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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,786 @@
+---
+base_model: BAAI/bge-m3
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dataset_size:1K<n<10K
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 하이브리다이저란 무엇인가요?
+  sentences:
+  - 하이퍼바이저는 보안에서 어떤 역할을 합니까?
+  - 지난 몇 년간 CUDA 생태계는 어떻게 발전해 왔나요?
+  - 로컬 메모리 액세스 성능을 결정하는 요소는 무엇입니까?
+- source_sentence: 임시 구독의 용도는 무엇입니까?
+  sentences:
+  - 메모리 액세스 최적화에서 프리패치의 역할은 무엇입니까?
+  - CUDA 인식 MPI는 확장 측면에서 어떻게 작동합니까?
+  - CUDA 8이 해결하는 계산상의 과제에는 어떤 것이 있습니까?
+- source_sentence: '''saxpy''는 무엇을 뜻하나요?'
+  sentences:
+  - CUDA C/C++의 맥락에서 SAXPY는 무엇입니까?
+  - Numba는 다른 GPU 가속 방법과 어떻게 다른가요?
+  - 장치 LTO는 CUDA 애플리케이션에 어떤 이점을 제공합니까?
+- source_sentence: USD/Hydra란 무엇인가요?
+  sentences:
+  - 쿠다란 무엇인가요?
+  - y 미분 계산에 사용되는 접근 방식의 단점은 무엇입니까?
+  - Pascal 아키텍처는 통합 메모리를 어떻게 개선합니까?
+- source_sentence: CUDAcast란 무엇인가요?
+  sentences:
+  - CUDACast 시리즈에서는 어떤 주제를 다룰 예정인가요?
+  - 이 게시물에 기여한 것으로 인정받은 사람은 누구입니까?
+  - WSL 2에서 NVML의 목적은 무엇입니까?
+model-index:
+- name: BGE base Financial Matryoshka
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5443037974683544
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7749648382559775
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8523206751054853
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9409282700421941
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5443037974683544
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2583216127519925
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17046413502109703
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09409282700421939
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5443037974683544
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7749648382559775
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8523206751054853
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9409282700421941
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7411108924386547
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.677065054807671
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6802131506478553
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5386779184247539
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7749648382559775
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8593530239099859
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9451476793248945
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5386779184247539
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2583216127519925
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17187060478199717
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09451476793248943
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5386779184247539
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7749648382559775
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8593530239099859
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9451476793248945
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7413571133247474
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6759917844306029
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.678939165210132
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.540084388185654
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7791842475386779
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8621659634317862
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9423347398030942
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.540084388185654
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.25972808251289264
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1724331926863572
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09423347398030943
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.540084388185654
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7791842475386779
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8621659634317862
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9423347398030942
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7403981257690416
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6756379344986938
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6787046866761269
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5218002812939522
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7679324894514767
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8635724331926864
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9367088607594937
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5218002812939522
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2559774964838256
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17271448663853725
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09367088607594935
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5218002812939522
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7679324894514767
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8635724331926864
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9367088607594937
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7305864977688176
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6641673922264634
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6671648971944116
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.509142053445851
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7426160337552743
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8284106891701828
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9310829817158931
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.509142053445851
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24753867791842477
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16568213783403654
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09310829817158929
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.509142053445851
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7426160337552743
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8284106891701828
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9310829817158931
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7135661304090457
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6444829549259928
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6474431148702396
+      name: Cosine Map@100
+---
+
+# BGE base Financial Matryoshka
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'CUDAcast란 무엇인가요?',
+    'CUDACast 시리즈에서는 어떤 주제를 다룰 예정인가요?',
+    '이 게시물에 기여한 것으로 인정받은 사람은 누구입니까?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* Dataset: `dim_768`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.5443     |
+| cosine_accuracy@3   | 0.775      |
+| cosine_accuracy@5   | 0.8523     |
+| cosine_accuracy@10  | 0.9409     |
