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language:
- ko
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:375895
- loss:MatryoshkaLoss
- loss:CachedMultipleNegativesRankingLoss
base_model: Qwen/Qwen3-VL-Embedding-2B
widget:
- source_sentence: 컴퓨터시스템설계 및 분석가와 시스템소프트웨어개발자의 2021-2031년 고용 증감률 차이는 어떤 요인에 기인하나요?
sentences:
- 2023년 일·가정 양립 실태조사에서 사업체 규모별 상시근로자 수와 표본 배분 수의 차이는 어떻게 다른가요?
- 「소재·부품·장비 2.0전략」으로 확대된 GVC 핵심품목 수와 2022년 국내 첨단화학소재 시장 규모는 각각 얼마인가요?
- 이차전지 장비 분야에서 고졸 인력의 퇴직률과 채용률은 사업체 규모별로 어떻게 다른가요?
- source_sentence: 난임치료휴가 사용일수 vs 태아검진시간 보장 vs 생리휴가 vs 수유시간 제공 모성보호제도 사용 가능 비율
sentences:
- 차세대세라믹소재 산업에서 분야별 및 사업체규모별 퇴직률과 채용률의 차이를 비교하라
- 2023년 기준 일·가정 양립 정책 중 근로자 이직률 감소와 기업 생산성 향상에 가장 큰 영향을 미친 상위 3개 정책을 순위별 응답 빈도와
지역별 성과 데이터를 비교하여 설명하라.
- 철로설치·보수원과 잠수기능원의 업무 환경, 필수 자격 요건, 그리고 작업 방식의 차이점을 비교하라.
- source_sentence: 재량근무제 원격근무제 재택근무제 도입률 평균 활용인원 비교
sentences:
- 자연과학시험원과 생물학연구원의 향후 10년간 일자리 전망 차이는 어떤 기술적·사회적 요인에 기인하나요?
- 식품 가공 관련 기계 조작원의 평균 연령대와 초과 근로시간이 월임금총액에서 차지하는 비율은 성별에 따라 어떻게 다른가요?
- 산업용 첨단화학소재 분야의 높은 연평균 증가율이 전체 산업기술인력 기여율에서 상대적으로 낮은 비중을 차지하는 이유는 무엇이며, 이와 관련된
직무별 인력 수요 변화는 어떻게 나타나는가?
- source_sentence: 이차전지 재사용·재활용 분야의 신입직 채용 어려움과 인력 부족 원인이 다른 분야와 어떻게 다른지 설명하시오.
sentences:
- 부동산중개인이 되기 위해 필요한 자격증과 향후 10년간 일자리 전망에 영향을 미치는 주요 요인은 무엇인가요?
- 가상(증강)현실전문가와 게임프로그래머의 일자리 증가 요인과 요구되는 능력은 어떻게 다른가요?
- 첨단화학소재 산업에서 생산기술 직무 퇴직률 증가와 R&D 및 공정설계 분야 고급인력 양성 요구 사이의 관계는 무엇인가요?
- source_sentence: 2023년 지역별 연차 평균 사용 일수와 연차 미사용 이유 간의 상관관계는 무엇이며, 이를 개선하기 위한 주요 조치는
무엇인가요?
sentences:
- 웹개발자 및 웹기획자 취업자 수 증감률 비교
- 이차전지 재사용·재활용 분야의 신입직 채용 어려움과 인력 부족 원인이 다른 분야와 어떻게 다른지 설명하시오.
- 가축사육종사원의 일자리 전망에 영향을 미치는 증가 요인과 감소 요인을 비교하여 설명하시오.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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@5
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: Qwen3-VL-Embedding-2B model trained on Korean Visual Document Retrieval query-document
screenshot pairs
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: kovidore hr eval
type: kovidore-hr-eval
metrics:
- type: cosine_accuracy@1
value: 0.3167420814479638
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5384615384615384
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6244343891402715
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.746606334841629
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3167420814479638
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.23981900452488686
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1936651583710407
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13891402714932127
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10555914673561732
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23277310924369748
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3139301874595992
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.42953027364792074
name: Cosine Recall@10
- type: cosine_ndcg@5
value: 0.29936477796940414
name: Cosine Ndcg@5
- type: cosine_ndcg@10
value: 0.3521916369275723
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4510019392372335
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.28612283041029246
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: kovidore hr eval 2048d
type: kovidore-hr-eval-2048d
metrics:
- type: cosine_accuracy@1
value: 0.3167420814479638
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5384615384615384
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6244343891402715
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.746606334841629
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3167420814479638
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.23981900452488686
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1936651583710407
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13891402714932127
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10555914673561732
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23277310924369748
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3139301874595992
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.42953027364792074
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3521916369275723
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4510019392372335
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.28612283041029246
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: kovidore hr eval 1024d
type: kovidore-hr-eval-1024d
metrics:
- type: cosine_accuracy@1
value: 0.28054298642533937
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.502262443438914
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6108597285067874
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7239819004524887
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28054298642533937
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22624434389140272
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19004524886877827
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13122171945701358
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.09145658263305322
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21945701357466063
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.30687351863822454
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.41164619694031457
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3299995662996147
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4146232852115207
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.27091077583689815
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: kovidore hr eval 512d
type: kovidore-hr-eval-512d
metrics:
- type: cosine_accuracy@1
value: 0.26244343891402716
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.497737556561086
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.583710407239819
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.751131221719457
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26244343891402716
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.21116138763197587
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17647058823529413
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13031674208144794
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08629605688429216
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.20309200603318248
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.27490842490842493
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4076923076923077
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3191360237740399
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4087050204697264
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.25493079095962123
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: kovidore hr eval 256d
type: kovidore-hr-eval-256d
metrics:
- type: cosine_accuracy@1
value: 0.2669683257918552
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.45248868778280543
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.579185520361991
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7058823529411765
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2669683257918552
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1975867269984917
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16832579185520363
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1253393665158371
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08584356819650937
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1872979961215255
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2654384830855419
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3909609997845292
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.30811152229006145
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3952111613876321
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24497983293286898
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: kovidore hr eval 128d
type: kovidore-hr-eval-128d
metrics:
- type: cosine_accuracy@1
value: 0.2398190045248869
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.38461538461538464
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4660633484162896
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5972850678733032
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2398190045248869
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16892911010558068
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.13846153846153844
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1013574660633484
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0781835811247576
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15757379875026933
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2132406808877397
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.31347769877181647
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2528783413141007
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.33766429648782603
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.20530881281856705
name: Cosine Map@100
---
# Qwen3-VL-Embedding-2B model trained on Korean Visual Document Retrieval query-document screenshot pairs
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B). It maps sentences & paragraphs to a 2048-dimensional dense vector space and can be used for retrieval.
> [!NOTE]
> The evaluation results reported in this README were obtained with `max_pixels = 1024 * 32 * 32`. When evaluated on **SDS KoPub-VDR** and **KoViDoRe v2** with `max_pixels = 1800 * 32 * 32`, the NDCG@10 scores are as follows:
>
> **KoViDoRe v2**
>
> | Domain | NDCG@10 |
> |---|---|
> | Cybersecurity | 0.7073 |
> | Energy | 0.6035 |
> | Hr | 0.4107 |
> | Economic | 0.2404 |
> | **Average** | **0.4905** |
>
> **SDS KoPub-VDR**
>
> | Domain | NDCG@10 |
> |---|---|
> | **Average** | **0.6522** |
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Qwen/Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) <!-- at revision 9f2f7e710d6d81056aa5c0a4f04764fec6bb7bda -->
- **Maximum Sequence Length:** 262144 tokens
- **Output Dimensionality:** 2048 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modalities:** Text, Image, Video, Message
<!-- - **Training Dataset:** Unknown -->
- **Language:** ko
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'processing_kwargs': {'chat_template': {'add_generation_prompt': True}}, 'unpad_inputs': False, 'architecture': 'Qwen3VLModel'})
(1): Pooling({'embedding_dimension': 2048, 'pooling_mode': 'lasttoken', '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
queries = [
'하이테크섬유소재 산업에서 산업용 테크니컬 섬유의 시장 성장 전망이 연구개발 인력 비중 변화에 어떤 영향을 미치고 있나요?',
]
documents = [
'assets/image_0.jpg',
'assets/image_1.jpg',
'assets/image_2.jpg',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 2048] [3, 2048]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.5445, 0.5695, 0.5247]])
```
<!--
### 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: `kovidore-hr-eval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"query_prompt": "Find a screenshot that is relevant to the user's question."
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3167 |
| cosine_accuracy@3 | 0.5385 |
| cosine_accuracy@5 | 0.6244 |
| cosine_accuracy@10 | 0.7466 |
| cosine_precision@1 | 0.3167 |
| cosine_precision@3 | 0.2398 |
| cosine_precision@5 | 0.1937 |
| cosine_precision@10 | 0.1389 |
| cosine_recall@1 | 0.1056 |
| cosine_recall@3 | 0.2328 |
| cosine_recall@5 | 0.3139 |
| cosine_recall@10 | 0.4295 |
| cosine_ndcg@5 | 0.2994 |
| **cosine_ndcg@10** | **0.3522** |
| cosine_mrr@10 | 0.451 |
| cosine_map@100 | 0.2861 |
#### Information Retrieval
* Dataset: `kovidore-hr-eval-2048d`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 2048,
"query_prompt": "Find a screenshot that is relevant to the user's question."
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3167 |
| cosine_accuracy@3 | 0.5385 |
| cosine_accuracy@5 | 0.6244 |
| cosine_accuracy@10 | 0.7466 |
| cosine_precision@1 | 0.3167 |
| cosine_precision@3 | 0.2398 |
| cosine_precision@5 | 0.1937 |
| cosine_precision@10 | 0.1389 |
| cosine_recall@1 | 0.1056 |
| cosine_recall@3 | 0.2328 |
| cosine_recall@5 | 0.3139 |
| cosine_recall@10 | 0.4295 |
| **cosine_ndcg@10** | **0.3522** |
| cosine_mrr@10 | 0.451 |
| cosine_map@100 | 0.2861 |
#### Information Retrieval
* Dataset: `kovidore-hr-eval-1024d`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 1024,
"query_prompt": "Find a screenshot that is relevant to the user's question."
