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checkpoint-1600/1_Pooling/config.json

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+}

checkpoint-1600/config_sentence_transformers.json

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+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
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+  "similarity_fn_name": "cosine"
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checkpoint-1600/optimizer.pt

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checkpoint-1600/scheduler.pt

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checkpoint-1600/sentence_bert_config.json

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checkpoint-1600/special_tokens_map.json

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checkpoint-1600/training_args.bin

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checkpoint-1800/README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:124788
+- loss:GISTEmbedLoss
+base_model: BAAI/bge-small-en-v1.5
+widget:
+- source_sentence: 其他机械、设备和有形货物租赁服务代表
+  sentences:
+  - 其他机械和设备租赁服务工作人员
+  - 电子和电信设备及零部件物流经理
+  - 工业主厨
+- source_sentence: 公交车司机
+  sentences:
+  - 表演灯光设计师
+  - 乙烯基地板安装工
+  - 国际巴士司机
+- source_sentence: online communication manager
+  sentences:
+  - trades union official
+  - social media manager
+  - budget manager
+- source_sentence: Projektmanagerin
+  sentences:
+  - Projektmanager/Projektmanagerin
+  - Category-Manager
+  - Infanterist
+- source_sentence: Volksvertreter
+  sentences:
+  - Parlamentarier
+  - Oberbürgermeister
+  - Konsul
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@20
+- cosine_accuracy@50
+- cosine_accuracy@100
+- cosine_accuracy@150
+- cosine_accuracy@200
+- cosine_precision@1
+- cosine_precision@20
+- cosine_precision@50
+- cosine_precision@100
+- cosine_precision@150
+- cosine_precision@200
+- cosine_recall@1
+- cosine_recall@20
+- cosine_recall@50
+- cosine_recall@100
+- cosine_recall@150
+- cosine_recall@200
+- cosine_ndcg@1
+- cosine_ndcg@20
+- cosine_ndcg@50
+- cosine_ndcg@100
+- cosine_ndcg@150
+- cosine_ndcg@200
+- cosine_mrr@1
+- cosine_mrr@20
+- cosine_mrr@50
+- cosine_mrr@100
+- cosine_mrr@150
+- cosine_mrr@200
+- cosine_map@1
+- cosine_map@20
+- cosine_map@50
+- cosine_map@100
+- cosine_map@150
+- cosine_map@200
+- cosine_map@500
+model-index:
+- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full en
+      type: full_en
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6571428571428571
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9904761904761905
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9904761904761905
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9904761904761905
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9904761904761905
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9904761904761905
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6571428571428571
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.5076190476190475
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.3089523809523809
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.1872380952380952
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.1321904761904762
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.1027142857142857
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0680237860830842
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5459242543214992
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.728483344815942
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.8382149119179341
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8762032488748317
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9059964336434017
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6571428571428571
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.6895375515490911
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7060633068166344
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+    - type: cosine_ndcg@100
+      value: 0.7619501692018719
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
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+      value: 0.8098412698412698
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+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5835322146366259
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full es
+      type: full_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.12432432432432433
+      name: Cosine Accuracy@1
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+      value: 1.0
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+      value: 1.0
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+      value: 0.31686486486486487
+      name: Cosine Precision@50
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+      name: Cosine Precision@100
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+      value: 0.14702702702702705
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.11762162162162161
+      name: Cosine Precision@200
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+      value: 0.003111544931768446
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+      value: 0.3235933309332048
+      name: Cosine Recall@20
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+      value: 0.4622883553307717
+      name: Cosine Recall@50
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+      value: 0.12432432432432433
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+      value: 0.5516816816816817
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+      value: 0.5516816816816817
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+      value: 0.5516816816816817
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+      value: 0.5516816816816817
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+      value: 0.12432432432432433
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+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full de
+      type: full_de
+    metrics:
+    - type: cosine_accuracy@1
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+      name: Cosine Accuracy@1
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+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.12884550949445583
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.13149151347084506
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.1329204280861929
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.13684460640814028
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix es
+      type: mix_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.40717628705148207
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.7581903276131046
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.8455538221528861
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9048361934477379
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9370774830993239
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9547581903276131
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.40717628705148207
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.08819552782111284
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.04139365574622985
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.022704108164326574
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.015822499566649332
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.012189287571502862
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.1547198078399326
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.652070178045217
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.7639576059232845
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.8390052744966942
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.877764158185375
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9016677809969541
