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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:124788 |
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- loss:GISTEmbedLoss |
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base_model: Alibaba-NLP/gte-multilingual-base |
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widget: |
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- source_sentence: 其他机械、设备和有形货物租赁服务代表 |
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sentences: |
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- 其他机械和设备租赁服务工作人员 |
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- 电子和电信设备及零部件物流经理 |
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- 工业主厨 |
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- source_sentence: 公交车司机 |
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sentences: |
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- 表演灯光设计师 |
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- 乙烯基地板安装工 |
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- 国际巴士司机 |
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- source_sentence: online communication manager |
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sentences: |
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- trades union official |
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- social media manager |
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- budget manager |
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- source_sentence: Projektmanagerin |
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sentences: |
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- Projektmanager/Projektmanagerin |
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- Category-Manager |
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- Infanterist |
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- source_sentence: Volksvertreter |
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sentences: |
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- Parlamentarier |
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- Oberbürgermeister |
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- Konsul |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@20 |
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- cosine_accuracy@50 |
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- cosine_accuracy@100 |
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- cosine_accuracy@150 |
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- cosine_accuracy@200 |
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- cosine_precision@1 |
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- cosine_precision@20 |
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- cosine_precision@50 |
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- cosine_precision@100 |
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- cosine_precision@150 |
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- cosine_precision@200 |
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- cosine_recall@1 |
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- cosine_recall@20 |
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- cosine_recall@50 |
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- cosine_recall@100 |
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- cosine_recall@150 |
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- cosine_recall@200 |
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- cosine_ndcg@1 |
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- cosine_ndcg@20 |
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- cosine_ndcg@50 |
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- cosine_ndcg@100 |
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- cosine_ndcg@150 |
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- cosine_ndcg@200 |
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- cosine_mrr@1 |
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- cosine_mrr@20 |
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- cosine_mrr@50 |
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- cosine_mrr@100 |
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- cosine_mrr@150 |
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- cosine_mrr@200 |
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- cosine_map@1 |
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- cosine_map@20 |
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- cosine_map@50 |
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- cosine_map@100 |
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- cosine_map@150 |
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- cosine_map@200 |
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- cosine_map@500 |
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model-index: |
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- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: full en |
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type: full_en |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6666666666666666 |
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|
name: Cosine Accuracy@1 |
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- type: cosine_accuracy@20 |
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value: 0.9904761904761905 |
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name: Cosine Accuracy@20 |
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- type: cosine_accuracy@50 |
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value: 0.9904761904761905 |
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|
name: Cosine Accuracy@50 |
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|
- type: cosine_accuracy@100 |
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|
value: 0.9904761904761905 |
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|
name: Cosine Accuracy@100 |
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|
- type: cosine_accuracy@150 |
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|
value: 0.9904761904761905 |
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name: Cosine Accuracy@150 |
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- type: cosine_accuracy@200 |
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value: 0.9904761904761905 |
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name: Cosine Accuracy@200 |