}
```
| Metric | Value |
|:--------------------|:---------|
| cosine_accuracy@1 | 0.2805 |
| cosine_accuracy@3 | 0.5023 |
| cosine_accuracy@5 | 0.6109 |
| cosine_accuracy@10 | 0.724 |
| cosine_precision@1 | 0.2805 |
| cosine_precision@3 | 0.2262 |
| cosine_precision@5 | 0.19 |
| cosine_precision@10 | 0.1312 |
| cosine_recall@1 | 0.0915 |
| cosine_recall@3 | 0.2195 |
| cosine_recall@5 | 0.3069 |
| cosine_recall@10 | 0.4116 |
| **cosine_ndcg@10** | **0.33** |
| cosine_mrr@10 | 0.4146 |
| cosine_map@100 | 0.2709 |
#### Information Retrieval
* Dataset: `kovidore-hr-eval-512d`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 512,
"query_prompt": "Find a screenshot that is relevant to the user's question."
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2624 |
| cosine_accuracy@3 | 0.4977 |
| cosine_accuracy@5 | 0.5837 |
| cosine_accuracy@10 | 0.7511 |
| cosine_precision@1 | 0.2624 |
| cosine_precision@3 | 0.2112 |
| cosine_precision@5 | 0.1765 |
| cosine_precision@10 | 0.1303 |
| cosine_recall@1 | 0.0863 |
| cosine_recall@3 | 0.2031 |
| cosine_recall@5 | 0.2749 |
| cosine_recall@10 | 0.4077 |
| **cosine_ndcg@10** | **0.3191** |
| cosine_mrr@10 | 0.4087 |
| cosine_map@100 | 0.2549 |
#### Information Retrieval
* Dataset: `kovidore-hr-eval-256d`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256,
"query_prompt": "Find a screenshot that is relevant to the user's question."
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.267 |
| cosine_accuracy@3 | 0.4525 |
| cosine_accuracy@5 | 0.5792 |
| cosine_accuracy@10 | 0.7059 |
| cosine_precision@1 | 0.267 |
| cosine_precision@3 | 0.1976 |
| cosine_precision@5 | 0.1683 |
| cosine_precision@10 | 0.1253 |
| cosine_recall@1 | 0.0858 |
| cosine_recall@3 | 0.1873 |
| cosine_recall@5 | 0.2654 |
| cosine_recall@10 | 0.391 |
| **cosine_ndcg@10** | **0.3081** |
| cosine_mrr@10 | 0.3952 |
| cosine_map@100 | 0.245 |
#### Information Retrieval
* Dataset: `kovidore-hr-eval-128d`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128,
"query_prompt": "Find a screenshot that is relevant to the user's question."
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2398 |
| cosine_accuracy@3 | 0.3846 |
| cosine_accuracy@5 | 0.4661 |
| cosine_accuracy@10 | 0.5973 |
| cosine_precision@1 | 0.2398 |
| cosine_precision@3 | 0.1689 |
| cosine_precision@5 | 0.1385 |
| cosine_precision@10 | 0.1014 |
| cosine_recall@1 | 0.0782 |
| cosine_recall@3 | 0.1576 |
| cosine_recall@5 | 0.2132 |
| cosine_recall@10 | 0.3135 |
| **cosine_ndcg@10** | **0.2529** |
| cosine_mrr@10 | 0.3377 |
| cosine_map@100 | 0.2053 |
<!--
## 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: 375,895 training samples
* Columns: <code>query</code> and <code>image</code>
* Approximate statistics based on the first 1000 samples:
| | query | image |
|:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| type | string | image |
| details | <ul><li>min: 20 tokens</li><li>mean: 61.49 tokens</li><li>max: 116 tokens</li></ul> | <ul><li>min: 339x178 px</li><li>mean: 2263x2460 px</li><li>max: 4961x4385 px</li></ul> |
* Samples:
| query | image |
|:--------------------------------------------------------------------------|:---------------------------------------------------|
| <code>시청사 안전 관리 정책과 세무조사 절차가 각각 위험 최소화와 예산 효율성 확보에 어떻게 기여하고 있는가?</code> | <img src="assets/example_image_0.jpg" width="200"> |
| <code>학교 밖 청소년 지원 확대는 언제 시작되었나요?</code> | <img src="assets/example_image_1.jpg" width="200"> |
| <code>소니의 게임 산업 전략과 2020년 방송영상 콘텐츠 혁신 계획 간의 기술 혁신 접근 방식 차이는 무엇인가요?</code> | <img src="assets/example_image_2.jpg" width="200"> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CachedMultipleNegativesRankingLoss",
"matryoshka_dims": [
2048,
1024,
512,
256,
128
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 726 evaluation samples
* Columns: <code>query</code> and <code>image</code>
* Approximate statistics based on the first 726 samples:
| | query | image |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|
| type | string | image |
| details | <ul><li>min: 38 tokens</li><li>mean: 65.45 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 2221x2480 px</li><li>mean: 2387x3333 px</li><li>max: 3505x3626 px</li></ul> |
* Samples:
| query | image |
|:---------------------------------------------------------------------------------|:-------------------------------------------|
| <code>하이테크섬유소재 산업에서 산업용 테크니컬 섬유의 시장 성장 전망이 연구개발 인력 비중 변화에 어떤 영향을 미치고 있나요?</code> | <img src="assets/image_0.jpg" width="200"> |
| <code>하이테크섬유소재 산업에서 산업용 테크니컬 섬유의 시장 성장 전망이 연구개발 인력 비중 변화에 어떤 영향을 미치고 있나요?</code> | <img src="assets/image_1.jpg" width="200"> |
| <code>하이테크섬유소재 산업에서 산업용 테크니컬 섬유의 시장 성장 전망이 연구개발 인력 비중 변화에 어떤 영향을 미치고 있나요?</code> | <img src="assets/image_2.jpg" width="200"> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CachedMultipleNegativesRankingLoss",
"matryoshka_dims": [
2048,
1024,
512,
256,
128
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 128
- `num_train_epochs`: 1.0
- `learning_rate`: 2e-05
- `lr_scheduler_type`: cosine
- `warmup_steps`: 0.05
- `gradient_accumulation_steps`: 2
- `bf16`: True
- `per_device_eval_batch_size`: 128
- `dataloader_num_workers`: 4
- `batch_sampler`: no_duplicates_hashed
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `per_device_train_batch_size`: 128
- `num_train_epochs`: 1.0
- `max_steps`: -1
- `learning_rate`: 2e-05
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.05
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 2
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: True
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: trackio
- `per_device_eval_batch_size`: 128
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: no_duplicates_hashed
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
- `mini_batch_size`: 16
- `matryoshka_dims`: [2048, 1024, 512, 256, 128]
- `use_lora`: False
- `lora_r`: 32
- `lora_alpha`: 32
- `lora_dropout`: 0.05
- `lora_target_modules`: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | kovidore-hr-eval_cosine_ndcg@10 | kovidore-hr-eval-2048d_cosine_ndcg@10 | kovidore-hr-eval-1024d_cosine_ndcg@10 | kovidore-hr-eval-512d_cosine_ndcg@10 | kovidore-hr-eval-256d_cosine_ndcg@10 | kovidore-hr-eval-128d_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:-------------------------------------:|:-------------------------------------:|:------------------------------------:|:------------------------------------:|:------------------------------------:|
| -1 | -1 | - | - | 0.1635 | - | - | - | - | - |
| 0.0014 | 1 | 12.5989 | - | - | - | - | - | - | - |
| 0.0027 | 2 | 11.7525 | - | - | - | - | - | - | - |
| 0.0041 | 3 | 11.9801 | - | - | - | - | - | - | - |