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.40717628705148207
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5052212563002627
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.5356805472279078
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.5521009256440798
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5595924746165437
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.5639157869620031
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.40717628705148207
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.49319826751048873
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.49612329710657893
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.4969826860284758
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.49724671253026886
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.49734552981578295
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.40717628705148207
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.419614969499609
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.427088485280225
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.4292014145714
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.4298202694277485
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.4300980044671579
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.43055010151569684
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix de
+      type: mix_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.29017160686427457
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.6484659386375455
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.7540301612064483
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.8419136765470618
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.8788351534061363
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9089963598543942
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.29017160686427457
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.07251690067602704
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.0355486219448778
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.02041601664066563
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.014505113537874847
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.01137545501820073
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.10890968972092217
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5299011960478419
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.647191887675507
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7442624371641533
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.7928583810019068
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.8289478245796498
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.29017160686427457
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.38598145754556046
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.41773491829410075
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.43906545567486005
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.4485955578737219
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.45520732213321263
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.29017160686427457
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.37232551415227233
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.375685507642469
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.3769348294784883
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.377239930826995
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.3774183771765249
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.29017160686427457
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.30311022602590254
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.31036427264538485
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.31304585670015317
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.3138396622777036
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.31426372512191
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.3150399864057635
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix zh
+      type: mix_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.09394572025052192
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.35281837160751567
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.48329853862212946
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.5918580375782881
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.6649269311064718
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.7004175365344467
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.09394572025052192
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.030897703549060546
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.018204592901878917
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.011362212943632568
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.008639526791927627
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.007019832985386221
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.03185455810716771
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.20592877025549258
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.30069837956059253
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.3754792557245584
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.4282591046160983
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.46372361401067036
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.09394572025052192
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.13433471892252347
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.16091824243484512
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.1780017996510726
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.1886875211403746
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.19541417908856412
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.09394572025052192
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.14710513443845905
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.15122849766144658
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.15275090014884107
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.1533445728241347
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.1535456563541225
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.09394572025052192
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.083759101073897
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.08908800548950695
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.09092612397080438
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.09168814149038751
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.09208168156532727
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.09301554391402207
+      name: Cosine Map@500
+---
+
+# SentenceTransformer based on BAAI/bge-small-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the full_en, full_de, full_es, full_zh and mix datasets. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+    - full_en
+    - full_de
+    - full_es
+    - full_zh
+    - mix
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Volksvertreter',
+    'Parlamentarier',
+    'Oberbürgermeister',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric               | full_en  | full_es    | full_de    | full_zh    | mix_es     | mix_de     | mix_zh     |
+|:---------------------|:---------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1    | 0.6571   | 0.1243     | 0.2956     | 0.301      | 0.4072     | 0.2902     | 0.0939     |
+| cosine_accuracy@20   | 0.9905   | 1.0        | 0.9212     | 0.7087     | 0.7582     | 0.6485     | 0.3528     |
+| cosine_accuracy@50   | 0.9905   | 1.0        | 0.9606     | 0.8252     | 0.8456     | 0.754      | 0.4833     |
+| cosine_accuracy@100  | 0.9905   | 1.0        | 0.9754     | 0.8544     | 0.9048     | 0.8419     | 0.5919     |