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- type: cosine_precision@1 |
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value: 0.6666666666666666 |
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|
name: Cosine Precision@1 |
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- type: cosine_precision@20 |
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value: 0.5147619047619048 |
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name: Cosine Precision@20 |
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- type: cosine_precision@50 |
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value: 0.31999999999999995 |
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name: Cosine Precision@50 |
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- type: cosine_precision@100 |
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value: 0.19047619047619047 |
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name: Cosine Precision@100 |
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- type: cosine_precision@150 |
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value: 0.1361904761904762 |
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name: Cosine Precision@150 |
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- type: cosine_precision@200 |
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value: 0.10542857142857143 |
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name: Cosine Precision@200 |
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- type: cosine_recall@1 |
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value: 0.06854687410617222 |
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name: Cosine Recall@1 |
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- type: cosine_recall@20 |
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value: 0.5491240579458434 |
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name: Cosine Recall@20 |
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- type: cosine_recall@50 |
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value: 0.7553654907661455 |
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name: Cosine Recall@50 |
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- type: cosine_recall@100 |
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value: 0.8503209224897438 |
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name: Cosine Recall@100 |
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|
- type: cosine_recall@150 |
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value: 0.8994749092946579 |
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|
name: Cosine Recall@150 |
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- type: cosine_recall@200 |
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|
value: 0.9207884118691805 |
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name: Cosine Recall@200 |
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|
- type: cosine_ndcg@1 |
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|
value: 0.6666666666666666 |
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|
name: Cosine Ndcg@1 |
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|
- type: cosine_ndcg@20 |
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value: 0.6952098522285352 |
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name: Cosine Ndcg@20 |
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- type: cosine_ndcg@50 |
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value: 0.7229572913271685 |
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name: Cosine Ndcg@50 |
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- type: cosine_ndcg@100 |
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value: 0.7732532874348539 |
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name: Cosine Ndcg@100 |
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|
- type: cosine_ndcg@150 |
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value: 0.7947334799125039 |
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name: Cosine Ndcg@150 |
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|
- type: cosine_ndcg@200 |
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value: 0.8038564389556094 |
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name: Cosine Ndcg@200 |
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- type: cosine_mrr@1 |
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value: 0.6666666666666666 |
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|
name: Cosine Mrr@1 |
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- type: cosine_mrr@20 |
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value: 0.8182539682539683 |
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name: Cosine Mrr@20 |
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|
- type: cosine_mrr@50 |
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|
value: 0.8182539682539683 |
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name: Cosine Mrr@50 |
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|
- type: cosine_mrr@100 |
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|
value: 0.8182539682539683 |
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|
name: Cosine Mrr@100 |
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|
- type: cosine_mrr@150 |
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value: 0.8182539682539683 |
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name: Cosine Mrr@150 |
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- type: cosine_mrr@200 |
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value: 0.8182539682539683 |
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name: Cosine Mrr@200 |
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|
- type: cosine_map@1 |
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|
value: 0.6666666666666666 |
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|
name: Cosine Map@1 |
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- type: cosine_map@20 |
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|
value: 0.5566401101002375 |
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name: Cosine Map@20 |
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- type: cosine_map@50 |
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|
value: 0.55344017265156 |
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|