| 0.0054 | 4 | 12.4201 | - | - | - | - | - | - | - |
| 0.0068 | 5 | 12.4029 | - | - | - | - | - | - | - |
| 0.0082 | 6 | 11.9316 | - | - | - | - | - | - | - |
| 0.0095 | 7 | 11.5481 | - | - | - | - | - | - | - |
| 0.0109 | 8 | 11.5434 | - | - | - | - | - | - | - |
| 0.0123 | 9 | 10.8382 | - | - | - | - | - | - | - |
| 0.0136 | 10 | 10.0838 | - | - | - | - | - | - | - |
| 0.0150 | 11 | 9.8435 | - | - | - | - | - | - | - |
| 0.0163 | 12 | 9.1532 | - | - | - | - | - | - | - |
| 0.0177 | 13 | 8.5349 | - | - | - | - | - | - | - |
| 0.0191 | 14 | 8.6398 | - | - | - | - | - | - | - |
| 0.0204 | 15 | 8.2873 | - | - | - | - | - | - | - |
| 0.0218 | 16 | 7.6626 | - | - | - | - | - | - | - |
| 0.0232 | 17 | 7.2096 | - | - | - | - | - | - | - |
| 0.0245 | 18 | 7.1897 | - | - | - | - | - | - | - |
| 0.0259 | 19 | 6.3010 | - | - | - | - | - | - | - |
| 0.0272 | 20 | 6.6421 | - | - | - | - | - | - | - |
| 0.0286 | 21 | 6.3039 | - | - | - | - | - | - | - |
| 0.0300 | 22 | 6.1375 | - | - | - | - | - | - | - |
| 0.0313 | 23 | 6.0474 | - | - | - | - | - | - | - |
| 0.0327 | 24 | 5.6578 | - | - | - | - | - | - | - |
| 0.0341 | 25 | 5.5311 | - | - | - | - | - | - | - |
| 0.0354 | 26 | 5.5291 | - | - | - | - | - | - | - |
| 0.0368 | 27 | 5.1779 | - | - | - | - | - | - | - |
| 0.0381 | 28 | 5.5651 | - | - | - | - | - | - | - |
| 0.0395 | 29 | 4.8693 | - | - | - | - | - | - | - |
| 0.0409 | 30 | 4.5870 | - | - | - | - | - | - | - |
| 0.0422 | 31 | 4.8026 | - | - | - | - | - | - | - |
| 0.0436 | 32 | 4.5821 | - | - | - | - | - | - | - |
| 0.0450 | 33 | 3.9704 | - | - | - | - | - | - | - |
| 0.0463 | 34 | 4.6102 | - | - | - | - | - | - | - |
| 0.0477 | 35 | 4.3860 | - | - | - | - | - | - | - |
| 0.0490 | 36 | 4.1360 | - | - | - | - | - | - | - |
| 0.0504 | 37 | 3.6120 | - | - | - | - | - | - | - |
| 0.0518 | 38 | 4.0341 | - | - | - | - | - | - | - |
| 0.0531 | 39 | 4.3009 | - | - | - | - | - | - | - |
| 0.0545 | 40 | 3.8860 | - | - | - | - | - | - | - |
| 0.0559 | 41 | 3.6376 | - | - | - | - | - | - | - |
| 0.0572 | 42 | 3.6823 | - | - | - | - | - | - | - |
| 0.0586 | 43 | 3.7491 | - | - | - | - | - | - | - |
| 0.0599 | 44 | 3.9204 | - | - | - | - | - | - | - |
| 0.0613 | 45 | 3.3923 | - | - | - | - | - | - | - |
| 0.0627 | 46 | 3.4663 | - | - | - | - | - | - | - |
| 0.0640 | 47 | 3.7050 | - | - | - | - | - | - | - |
| 0.0654 | 48 | 3.9178 | - | - | - | - | - | - | - |
| 0.0668 | 49 | 3.8461 | - | - | - | - | - | - | - |
| 0.0681 | 50 | 3.4688 | - | - | - | - | - | - | - |
| 0.0695 | 51 | 3.4001 | - | - | - | - | - | - | - |
| 0.0708 | 52 | 3.6708 | - | - | - | - | - | - | - |
| 0.0722 | 53 | 3.6082 | - | - | - | - | - | - | - |
| 0.0736 | 54 | 3.3912 | - | - | - | - | - | - | - |
| 0.0749 | 55 | 2.7133 | - | - | - | - | - | - | - |
| 0.0763 | 56 | 3.2332 | - | - | - | - | - | - | - |
| 0.0777 | 57 | 2.9877 | - | - | - | - | - | - | - |
| 0.0790 | 58 | 3.2032 | - | - | - | - | - | - | - |
| 0.0804 | 59 | 3.3414 | - | - | - | - | - | - | - |
| 0.0817 | 60 | 3.4641 | - | - | - | - | - | - | - |
| 0.0831 | 61 | 3.1595 | - | - | - | - | - | - | - |
| 0.0845 | 62 | 2.9798 | - | - | - | - | - | - | - |
| 0.0858 | 63 | 3.4362 | - | - | - | - | - | - | - |
| 0.0872 | 64 | 2.6839 | - | - | - | - | - | - | - |
| 0.0886 | 65 | 3.1535 | - | - | - | - | - | - | - |
| 0.0899 | 66 | 3.2873 | - | - | - | - | - | - | - |
| 0.0913 | 67 | 3.4748 | - | - | - | - | - | - | - |
| 0.0926 | 68 | 3.2044 | - | - | - | - | - | - | - |
| 0.0940 | 69 | 3.3770 | - | - | - | - | - | - | - |
| 0.0954 | 70 | 2.7455 | - | - | - | - | - | - | - |
| 0.0967 | 71 | 2.4288 | - | - | - | - | - | - | - |
| 0.0981 | 72 | 2.9685 | - | - | - | - | - | - | - |
| 0.0995 | 73 | 3.0441 | - | - | - | - | - | - | - |
| 0.1008 | 74 | 2.8841 | - | - | - | - | - | - | - |
| 0.1022 | 75 | 2.8333 | - | - | - | - | - | - | - |
| 0.1035 | 76 | 2.9206 | - | - | - | - | - | - | - |
| 0.1049 | 77 | 3.0215 | - | - | - | - | - | - | - |
| 0.1063 | 78 | 3.0514 | - | - | - | - | - | - | - |
| 0.1076 | 79 | 2.9484 | - | - | - | - | - | - | - |
| 0.1090 | 80 | 2.7305 | - | - | - | - | - | - | - |
| 0.1104 | 81 | 3.4594 | - | - | - | - | - | - | - |
| 0.1117 | 82 | 2.2943 | - | - | - | - | - | - | - |
| 0.1131 | 83 | 2.8816 | - | - | - | - | - | - | - |
| 0.1144 | 84 | 3.2173 | - | - | - | - | - | - | - |
| 0.1158 | 85 | 2.7779 | - | - | - | - | - | - | - |
| 0.1172 | 86 | 2.9407 | - | - | - | - | - | - | - |
| 0.1185 | 87 | 2.6579 | - | - | - | - | - | - | - |
| 0.1199 | 88 | 2.4952 | - | - | - | - | - | - | - |
| 0.1213 | 89 | 2.9427 | - | - | - | - | - | - | - |
| 0.1226 | 90 | 2.7977 | - | - | - | - | - | - | - |
| 0.1240 | 91 | 2.7015 | - | - | - | - | - | - | - |
| 0.1253 | 92 | 2.9378 | - | - | - | - | - | - | - |
| 0.1267 | 93 | 2.6606 | - | - | - | - | - | - | - |
| 0.1281 | 94 | 2.6318 | - | - | - | - | - | - | - |
| 0.1294 | 95 | 2.7911 | - | - | - | - | - | - | - |
| 0.1308 | 96 | 3.0715 | - | - | - | - | - | - | - |
| 0.1322 | 97 | 2.9101 | - | - | - | - | - | - | - |
| 0.1335 | 98 | 2.4365 | - | - | - | - | - | - | - |
| 0.1349 | 99 | 2.3475 | - | - | - | - | - | - | - |
| 0.1362 | 100 | 2.3083 | 12.6454 | 0.2856 | - | - | - | - | - |
| 0.1376 | 101 | 2.4004 | - | - | - | - | - | - | - |
| 0.1390 | 102 | 2.7838 | - | - | - | - | - | - | - |
| 0.1403 | 103 | 2.7071 | - | - | - | - | - | - | - |
| 0.1417 | 104 | 2.5176 | - | - | - | - | - | - | - |
| 0.1431 | 105 | 2.6188 | - | - | - | - | - | - | - |
| 0.1444 | 106 | 2.5974 | - | - | - | - | - | - | - |
| 0.1458 | 107 | 2.4591 | - | - | - | - | - | - | - |
| 0.1471 | 108 | 2.9671 | - | - | - | - | - | - | - |
| 0.1485 | 109 | 2.8031 | - | - | - | - | - | - | - |
| 0.1499 | 110 | 2.4146 | - | - | - | - | - | - | - |
| 0.1512 | 111 | 2.5816 | - | - | - | - | - | - | - |
| 0.1526 | 112 | 2.6721 | - | - | - | - | - | - | - |
| 0.1540 | 113 | 2.7648 | - | - | - | - | - | - | - |
| 0.1553 | 114 | 2.8498 | - | - | - | - | - | - | - |
| 0.1567 | 115 | 2.6547 | - | - | - | - | - | - | - |
| 0.1580 | 116 | 2.8596 | - | - | - | - | - | - | - |
| 0.1594 | 117 | 3.0361 | - | - | - | - | - | - | - |
| 0.1608 | 118 | 2.8527 | - | - | - | - | - | - | - |
| 0.1621 | 119 | 2.5922 | - | - | - | - | - | - | - |
| 0.1635 | 120 | 2.7961 | - | - | - | - | - | - | - |
| 0.1649 | 121 | 2.3379 | - | - | - | - | - | - | - |
| 0.1662 | 122 | 2.6011 | - | - | - | - | - | - | - |
| 0.1676 | 123 | 2.5183 | - | - | - | - | - | - | - |
| 0.1689 | 124 | 2.3978 | - | - | - | - | - | - | - |
| 0.1703 | 125 | 2.5928 | - | - | - | - | - | - | - |
| 0.1717 | 126 | 2.8163 | - | - | - | - | - | - | - |
| 0.1730 | 127 | 2.6082 | - | - | - | - | - | - | - |
| 0.1744 | 128 | 2.5493 | - | - | - | - | - | - | - |
