真实分布。 |
|
|
|
**OOD的性能：** 虽然近期出现了很多向量编码模型，但是对于不是那么通用的domain，这一众模型包括stella、openai和cohere, |
|
它们的效果均比不上BM25。 |
|
|
|
## Reference |
|
|
|
1. https://www.scidb.cn/en/detail?dataSetId=c6a3fe684227415a9db8e21bac4a15ab |
|
2. https://github.com/wangyuxinwhy/uniem |
|
3. https://github.com/CLUEbenchmark/SimCLUE |
|
4. https://arxiv.org/abs/1612.00796 |
|
5. https://kexue.fm/archives/8847 |
|
6. https://huggingface.co/sensenova/piccolo-base-zh |
|
7. https://kexue.fm/archives/7947 |
|
8. https://github.com/FlagOpen/FlagEmbedding |
|
9. https://github.com/THUDM/LongBench |
|
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|