name: Cosine Map@50 |
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|
- type: cosine_map@100 |
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|
value: 0.5852249415484134 |
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|
name: Cosine Map@100 |
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|
- type: cosine_map@150 |
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|
value: 0.5943042662925763 |
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name: Cosine Map@150 |
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- type: cosine_map@200 |
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|
value: 0.5975837437975446 |
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name: Cosine Map@200 |
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- type: cosine_map@500 |
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|
value: 0.6015742986218369 |
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name: Cosine Map@500 |
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|
- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: full es |
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type: full_es |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.12432432432432433 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@20 |
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value: 1.0 |
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name: Cosine Accuracy@20 |
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- type: cosine_accuracy@50 |
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value: 1.0 |
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name: Cosine Accuracy@50 |
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- type: cosine_accuracy@100 |
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value: 1.0 |
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name: Cosine Accuracy@100 |
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- type: cosine_accuracy@150 |
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value: 1.0 |
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name: Cosine Accuracy@150 |
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- type: cosine_accuracy@200 |
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value: 1.0 |
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name: Cosine Accuracy@200 |
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- type: cosine_precision@1 |
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value: 0.12432432432432433 |
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|
name: Cosine Precision@1 |
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- type: cosine_precision@20 |
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value: 0.575945945945946 |
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name: Cosine Precision@20 |
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|
- type: cosine_precision@50 |
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|
value: 0.3923243243243244 |
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|
name: Cosine Precision@50 |
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|
- type: cosine_precision@100 |
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value: 0.2565945945945946 |
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name: Cosine Precision@100 |
|
|
- type: cosine_precision@150 |
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value: 0.19282882882882882 |
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name: Cosine Precision@150 |
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- type: cosine_precision@200 |
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value: 0.1527837837837838 |
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name: Cosine Precision@200 |
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- type: cosine_recall@1 |
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value: 0.0036138931714884822 |
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|
name: Cosine Recall@1 |
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- type: cosine_recall@20 |
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value: 0.3852888120551914 |
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|
name: Cosine Recall@20 |
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- type: cosine_recall@50 |
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value: 0.5659574514538841 |
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|
name: Cosine Recall@50 |
|
|
- type: cosine_recall@100 |
|
|
value: 0.6898678629281393 |
|
|
name: Cosine Recall@100 |
|
|
- type: cosine_recall@150 |
|
|
value: 0.7540209165372845 |
|
|
name: Cosine Recall@150 |
|
|
- type: cosine_recall@200 |
|
|
value: 0.7858170054407897 |
|
|
name: Cosine Recall@200 |
|
|
- type: cosine_ndcg@1 |
|
|
value: 0.12432432432432433 |
|
|
name: Cosine Ndcg@1 |
|
|
- type: cosine_ndcg@20 |
|
|
value: 0.6168674053047035 |
|
|
name: Cosine Ndcg@20 |
|
|
- type: cosine_ndcg@50 |
|
|
value: 0.5913690595071309 |
|
|
name: Cosine Ndcg@50 |
|
|
- type: cosine_ndcg@100 |
|
|
value: 0.62350509928888 |
|
|
name: Cosine Ndcg@100 |
|
|
- type: cosine_ndcg@150 |
|
|
value: 0.6556716735369459 |
|
|
name: Cosine Ndcg@150 |
|
|
- type: cosine_ndcg@200 |
|
|
value: 0.6716557949894583 |
|
|
name: Cosine Ndcg@200 |
|
|
- type: cosine_mrr@1 |
|
|
value: 0.12432432432432433 |
|
|
name: Cosine Mrr@1 |
|
|
- type: cosine_mrr@20 |
|
|
value: 0.5581081081081081 |
|
|
name: Cosine Mrr@20 |
|
|
- type: cosine_mrr@50 |
|
|
value: 0.5581081081081081 |
|
|
name: Cosine Mrr@50 |
|
|
- type: cosine_mrr@100 |
|
|
value: 0.5581081081081081 |
|
|
name: Cosine Mrr@100 |
|
|
- type: cosine_mrr@150 |
|
|
value: 0.5581081081081081 |
|
|
name: Cosine Mrr@150 |
|
|
- type: cosine_mrr@200 |
|
|
value: 0.5581081081081081 |
|
|
name: Cosine Mrr@200 |
|
|
- type: cosine_map@1 |
|
|
value: 0.12432432432432433 |
|
|
name: Cosine Map@1 |
|
|
- type: cosine_map@20 |
|
|
value: 0.48407152706202555 |
|
|
name: Cosine Map@20 |
|
|
- type: cosine_map@50 |
|
|
value: 0.43043374125481026 |
|
|
name: Cosine Map@50 |
|
|
- type: cosine_map@100 |
|
|
value: 0.43735327570764515 |
|
|
name: Cosine Map@100 |
|
|
- type: cosine_map@150 |
|
|
value: 0.45269435912524697 |
|
|
name: Cosine Map@150 |
|
|
- type: cosine_map@200 |
|
|
value: 0.45930097680668164 |
|
|
name: Cosine Map@200 |
|
|
- type: cosine_map@500 |
|
|
value: 0.47204219228541466 |
|
|
name: Cosine Map@500 |
|
|