| 0.1757 | 129 | 2.3846 | - | - | - | - | - | - | - |
| 0.1771 | 130 | 2.7151 | - | - | - | - | - | - | - |
| 0.1785 | 131 | 2.2919 | - | - | - | - | - | - | - |
| 0.1798 | 132 | 2.7926 | - | - | - | - | - | - | - |
| 0.1812 | 133 | 2.5687 | - | - | - | - | - | - | - |
| 0.1826 | 134 | 3.0042 | - | - | - | - | - | - | - |
| 0.1839 | 135 | 2.1077 | - | - | - | - | - | - | - |
| 0.1853 | 136 | 2.4163 | - | - | - | - | - | - | - |
| 0.1866 | 137 | 2.2914 | - | - | - | - | - | - | - |
| 0.1880 | 138 | 2.3251 | - | - | - | - | - | - | - |
| 0.1894 | 139 | 2.6317 | - | - | - | - | - | - | - |
| 0.1907 | 140 | 2.8224 | - | - | - | - | - | - | - |
| 0.1921 | 141 | 2.2333 | - | - | - | - | - | - | - |
| 0.1935 | 142 | 2.5090 | - | - | - | - | - | - | - |
| 0.1948 | 143 | 2.3920 | - | - | - | - | - | - | - |
| 0.1962 | 144 | 2.4799 | - | - | - | - | - | - | - |
| 0.1975 | 145 | 2.5125 | - | - | - | - | - | - | - |
| 0.1989 | 146 | 2.6033 | - | - | - | - | - | - | - |
| 0.2003 | 147 | 2.3994 | - | - | - | - | - | - | - |
| 0.2016 | 148 | 2.7508 | - | - | - | - | - | - | - |
| 0.2030 | 149 | 2.5441 | - | - | - | - | - | - | - |
| 0.2044 | 150 | 2.8192 | - | - | - | - | - | - | - |
| 0.2057 | 151 | 2.5756 | - | - | - | - | - | - | - |
| 0.2071 | 152 | 2.7869 | - | - | - | - | - | - | - |
| 0.2084 | 153 | 2.5446 | - | - | - | - | - | - | - |
| 0.2098 | 154 | 2.5355 | - | - | - | - | - | - | - |
| 0.2112 | 155 | 2.6220 | - | - | - | - | - | - | - |
| 0.2125 | 156 | 2.6144 | - | - | - | - | - | - | - |
| 0.2139 | 157 | 2.4171 | - | - | - | - | - | - | - |
| 0.2153 | 158 | 2.3553 | - | - | - | - | - | - | - |
| 0.2166 | 159 | 2.4939 | - | - | - | - | - | - | - |
| 0.2180 | 160 | 2.5188 | - | - | - | - | - | - | - |
| 0.2193 | 161 | 2.2849 | - | - | - | - | - | - | - |
| 0.2207 | 162 | 2.4986 | - | - | - | - | - | - | - |
| 0.2221 | 163 | 2.2168 | - | - | - | - | - | - | - |
| 0.2234 | 164 | 2.5498 | - | - | - | - | - | - | - |
| 0.2248 | 165 | 2.3878 | - | - | - | - | - | - | - |
| 0.2262 | 166 | 1.9777 | - | - | - | - | - | - | - |
| 0.2275 | 167 | 2.1589 | - | - | - | - | - | - | - |
| 0.2289 | 168 | 2.3726 | - | - | - | - | - | - | - |
| 0.2302 | 169 | 2.1589 | - | - | - | - | - | - | - |
| 0.2316 | 170 | 2.4009 | - | - | - | - | - | - | - |
| 0.2330 | 171 | 2.2393 | - | - | - | - | - | - | - |
| 0.2343 | 172 | 2.3541 | - | - | - | - | - | - | - |
| 0.2357 | 173 | 2.3611 | - | - | - | - | - | - | - |
| 0.2371 | 174 | 2.2913 | - | - | - | - | - | - | - |
| 0.2384 | 175 | 2.6696 | - | - | - | - | - | - | - |
| 0.2398 | 176 | 2.6759 | - | - | - | - | - | - | - |
| 0.2411 | 177 | 2.1232 | - | - | - | - | - | - | - |
| 0.2425 | 178 | 2.4155 | - | - | - | - | - | - | - |
| 0.2439 | 179 | 2.6672 | - | - | - | - | - | - | - |
| 0.2452 | 180 | 2.4271 | - | - | - | - | - | - | - |
| 0.2466 | 181 | 2.1856 | - | - | - | - | - | - | - |
| 0.2480 | 182 | 2.5975 | - | - | - | - | - | - | - |
| 0.2493 | 183 | 2.6202 | - | - | - | - | - | - | - |
| 0.2507 | 184 | 2.4713 | - | - | - | - | - | - | - |
| 0.2520 | 185 | 2.6800 | - | - | - | - | - | - | - |
| 0.2534 | 186 | 2.4214 | - | - | - | - | - | - | - |
| 0.2548 | 187 | 2.4757 | - | - | - | - | - | - | - |
| 0.2561 | 188 | 2.0876 | - | - | - | - | - | - | - |
| 0.2575 | 189 | 1.9946 | - | - | - | - | - | - | - |
| 0.2589 | 190 | 2.1938 | - | - | - | - | - | - | - |
| 0.2602 | 191 | 2.3078 | - | - | - | - | - | - | - |
| 0.2616 | 192 | 2.0943 | - | - | - | - | - | - | - |
| 0.2629 | 193 | 2.3893 | - | - | - | - | - | - | - |
| 0.2643 | 194 | 2.3438 | - | - | - | - | - | - | - |
| 0.2657 | 195 | 2.2656 | - | - | - | - | - | - | - |
| 0.2670 | 196 | 2.4710 | - | - | - | - | - | - | - |
| 0.2684 | 197 | 2.3922 | - | - | - | - | - | - | - |
| 0.2698 | 198 | 2.4481 | - | - | - | - | - | - | - |
| 0.2711 | 199 | 2.3725 | - | - | - | - | - | - | - |
| 0.2725 | 200 | 2.6946 | 12.7048 | 0.2944 | - | - | - | - | - |
| 0.2738 | 201 | 2.1826 | - | - | - | - | - | - | - |
| 0.2752 | 202 | 2.1741 | - | - | - | - | - | - | - |
| 0.2766 | 203 | 2.1160 | - | - | - | - | - | - | - |
| 0.2779 | 204 | 2.6354 | - | - | - | - | - | - | - |
| 0.2793 | 205 | 2.3611 | - | - | - | - | - | - | - |
| 0.2807 | 206 | 2.0879 | - | - | - | - | - | - | - |
| 0.2820 | 207 | 2.3140 | - | - | - | - | - | - | - |
| 0.2834 | 208 | 2.4683 | - | - | - | - | - | - | - |
| 0.2847 | 209 | 2.6136 | - | - | - | - | - | - | - |
| 0.2861 | 210 | 2.3649 | - | - | - | - | - | - | - |
| 0.2875 | 211 | 2.3117 | - | - | - | - | - | - | - |
| 0.2888 | 212 | 2.5930 | - | - | - | - | - | - | - |
| 0.2902 | 213 | 2.0142 | - | - | - | - | - | - | - |
| 0.2916 | 214 | 2.4329 | - | - | - | - | - | - | - |
| 0.2929 | 215 | 2.5556 | - | - | - | - | - | - | - |
| 0.2943 | 216 | 2.4058 | - | - | - | - | - | - | - |
| 0.2956 | 217 | 2.1328 | - | - | - | - | - | - | - |
| 0.2970 | 218 | 2.3350 | - | - | - | - | - | - | - |
| 0.2984 | 219 | 2.1800 | - | - | - | - | - | - | - |
| 0.2997 | 220 | 2.3129 | - | - | - | - | - | - | - |
| 0.3011 | 221 | 2.3166 | - | - | - | - | - | - | - |
| 0.3025 | 222 | 2.0943 | - | - | - | - | - | - | - |
| 0.3038 | 223 | 2.2954 | - | - | - | - | - | - | - |
| 0.3052 | 224 | 2.3178 | - | - | - | - | - | - | - |
| 0.3065 | 225 | 2.4639 | - | - | - | - | - | - | - |
| 0.3079 | 226 | 1.9902 | - | - | - | - | - | - | - |
| 0.3093 | 227 | 2.2004 | - | - | - | - | - | - | - |
| 0.3106 | 228 | 2.1586 | - | - | - | - | - | - | - |
| 0.3120 | 229 | 2.1834 | - | - | - | - | - | - | - |
| 0.3134 | 230 | 2.4140 | - | - | - | - | - | - | - |
| 0.3147 | 231 | 2.2707 | - | - | - | - | - | - | - |
| 0.3161 | 232 | 2.4095 | - | - | - | - | - | - | - |
| 0.3174 | 233 | 2.3388 | - | - | - | - | - | - | - |
| 0.3188 | 234 | 2.1020 | - | - | - | - | - | - | - |
| 0.3202 | 235 | 2.1115 | - | - | - | - | - | - | - |
| 0.3215 | 236 | 2.2724 | - | - | - | - | - | - | - |
| 0.3229 | 237 | 2.0170 | - | - | - | - | - | - | - |
| 0.3243 | 238 | 2.0427 | - | - | - | - | - | - | - |
| 0.3256 | 239 | 2.1623 | - | - | - | - | - | - | - |
| 0.3270 | 240 | 2.2005 | - | - | - | - | - | - | - |
| 0.3283 | 241 | 2.2860 | - | - | - | - | - | - | - |
| 0.3297 | 242 | 2.3482 | - | - | - | - | - | - | - |
| 0.3311 | 243 | 2.0765 | - | - | - | - | - | - | - |
| 0.3324 | 244 | 2.2380 | - | - | - | - | - | - | - |
| 0.3338 | 245 | 2.1407 | - | - | - | - | - | - | - |
| 0.3351 | 246 | 2.0575 | - | - | - | - | - | - | - |
| 0.3365 | 247 | 2.2492 | - | - | - | - | - | - | - |
| 0.3379 | 248 | 1.9443 | - | - | - | - | - | - | - |
| 0.3392 | 249 | 2.4679 | - | - | - | - | - | - | - |
| 0.3406 | 250 | 1.9008 | - | - | - | - | - | - | - |
| 0.3420 | 251 | 2.2967 | - | - | - | - | - | - | - |