+| cosine_accuracy@150  | 0.9905   | 1.0        | 0.9852     | 0.9126     | 0.9371     | 0.8788     | 0.6649     |
+| cosine_accuracy@200  | 0.9905   | 1.0        | 0.9852     | 0.9417     | 0.9548     | 0.909      | 0.7004     |
+| cosine_precision@1   | 0.6571   | 0.1243     | 0.2956     | 0.301      | 0.4072     | 0.2902     | 0.0939     |
+| cosine_precision@20  | 0.5076   | 0.4924     | 0.4227     | 0.1684     | 0.0882     | 0.0725     | 0.0309     |
+| cosine_precision@50  | 0.309    | 0.3169     | 0.2775     | 0.0944     | 0.0414     | 0.0355     | 0.0182     |
+| cosine_precision@100 | 0.1872   | 0.1984     | 0.1787     | 0.0584     | 0.0227     | 0.0204     | 0.0114     |
+| cosine_precision@150 | 0.1322   | 0.147      | 0.135      | 0.046      | 0.0158     | 0.0145     | 0.0086     |
+| cosine_precision@200 | 0.1027   | 0.1176     | 0.1096     | 0.0382     | 0.0122     | 0.0114     | 0.007      |
+| cosine_recall@1      | 0.068    | 0.0031     | 0.0111     | 0.0244     | 0.1547     | 0.1089     | 0.0319     |
+| cosine_recall@20     | 0.5459   | 0.3236     | 0.2579     | 0.1751     | 0.6521     | 0.5299     | 0.2059     |
+| cosine_recall@50     | 0.7285   | 0.4623     | 0.3785     | 0.2395     | 0.764      | 0.6472     | 0.3007     |
+| cosine_recall@100    | 0.8382   | 0.5424     | 0.4647     | 0.286      | 0.839      | 0.7443     | 0.3755     |
+| cosine_recall@150    | 0.8762   | 0.5823     | 0.5141     | 0.3291     | 0.8778     | 0.7929     | 0.4283     |
+| cosine_recall@200    | 0.906    | 0.6126     | 0.5479     | 0.363      | 0.9017     | 0.8289     | 0.4637     |
+| cosine_ndcg@1        | 0.6571   | 0.1243     | 0.2956     | 0.301      | 0.4072     | 0.2902     | 0.0939     |
+| cosine_ndcg@20       | 0.6895   | 0.5407     | 0.4572     | 0.2385     | 0.5052     | 0.386      | 0.1343     |
+| cosine_ndcg@50       | 0.7061   | 0.5008     | 0.4186     | 0.2342     | 0.5357     | 0.4177     | 0.1609     |
+| cosine_ndcg@100      | 0.762    | 0.5143     | 0.4341     | 0.2559     | 0.5521     | 0.4391     | 0.178      |
+| cosine_ndcg@150      | 0.7788   | 0.535      | 0.4594     | 0.2734     | 0.5596     | 0.4486     | 0.1887     |
+| **cosine_ndcg@200**  | **0.79** | **0.5498** | **0.4763** | **0.2857** | **0.5639** | **0.4552** | **0.1954** |
+| cosine_mrr@1         | 0.6571   | 0.1243     | 0.2956     | 0.301      | 0.4072     | 0.2902     | 0.0939     |
+| cosine_mrr@20        | 0.8098   | 0.5517     | 0.4885     | 0.4211     | 0.4932     | 0.3723     | 0.1471     |
+| cosine_mrr@50        | 0.8098   | 0.5517     | 0.4898     | 0.425      | 0.4961     | 0.3757     | 0.1512     |
+| cosine_mrr@100       | 0.8098   | 0.5517     | 0.49       | 0.4254     | 0.497      | 0.3769     | 0.1528     |
+| cosine_mrr@150       | 0.8098   | 0.5517     | 0.4901     | 0.4259     | 0.4972     | 0.3772     | 0.1533     |
+| cosine_mrr@200       | 0.8098   | 0.5517     | 0.4901     | 0.426      | 0.4973     | 0.3774     | 0.1535     |
+| cosine_map@1         | 0.6571   | 0.1243     | 0.2956     | 0.301      | 0.4072     | 0.2902     | 0.0939     |
+| cosine_map@20        | 0.5465   | 0.4062     | 0.3211     | 0.1416     | 0.4196     | 0.3031     | 0.0838     |
+| cosine_map@50        | 0.5352   | 0.3426     | 0.2619     | 0.1233     | 0.4271     | 0.3104     | 0.0891     |
+| cosine_map@100       | 0.5685   | 0.3398     | 0.2559     | 0.1288     | 0.4292     | 0.313      | 0.0909     |
+| cosine_map@150       | 0.5757   | 0.3482     | 0.265      | 0.1315     | 0.4298     | 0.3138     | 0.0917     |
+| cosine_map@200       | 0.5792   | 0.3535     | 0.2701     | 0.1329     | 0.4301     | 0.3143     | 0.0921     |
+| cosine_map@500       | 0.5835   | 0.3635     | 0.2811     | 0.1368     | 0.4306     | 0.315      | 0.093      |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Datasets
+<details><summary>full_en</summary>
+
+#### full_en
+
+* Dataset: full_en
+* Size: 28,880 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                          | positive                                                                         |
+  |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                          | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.0 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.01 tokens</li><li>max: 13 tokens</li></ul> |
+* Samples:
+  | anchor                                   | positive                                 |
+  |:-----------------------------------------|:-----------------------------------------|
+  | <code>air commodore</code>               | <code>flight lieutenant</code>           |
+  | <code>command and control officer</code> | <code>flight officer</code>              |
+  | <code>air commodore</code>               | <code>command and control officer</code> |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>full_de</summary>
+
+#### full_de
+
+* Dataset: full_de
+* Size: 23,023 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 11.05 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.43 tokens</li><li>max: 45 tokens</li></ul> |
+* Samples:
+  | anchor                            | positive                                             |
+  |:----------------------------------|:-----------------------------------------------------|
+  | <code>Staffelkommandantin</code>  | <code>Kommodore</code>                               |
+  | <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
+  | <code>Staffelkommandantin</code>  | <code>Luftwaffenoffizierin</code>                    |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>full_es</summary>
+
+#### full_es
+
+* Dataset: full_es
+* Size: 20,724 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 12.95 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.57 tokens</li><li>max: 50 tokens</li></ul> |
+* Samples:
+  | anchor                              | positive                                   |
+  |:------------------------------------|:-------------------------------------------|
+  | <code>jefe de escuadrón</code>      | <code>instructor</code>                    |
+  | <code>comandante de aeronave</code> | <code>instructor de simulador</code>       |
+  | <code>instructor</code>             | <code>oficial del Ejército del Aire</code> |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>full_zh</summary>
+
+#### full_zh
+
+* Dataset: full_zh
+* Size: 30,401 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                         |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 8.36 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.95 tokens</li><li>max: 27 tokens</li></ul> |
+* Samples:
+  | anchor            | positive             |
+  |:------------------|:---------------------|
+  | <code>技术总监</code> | <code>技术和运营总监</code> |
+  | <code>技术总监</code> | <code>技术主管</code>    |
+  | <code>技术总监</code> | <code>技术艺术总监</code>  |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>mix</summary>
+
+#### mix
+
+* Dataset: mix
+* Size: 21,760 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 5.65 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 10.08 tokens</li><li>max: 30 tokens</li></ul> |
+* Samples:
+  | anchor                                    | positive                                                        |
+  |:------------------------------------------|:----------------------------------------------------------------|
+  | <code>technical manager</code>            | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
+  | <code>head of technical</code>            | <code>directora técnica</code>                                  |
+  | <code>head of technical department</code> | <code>技术艺术总监</code>                                             |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `gradient_accumulation_steps`: 2