- task: |
|
|
type: information-retrieval |
|
|
name: Information Retrieval |
|
|
dataset: |
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name: full de |
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type: full_de |
|
|
metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.2955665024630542 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@20 |
|
|
value: 0.9753694581280788 |
|
|
name: Cosine Accuracy@20 |
|
|
- type: cosine_accuracy@50 |
|
|
value: 0.9852216748768473 |
|
|
name: Cosine Accuracy@50 |
|
|
- type: cosine_accuracy@100 |
|
|
value: 0.9901477832512315 |
|
|
name: Cosine Accuracy@100 |
|
|
- type: cosine_accuracy@150 |
|
|
value: 0.9901477832512315 |
|
|
name: Cosine Accuracy@150 |
|
|
- type: cosine_accuracy@200 |
|
|
value: 0.9901477832512315 |
|
|
name: Cosine Accuracy@200 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.2955665024630542 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@20 |
|
|
value: 0.5103448275862069 |
|
|
name: Cosine Precision@20 |
|
|
- type: cosine_precision@50 |
|
|
value: 0.36935960591133016 |
|
|
name: Cosine Precision@50 |
|
|
- type: cosine_precision@100 |
|
|
value: 0.23965517241379314 |
|
|
name: Cosine Precision@100 |
|
|
- type: cosine_precision@150 |
|
|
value: 0.1807881773399015 |
|
|
name: Cosine Precision@150 |
|
|
- type: cosine_precision@200 |
|
|
value: 0.1461576354679803 |
|
|
name: Cosine Precision@200 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.01108543831680986 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@20 |
|
|
value: 0.3207974783481294 |
|
|
name: Cosine Recall@20 |
|
|
- type: cosine_recall@50 |
|
|
value: 0.5042046446720455 |
|
|
name: Cosine Recall@50 |
|
|
- type: cosine_recall@100 |
|
|
value: 0.6172666777909689 |
|
|
name: Cosine Recall@100 |
|
|
- type: cosine_recall@150 |
|
|
value: 0.6848138831682932 |
|
|
name: Cosine Recall@150 |
|
|
- type: cosine_recall@200 |
|
|
value: 0.7253195006357535 |
|
|
name: Cosine Recall@200 |
|
|
- type: cosine_ndcg@1 |
|
|
value: 0.2955665024630542 |
|
|
name: Cosine Ndcg@1 |
|
|
- type: cosine_ndcg@20 |
|
|
value: 0.537849085734973 |
|
|
name: Cosine Ndcg@20 |
|
|
- type: cosine_ndcg@50 |
|
|
value: 0.5288037060639387 |
|
|
name: Cosine Ndcg@50 |
|
|
- type: cosine_ndcg@100 |
|
|
value: 0.5551941695921919 |
|
|
name: Cosine Ndcg@100 |
|
|
- type: cosine_ndcg@150 |
|
|
value: 0.5887611959940118 |
|
|
name: Cosine Ndcg@150 |
|
|
- type: cosine_ndcg@200 |
|
|
value: 0.6092219717029682 |
|
|
name: Cosine Ndcg@200 |
|
|
- type: cosine_mrr@1 |
|
|
value: 0.2955665024630542 |
|
|
name: Cosine Mrr@1 |
|
|
- type: cosine_mrr@20 |
|
|
value: 0.5164773875147672 |
|
|
name: Cosine Mrr@20 |
|
|
- type: cosine_mrr@50 |
|
|
value: 0.5167647438366063 |
|
|
name: Cosine Mrr@50 |
|
|
- type: cosine_mrr@100 |
|
|
value: 0.5168213657719442 |
|
|
name: Cosine Mrr@100 |
|
|
- type: cosine_mrr@150 |
|
|
value: 0.5168213657719442 |
|
|
name: Cosine Mrr@150 |
|
|
- type: cosine_mrr@200 |
|
|
value: 0.5168213657719442 |
|
|
name: Cosine Mrr@200 |
|
|
- type: cosine_map@1 |
|
|
value: 0.2955665024630542 |
|
|
name: Cosine Map@1 |
|
|
- type: cosine_map@20 |
|
|
value: 0.398398563122481 |
|
|
name: Cosine Map@20 |
|
|
- type: cosine_map@50 |
|
|
value: 0.36032758502543594 |
|
|
name: Cosine Map@50 |
|
|
- type: cosine_map@100 |
|
|
value: 0.3632259128424842 |
|
|
name: Cosine Map@100 |
|
|
- type: cosine_map@150 |
|
|
value: 0.37822275477623696 |
|
|
name: Cosine Map@150 |
|
|
- type: cosine_map@200 |
|
|
value: 0.3863148456840816 |
|
|
name: Cosine Map@200 |
|
|
- type: cosine_map@500 |
|
|
value: 0.399227009561676 |
|
|
name: Cosine Map@500 |
|
|
- task: |
|
|
type: information-retrieval |
|
|
name: Information Retrieval |
|
|
dataset: |
|
|
name: full zh |
|
|
type: full_zh |
|
|
metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.6796116504854369 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@20 |
|
|
value: 0.9805825242718447 |
|
|
name: Cosine Accuracy@20 |
|
|
- type: cosine_accuracy@50 |
|
|
value: 0.9902912621359223 |
|
|
name: Cosine Accuracy@50 |
|
|
- type: cosine_accuracy@100 |
|
|
value: 0.9902912621359223 |
|
|
name: Cosine Accuracy@100 |
|
|
- type: cosine_accuracy@150 |
|
|
value: 0.9902912621359223 |
|
|
name: Cosine Accuracy@150 |
|
|
- type: cosine_accuracy@200 |
|
|
value: 0.9902912621359223 |
|
|
name: Cosine Accuracy@200 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.6796116504854369 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@20 |
|
|
value: 0.488349514563107 |
|
|
name: Cosine Precision@20 |
|
|
- type: cosine_precision@50 |
|
|
value: 0.29631067961165053 |
|
|
name: Cosine Precision@50 |
|
|
- type: cosine_precision@100 |
|
|
value: 0.17883495145631062 |
|
|
name: Cosine Precision@100 |
|
|
- type: cosine_precision@150 |
|
|
value: 0.12776699029126212 |
|
|
name: Cosine Precision@150 |
|
|
- type: cosine_precision@200 |
|
|
value: 0.09990291262135924 |
|
|
name: Cosine Precision@200 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.06931865009287731 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@20 |
|
|
value: 0.5250914458143515 |
|
|
name: Cosine Recall@20 |
|
|
- type: cosine_recall@50 |
|
|
value: 0.7082715439925011 |
|
|
name: Cosine Recall@50 |
|
|
- type: cosine_recall@100 |
|
|
value: 0.8169166539243944 |
|
|
name: Cosine Recall@100 |
|
|
- type: cosine_recall@150 |
|
|
value: 0.8613232254521018 |
|
|
name: Cosine Recall@150 |
|
|
- type: cosine_recall@200 |
|
|
value: 0.8898175710074696 |
|
|
name: Cosine Recall@200 |
|
|
- type: cosine_ndcg@1 |
|
|
value: 0.6796116504854369 |
|
|
name: Cosine Ndcg@1 |
|
|
- type: cosine_ndcg@20 |
|
|
value: 0.6680745295820606 |
|
|
name: Cosine Ndcg@20 |
|
|
- type: cosine_ndcg@50 |
|
|
value: 0.6856578240865067 |
|
|
name: Cosine Ndcg@50 |
|
|
- type: cosine_ndcg@100 |
|
|
value: 0.7378907298421352 |
|
|
name: Cosine Ndcg@100 |
|
|
- type: cosine_ndcg@150 |
|
|
value: 0.7576651805692517 |
|
|
name: Cosine Ndcg@150 |
|
|
- type: cosine_ndcg@200 |
|
|
value: 0.7696718049970358 |
|
|
name: Cosine Ndcg@200 |
|
|
- type: cosine_mrr@1 |
|
|
value: 0.6796116504854369 |
|
|
name: Cosine Mrr@1 |
|
|
- type: cosine_mrr@20 |
|
|
value: 0.8158576051779936 |
|
|
name: Cosine Mrr@20 |
|
|
- type: cosine_mrr@50 |
|
|
value: 0.816279724215562 |
|
|
name: Cosine Mrr@50 |
|
|
- type: cosine_mrr@100 |
|
|
value: 0.816279724215562 |
|
|
name: Cosine Mrr@100 |
|
|
- type: cosine_mrr@150 |
|
|
value: 0.816279724215562 |
|
|
name: Cosine Mrr@150 |