| 0.3433 | 252 | 2.5148 | - | - | - | - | - | - | - |
| 0.3447 | 253 | 2.3991 | - | - | - | - | - | - | - |
| 0.3460 | 254 | 2.4203 | - | - | - | - | - | - | - |
| 0.3474 | 255 | 2.1369 | - | - | - | - | - | - | - |
| 0.3488 | 256 | 2.3870 | - | - | - | - | - | - | - |
| 0.3501 | 257 | 2.6039 | - | - | - | - | - | - | - |
| 0.3515 | 258 | 2.2899 | - | - | - | - | - | - | - |
| 0.3529 | 259 | 2.0916 | - | - | - | - | - | - | - |
| 0.3542 | 260 | 2.2044 | - | - | - | - | - | - | - |
| 0.3556 | 261 | 1.9944 | - | - | - | - | - | - | - |
| 0.3569 | 262 | 2.5242 | - | - | - | - | - | - | - |
| 0.3583 | 263 | 2.2430 | - | - | - | - | - | - | - |
| 0.3597 | 264 | 2.3481 | - | - | - | - | - | - | - |
| 0.3610 | 265 | 2.3132 | - | - | - | - | - | - | - |
| 0.3624 | 266 | 2.1954 | - | - | - | - | - | - | - |
| 0.3638 | 267 | 2.3599 | - | - | - | - | - | - | - |
| 0.3651 | 268 | 2.1727 | - | - | - | - | - | - | - |
| 0.3665 | 269 | 2.1973 | - | - | - | - | - | - | - |
| 0.3678 | 270 | 2.1254 | - | - | - | - | - | - | - |
| 0.3692 | 271 | 2.1877 | - | - | - | - | - | - | - |
| 0.3706 | 272 | 2.2401 | - | - | - | - | - | - | - |
| 0.3719 | 273 | 2.2340 | - | - | - | - | - | - | - |
| 0.3733 | 274 | 2.4150 | - | - | - | - | - | - | - |
| 0.3747 | 275 | 1.8598 | - | - | - | - | - | - | - |
| 0.3760 | 276 | 2.1595 | - | - | - | - | - | - | - |
| 0.3774 | 277 | 2.3997 | - | - | - | - | - | - | - |
| 0.3787 | 278 | 2.2813 | - | - | - | - | - | - | - |
| 0.3801 | 279 | 2.1888 | - | - | - | - | - | - | - |
| 0.3815 | 280 | 2.3886 | - | - | - | - | - | - | - |
| 0.3828 | 281 | 2.2756 | - | - | - | - | - | - | - |
| 0.3842 | 282 | 2.0469 | - | - | - | - | - | - | - |
| 0.3856 | 283 | 2.1666 | - | - | - | - | - | - | - |
| 0.3869 | 284 | 2.0312 | - | - | - | - | - | - | - |
| 0.3883 | 285 | 2.1354 | - | - | - | - | - | - | - |
| 0.3896 | 286 | 2.1032 | - | - | - | - | - | - | - |
| 0.3910 | 287 | 2.1454 | - | - | - | - | - | - | - |
| 0.3924 | 288 | 2.2448 | - | - | - | - | - | - | - |
| 0.3937 | 289 | 2.0035 | - | - | - | - | - | - | - |
| 0.3951 | 290 | 2.0297 | - | - | - | - | - | - | - |
| 0.3965 | 291 | 2.1220 | - | - | - | - | - | - | - |
| 0.3978 | 292 | 1.9975 | - | - | - | - | - | - | - |
| 0.3992 | 293 | 2.1936 | - | - | - | - | - | - | - |
| 0.4005 | 294 | 1.9374 | - | - | - | - | - | - | - |
| 0.4019 | 295 | 2.1295 | - | - | - | - | - | - | - |
| 0.4033 | 296 | 2.1893 | - | - | - | - | - | - | - |
| 0.4046 | 297 | 2.0841 | - | - | - | - | - | - | - |
| 0.4060 | 298 | 1.9163 | - | - | - | - | - | - | - |
| 0.4074 | 299 | 2.1985 | - | - | - | - | - | - | - |
| 0.4087 | 300 | 2.0377 | 11.6451 | 0.3291 | - | - | - | - | - |
| 0.4101 | 301 | 2.0648 | - | - | - | - | - | - | - |
| 0.4114 | 302 | 1.9697 | - | - | - | - | - | - | - |
| 0.4128 | 303 | 2.0732 | - | - | - | - | - | - | - |
| 0.4142 | 304 | 2.1077 | - | - | - | - | - | - | - |
| 0.4155 | 305 | 2.0870 | - | - | - | - | - | - | - |
| 0.4169 | 306 | 1.9957 | - | - | - | - | - | - | - |
| 0.4183 | 307 | 2.5347 | - | - | - | - | - | - | - |
| 0.4196 | 308 | 2.2885 | - | - | - | - | - | - | - |
| 0.4210 | 309 | 2.1144 | - | - | - | - | - | - | - |
| 0.4223 | 310 | 2.2093 | - | - | - | - | - | - | - |
| 0.4237 | 311 | 1.8915 | - | - | - | - | - | - | - |
| 0.4251 | 312 | 2.1871 | - | - | - | - | - | - | - |
| 0.4264 | 313 | 1.9350 | - | - | - | - | - | - | - |
| 0.4278 | 314 | 2.1651 | - | - | - | - | - | - | - |
| 0.4292 | 315 | 2.2124 | - | - | - | - | - | - | - |
| 0.4305 | 316 | 2.3180 | - | - | - | - | - | - | - |
| 0.4319 | 317 | 2.2554 | - | - | - | - | - | - | - |
| 0.4332 | 318 | 2.1139 | - | - | - | - | - | - | - |
| 0.4346 | 319 | 2.1924 | - | - | - | - | - | - | - |
| 0.4360 | 320 | 2.2069 | - | - | - | - | - | - | - |
| 0.4373 | 321 | 1.8523 | - | - | - | - | - | - | - |
| 0.4387 | 322 | 2.1917 | - | - | - | - | - | - | - |
| 0.4401 | 323 | 2.1502 | - | - | - | - | - | - | - |
| 0.4414 | 324 | 2.3009 | - | - | - | - | - | - | - |
| 0.4428 | 325 | 1.9152 | - | - | - | - | - | - | - |
| 0.4441 | 326 | 2.1992 | - | - | - | - | - | - | - |
| 0.4455 | 327 | 2.0896 | - | - | - | - | - | - | - |
| 0.4469 | 328 | 2.4201 | - | - | - | - | - | - | - |
| 0.4482 | 329 | 2.1941 | - | - | - | - | - | - | - |
| 0.4496 | 330 | 1.9147 | - | - | - | - | - | - | - |
| 0.4510 | 331 | 2.0940 | - | - | - | - | - | - | - |
| 0.4523 | 332 | 1.9271 | - | - | - | - | - | - | - |
| 0.4537 | 333 | 2.1263 | - | - | - | - | - | - | - |
| 0.4550 | 334 | 1.9206 | - | - | - | - | - | - | - |
| 0.4564 | 335 | 1.8646 | - | - | - | - | - | - | - |
| 0.4578 | 336 | 2.1363 | - | - | - | - | - | - | - |
| 0.4591 | 337 | 2.0075 | - | - | - | - | - | - | - |
| 0.4605 | 338 | 2.0966 | - | - | - | - | - | - | - |
| 0.4619 | 339 | 2.0636 | - | - | - | - | - | - | - |
| 0.4632 | 340 | 2.0917 | - | - | - | - | - | - | - |
| 0.4646 | 341 | 2.1554 | - | - | - | - | - | - | - |
| 0.4659 | 342 | 1.7886 | - | - | - | - | - | - | - |
| 0.4673 | 343 | 2.0093 | - | - | - | - | - | - | - |
| 0.4687 | 344 | 2.0364 | - | - | - | - | - | - | - |
| 0.4700 | 345 | 2.0864 | - | - | - | - | - | - | - |
| 0.4714 | 346 | 2.3067 | - | - | - | - | - | - | - |
| 0.4728 | 347 | 2.0413 | - | - | - | - | - | - | - |
| 0.4741 | 348 | 1.8104 | - | - | - | - | - | - | - |
| 0.4755 | 349 | 1.8667 | - | - | - | - | - | - | - |
| 0.4768 | 350 | 2.0065 | - | - | - | - | - | - | - |
| 0.4782 | 351 | 2.0968 | - | - | - | - | - | - | - |
| 0.4796 | 352 | 1.9879 | - | - | - | - | - | - | - |
| 0.4809 | 353 | 2.2470 | - | - | - | - | - | - | - |
| 0.4823 | 354 | 2.1065 | - | - | - | - | - | - | - |
| 0.4837 | 355 | 2.2464 | - | - | - | - | - | - | - |
| 0.4850 | 356 | 2.1306 | - | - | - | - | - | - | - |
| 0.4864 | 357 | 2.1439 | - | - | - | - | - | - | - |
| 0.4877 | 358 | 2.3876 | - | - | - | - | - | - | - |
| 0.4891 | 359 | 2.1987 | - | - | - | - | - | - | - |
| 0.4905 | 360 | 2.0984 | - | - | - | - | - | - | - |
| 0.4918 | 361 | 1.9936 | - | - | - | - | - | - | - |
| 0.4932 | 362 | 1.8873 | - | - | - | - | - | - | - |
| 0.4946 | 363 | 1.8510 | - | - | - | - | - | - | - |
| 0.4959 | 364 | 2.1752 | - | - | - | - | - | - | - |
| 0.4973 | 365 | 2.0736 | - | - | - | - | - | - | - |
| 0.4986 | 366 | 2.3679 | - | - | - | - | - | - | - |
| 0.5 | 367 | 2.0347 | - | - | - | - | - | - | - |
| 0.5014 | 368 | 2.0108 | - | - | - | - | - | - | - |
| 0.5027 | 369 | 2.1860 | - | - | - | - | - | - | - |
| 0.5041 | 370 | 2.1617 | - | - | - | - | - | - | - |
| 0.5054 | 371 | 1.9222 | - | - | - | - | - | - | - |
| 0.5068 | 372 | 2.1139 | - | - | - | - | - | - | - |
| 0.5082 | 373 | 2.1473 | - | - | - | - | - | - | - |
| 0.5095 | 374 | 2.0438 | - | - | - | - | - | - | - |