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.05
+- `log_on_each_node`: False
+- `fp16`: True
+- `dataloader_num_workers`: 4
+- `ddp_find_unused_parameters`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: False
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: True
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
+|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
+| -1     | -1   | -             | 0.7322                  | 0.4690                  | 0.3853                  | 0.2723                  | 0.3209                 | 0.2244                 | 0.0919                 |
+| 0.0021 | 1    | 23.8878       | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.2058 | 100  | 7.2098        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.4115 | 200  | 4.2635        | 0.7800                  | 0.5132                  | 0.4268                  | 0.2798                  | 0.4372                 | 0.2996                 | 0.1447                 |
+| 0.6173 | 300  | 4.1931        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.8230 | 400  | 3.73          | 0.7863                  | 0.5274                  | 0.4451                  | 0.2805                  | 0.4762                 | 0.3455                 | 0.1648                 |
+| 1.0309 | 500  | 3.3569        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.2366 | 600  | 3.6464        | 0.7868                  | 0.5372                  | 0.4540                  | 0.2813                  | 0.5063                 | 0.3794                 | 0.1755                 |
+| 1.4424 | 700  | 3.0772        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.6481 | 800  | 3.114         | 0.7906                  | 0.5391                  | 0.4576                  | 0.2832                  | 0.5221                 | 0.4047                 | 0.1779                 |
+| 1.8539 | 900  | 2.9246        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.0617 | 1000 | 2.7479        | 0.7873                  | 0.5423                  | 0.4631                  | 0.2871                  | 0.5323                 | 0.4143                 | 0.1843                 |
+| 2.2675 | 1100 | 3.049         | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.4733 | 1200 | 2.6137        | 0.7878                  | 0.5418                  | 0.4685                  | 0.2870                  | 0.5470                 | 0.4339                 | 0.1932                 |
+| 2.6790 | 1300 | 2.8607        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.8848 | 1400 | 2.7071        | 0.7889                  | 0.5465                  | 0.4714                  | 0.2891                  | 0.5504                 | 0.4362                 | 0.1944                 |
+| 3.0926 | 1500 | 2.7012        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 3.2984 | 1600 | 2.7423        | 0.7882                  | 0.5471                  | 0.4748                  | 0.2868                  | 0.5542                 | 0.4454                 | 0.1976                 |
+| 3.5041 | 1700 | 2.5316        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 3.7099 | 1800 | 2.6344        | 0.7900                  | 0.5498                  | 0.4763                  | 0.2857                  | 0.5639                 | 0.4552                 | 0.1954                 |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### GISTEmbedLoss
+```bibtex
+@misc{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
+    author={Aivin V. Solatorio},
+    year={2024},
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-1800/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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checkpoint-1800/sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

checkpoint-1800/special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "single_word": false
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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checkpoint-2200/1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-2200/README.md

@@ -0,0 +1,1418 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:124788
+- loss:GISTEmbedLoss
+base_model: BAAI/bge-small-en-v1.5
+widget:
+- source_sentence: 其他机械、设备和有形货物租赁服务代表
+  sentences:
+  - 其他机械和设备租赁服务工作人员
+  - 电子和电信设备及零部件物流经理
+  - 工业主厨
+- source_sentence: 公交车司机
+  sentences:
+  - 表演灯光设计师
+  - 乙烯基地板安装工
+  - 国际巴士司机
+- source_sentence: online communication manager
+  sentences:
+  - trades union official
+  - social media manager
+  - budget manager
+- source_sentence: Projektmanagerin
+  sentences:
+  - Projektmanager/Projektmanagerin
+  - Category-Manager
+  - Infanterist
+- source_sentence: Volksvertreter
+  sentences:
+  - Parlamentarier
+  - Oberbürgermeister
+  - Konsul
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@20
+- cosine_accuracy@50
+- cosine_accuracy@100
+- cosine_accuracy@150
+- cosine_accuracy@200
+- cosine_precision@1
+- cosine_precision@20
+- cosine_precision@50
+- cosine_precision@100
+- cosine_precision@150
+- cosine_precision@200
+- cosine_recall@1
+- cosine_recall@20
+- cosine_recall@50
+- cosine_recall@100
+- cosine_recall@150
+- cosine_recall@200
+- cosine_ndcg@1
+- cosine_ndcg@20
+- cosine_ndcg@50
+- cosine_ndcg@100
+- cosine_ndcg@150
+- cosine_ndcg@200
+- cosine_mrr@1
+- cosine_mrr@20
+- cosine_mrr@50
+- cosine_mrr@100
+- cosine_mrr@150
+- cosine_mrr@200
+- cosine_map@1
+- cosine_map@20
+- cosine_map@50
+- cosine_map@100
+- cosine_map@150
+- cosine_map@200
+- cosine_map@500
+model-index:
+- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full en
+      type: full_en
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.6571428571428571
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9904761904761905
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9904761904761905
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9904761904761905
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9904761904761905
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9904761904761905
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.6571428571428571
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.5047619047619047
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.30857142857142855
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.18666666666666668
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.13269841269841268
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.1029047619047619
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.0680237860830842
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.539060339827615
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.7269844521994231
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.8337131628681403
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.879935375805825
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9050529457831012
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.6571428571428571
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.686462471196106
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.7052824081502371
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.7601614355798527
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.7798476891938094
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.7898871141566125
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.6571428571428571
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.8095238095238095
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.8095238095238095
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.8095238095238095
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.8095238095238095
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.8095238095238095
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.6571428571428571