|
|
- type: cosine_mrr@200 |
|
|
value: 0.816279724215562 |
|
|
name: Cosine Mrr@200 |
|
|
- type: cosine_map@1 |
|
|
value: 0.6796116504854369 |
|
|
name: Cosine Map@1 |
|
|
- type: cosine_map@20 |
|
|
value: 0.522177160195635 |
|
|
name: Cosine Map@20 |
|
|
- type: cosine_map@50 |
|
|
value: 0.5082601209392789 |
|
|
name: Cosine Map@50 |
|
|
- type: cosine_map@100 |
|
|
value: 0.5371705298206915 |
|
|
name: Cosine Map@100 |
|
|
- type: cosine_map@150 |
|
|
value: 0.5454012672534121 |
|
|
name: Cosine Map@150 |
|
|
- type: cosine_map@200 |
|
|
value: 0.5494570875591636 |
|
|
name: Cosine Map@200 |
|
|
- type: cosine_map@500 |
|
|
value: 0.5542116087189223 |
|
|
name: Cosine Map@500 |
|
|
- task: |
|
|
type: information-retrieval |
|
|
name: Information Retrieval |
|
|
dataset: |
|
|
name: mix es |
|
|
type: mix_es |
|
|
metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.7087883515340614 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@20 |
|
|
value: 0.9552782111284451 |
|
|
name: Cosine Accuracy@20 |
|
|
- type: cosine_accuracy@50 |
|
|
value: 0.9802392095683827 |
|
|
name: Cosine Accuracy@50 |
|
|
- type: cosine_accuracy@100 |
|
|
value: 0.9901196047841914 |
|
|
name: Cosine Accuracy@100 |
|
|
- type: cosine_accuracy@150 |
|
|
value: 0.9937597503900156 |
|
|
name: Cosine Accuracy@150 |
|
|
- type: cosine_accuracy@200 |
|
|
value: 0.9958398335933437 |
|
|
name: Cosine Accuracy@200 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.7087883515340614 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@20 |
|
|
value: 0.12158086323452937 |
|
|
name: Cosine Precision@20 |
|
|
- type: cosine_precision@50 |
|
|
value: 0.05122204888195529 |
|
|
name: Cosine Precision@50 |
|
|
- type: cosine_precision@100 |
|
|
value: 0.026125845033801356 |
|
|
name: Cosine Precision@100 |
|
|
- type: cosine_precision@150 |
|
|
value: 0.017548968625411682 |
|
|
name: Cosine Precision@150 |
|
|
- type: cosine_precision@200 |
|
|
value: 0.013239729589183572 |
|
|
name: Cosine Precision@200 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.2737959042171211 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@20 |
|
|
value: 0.8990032934650719 |
|
|
name: Cosine Recall@20 |
|
|
- type: cosine_recall@50 |
|
|
value: 0.9459438377535101 |
|
|
name: Cosine Recall@50 |
|
|
- type: cosine_recall@100 |
|
|
value: 0.9650979372508233 |
|
|
name: Cosine Recall@100 |
|
|
- type: cosine_recall@150 |
|
|
value: 0.9731582596637198 |
|
|
name: Cosine Recall@150 |
|
|
- type: cosine_recall@200 |
|
|
value: 0.979086496793205 |
|
|
name: Cosine Recall@200 |
|
|
- type: cosine_ndcg@1 |
|
|
value: 0.7087883515340614 |
|
|
name: Cosine Ndcg@1 |
|
|
- type: cosine_ndcg@20 |
|
|
value: 0.7814741332820433 |
|
|
name: Cosine Ndcg@20 |
|
|
- type: cosine_ndcg@50 |
|
|
value: 0.7944033394497885 |
|
|
name: Cosine Ndcg@50 |
|
|
- type: cosine_ndcg@100 |
|
|
value: 0.7986024294603647 |
|
|
name: Cosine Ndcg@100 |
|
|
- type: cosine_ndcg@150 |
|
|
value: 0.8001222520801115 |
|
|
name: Cosine Ndcg@150 |
|
|
- type: cosine_ndcg@200 |
|
|
value: 0.801183843730514 |
|
|
name: Cosine Ndcg@200 |
|
|
- type: cosine_mrr@1 |
|
|
value: 0.7087883515340614 |
|
|
name: Cosine Mrr@1 |
|
|
- type: cosine_mrr@20 |
|
|
value: 0.7804158804359833 |
|
|
name: Cosine Mrr@20 |
|
|
- type: cosine_mrr@50 |
|
|
value: 0.7812547046826683 |
|
|
name: Cosine Mrr@50 |
|
|
- type: cosine_mrr@100 |
|
|
value: 0.7813961782842836 |
|
|
name: Cosine Mrr@100 |
|
|
- type: cosine_mrr@150 |
|
|
value: 0.7814280971923943 |
|
|
name: Cosine Mrr@150 |
|
|
- type: cosine_mrr@200 |
|
|
value: 0.7814392363829243 |
|
|
name: Cosine Mrr@200 |
|
|
- type: cosine_map@1 |
|
|
value: 0.7087883515340614 |
|
|
name: Cosine Map@1 |
|
|
- type: cosine_map@20 |
|
|
value: 0.7070596364024803 |
|
|
name: Cosine Map@20 |
|
|
- type: cosine_map@50 |
|
|
value: 0.7106867578203881 |
|
|
name: Cosine Map@50 |
|
|
- type: cosine_map@100 |
|
|
value: 0.7112928928384499 |
|
|
name: Cosine Map@100 |
|
|
- type: cosine_map@150 |
|
|
value: 0.7114314004578745 |
|
|
name: Cosine Map@150 |
|
|
- type: cosine_map@200 |
|
|
value: 0.711504950521157 |
|
|
name: Cosine Map@200 |
|
|
- type: cosine_map@500 |
|
|
value: 0.7116431478000537 |
|
|
name: Cosine Map@500 |
|
|
- task: |
|
|
type: information-retrieval |
|
|
name: Information Retrieval |
|
|
dataset: |
|
|
name: mix de |
|
|
type: mix_de |
|
|
metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.6484659386375455 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@20 |
|
|
value: 0.9323972958918356 |
|
|
name: Cosine Accuracy@20 |
|
|
- type: cosine_accuracy@50 |
|
|
value: 0.968278731149246 |
|
|
name: Cosine Accuracy@50 |
|
|
- type: cosine_accuracy@100 |
|
|
value: 0.984919396775871 |
|
|
name: Cosine Accuracy@100 |
|
|
- type: cosine_accuracy@150 |
|
|
value: 0.9885595423816953 |
|
|
name: Cosine Accuracy@150 |
|
|
- type: cosine_accuracy@200 |
|
|
value: 0.9937597503900156 |
|
|
name: Cosine Accuracy@200 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.6484659386375455 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@20 |
|
|
value: 0.12093083723348932 |
|
|
name: Cosine Precision@20 |
|
|
- type: cosine_precision@50 |
|
|
value: 0.05140925637025482 |
|
|
name: Cosine Precision@50 |
|
|
- type: cosine_precision@100 |
|
|
value: 0.02647425897035882 |
|
|
name: Cosine Precision@100 |
|
|
- type: cosine_precision@150 |
|
|
value: 0.017892182353960822 |
|
|
name: Cosine Precision@150 |
|
|
- type: cosine_precision@200 |
|
|
value: 0.013530941237649509 |
|
|
name: Cosine Precision@200 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.2435517420696828 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@20 |
|
|
value: 0.87873114924597 |
|
|
name: Cosine Recall@20 |
|
|
- type: cosine_recall@50 |
|
|
value: 0.9319899462645173 |
|
|
name: Cosine Recall@50 |
|
|
- type: cosine_recall@100 |
|
|
value: 0.9596117178020455 |
|
|
name: Cosine Recall@100 |
|
|
- type: cosine_recall@150 |
|
|
value: 0.9718322066215982 |
|
|
name: Cosine Recall@150 |
|
|
- type: cosine_recall@200 |
|
|
value: 0.9799791991679667 |
|
|
name: Cosine Recall@200 |
|
|
- type: cosine_ndcg@1 |
|
|
value: 0.6484659386375455 |
|
|
name: Cosine Ndcg@1 |
|
|
- type: cosine_ndcg@20 |
|
|
value: 0.7448150588358 |
|
|
name: Cosine Ndcg@20 |
|
|
- type: cosine_ndcg@50 |
|
|
value: 0.7595232400510039 |
|
|
name: Cosine Ndcg@50 |
|
|
- type: cosine_ndcg@100 |
|
|
value: 0.7656851368194345 |
|
|
name: Cosine Ndcg@100 |
|