| 0.5109 | 375 | 1.8796 | - | - | - | - | - | - | - |
| 0.5123 | 376 | 2.3586 | - | - | - | - | - | - | - |
| 0.5136 | 377 | 1.9794 | - | - | - | - | - | - | - |
| 0.5150 | 378 | 2.0138 | - | - | - | - | - | - | - |
| 0.5163 | 379 | 1.9307 | - | - | - | - | - | - | - |
| 0.5177 | 380 | 2.0385 | - | - | - | - | - | - | - |
| 0.5191 | 381 | 1.8926 | - | - | - | - | - | - | - |
| 0.5204 | 382 | 1.9191 | - | - | - | - | - | - | - |
| 0.5218 | 383 | 1.8955 | - | - | - | - | - | - | - |
| 0.5232 | 384 | 1.9496 | - | - | - | - | - | - | - |
| 0.5245 | 385 | 1.8697 | - | - | - | - | - | - | - |
| 0.5259 | 386 | 2.1003 | - | - | - | - | - | - | - |
| 0.5272 | 387 | 1.9338 | - | - | - | - | - | - | - |
| 0.5286 | 388 | 2.0523 | - | - | - | - | - | - | - |
| 0.5300 | 389 | 1.9691 | - | - | - | - | - | - | - |
| 0.5313 | 390 | 2.0548 | - | - | - | - | - | - | - |
| 0.5327 | 391 | 1.9817 | - | - | - | - | - | - | - |
| 0.5341 | 392 | 2.0298 | - | - | - | - | - | - | - |
| 0.5354 | 393 | 2.1958 | - | - | - | - | - | - | - |
| 0.5368 | 394 | 1.7758 | - | - | - | - | - | - | - |
| 0.5381 | 395 | 1.9399 | - | - | - | - | - | - | - |
| 0.5395 | 396 | 1.8805 | - | - | - | - | - | - | - |
| 0.5409 | 397 | 2.1010 | - | - | - | - | - | - | - |
| 0.5422 | 398 | 1.8378 | - | - | - | - | - | - | - |
| 0.5436 | 399 | 1.8905 | - | - | - | - | - | - | - |
| 0.5450 | 400 | 1.8815 | 11.2060 | 0.3277 | - | - | - | - | - |
| 0.5463 | 401 | 2.1247 | - | - | - | - | - | - | - |
| 0.5477 | 402 | 2.1008 | - | - | - | - | - | - | - |
| 0.5490 | 403 | 1.9697 | - | - | - | - | - | - | - |
| 0.5504 | 404 | 1.9233 | - | - | - | - | - | - | - |
| 0.5518 | 405 | 1.9048 | - | - | - | - | - | - | - |
| 0.5531 | 406 | 2.1716 | - | - | - | - | - | - | - |
| 0.5545 | 407 | 1.7416 | - | - | - | - | - | - | - |
| 0.5559 | 408 | 2.1333 | - | - | - | - | - | - | - |
| 0.5572 | 409 | 2.0244 | - | - | - | - | - | - | - |
| 0.5586 | 410 | 1.7340 | - | - | - | - | - | - | - |
| 0.5599 | 411 | 1.9839 | - | - | - | - | - | - | - |
| 0.5613 | 412 | 1.6663 | - | - | - | - | - | - | - |
| 0.5627 | 413 | 1.7062 | - | - | - | - | - | - | - |
| 0.5640 | 414 | 1.6888 | - | - | - | - | - | - | - |
| 0.5654 | 415 | 1.9451 | - | - | - | - | - | - | - |
| 0.5668 | 416 | 1.9624 | - | - | - | - | - | - | - |
| 0.5681 | 417 | 2.0927 | - | - | - | - | - | - | - |
| 0.5695 | 418 | 2.3091 | - | - | - | - | - | - | - |
| 0.5708 | 419 | 1.9053 | - | - | - | - | - | - | - |
| 0.5722 | 420 | 1.7858 | - | - | - | - | - | - | - |
| 0.5736 | 421 | 2.1890 | - | - | - | - | - | - | - |
| 0.5749 | 422 | 2.1475 | - | - | - | - | - | - | - |
| 0.5763 | 423 | 2.1070 | - | - | - | - | - | - | - |
| 0.5777 | 424 | 2.0707 | - | - | - | - | - | - | - |
| 0.5790 | 425 | 2.1197 | - | - | - | - | - | - | - |
| 0.5804 | 426 | 2.1200 | - | - | - | - | - | - | - |
| 0.5817 | 427 | 2.2154 | - | - | - | - | - | - | - |
| 0.5831 | 428 | 2.0721 | - | - | - | - | - | - | - |
| 0.5845 | 429 | 1.9294 | - | - | - | - | - | - | - |
| 0.5858 | 430 | 2.0615 | - | - | - | - | - | - | - |
| 0.5872 | 431 | 1.8233 | - | - | - | - | - | - | - |
| 0.5886 | 432 | 2.0925 | - | - | - | - | - | - | - |
| 0.5899 | 433 | 2.0686 | - | - | - | - | - | - | - |
| 0.5913 | 434 | 2.2190 | - | - | - | - | - | - | - |
| 0.5926 | 435 | 1.8144 | - | - | - | - | - | - | - |
| 0.5940 | 436 | 2.0964 | - | - | - | - | - | - | - |
| 0.5954 | 437 | 2.2258 | - | - | - | - | - | - | - |
| 0.5967 | 438 | 1.9573 | - | - | - | - | - | - | - |
| 0.5981 | 439 | 2.1186 | - | - | - | - | - | - | - |
| 0.5995 | 440 | 1.9568 | - | - | - | - | - | - | - |
| 0.6008 | 441 | 1.6988 | - | - | - | - | - | - | - |
| 0.6022 | 442 | 1.8325 | - | - | - | - | - | - | - |
| 0.6035 | 443 | 2.0409 | - | - | - | - | - | - | - |
| 0.6049 | 444 | 2.2048 | - | - | - | - | - | - | - |
| 0.6063 | 445 | 1.8539 | - | - | - | - | - | - | - |
| 0.6076 | 446 | 2.1122 | - | - | - | - | - | - | - |
| 0.6090 | 447 | 2.0135 | - | - | - | - | - | - | - |
| 0.6104 | 448 | 1.7166 | - | - | - | - | - | - | - |
| 0.6117 | 449 | 1.9002 | - | - | - | - | - | - | - |
| 0.6131 | 450 | 2.0630 | - | - | - | - | - | - | - |
| 0.6144 | 451 | 2.2063 | - | - | - | - | - | - | - |
| 0.6158 | 452 | 1.9474 | - | - | - | - | - | - | - |
| 0.6172 | 453 | 1.7995 | - | - | - | - | - | - | - |
| 0.6185 | 454 | 1.8752 | - | - | - | - | - | - | - |
| 0.6199 | 455 | 1.8314 | - | - | - | - | - | - | - |
| 0.6213 | 456 | 1.9715 | - | - | - | - | - | - | - |
| 0.6226 | 457 | 2.0093 | - | - | - | - | - | - | - |
| 0.6240 | 458 | 2.0183 | - | - | - | - | - | - | - |
| 0.6253 | 459 | 1.8633 | - | - | - | - | - | - | - |
| 0.6267 | 460 | 1.8368 | - | - | - | - | - | - | - |
| 0.6281 | 461 | 1.9607 | - | - | - | - | - | - | - |
| 0.6294 | 462 | 2.0700 | - | - | - | - | - | - | - |
| 0.6308 | 463 | 2.2223 | - | - | - | - | - | - | - |
| 0.6322 | 464 | 1.4965 | - | - | - | - | - | - | - |
| 0.6335 | 465 | 2.0539 | - | - | - | - | - | - | - |
| 0.6349 | 466 | 1.8858 | - | - | - | - | - | - | - |
| 0.6362 | 467 | 1.7968 | - | - | - | - | - | - | - |
| 0.6376 | 468 | 1.7847 | - | - | - | - | - | - | - |
| 0.6390 | 469 | 2.0254 | - | - | - | - | - | - | - |
| 0.6403 | 470 | 2.1478 | - | - | - | - | - | - | - |
| 0.6417 | 471 | 2.0867 | - | - | - | - | - | - | - |
| 0.6431 | 472 | 1.9708 | - | - | - | - | - | - | - |
| 0.6444 | 473 | 2.1471 | - | - | - | - | - | - | - |
| 0.6458 | 474 | 1.9555 | - | - | - | - | - | - | - |
| 0.6471 | 475 | 1.8963 | - | - | - | - | - | - | - |
| 0.6485 | 476 | 1.8706 | - | - | - | - | - | - | - |
| 0.6499 | 477 | 1.9314 | - | - | - | - | - | - | - |
| 0.6512 | 478 | 2.1749 | - | - | - | - | - | - | - |
| 0.6526 | 479 | 2.2334 | - | - | - | - | - | - | - |
| 0.6540 | 480 | 1.5540 | - | - | - | - | - | - | - |
| 0.6553 | 481 | 1.8927 | - | - | - | - | - | - | - |
| 0.6567 | 482 | 2.1292 | - | - | - | - | - | - | - |
| 0.6580 | 483 | 2.2297 | - | - | - | - | - | - | - |
| 0.6594 | 484 | 2.0329 | - | - | - | - | - | - | - |
| 0.6608 | 485 | 1.8981 | - | - | - | - | - | - | - |
| 0.6621 | 486 | 2.0926 | - | - | - | - | - | - | - |
| 0.6635 | 487 | 1.8557 | - | - | - | - | - | - | - |
| 0.6649 | 488 | 2.0754 | - | - | - | - | - | - | - |
| 0.6662 | 489 | 1.8098 | - | - | - | - | - | - | - |
| 0.6676 | 490 | 1.8887 | - | - | - | - | - | - | - |
| 0.6689 | 491 | 1.9659 | - | - | - | - | - | - | - |
| 0.6703 | 492 | 2.1589 | - | - | - | - | - | - | - |
| 0.6717 | 493 | 1.9188 | - | - | - | - | - | - | - |
| 0.6730 | 494 | 1.9179 | - | - | - | - | - | - | - |
| 0.6744 | 495 | 1.7912 | - | - | - | - | - | - | - |
| 0.6757 | 496 | 1.6912 | - | - | - | - | - | - | - |
| 0.6771 | 497 | 1.8042 | - | - | - | - | - | - | - |
| 0.6785 | 498 | 2.1336 | - | - | - | - | - | - | - |