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.5451065538458748
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.5347802076206865
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.567702602098158
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.5756725358487015
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.5789669196636947
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.5832808543489026
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full es
+      type: full_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11351351351351352
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 1.0
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 1.0
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 1.0
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 1.0
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 1.0
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.11351351351351352
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.4913513513513514
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.316972972972973
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.19843243243243244
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.146990990990991
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.11778378378378378
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.002992884071419607
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.32341666838263944
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.4630260221149236
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.5419804526017848
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5826718468403144
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.6149262657286421
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.11351351351351352
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5389058089458943
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.5002442028172164
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.5138591255215345
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5346372349516221
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.5502474315848075
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.11351351351351352
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.5444744744744745
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.5444744744744745
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5444744744744745
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5444744744744745
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5444744744744745
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.11351351351351352
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.40352984921129137
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.3418539578142162
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.339373689987275
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.3478760829213016
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.3533435915341769
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.363222785830563
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full de
+      type: full_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2955665024630542
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.9211822660098522
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.9655172413793104
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9753694581280788
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9852216748768473
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9852216748768473
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.2955665024630542
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.424384236453202
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.28167487684729065
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.17995073891625615
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.13589490968801315
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.1108128078817734
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.01108543831680986
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.2600945586038909
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.3844030994839744
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.4672649807153451
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.5171228717670064
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.5533299912627624
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.2955665024630542
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.4593107411252075
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.42313178566078624
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.4367043857530601
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.4621847371016286
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.48019099347834654
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.2955665024630542
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.4892678749821603
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.49065090899064223
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.49080251743966435
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.4908799208299932
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.4908799208299932
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.2955665024630542
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.3228620941051522
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.2644260812747752
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.2576011230547815
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.2666548881846307
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.27224102651692533
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.28312561300678324
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: full zh
+      type: full_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.3300970873786408
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.7184466019417476
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.8155339805825242
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.8932038834951457
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9223300970873787
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9320388349514563
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.3300970873786408
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.16796116504854372
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.09262135922330093
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.05815533980582525
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.04563106796116505
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.03771844660194174
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.02573649124630195
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.17402459309945448
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.23816219248808224
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.28291725637657983
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.32619122038725784
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.3543394793587958
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.3300970873786408
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.23956118764265208
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.2341910409667355
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.2559822552765659
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.27344655996496936
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.28432223965649855
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.3300970873786408
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.43064643766798927
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.43374387043765733
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.4348781442268605