|
- type: cosine_ndcg@150 |
|
|
value: 0.7681576326024331 |
|
|
name: Cosine Ndcg@150 |
|
|
- type: cosine_ndcg@200 |
|
|
value: 0.7696474672652458 |
|
|
name: Cosine Ndcg@200 |
|
|
- type: cosine_mrr@1 |
|
|
value: 0.6484659386375455 |
|
|
name: Cosine Mrr@1 |
|
|
- type: cosine_mrr@20 |
|
|
value: 0.7323691045739125 |
|
|
name: Cosine Mrr@20 |
|
|
- type: cosine_mrr@50 |
|
|
value: 0.733538875120878 |
|
|
name: Cosine Mrr@50 |
|
|
- type: cosine_mrr@100 |
|
|
value: 0.733776247038599 |
|
|
name: Cosine Mrr@100 |
|
|
- type: cosine_mrr@150 |
|
|
value: 0.7338087409764548 |
|
|
name: Cosine Mrr@150 |
|
|
- type: cosine_mrr@200 |
|
|
value: 0.7338398642058079 |
|
|
name: Cosine Mrr@200 |
|
|
- type: cosine_map@1 |
|
|
value: 0.6484659386375455 |
|
|
name: Cosine Map@1 |
|
|
- type: cosine_map@20 |
|
|
value: 0.6646138211839377 |
|
|
name: Cosine Map@20 |
|
|
- type: cosine_map@50 |
|
|
value: 0.6683657128313888 |
|
|
name: Cosine Map@50 |
|
|
- type: cosine_map@100 |
|
|
value: 0.6692634410264182 |
|
|
name: Cosine Map@100 |
|
|
- type: cosine_map@150 |
|
|
value: 0.669518875077899 |
|
|
name: Cosine Map@150 |
|
|
- type: cosine_map@200 |
|
|
value: 0.6696171599377958 |
|
|
name: Cosine Map@200 |
|
|
- type: cosine_map@500 |
|
|
value: 0.6697127210085475 |
|
|
name: Cosine Map@500 |
|
|
- task: |
|
|
type: information-retrieval |
|
|
name: Information Retrieval |
|
|
dataset: |
|
|
name: mix zh |
|
|
type: mix_zh |
|
|
metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.7667014613778705 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@20 |
|
|
value: 0.9843423799582464 |
|
|
name: Cosine Accuracy@20 |
|
|
- type: cosine_accuracy@50 |
|
|
value: 0.9932150313152401 |
|
|
name: Cosine Accuracy@50 |
|
|
- type: cosine_accuracy@100 |
|
|
value: 0.9958246346555324 |
|
|
name: Cosine Accuracy@100 |
|
|
- type: cosine_accuracy@150 |
|
|
value: 0.9973903966597077 |
|
|
name: Cosine Accuracy@150 |
|
|
- type: cosine_accuracy@200 |
|
|
value: 0.9979123173277662 |
|
|
name: Cosine Accuracy@200 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.7667014613778705 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@20 |
|
|
value: 0.13870041753653445 |
|
|
name: Cosine Precision@20 |
|
|
- type: cosine_precision@50 |
|
|
value: 0.05810020876826725 |
|
|
name: Cosine Precision@50 |
|
|
- type: cosine_precision@100 |
|
|
value: 0.029598121085595 |
|
|
name: Cosine Precision@100 |
|
|
- type: cosine_precision@150 |
|
|
value: 0.01986778009742519 |
|
|
name: Cosine Precision@150 |
|
|
- type: cosine_precision@200 |
|
|
value: 0.014945198329853866 |
|
|
name: Cosine Precision@200 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.25692041952480366 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@20 |
|
|
value: 0.9156576200417536 |
|
|
name: Cosine Recall@20 |
|
|
- type: cosine_recall@50 |
|
|
value: 0.9582637439109255 |
|
|
name: Cosine Recall@50 |
|
|
- type: cosine_recall@100 |
|
|
value: 0.9765483646485734 |
|
|
name: Cosine Recall@100 |
|
|
- type: cosine_recall@150 |
|
|
value: 0.9833768267223383 |
|
|
name: Cosine Recall@150 |
|
|
- type: cosine_recall@200 |
|
|
value: 0.986464857341684 |
|
|
name: Cosine Recall@200 |
|
|
- type: cosine_ndcg@1 |
|
|
value: 0.7667014613778705 |
|
|
name: Cosine Ndcg@1 |
|
|
- type: cosine_ndcg@20 |
|
|
value: 0.8002168358295473 |
|
|
name: Cosine Ndcg@20 |
|
|
- type: cosine_ndcg@50 |
|
|
value: 0.8125113081884888 |
|
|
name: Cosine Ndcg@50 |
|
|
- type: cosine_ndcg@100 |
|
|
value: 0.8167350090334409 |
|
|
name: Cosine Ndcg@100 |
|
|
- type: cosine_ndcg@150 |
|
|
value: 0.8181122471507385 |
|
|
name: Cosine Ndcg@150 |
|
|
- type: cosine_ndcg@200 |
|
|
value: 0.8186874070081017 |
|
|
name: Cosine Ndcg@200 |
|
|
- type: cosine_mrr@1 |
|
|
value: 0.7667014613778705 |
|
|
name: Cosine Mrr@1 |
|
|
- type: cosine_mrr@20 |
|
|
value: 0.8421752732824312 |
|
|
name: Cosine Mrr@20 |
|
|
- type: cosine_mrr@50 |
|
|
value: 0.8424954415974232 |
|
|
name: Cosine Mrr@50 |
|
|
- type: cosine_mrr@100 |
|
|
value: 0.8425358910333786 |
|
|
name: Cosine Mrr@100 |
|
|
- type: cosine_mrr@150 |
|
|
value: 0.8425483391786986 |
|
|
name: Cosine Mrr@150 |
|
|
- type: cosine_mrr@200 |
|
|
value: 0.8425515411459873 |
|
|
name: Cosine Mrr@200 |
|
|
- type: cosine_map@1 |
|
|
value: 0.7667014613778705 |
|
|
name: Cosine Map@1 |
|
|
- type: cosine_map@20 |
|
|
value: 0.7007206423896271 |
|
|
name: Cosine Map@20 |
|
|
- type: cosine_map@50 |
|
|
value: 0.7046277360194696 |
|
|
name: Cosine Map@50 |
|
|
- type: cosine_map@100 |
|
|
value: 0.7053668771050886 |
|
|
name: Cosine Map@100 |
|
|
- type: cosine_map@150 |
|
|
value: 0.7055166914145262 |
|
|
name: Cosine Map@150 |
|
|
- type: cosine_map@200 |
|
|
value: 0.7055658329670217 |
|
|
name: Cosine Map@200 |
|
|
- type: cosine_map@500 |
|
|
value: 0.7056512281794008 |
|
|
name: Cosine Map@500 |
|
|
--- |
|
|
|
|
|
# Job - Job matching Alibaba-NLP/gte-multilingual-base (v2) |
|
|
|
|
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Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Datasets:** |
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- full_en |
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- full_de |
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- full_es |
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- full_zh |
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- mix |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel |
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(1): Pooling({'word_embedding_dimension': 768, '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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-v2") |
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# Run inference |
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sentences = [ |
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'Volksvertreter', |
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'Parlamentarier', |
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'Oberbürgermeister', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh | |
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|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 | |
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| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9754 | 0.9806 | 0.9553 | 0.9324 | 0.9843 | |
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| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9802 | 0.9683 | 0.9932 | |
