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README.md
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---
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!--
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<!--
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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## Model Card Contact
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:392702
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- loss:CosineSimilarityLoss
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base_model: x2bee/KoModernBERT-base-mlm-v03-ckp00
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widget:
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- source_sentence: 우리는 움직이는 동행 우주 정지 좌표계에 비례하여 이동하고 있습니다 ... 약 371km / s에서 별자리 leo
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쪽으로. "
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sentences:
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- 두 마리의 독수리가 가지에 앉는다.
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- 다른 물체와는 관련이 없는 '정지'는 없다.
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- 소녀는 버스의 열린 문 앞에 서 있다.
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- source_sentence: 숲에는 개들이 있다.
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sentences:
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- 양을 보는 아이들.
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- 여왕의 배우자를 "왕"이라고 부르지 않는 것은 아주 좋은 이유가 있다. 왜냐하면 그들은 왕이 아니기 때문이다.
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- 개들은 숲속에 혼자 있다.
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- source_sentence: '첫째, 두 가지 다른 종류의 대시가 있다는 것을 알아야 합니다 : en 대시와 em 대시.'
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sentences:
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- 그들은 그 물건들을 집 주변에 두고 가거나 집의 정리를 해칠 의도가 없다.
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- 세미콜론은 혼자 있을 수 있는 문장에 참여하는데 사용되지만, 그들의 관계를 강조하기 위해 결합됩니다.
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- 그의 남동생이 지켜보는 동안 집 앞에서 트럼펫을 연주하는 금발의 아이.
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- source_sentence: 한 여성이 생선 껍질을 벗기고 있다.
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sentences:
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- 한 남자가 수영장으로 뛰어들었다.
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- 한 여성이 프라이팬에 노란 혼합물을 부어 넣고 있다.
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- 두 마리의 갈색 개가 눈 속에서 서로 놀고 있다.
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- source_sentence: 버스가 바쁜 길을 따라 운전한다.
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sentences:
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- 우리와 같은 태양계가 은하계 밖에서 존재할 수도 있을 것입니다.
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- 그 여자는 데이트하러 가는 중이다.
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- 녹색 버스가 도로를 따라 내려간다.
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datasets:
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- x2bee/Korean_NLI_dataset
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- CocoRoF/sts_dev
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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metrics:
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- pearson_cosine
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- spearman_cosine
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- pearson_euclidean
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- spearman_euclidean
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- pearson_manhattan
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- spearman_manhattan
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- pearson_dot
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- spearman_dot
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- pearson_max
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- spearman_max
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model-index:
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- name: SentenceTransformer based on x2bee/KoModernBERT-base-mlm-v03-ckp00
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results:
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- task:
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type: semantic-similarity
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name: Semantic Similarity
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dataset:
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name: sts dev
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type: sts_dev
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metrics:
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- type: pearson_cosine
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value: 0.6463764324668821
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name: Pearson Cosine
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- type: spearman_cosine
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value: 0.668749120795344
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name: Spearman Cosine
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- type: pearson_euclidean
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value: 0.6434649881382908
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name: Pearson Euclidean
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- type: spearman_euclidean
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value: 0.6535107003038169
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name: Spearman Euclidean
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- type: pearson_manhattan
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value: 0.6516759845194007
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name: Pearson Manhattan
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- type: spearman_manhattan
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value: 0.6679435004022668
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name: Spearman Manhattan
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- type: pearson_dot
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value: 0.6306152465572834
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name: Pearson Dot
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- type: spearman_dot
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value: 0.6496717700503837
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name: Spearman Dot
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- type: pearson_max
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value: 0.6516759845194007
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name: Pearson Max
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- type: spearman_max
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value: 0.668749120795344
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name: Spearman Max
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---
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# SentenceTransformer based on x2bee/KoModernBERT-base-mlm-v03-ckp00
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [x2bee/KoModernBERT-base-mlm-v03-ckp00](https://huggingface.co/x2bee/KoModernBERT-base-mlm-v03-ckp00) on the [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [x2bee/KoModernBERT-base-mlm-v03-ckp00](https://huggingface.co/x2bee/KoModernBERT-base-mlm-v03-ckp00) <!-- at revision addb15798678d7f76904915cf8045628d402b3ce -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 768 dimensions
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset)
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel
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+