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.4351279925655956
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.4351822313246128
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.3300970873786408
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.14301006319225865
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.12425793473074002
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.12962575663735706
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.13242860022521366
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.13374255185989983
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.13779434547799502
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix es
+      type: mix_es
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.40977639105564223
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.7618304732189287
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.8512740509620385
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.9105564222568903
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.9381175247009881
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9542381695267811
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.40977639105564223
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.0890015600624025
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.04168486739469579
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.022854914196567863
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.01585370081469925
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.012220488819552783
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.15567317930812472
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.6574783943738703
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.7691404799049105
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.8454015303469281
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.8795148948815096
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.9035051878265606
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.40977639105564223
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.5094055696124096
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.5398029704628499
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.5563939454831869
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.5630335952477792
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.5674217099859529
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.40977639105564223
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.4963374711503733
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.49930745416180927
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.5001571935146001
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.5003842041203103
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.5004783417497985
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.40977639105564223
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.4236549905504724
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.4311498037279026
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.43327838927965695
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.4338451382952763
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.4341307997461715
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.4345995592976099
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix de
+      type: mix_de
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2912116484659386
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.6526261050442018
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.7550702028081123
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.8460738429537181
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.8876755070202809
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.9173166926677067
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.2912116484659386
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.07308892355694228
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.03583983359334374
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.02058242329693188
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.014609117698041255
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.011515860634425378
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.10977639105564223
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.5342520367481365
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.6529207834980065
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.7505633558675681
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.7989166233315999
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.8393482405962905
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.2912116484659386
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.39027078330836906
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.4224011615840446
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.4438393956774872
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.45327900259303716
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.4606831999024183
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.2912116484659386
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.37544207546115405
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.37870409367323543
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.37999194359776256
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.3803335431113417
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.3805079454038972
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.2912116484659386
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.3075927383942124
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.31502827814698436
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.31767149302992986
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.31842095656425334
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.3189017921904424
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.31963709557315734
+      name: Cosine Map@500
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: mix zh
+      type: mix_zh
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.09498956158663883
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@20
+      value: 0.35281837160751567
+      name: Cosine Accuracy@20
+    - type: cosine_accuracy@50
+      value: 0.48851774530271397
+      name: Cosine Accuracy@50
+    - type: cosine_accuracy@100
+      value: 0.5960334029227558
+      name: Cosine Accuracy@100
+    - type: cosine_accuracy@150
+      value: 0.657098121085595
+      name: Cosine Accuracy@150
+    - type: cosine_accuracy@200
+      value: 0.7025052192066806
+      name: Cosine Accuracy@200
+    - type: cosine_precision@1
+      value: 0.09498956158663883
+      name: Cosine Precision@1
+    - type: cosine_precision@20
+      value: 0.03102818371607516
+      name: Cosine Precision@20
+    - type: cosine_precision@50
+      value: 0.018528183716075158
+      name: Cosine Precision@50
+    - type: cosine_precision@100
+      value: 0.011550104384133612
+      name: Cosine Precision@100
+    - type: cosine_precision@150
+      value: 0.008601252609603338
+      name: Cosine Precision@150
+    - type: cosine_precision@200
+      value: 0.007074634655532359
+      name: Cosine Precision@200
+    - type: cosine_recall@1
+      value: 0.03218510786360473
+      name: Cosine Recall@1
+    - type: cosine_recall@20
+      value: 0.20682473406899293
+      name: Cosine Recall@20
+    - type: cosine_recall@50
+      value: 0.30616239188786165
+      name: Cosine Recall@50
+    - type: cosine_recall@100
+      value: 0.38175970109686186
+      name: Cosine Recall@100
+    - type: cosine_recall@150
+      value: 0.4266063558339132
