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--- |
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base_model: harheem/bge-m3-nvidia-ko-v1 |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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pipeline_tag: sentence-similarity |
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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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- dataset_size:1K<n<10K |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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- llama-cpp |
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- gguf-my-repo |
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widget: |
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- source_sentence: 하이브리다이저란 무엇인가요? |
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sentences: |
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- 하이퍼바이저는 보안에서 어떤 역할을 합니까? |
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- 지난 몇 년간 CUDA 생태계는 어떻게 발전해 왔나요? |
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- 로컬 메모리 액세스 성능을 결정하는 요소는 무엇입니까? |
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- source_sentence: 임시 구독의 용도는 무엇입니까? |
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sentences: |
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- 메모리 액세스 최적화에서 프리패치의 역할은 무엇입니까? |
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- CUDA 인식 MPI는 확장 측면에서 어떻게 작동합니까? |
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- CUDA 8이 해결하는 계산상의 과제에는 어떤 것이 있습니까? |
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- source_sentence: '''saxpy''는 무엇을 뜻하나요?' |
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sentences: |
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- CUDA C/C++의 맥락에서 SAXPY는 무엇입니까? |
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- Numba는 다른 GPU 가속 방법과 어떻게 다른가요? |
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- 장치 LTO는 CUDA 애플리케이션에 어떤 이점을 제공합니까? |
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- source_sentence: USD/Hydra란 무엇인가요? |
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sentences: |
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- 쿠다란 무엇인가요? |
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- y 미분 계산에 사용되는 접근 방식의 단점은 무엇입니까? |
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- Pascal 아키텍처는 통합 메모리를 어떻게 개선합니까? |
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- source_sentence: CUDAcast란 무엇인가요? |
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sentences: |
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- CUDACast 시리즈에서는 어떤 주제를 다룰 예정인가요? |
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- 이 게시물에 기여한 것으로 인정받은 사람은 누구입니까? |
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- WSL 2에서 NVML의 목적은 무엇입니까? |
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model-index: |
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- name: BGE base Financial Matryoshka |
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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: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5443037974683544 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.7749648382559775 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8523206751054853 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9409282700421941 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5443037974683544 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2583216127519925 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17046413502109703 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09409282700421939 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5443037974683544 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.7749648382559775 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8523206751054853 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9409282700421941 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7411108924386547 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.677065054807671 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.6802131506478553 |
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name: Cosine Map@100 |
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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: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5386779184247539 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.7749648382559775 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8593530239099859 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9451476793248945 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5386779184247539 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2583216127519925 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17187060478199717 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09451476793248943 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5386779184247539 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.7749648382559775 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8593530239099859 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9451476793248945 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7413571133247474 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.6759917844306029 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.678939165210132 |
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name: Cosine Map@100 |
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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: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.540084388185654 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.7791842475386779 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8621659634317862 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9423347398030942 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.540084388185654 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.25972808251289264 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1724331926863572 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09423347398030943 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.540084388185654 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.7791842475386779 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8621659634317862 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9423347398030942 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7403981257690416 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.6756379344986938 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.6787046866761269 |
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name: Cosine Map@100 |
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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: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.5218002812939522 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.7679324894514767 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8635724331926864 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9367088607594937 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5218002812939522 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2559774964838256 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17271448663853725 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09367088607594935 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5218002812939522 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.7679324894514767 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8635724331926864 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9367088607594937 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7305864977688176 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.6641673922264634 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.6671648971944116 |
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name: Cosine Map@100 |
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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: dim 64 |
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type: dim_64 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.509142053445851 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.7426160337552743 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8284106891701828 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9310829817158931 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.509142053445851 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.24753867791842477 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.16568213783403654 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09310829817158929 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.509142053445851 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.7426160337552743 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
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value: 0.8284106891701828 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9310829817158931 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7135661304090457 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
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value: 0.6444829549259928 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.6474431148702396 |
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name: Cosine Map@100 |
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--- |
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|
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# hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF |
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This model was converted to GGUF format from [`harheem/bge-m3-nvidia-ko-v1`](https://huggingface.co./harheem/bge-m3-nvidia-ko-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co./spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co./harheem/bge-m3-nvidia-ko-v1) for more details on the model. |
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|
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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|
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```bash |
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brew install llama.cpp |
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|
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``` |
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Invoke the llama.cpp server or the CLI. |
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|
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### CLI: |
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```bash |
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llama-cli --hf-repo hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF --hf-file bge-m3-nvidia-ko-v1-q4_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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|
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### Server: |
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```bash |
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llama-server --hf-repo hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF --hf-file bge-m3-nvidia-ko-v1-q4_k_m.gguf -c 2048 |
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``` |
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|
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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|
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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|
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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|
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF --hf-file bge-m3-nvidia-ko-v1-q4_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF --hf-file bge-m3-nvidia-ko-v1-q4_k_m.gguf -c 2048 |
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``` |
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