+| cosine_precision@1  | 0.5443     |
+| cosine_precision@3  | 0.2583     |
+| cosine_precision@5  | 0.1705     |
+| cosine_precision@10 | 0.0941     |
+| cosine_recall@1     | 0.5443     |
+| cosine_recall@3     | 0.775      |
+| cosine_recall@5     | 0.8523     |
+| cosine_recall@10    | 0.9409     |
+| cosine_ndcg@10      | 0.7411     |
+| cosine_mrr@10       | 0.6771     |
+| **cosine_map@100**  | **0.6802** |
+
+#### Information Retrieval
+* Dataset: `dim_512`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.5387     |
+| cosine_accuracy@3   | 0.775      |
+| cosine_accuracy@5   | 0.8594     |
+| cosine_accuracy@10  | 0.9451     |
+| cosine_precision@1  | 0.5387     |
+| cosine_precision@3  | 0.2583     |
+| cosine_precision@5  | 0.1719     |
+| cosine_precision@10 | 0.0945     |
+| cosine_recall@1     | 0.5387     |
+| cosine_recall@3     | 0.775      |
+| cosine_recall@5     | 0.8594     |
+| cosine_recall@10    | 0.9451     |
+| cosine_ndcg@10      | 0.7414     |
+| cosine_mrr@10       | 0.676      |
+| **cosine_map@100**  | **0.6789** |
+
+#### Information Retrieval
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.5401     |
+| cosine_accuracy@3   | 0.7792     |
+| cosine_accuracy@5   | 0.8622     |
+| cosine_accuracy@10  | 0.9423     |
+| cosine_precision@1  | 0.5401     |
+| cosine_precision@3  | 0.2597     |
+| cosine_precision@5  | 0.1724     |
+| cosine_precision@10 | 0.0942     |
+| cosine_recall@1     | 0.5401     |
+| cosine_recall@3     | 0.7792     |
+| cosine_recall@5     | 0.8622     |
+| cosine_recall@10    | 0.9423     |
+| cosine_ndcg@10      | 0.7404     |
+| cosine_mrr@10       | 0.6756     |
+| **cosine_map@100**  | **0.6787** |
+
+#### Information Retrieval
+* Dataset: `dim_128`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.5218     |
+| cosine_accuracy@3   | 0.7679     |
+| cosine_accuracy@5   | 0.8636     |
+| cosine_accuracy@10  | 0.9367     |
+| cosine_precision@1  | 0.5218     |
+| cosine_precision@3  | 0.256      |
+| cosine_precision@5  | 0.1727     |
+| cosine_precision@10 | 0.0937     |
+| cosine_recall@1     | 0.5218     |
+| cosine_recall@3     | 0.7679     |
+| cosine_recall@5     | 0.8636     |
+| cosine_recall@10    | 0.9367     |
+| cosine_ndcg@10      | 0.7306     |
+| cosine_mrr@10       | 0.6642     |
+| **cosine_map@100**  | **0.6672** |
+
+#### Information Retrieval
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.5091     |
+| cosine_accuracy@3   | 0.7426     |
+| cosine_accuracy@5   | 0.8284     |
+| cosine_accuracy@10  | 0.9311     |
+| cosine_precision@1  | 0.5091     |
+| cosine_precision@3  | 0.2475     |
+| cosine_precision@5  | 0.1657     |
+| cosine_precision@10 | 0.0931     |
+| cosine_recall@1     | 0.5091     |
+| cosine_recall@3     | 0.7426     |
+| cosine_recall@5     | 0.8284     |
+| cosine_recall@10    | 0.9311     |
+| cosine_ndcg@10      | 0.7136     |
+| cosine_mrr@10       | 0.6445     |
+| **cosine_map@100**  | **0.6474** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 6,397 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                            | anchor                                                                           |
+  |:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                           |
+  | details | <ul><li>min: 11 tokens</li><li>mean: 48.46 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.0 tokens</li><li>max: 48 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                              | anchor                                                        |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | <code>Warp-stride 및 block-stride 루프는 스레드 동작을 재구성하고 공유 메모리 액세스 패턴을 최적화하는 데 사용되었습니다.</code>                                                             | <code>코드에서 공유 메모리 액세스 패턴을 최적화하기 위해 어떤 유형의 루프가 사용되었습니까?</code> |
+  | <code>Nsight Compute의 규칙은 성능 병목 현상을 식별하기 위한 구조화된 프레임워크를 제공하고 최적화 프로세스를 간소화하기 위한 실행 가능한 통찰력을 제공합니다.</code>                                             | <code>Nsight Compute의 맥락에서 규칙이 중요한 이유는 무엇입니까?</code>          |
+  | <code>NVIDIA Nsight와 같은 도구의 가용성으로 인해 개발자가 단일 GPU에서 디버깅할 수 있게 되어 CUDA 개발 속도가 크게 향상되었습니다. CUDA 메모리 검사기는 메모리 액세스 문제를 식별하여 코드 품질을 향상시키는 데 도움이 됩니다.</code> | <code>디버깅 도구의 가용성이 CUDA 개발에 어떤 영향을 미쳤습니까?</code>              |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `eval_accumulation_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `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}
+- `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_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `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`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch    | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
+|:--------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.8      | 10     | 1.3103        | -                      | -                      | -                      | -                     | -                      |
+| 0.96     | 12     | -             | 0.6512                 | 0.6539                 | 0.6688                 | 0.6172                | 0.6679                 |
+| 1.6      | 20     | 0.4148        | -                      | -                      | -                      | -                     | -                      |
+| 2.0      | 25     | -             | 0.6615                 | 0.6688                 | 0.6783                 | 0.6417                | 0.6763                 |
+| 2.4      | 30     | 0.2683        | -                      | -                      | -                      | -                     | -                      |
+| **2.88** | **36** | **-**         | **0.6672**             | **0.6787**             | **0.6789**             | **0.6474**            | **0.6802**             |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.0
+- Transformers: 4.41.2
+- PyTorch: 2.1.2+cu121
+- Accelerate: 0.31.0
+- Datasets: 2.18.0
+- Tokenizers: 0.19.1
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning}, 
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
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+  "model_type": "xlm-roberta",
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+  "use_cache": true,
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+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.33.0",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
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modules.json

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+    "path": "",
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+  }
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sentence_bert_config.json

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+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

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