+      name: Cosine Recall@150
+    - type: cosine_recall@200
+      value: 0.4677598005103224
+      name: Cosine Recall@200
+    - type: cosine_ndcg@1
+      value: 0.09498956158663883
+      name: Cosine Ndcg@1
+    - type: cosine_ndcg@20
+      value: 0.13726194438538974
+      name: Cosine Ndcg@20
+    - type: cosine_ndcg@50
+      value: 0.16515347653846224
+      name: Cosine Ndcg@50
+    - type: cosine_ndcg@100
+      value: 0.18245718935168395
+      name: Cosine Ndcg@100
+    - type: cosine_ndcg@150
+      value: 0.1915123607890909
+      name: Cosine Ndcg@150
+    - type: cosine_ndcg@200
+      value: 0.1993072789458329
+      name: Cosine Ndcg@200
+    - type: cosine_mrr@1
+      value: 0.09498956158663883
+      name: Cosine Mrr@1
+    - type: cosine_mrr@20
+      value: 0.15082760305134044
+      name: Cosine Mrr@20
+    - type: cosine_mrr@50
+      value: 0.1552139914541245
+      name: Cosine Mrr@50
+    - type: cosine_mrr@100
+      value: 0.1567682757261486
+      name: Cosine Mrr@100
+    - type: cosine_mrr@150
+      value: 0.1572599746321091
+      name: Cosine Mrr@150
+    - type: cosine_mrr@200
+      value: 0.15752063728764779
+      name: Cosine Mrr@200
+    - type: cosine_map@1
+      value: 0.09498956158663883
+      name: Cosine Map@1
+    - type: cosine_map@20
+      value: 0.08696228866764828
+      name: Cosine Map@20
+    - type: cosine_map@50
+      value: 0.0925585898977933
+      name: Cosine Map@50
+    - type: cosine_map@100
+      value: 0.09443690504503688
+      name: Cosine Map@100
+    - type: cosine_map@150
+      value: 0.09508196706389692
+      name: Cosine Map@150
+    - type: cosine_map@200
+      value: 0.09552658777692054
+      name: Cosine Map@200
+    - type: cosine_map@500
+      value: 0.09647934265199021
+      name: Cosine Map@500
+---
+
+# SentenceTransformer based on BAAI/bge-small-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the full_en, full_de, full_es, full_zh and mix datasets. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+    - full_en
+    - full_de
+    - full_es
+    - full_zh
+    - mix
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Volksvertreter',
+    'Parlamentarier',
+    'Oberbürgermeister',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric               | full_en    | full_es    | full_de    | full_zh    | mix_es     | mix_de     | mix_zh     |
+|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1    | 0.6571     | 0.1135     | 0.2956     | 0.3301     | 0.4098     | 0.2912     | 0.095      |
+| cosine_accuracy@20   | 0.9905     | 1.0        | 0.9212     | 0.7184     | 0.7618     | 0.6526     | 0.3528     |
+| cosine_accuracy@50   | 0.9905     | 1.0        | 0.9655     | 0.8155     | 0.8513     | 0.7551     | 0.4885     |
+| cosine_accuracy@100  | 0.9905     | 1.0        | 0.9754     | 0.8932     | 0.9106     | 0.8461     | 0.596      |
+| cosine_accuracy@150  | 0.9905     | 1.0        | 0.9852     | 0.9223     | 0.9381     | 0.8877     | 0.6571     |
+| cosine_accuracy@200  | 0.9905     | 1.0        | 0.9852     | 0.932      | 0.9542     | 0.9173     | 0.7025     |
+| cosine_precision@1   | 0.6571     | 0.1135     | 0.2956     | 0.3301     | 0.4098     | 0.2912     | 0.095      |
+| cosine_precision@20  | 0.5048     | 0.4914     | 0.4244     | 0.168      | 0.089      | 0.0731     | 0.031      |
+| cosine_precision@50  | 0.3086     | 0.317      | 0.2817     | 0.0926     | 0.0417     | 0.0358     | 0.0185     |
+| cosine_precision@100 | 0.1867     | 0.1984     | 0.18       | 0.0582     | 0.0229     | 0.0206     | 0.0116     |
+| cosine_precision@150 | 0.1327     | 0.147      | 0.1359     | 0.0456     | 0.0159     | 0.0146     | 0.0086     |
+| cosine_precision@200 | 0.1029     | 0.1178     | 0.1108     | 0.0377     | 0.0122     | 0.0115     | 0.0071     |
+| cosine_recall@1      | 0.068      | 0.003      | 0.0111     | 0.0257     | 0.1557     | 0.1098     | 0.0322     |
+| cosine_recall@20     | 0.5391     | 0.3234     | 0.2601     | 0.174      | 0.6575     | 0.5343     | 0.2068     |
+| cosine_recall@50     | 0.727      | 0.463      | 0.3844     | 0.2382     | 0.7691     | 0.6529     | 0.3062     |
+| cosine_recall@100    | 0.8337     | 0.542      | 0.4673     | 0.2829     | 0.8454     | 0.7506     | 0.3818     |
+| cosine_recall@150    | 0.8799     | 0.5827     | 0.5171     | 0.3262     | 0.8795     | 0.7989     | 0.4266     |
+| cosine_recall@200    | 0.9051     | 0.6149     | 0.5533     | 0.3543     | 0.9035     | 0.8393     | 0.4678     |
+| cosine_ndcg@1        | 0.6571     | 0.1135     | 0.2956     | 0.3301     | 0.4098     | 0.2912     | 0.095      |
+| cosine_ndcg@20       | 0.6865     | 0.5389     | 0.4593     | 0.2396     | 0.5094     | 0.3903     | 0.1373     |
+| cosine_ndcg@50       | 0.7053     | 0.5002     | 0.4231     | 0.2342     | 0.5398     | 0.4224     | 0.1652     |
+| cosine_ndcg@100      | 0.7602     | 0.5139     | 0.4367     | 0.256      | 0.5564     | 0.4438     | 0.1825     |
+| cosine_ndcg@150      | 0.7798     | 0.5346     | 0.4622     | 0.2734     | 0.563      | 0.4533     | 0.1915     |
+| **cosine_ndcg@200**  | **0.7899** | **0.5502** | **0.4802** | **0.2843** | **0.5674** | **0.4607** | **0.1993** |
+| cosine_mrr@1         | 0.6571     | 0.1135     | 0.2956     | 0.3301     | 0.4098     | 0.2912     | 0.095      |
+| cosine_mrr@20        | 0.8095     | 0.5445     | 0.4893     | 0.4306     | 0.4963     | 0.3754     | 0.1508     |
+| cosine_mrr@50        | 0.8095     | 0.5445     | 0.4907     | 0.4337     | 0.4993     | 0.3787     | 0.1552     |
+| cosine_mrr@100       | 0.8095     | 0.5445     | 0.4908     | 0.4349     | 0.5002     | 0.38       | 0.1568     |
+| cosine_mrr@150       | 0.8095     | 0.5445     | 0.4909     | 0.4351     | 0.5004     | 0.3803     | 0.1573     |
+| cosine_mrr@200       | 0.8095     | 0.5445     | 0.4909     | 0.4352     | 0.5005     | 0.3805     | 0.1575     |
+| cosine_map@1         | 0.6571     | 0.1135     | 0.2956     | 0.3301     | 0.4098     | 0.2912     | 0.095      |
+| cosine_map@20        | 0.5451     | 0.4035     | 0.3229     | 0.143      | 0.4237     | 0.3076     | 0.087      |
+| cosine_map@50        | 0.5348     | 0.3419     | 0.2644     | 0.1243     | 0.4311     | 0.315      | 0.0926     |
+| cosine_map@100       | 0.5677     | 0.3394     | 0.2576     | 0.1296     | 0.4333     | 0.3177     | 0.0944     |
+| cosine_map@150       | 0.5757     | 0.3479     | 0.2667     | 0.1324     | 0.4338     | 0.3184     | 0.0951     |
+| cosine_map@200       | 0.579      | 0.3533     | 0.2722     | 0.1337     | 0.4341     | 0.3189     | 0.0955     |
+| cosine_map@500       | 0.5833     | 0.3632     | 0.2831     | 0.1378     | 0.4346     | 0.3196     | 0.0965     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Datasets
+<details><summary>full_en</summary>
+
+#### full_en
+
+* Dataset: full_en
+* Size: 28,880 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                          | positive                                                                         |
+  |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                          | string                                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 5.0 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.01 tokens</li><li>max: 13 tokens</li></ul> |
+* Samples:
+  | anchor                                   | positive                                 |
+  |:-----------------------------------------|:-----------------------------------------|
+  | <code>air commodore</code>               | <code>flight lieutenant</code>           |
+  | <code>command and control officer</code> | <code>flight officer</code>              |
+  | <code>air commodore</code>               | <code>command and control officer</code> |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>full_de</summary>