| 0.6798 | 499 | 1.8140 | - | - | - | - | - | - | - |
| 0.6812 | 500 | 2.2092 | 11.2911 | 0.3514 | - | - | - | - | - |
| 0.6826 | 501 | 1.8917 | - | - | - | - | - | - | - |
| 0.6839 | 502 | 1.6998 | - | - | - | - | - | - | - |
| 0.6853 | 503 | 1.9427 | - | - | - | - | - | - | - |
| 0.6866 | 504 | 1.8354 | - | - | - | - | - | - | - |
| 0.6880 | 505 | 1.8718 | - | - | - | - | - | - | - |
| 0.6894 | 506 | 1.7392 | - | - | - | - | - | - | - |
| 0.6907 | 507 | 1.9486 | - | - | - | - | - | - | - |
| 0.6921 | 508 | 2.0640 | - | - | - | - | - | - | - |
| 0.6935 | 509 | 1.7439 | - | - | - | - | - | - | - |
| 0.6948 | 510 | 2.0738 | - | - | - | - | - | - | - |
| 0.6962 | 511 | 1.9561 | - | - | - | - | - | - | - |
| 0.6975 | 512 | 2.0381 | - | - | - | - | - | - | - |
| 0.6989 | 513 | 1.9892 | - | - | - | - | - | - | - |
| 0.7003 | 514 | 1.8207 | - | - | - | - | - | - | - |
| 0.7016 | 515 | 1.9467 | - | - | - | - | - | - | - |
| 0.7030 | 516 | 1.9587 | - | - | - | - | - | - | - |
| 0.7044 | 517 | 1.8267 | - | - | - | - | - | - | - |
| 0.7057 | 518 | 1.8500 | - | - | - | - | - | - | - |
| 0.7071 | 519 | 2.2125 | - | - | - | - | - | - | - |
| 0.7084 | 520 | 2.0397 | - | - | - | - | - | - | - |
| 0.7098 | 521 | 1.9400 | - | - | - | - | - | - | - |
| 0.7112 | 522 | 1.8753 | - | - | - | - | - | - | - |
| 0.7125 | 523 | 2.0385 | - | - | - | - | - | - | - |
| 0.7139 | 524 | 2.0365 | - | - | - | - | - | - | - |
| 0.7153 | 525 | 2.2615 | - | - | - | - | - | - | - |
| 0.7166 | 526 | 1.8104 | - | - | - | - | - | - | - |
| 0.7180 | 527 | 1.7855 | - | - | - | - | - | - | - |
| 0.7193 | 528 | 1.8222 | - | - | - | - | - | - | - |
| 0.7207 | 529 | 2.3293 | - | - | - | - | - | - | - |
| 0.7221 | 530 | 1.9503 | - | - | - | - | - | - | - |
| 0.7234 | 531 | 2.2242 | - | - | - | - | - | - | - |
| 0.7248 | 532 | 1.9047 | - | - | - | - | - | - | - |
| 0.7262 | 533 | 2.1315 | - | - | - | - | - | - | - |
| 0.7275 | 534 | 2.3465 | - | - | - | - | - | - | - |
| 0.7289 | 535 | 1.6882 | - | - | - | - | - | - | - |
| 0.7302 | 536 | 2.1036 | - | - | - | - | - | - | - |
| 0.7316 | 537 | 2.0365 | - | - | - | - | - | - | - |
| 0.7330 | 538 | 2.3878 | - | - | - | - | - | - | - |
| 0.7343 | 539 | 2.4062 | - | - | - | - | - | - | - |
| 0.7357 | 540 | 1.8239 | - | - | - | - | - | - | - |
| 0.7371 | 541 | 1.9770 | - | - | - | - | - | - | - |
| 0.7384 | 542 | 1.7179 | - | - | - | - | - | - | - |
| 0.7398 | 543 | 1.8086 | - | - | - | - | - | - | - |
| 0.7411 | 544 | 1.4876 | - | - | - | - | - | - | - |
| 0.7425 | 545 | 2.0294 | - | - | - | - | - | - | - |
| 0.7439 | 546 | 1.5364 | - | - | - | - | - | - | - |
| 0.7452 | 547 | 1.8511 | - | - | - | - | - | - | - |
| 0.7466 | 548 | 2.2621 | - | - | - | - | - | - | - |
| 0.7480 | 549 | 1.8094 | - | - | - | - | - | - | - |
| 0.7493 | 550 | 1.9640 | - | - | - | - | - | - | - |
| 0.7507 | 551 | 2.1765 | - | - | - | - | - | - | - |
| 0.7520 | 552 | 1.8695 | - | - | - | - | - | - | - |
| 0.7534 | 553 | 2.0451 | - | - | - | - | - | - | - |
| 0.7548 | 554 | 2.0232 | - | - | - | - | - | - | - |
| 0.7561 | 555 | 1.8545 | - | - | - | - | - | - | - |
| 0.7575 | 556 | 1.8301 | - | - | - | - | - | - | - |
| 0.7589 | 557 | 2.1014 | - | - | - | - | - | - | - |
| 0.7602 | 558 | 1.9559 | - | - | - | - | - | - | - |
| 0.7616 | 559 | 1.8454 | - | - | - | - | - | - | - |
| 0.7629 | 560 | 2.0682 | - | - | - | - | - | - | - |
| 0.7643 | 561 | 2.0796 | - | - | - | - | - | - | - |
| 0.7657 | 562 | 1.9840 | - | - | - | - | - | - | - |
| 0.7670 | 563 | 2.1964 | - | - | - | - | - | - | - |
| 0.7684 | 564 | 1.7040 | - | - | - | - | - | - | - |
| 0.7698 | 565 | 2.0675 | - | - | - | - | - | - | - |
| 0.7711 | 566 | 1.9507 | - | - | - | - | - | - | - |
| 0.7725 | 567 | 2.0867 | - | - | - | - | - | - | - |
| 0.7738 | 568 | 2.0394 | - | - | - | - | - | - | - |
| 0.7752 | 569 | 1.8570 | - | - | - | - | - | - | - |
| 0.7766 | 570 | 1.8720 | - | - | - | - | - | - | - |
| 0.7779 | 571 | 2.2958 | - | - | - | - | - | - | - |
| 0.7793 | 572 | 1.6687 | - | - | - | - | - | - | - |
| 0.7807 | 573 | 2.0030 | - | - | - | - | - | - | - |
| 0.7820 | 574 | 2.1554 | - | - | - | - | - | - | - |
| 0.7834 | 575 | 2.1252 | - | - | - | - | - | - | - |
| 0.7847 | 576 | 2.1188 | - | - | - | - | - | - | - |
| 0.7861 | 577 | 2.0241 | - | - | - | - | - | - | - |
| 0.7875 | 578 | 1.7107 | - | - | - | - | - | - | - |
| 0.7888 | 579 | 1.9787 | - | - | - | - | - | - | - |
| 0.7902 | 580 | 1.9117 | - | - | - | - | - | - | - |
| 0.7916 | 581 | 1.9882 | - | - | - | - | - | - | - |
| 0.7929 | 582 | 2.0346 | - | - | - | - | - | - | - |
| 0.7943 | 583 | 2.0276 | - | - | - | - | - | - | - |
| 0.7956 | 584 | 1.9577 | - | - | - | - | - | - | - |
| 0.7970 | 585 | 2.0884 | - | - | - | - | - | - | - |
| 0.7984 | 586 | 1.8276 | - | - | - | - | - | - | - |
| 0.7997 | 587 | 1.9217 | - | - | - | - | - | - | - |
| 0.8011 | 588 | 1.9906 | - | - | - | - | - | - | - |
| 0.8025 | 589 | 1.4051 | - | - | - | - | - | - | - |
| 0.8038 | 590 | 2.0875 | - | - | - | - | - | - | - |
| 0.8052 | 591 | 1.5603 | - | - | - | - | - | - | - |
| 0.8065 | 592 | 2.1195 | - | - | - | - | - | - | - |
| 0.8079 | 593 | 1.7710 | - | - | - | - | - | - | - |
| 0.8093 | 594 | 2.0018 | - | - | - | - | - | - | - |
| 0.8106 | 595 | 1.9722 | - | - | - | - | - | - | - |
| 0.8120 | 596 | 1.5888 | - | - | - | - | - | - | - |
| 0.8134 | 597 | 1.9500 | - | - | - | - | - | - | - |
| 0.8147 | 598 | 1.7978 | - | - | - | - | - | - | - |
| 0.8161 | 599 | 1.7421 | - | - | - | - | - | - | - |
| 0.8174 | 600 | 2.2193 | 11.3593 | 0.3553 | - | - | - | - | - |
| 0.8188 | 601 | 2.1397 | - | - | - | - | - | - | - |
| 0.8202 | 602 | 2.2487 | - | - | - | - | - | - | - |
| 0.8215 | 603 | 2.0582 | - | - | - | - | - | - | - |
| 0.8229 | 604 | 1.8363 | - | - | - | - | - | - | - |
| 0.8243 | 605 | 2.0783 | - | - | - | - | - | - | - |
| 0.8256 | 606 | 1.8499 | - | - | - | - | - | - | - |
| 0.8270 | 607 | 2.1881 | - | - | - | - | - | - | - |
| 0.8283 | 608 | 1.9396 | - | - | - | - | - | - | - |
| 0.8297 | 609 | 1.8631 | - | - | - | - | - | - | - |
| 0.8311 | 610 | 1.7273 | - | - | - | - | - | - | - |
| 0.8324 | 611 | 2.0882 | - | - | - | - | - | - | - |
| 0.8338 | 612 | 1.9253 | - | - | - | - | - | - | - |
| 0.8351 | 613 | 2.0465 | - | - | - | - | - | - | - |
| 0.8365 | 614 | 2.1520 | - | - | - | - | - | - | - |
| 0.8379 | 615 | 1.9532 | - | - | - | - | - | - | - |
| 0.8392 | 616 | 1.8759 | - | - | - | - | - | - | - |
| 0.8406 | 617 | 2.1780 | - | - | - | - | - | - | - |
| 0.8420 | 618 | 2.0435 | - | - | - | - | - | - | - |
| 0.8433 | 619 | 2.2467 | - | - | - | - | - | - | - |
| 0.8447 | 620 | 1.6342 | - | - | - | - | - | - | - |
| 0.8460 | 621 | 2.1642 | - | - | - | - | - | - | - |