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| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9901 | 0.9849 | 0.9958 | |
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| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9938 | 0.9886 | 0.9974 | |
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| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9958 | 0.9938 | 0.9979 | |
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| cosine_precision@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 | |
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| cosine_precision@20 | 0.5148 | 0.5759 | 0.5103 | 0.4883 | 0.1216 | 0.1209 | 0.1387 | |
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| cosine_precision@50 | 0.32 | 0.3923 | 0.3694 | 0.2963 | 0.0512 | 0.0514 | 0.0581 | |
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| cosine_precision@100 | 0.1905 | 0.2566 | 0.2397 | 0.1788 | 0.0261 | 0.0265 | 0.0296 | |
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| cosine_precision@150 | 0.1362 | 0.1928 | 0.1808 | 0.1278 | 0.0175 | 0.0179 | 0.0199 | |
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| cosine_precision@200 | 0.1054 | 0.1528 | 0.1462 | 0.0999 | 0.0132 | 0.0135 | 0.0149 | |
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| cosine_recall@1 | 0.0685 | 0.0036 | 0.0111 | 0.0693 | 0.2738 | 0.2436 | 0.2569 | |
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| cosine_recall@20 | 0.5491 | 0.3853 | 0.3208 | 0.5251 | 0.899 | 0.8787 | 0.9157 | |
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| cosine_recall@50 | 0.7554 | 0.566 | 0.5042 | 0.7083 | 0.9459 | 0.932 | 0.9583 | |
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| cosine_recall@100 | 0.8503 | 0.6899 | 0.6173 | 0.8169 | 0.9651 | 0.9596 | 0.9765 | |
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| cosine_recall@150 | 0.8995 | 0.754 | 0.6848 | 0.8613 | 0.9732 | 0.9718 | 0.9834 | |
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| cosine_recall@200 | 0.9208 | 0.7858 | 0.7253 | 0.8898 | 0.9791 | 0.98 | 0.9865 | |
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| cosine_ndcg@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 | |
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| cosine_ndcg@20 | 0.6952 | 0.6169 | 0.5378 | 0.6681 | 0.7815 | 0.7448 | 0.8002 | |
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| cosine_ndcg@50 | 0.723 | 0.5914 | 0.5288 | 0.6857 | 0.7944 | 0.7595 | 0.8125 | |
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| cosine_ndcg@100 | 0.7733 | 0.6235 | 0.5552 | 0.7379 | 0.7986 | 0.7657 | 0.8167 | |
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| cosine_ndcg@150 | 0.7947 | 0.6557 | 0.5888 | 0.7577 | 0.8001 | 0.7682 | 0.8181 | |
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| **cosine_ndcg@200** | **0.8039** | **0.6717** | **0.6092** | **0.7697** | **0.8012** | **0.7696** | **0.8187** | |
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| cosine_mrr@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 | |
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| cosine_mrr@20 | 0.8183 | 0.5581 | 0.5165 | 0.8159 | 0.7804 | 0.7324 | 0.8422 | |
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| cosine_mrr@50 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7813 | 0.7335 | 0.8425 | |
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| cosine_mrr@100 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7814 | 0.7338 | 0.8425 | |
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| cosine_mrr@150 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7814 | 0.7338 | 0.8425 | |
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| cosine_mrr@200 | 0.8183 | 0.5581 | 0.5168 | 0.8163 | 0.7814 | 0.7338 | 0.8426 | |
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| cosine_map@1 | 0.6667 | 0.1243 | 0.2956 | 0.6796 | 0.7088 | 0.6485 | 0.7667 | |
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| cosine_map@20 | 0.5566 | 0.4841 | 0.3984 | 0.5222 | 0.7071 | 0.6646 | 0.7007 | |
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| cosine_map@50 | 0.5534 | 0.4304 | 0.3603 | 0.5083 | 0.7107 | 0.6684 | 0.7046 | |
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| cosine_map@100 | 0.5852 | 0.4374 | 0.3632 | 0.5372 | 0.7113 | 0.6693 | 0.7054 | |
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| cosine_map@150 | 0.5943 | 0.4527 | 0.3782 | 0.5454 | 0.7114 | 0.6695 | 0.7055 | |
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| cosine_map@200 | 0.5976 | 0.4593 | 0.3863 | 0.5495 | 0.7115 | 0.6696 | 0.7056 | |
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| cosine_map@500 | 0.6016 | 0.472 | 0.3992 | 0.5542 | 0.7116 | 0.6697 | 0.7057 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Datasets |
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<details><summary>full_en</summary> |
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#### full_en |
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* Dataset: full_en |
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* Size: 28,880 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.76 tokens</li><li>max: 12 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:-----------------------------------------|:-----------------------------------------| |
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| <code>air commodore</code> | <code>flight lieutenant</code> | |
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| <code>command and control officer</code> | <code>flight officer</code> | |
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| <code>air commodore</code> | <code>command and control officer</code> | |
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* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: |
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```json |
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{'guide': SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} |
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``` |
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</details> |
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<details><summary>full_de</summary> |
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#### full_de |
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* Dataset: full_de |
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* Size: 23,023 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.19 tokens</li><li>max: 30 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:----------------------------------|:-----------------------------------------------------| |