+
+#### full_de
+
+* Dataset: full_de
+* Size: 23,023 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 11.05 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.43 tokens</li><li>max: 45 tokens</li></ul> |
+* Samples:
+  | anchor                            | positive                                             |
+  |:----------------------------------|:-----------------------------------------------------|
+  | <code>Staffelkommandantin</code>  | <code>Kommodore</code>                               |
+  | <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
+  | <code>Staffelkommandantin</code>  | <code>Luftwaffenoffizierin</code>                    |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>full_es</summary>
+
+#### full_es
+
+* Dataset: full_es
+* Size: 20,724 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 12.95 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.57 tokens</li><li>max: 50 tokens</li></ul> |
+* Samples:
+  | anchor                              | positive                                   |
+  |:------------------------------------|:-------------------------------------------|
+  | <code>jefe de escuadrón</code>      | <code>instructor</code>                    |
+  | <code>comandante de aeronave</code> | <code>instructor de simulador</code>       |
+  | <code>instructor</code>             | <code>oficial del Ejército del Aire</code> |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>full_zh</summary>
+
+#### full_zh
+
+* Dataset: full_zh
+* Size: 30,401 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                         |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 8.36 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.95 tokens</li><li>max: 27 tokens</li></ul> |
+* Samples:
+  | anchor            | positive             |
+  |:------------------|:---------------------|
+  | <code>技术总监</code> | <code>技术和运营总监</code> |
+  | <code>技术总监</code> | <code>技术主管</code>    |
+  | <code>技术总监</code> | <code>技术艺术总监</code>  |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+<details><summary>mix</summary>
+
+#### mix
+
+* Dataset: mix
+* Size: 21,760 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 5.65 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 10.08 tokens</li><li>max: 30 tokens</li></ul> |
+* Samples:
+  | anchor                                    | positive                                                        |
+  |:------------------------------------------|:----------------------------------------------------------------|
+  | <code>technical manager</code>            | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
+  | <code>head of technical</code>            | <code>directora técnica</code>                                  |
+  | <code>head of technical department</code> | <code>技术艺术总监</code>                                             |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
+  ```
+</details>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `gradient_accumulation_steps`: 2
+- `num_train_epochs`: 5
+- `warmup_ratio`: 0.05
+- `log_on_each_node`: False
+- `fp16`: True
+- `dataloader_num_workers`: 4
+- `ddp_find_unused_parameters`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: False
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: True
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
+|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
+| -1     | -1   | -             | 0.7322                  | 0.4690                  | 0.3853                  | 0.2723                  | 0.3209                 | 0.2244                 | 0.0919                 |
+| 0.0021 | 1    | 23.8878       | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.2058 | 100  | 7.2098        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.4115 | 200  | 4.2635        | 0.7800                  | 0.5132                  | 0.4268                  | 0.2798                  | 0.4372                 | 0.2996                 | 0.1447                 |
+| 0.6173 | 300  | 4.1931        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 0.8230 | 400  | 3.73          | 0.7863                  | 0.5274                  | 0.4451                  | 0.2805                  | 0.4762                 | 0.3455                 | 0.1648                 |
+| 1.0309 | 500  | 3.3569        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.2366 | 600  | 3.6464        | 0.7868                  | 0.5372                  | 0.4540                  | 0.2813                  | 0.5063                 | 0.3794                 | 0.1755                 |
+| 1.4424 | 700  | 3.0772        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 1.6481 | 800  | 3.114         | 0.7906                  | 0.5391                  | 0.4576                  | 0.2832                  | 0.5221                 | 0.4047                 | 0.1779                 |
+| 1.8539 | 900  | 2.9246        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.0617 | 1000 | 2.7479        | 0.7873                  | 0.5423                  | 0.4631                  | 0.2871                  | 0.5323                 | 0.4143                 | 0.1843                 |
+| 2.2675 | 1100 | 3.049         | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.4733 | 1200 | 2.6137        | 0.7878                  | 0.5418                  | 0.4685                  | 0.2870                  | 0.5470                 | 0.4339                 | 0.1932                 |
+| 2.6790 | 1300 | 2.8607        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 2.8848 | 1400 | 2.7071        | 0.7889                  | 0.5465                  | 0.4714                  | 0.2891                  | 0.5504                 | 0.4362                 | 0.1944                 |
+| 3.0926 | 1500 | 2.7012        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 3.2984 | 1600 | 2.7423        | 0.7882                  | 0.5471                  | 0.4748                  | 0.2868                  | 0.5542                 | 0.4454                 | 0.1976                 |
+| 3.5041 | 1700 | 2.5316        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 3.7099 | 1800 | 2.6344        | 0.7900                  | 0.5498                  | 0.4763                  | 0.2857                  | 0.5639                 | 0.4552                 | 0.1954                 |
+| 3.9156 | 1900 | 2.4983        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.1235 | 2000 | 2.5423        | 0.7894                  | 0.5499                  | 0.4786                  | 0.2870                  | 0.5644                 | 0.4576                 | 0.1974                 |
+| 4.3292 | 2100 | 2.5674        | -                       | -                       | -                       | -                       | -                      | -                      | -                      |
+| 4.5350 | 2200 | 2.6237        | 0.7899                  | 0.5502                  | 0.4802                  | 0.2843                  | 0.5674                 | 0.4607                 | 0.1993                 |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 4.1.0
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.6.0
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### GISTEmbedLoss
+```bibtex
+@misc{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
+    author={Aivin V. Solatorio},
+    year={2024},
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

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+    "BertModel"
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+  "vocab_size": 30522
+}

checkpoint-2200/config_sentence_transformers.json

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+  "do_lower_case": true
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