| 0.8474 | 622 | 1.9904 | - | - | - | - | - | - | - |
| 0.8488 | 623 | 2.1406 | - | - | - | - | - | - | - |
| 0.8501 | 624 | 1.7405 | - | - | - | - | - | - | - |
| 0.8515 | 625 | 1.6236 | - | - | - | - | - | - | - |
| 0.8529 | 626 | 2.0281 | - | - | - | - | - | - | - |
| 0.8542 | 627 | 1.8210 | - | - | - | - | - | - | - |
| 0.8556 | 628 | 2.0766 | - | - | - | - | - | - | - |
| 0.8569 | 629 | 2.1171 | - | - | - | - | - | - | - |
| 0.8583 | 630 | 2.0518 | - | - | - | - | - | - | - |
| 0.8597 | 631 | 1.8610 | - | - | - | - | - | - | - |
| 0.8610 | 632 | 1.6426 | - | - | - | - | - | - | - |
| 0.8624 | 633 | 2.0160 | - | - | - | - | - | - | - |
| 0.8638 | 634 | 1.9798 | - | - | - | - | - | - | - |
| 0.8651 | 635 | 1.8205 | - | - | - | - | - | - | - |
| 0.8665 | 636 | 2.1837 | - | - | - | - | - | - | - |
| 0.8678 | 637 | 2.2531 | - | - | - | - | - | - | - |
| 0.8692 | 638 | 2.1098 | - | - | - | - | - | - | - |
| 0.8706 | 639 | 1.6472 | - | - | - | - | - | - | - |
| 0.8719 | 640 | 1.9857 | - | - | - | - | - | - | - |
| 0.8733 | 641 | 1.9248 | - | - | - | - | - | - | - |
| 0.8747 | 642 | 2.1199 | - | - | - | - | - | - | - |
| 0.8760 | 643 | 1.7253 | - | - | - | - | - | - | - |
| 0.8774 | 644 | 2.0370 | - | - | - | - | - | - | - |
| 0.8787 | 645 | 1.7325 | - | - | - | - | - | - | - |
| 0.8801 | 646 | 2.1499 | - | - | - | - | - | - | - |
| 0.8815 | 647 | 1.8849 | - | - | - | - | - | - | - |
| 0.8828 | 648 | 1.8569 | - | - | - | - | - | - | - |
| 0.8842 | 649 | 2.1557 | - | - | - | - | - | - | - |
| 0.8856 | 650 | 1.8378 | - | - | - | - | - | - | - |
| 0.8869 | 651 | 2.0249 | - | - | - | - | - | - | - |
| 0.8883 | 652 | 2.0020 | - | - | - | - | - | - | - |
| 0.8896 | 653 | 1.6973 | - | - | - | - | - | - | - |
| 0.8910 | 654 | 1.9207 | - | - | - | - | - | - | - |
| 0.8924 | 655 | 2.0978 | - | - | - | - | - | - | - |
| 0.8937 | 656 | 1.9925 | - | - | - | - | - | - | - |
| 0.8951 | 657 | 2.3711 | - | - | - | - | - | - | - |
| 0.8965 | 658 | 1.7138 | - | - | - | - | - | - | - |
| 0.8978 | 659 | 2.1374 | - | - | - | - | - | - | - |
| 0.8992 | 660 | 1.6448 | - | - | - | - | - | - | - |
| 0.9005 | 661 | 1.7745 | - | - | - | - | - | - | - |
| 0.9019 | 662 | 1.7290 | - | - | - | - | - | - | - |
| 0.9033 | 663 | 1.9564 | - | - | - | - | - | - | - |
| 0.9046 | 664 | 2.0760 | - | - | - | - | - | - | - |
| 0.9060 | 665 | 2.0595 | - | - | - | - | - | - | - |
| 0.9074 | 666 | 1.9363 | - | - | - | - | - | - | - |
| 0.9087 | 667 | 1.7733 | - | - | - | - | - | - | - |
| 0.9101 | 668 | 2.0236 | - | - | - | - | - | - | - |
| 0.9114 | 669 | 1.5634 | - | - | - | - | - | - | - |
| 0.9128 | 670 | 2.0639 | - | - | - | - | - | - | - |
| 0.9142 | 671 | 2.0204 | - | - | - | - | - | - | - |
| 0.9155 | 672 | 2.2900 | - | - | - | - | - | - | - |
| 0.9169 | 673 | 1.8201 | - | - | - | - | - | - | - |
| 0.9183 | 674 | 2.1656 | - | - | - | - | - | - | - |
| 0.9196 | 675 | 2.0076 | - | - | - | - | - | - | - |
| 0.9210 | 676 | 1.9645 | - | - | - | - | - | - | - |
| 0.9223 | 677 | 1.8221 | - | - | - | - | - | - | - |
| 0.9237 | 678 | 2.0237 | - | - | - | - | - | - | - |
| 0.9251 | 679 | 1.9136 | - | - | - | - | - | - | - |
| 0.9264 | 680 | 1.9702 | - | - | - | - | - | - | - |
| 0.9278 | 681 | 1.8147 | - | - | - | - | - | - | - |
| 0.9292 | 682 | 2.1164 | - | - | - | - | - | - | - |
| 0.9305 | 683 | 2.0116 | - | - | - | - | - | - | - |
| 0.9319 | 684 | 2.0340 | - | - | - | - | - | - | - |
| 0.9332 | 685 | 1.9375 | - | - | - | - | - | - | - |
| 0.9346 | 686 | 2.2812 | - | - | - | - | - | - | - |
| 0.9360 | 687 | 1.8680 | - | - | - | - | - | - | - |
| 0.9373 | 688 | 1.8893 | - | - | - | - | - | - | - |
| 0.9387 | 689 | 2.2570 | - | - | - | - | - | - | - |
| 0.9401 | 690 | 2.1660 | - | - | - | - | - | - | - |
| 0.9414 | 691 | 2.0101 | - | - | - | - | - | - | - |
| 0.9428 | 692 | 1.9484 | - | - | - | - | - | - | - |
| 0.9441 | 693 | 2.0350 | - | - | - | - | - | - | - |
| 0.9455 | 694 | 2.1707 | - | - | - | - | - | - | - |
| 0.9469 | 695 | 1.8573 | - | - | - | - | - | - | - |
| 0.9482 | 696 | 1.9730 | - | - | - | - | - | - | - |
| 0.9496 | 697 | 1.9259 | - | - | - | - | - | - | - |
| 0.9510 | 698 | 1.4627 | - | - | - | - | - | - | - |
| 0.9523 | 699 | 2.4841 | - | - | - | - | - | - | - |
| 0.9537 | 700 | 1.9361 | 11.3187 | 0.3530 | - | - | - | - | - |
| 0.9550 | 701 | 2.1124 | - | - | - | - | - | - | - |
| 0.9564 | 702 | 2.1921 | - | - | - | - | - | - | - |
| 0.9578 | 703 | 1.8938 | - | - | - | - | - | - | - |
| 0.9591 | 704 | 2.1081 | - | - | - | - | - | - | - |
| 0.9605 | 705 | 1.8599 | - | - | - | - | - | - | - |
| 0.9619 | 706 | 2.0760 | - | - | - | - | - | - | - |
| 0.9632 | 707 | 2.1696 | - | - | - | - | - | - | - |
| 0.9646 | 708 | 1.9780 | - | - | - | - | - | - | - |
| 0.9659 | 709 | 1.8767 | - | - | - | - | - | - | - |
| 0.9673 | 710 | 1.9442 | - | - | - | - | - | - | - |
| 0.9687 | 711 | 2.0872 | - | - | - | - | - | - | - |
| 0.9700 | 712 | 2.1241 | - | - | - | - | - | - | - |
| 0.9714 | 713 | 1.8670 | - | - | - | - | - | - | - |
| 0.9728 | 714 | 2.3039 | - | - | - | - | - | - | - |
| 0.9741 | 715 | 1.9972 | - | - | - | - | - | - | - |
| 0.9755 | 716 | 2.2666 | - | - | - | - | - | - | - |
| 0.9768 | 717 | 1.7745 | - | - | - | - | - | - | - |
| 0.9782 | 718 | 2.1228 | - | - | - | - | - | - | - |
| 0.9796 | 719 | 2.0808 | - | - | - | - | - | - | - |
| 0.9809 | 720 | 2.0337 | - | - | - | - | - | - | - |
| 0.9823 | 721 | 1.9328 | - | - | - | - | - | - | - |
| 0.9837 | 722 | 1.8424 | - | - | - | - | - | - | - |
| 0.9850 | 723 | 1.7553 | - | - | - | - | - | - | - |
| 0.9864 | 724 | 1.9440 | - | - | - | - | - | - | - |
| 0.9877 | 725 | 1.7839 | - | - | - | - | - | - | - |
| 0.9891 | 726 | 2.1347 | - | - | - | - | - | - | - |
| 0.9905 | 727 | 1.7486 | - | - | - | - | - | - | - |
| 0.9918 | 728 | 1.9341 | - | - | - | - | - | - | - |
| 0.9932 | 729 | 1.9954 | - | - | - | - | - | - | - |
| 0.9946 | 730 | 2.3272 | - | - | - | - | - | - | - |
| 0.9959 | 731 | 2.0899 | - | - | - | - | - | - | - |
| 0.9973 | 732 | 2.0678 | - | - | - | - | - | - | - |
| 0.9986 | 733 | 1.7371 | - | - | - | - | - | - | - |
| 1.0 | 734 | 1.8810 | 11.2500 | 0.3522 | - | - | - | - | - |
| -1 | -1 | - | - | 0.3522 | 0.3522 | 0.3300 | 0.3191 | 0.3081 | 0.2529 |
</details>
### Training Time
- **Training**: 5.0 hours
- **Evaluation**: 35.6 minutes
- **Total**: 5.6 hours
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 5.4.1
- Transformers: 5.5.4
- PyTorch: 2.11.0+cu130
- Accelerate: 1.13.0
- Datasets: 4.8.4
- Tokenizers: 0.22.2
## 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}
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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