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| <code>Staffelkommandantin</code> | <code>Kommodore</code> | |
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| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> | |
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| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> | |
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* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: |
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```json |
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{'guide': SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} |
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``` |
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</details> |
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<details><summary>full_es</summary> |
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#### full_es |
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* Dataset: full_es |
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* Size: 20,724 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 9.13 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.84 tokens</li><li>max: 32 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:------------------------------------|:-------------------------------------------| |
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| <code>jefe de escuadrón</code> | <code>instructor</code> | |
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| <code>comandante de aeronave</code> | <code>instructor de simulador</code> | |
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| <code>instructor</code> | <code>oficial del Ejército del Aire</code> | |
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* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: |
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```json |
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{'guide': SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} |
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``` |
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</details> |
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<details><summary>full_zh</summary> |
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#### full_zh |
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* Dataset: full_zh |
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* Size: 30,401 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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| type | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 7.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.46 tokens</li><li>max: 21 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:------------------|:---------------------| |
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| <code>技术总监</code> | <code>技术和运营总监</code> | |
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| <code>技术总监</code> | <code>技术主管</code> | |
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| <code>技术总监</code> | <code>技术艺术总监</code> | |
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* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: |
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```json |
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{'guide': SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} |
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``` |
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</details> |
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<details><summary>mix</summary> |
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#### mix |
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* Dataset: mix |
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* Size: 21,760 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 2 tokens</li><li>mean: 6.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 7.69 tokens</li><li>max: 19 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:------------------------------------------|:----------------------------------------------------------------| |
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| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> | |
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| <code>head of technical</code> | <code>directora técnica</code> | |
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| <code>head of technical department</code> | <code>技术艺术总监</code> | |
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* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: |
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```json |
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{'guide': SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0} |
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``` |
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</details> |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `gradient_accumulation_steps`: 2 |
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- `num_train_epochs`: 5 |
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- `warmup_ratio`: 0.05 |
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- `log_on_each_node`: False |
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- `fp16`: True |
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- `dataloader_num_workers`: 4 |
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- `ddp_find_unused_parameters`: True |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 2 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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|
- `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 | - | 0.7447 | 0.6125 | 0.5378 | 0.7240 | 0.7029 | 0.6345 | 0.7437 | |
|
|
| 0.0082 | 1 | 4.3088 | - | - | - | - | - | - | - | |
|
|
| 0.8230 | 100 | 1.9026 | - | - | - | - | - | - | - | |
|
|
| 1.6502 | 200 | 0.9336 | 0.8024 | 0.6703 | 0.6109 | 0.7695 | 0.7914 | 0.7594 | 0.8136 | |
|
|
| 2.4774 | 300 | 0.161 | - | - | - | - | - | - | - | |
|
|
| 3.3045 | 400 | 0.1398 | 0.8039 | 0.6717 | 0.6092 | 0.7697 | 0.8012 | 0.7696 | 0.8187 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.51.2 |
|
|
- 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} |
|
|
} |
|
|
``` |
|
|
|
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