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': True, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("x2bee/sts_nli_tune_test")
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# Run inference
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sentences = [
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'버스가 바쁜 길을 따라 운전한다.',
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'녹색 버스가 도로를 따라 내려간다.',
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'그 여자는 데이트하러 가는 중이다.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 768]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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|
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</details>
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+
-->
|
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|
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+
<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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+
-->
|
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+
<!--
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### Out-of-Scope Use
|
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+
-->
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|
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## Evaluation
|
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### Metrics
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|
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#### Semantic Similarity
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* Dataset: `sts_dev`
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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| Metric | Value |
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|:-------------------|:-----------|
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+
| pearson_cosine | 0.6464 |
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+
| spearman_cosine | 0.6687 |
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| pearson_euclidean | 0.6435 |
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| spearman_euclidean | 0.6535 |
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| pearson_manhattan | 0.6517 |
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| spearman_manhattan | 0.6679 |
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| pearson_dot | 0.6306 |
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| spearman_dot | 0.6497 |
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+
| pearson_max | 0.6517 |
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| **spearman_max** | **0.6687** |
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206 |
+
<!--
|
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+
## Bias, Risks and Limitations
|
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+
-->
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212 |
+
<!--
|
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+
### Recommendations
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+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
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+
-->
|
217 |
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218 |
+
## Training Details
|
219 |
|
220 |
+
### Training Dataset
|
221 |
+
|
222 |
+
#### korean_nli_dataset
|
223 |
+
|
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+
* Dataset: [korean_nli_dataset](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset) at [ef305ef](https://huggingface.co/datasets/x2bee/Korean_NLI_dataset/tree/ef305ef8e2d83c6991f30f2322f321efb5a3b9d1)
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+
* Size: 392,702 training samples
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+
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
|
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+
* Approximate statistics based on the first 1000 samples:
|
228 |
+
| | sentence1 | sentence2 | score |
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+
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+
| type | string | string | float |
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+
| details | <ul><li>min: 4 tokens</li><li>mean: 35.7 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.92 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
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+
* Samples:
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233 |
+
| sentence1 | sentence2 | score |
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+
|:----------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|:-----------------|
|
235 |
+
| <code>개념적으로 크림 스키밍은 제품과 지리라는 두 가지 기본 차원을 가지고 있다.</code> | <code>제품과 지리학은 크림 스키밍을 작동시키는 것이다.</code> | <code>0.5</code> |
|
236 |
+
| <code>시즌 중에 알고 있는 거 알아? 네 레벨에서 다음 레벨로 잃어버리는 거야 브레이브스가 모팀을 떠올���기로 결정하면 브레이브스가 트리플 A에서 한 남자를 떠올리기로 결정하면 더블 A가 그를 대신하러 올라가고 A 한 명이 그를 대신하러 올라간다.</code> | <code>사람들이 기억하면 다음 수준으로 물건을 잃는다.</code> | <code>1.0</code> |
|
237 |
+
| <code>우리 번호 중 하나가 당신의 지시를 세밀하게 수행할 것이다.</code> | <code>우리 팀의 일원이 당신의 명령을 엄청나게 정확하게 실행할 것이다.</code> | <code>1.0</code> |
|
238 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
239 |
+
```json
|
240 |
+
{
|
241 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
242 |
+
}
|
243 |
+
```
|
244 |
+
|
245 |
+
### Evaluation Dataset
|
246 |
+
|
247 |
+
#### sts_dev
|
248 |
+
|
249 |
+
* Dataset: [sts_dev](https://huggingface.co/datasets/CocoRoF/sts_dev) at [1de0cdf](https://huggingface.co/datasets/CocoRoF/sts_dev/tree/1de0cdfb2c238786ee61c5765aa60eed4a782371)
|
250 |
+
* Size: 1,500 evaluation samples
|
251 |
+
* Columns: <code>text</code>, <code>pair</code>, and <code>label</code>
|
252 |
+
* Approximate statistics based on the first 1000 samples:
|
253 |
+
| | text | pair | label |
|
254 |
+
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
|
255 |
+
| type | string | string | float |
|
256 |
+
| details | <ul><li>min: 7 tokens</li><li>mean: 20.38 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 20.52 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
|
257 |
+
* Samples:
|
258 |
+
| text | pair | label |
|
259 |
+
|:-------------------------------------|:------------------------------------|:------------------|
|
260 |
+
| <code>안전모를 가진 한 남자가 춤을 추고 있다.</code> | <code>안전모를 쓴 한 남자가 춤을 추고 있다.</code> | <code>1.0</code> |
|
261 |
+
| <code>어린아이가 말을 타고 있다.</code> | <code>아이가 말을 타고 있다.</code> | <code>0.95</code> |
|
262 |
+
| <code>한 남자가 뱀에게 쥐를 먹이고 있다.</code> | <code>남자가 뱀에게 쥐를 먹이고 있다.</code> | <code>1.0</code> |
|
263 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
264 |
+
```json
|
265 |
+
{
|
266 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
267 |
+
}
|
268 |
+
```
|
269 |
+
|
270 |
+
### Training Hyperparameters
|
271 |
+
#### Non-Default Hyperparameters
|
272 |
+
|
273 |
+
- `overwrite_output_dir`: True
|
274 |
+
- `eval_strategy`: steps
|
275 |
+
- `per_device_train_batch_size`: 16
|
276 |
+
- `per_device_eval_batch_size`: 16
|
277 |
+
- `gradient_accumulation_steps`: 8
|
278 |
+
- `warmup_ratio`: 0.1
|
279 |
+
- `push_to_hub`: True
|
280 |
+
- `hub_model_id`: x2bee/sts_nli_tune_test
|
281 |
+
- `hub_strategy`: checkpoint
|
282 |
+
- `batch_sampler`: no_duplicates
|
283 |
+
|
284 |
+
#### All Hyperparameters
|
285 |
+
<details><summary>Click to expand</summary>
|
286 |
+
|
287 |
+
- `overwrite_output_dir`: True
|
288 |
+
- `do_predict`: False
|
289 |
+
- `eval_strategy`: steps
|
290 |
+
- `prediction_loss_only`: True
|
291 |
+
- `per_device_train_batch_size`: 16
|
292 |
+
- `per_device_eval_batch_size`: 16
|
293 |
+
- `per_gpu_train_batch_size`: None
|
294 |
+
- `per_gpu_eval_batch_size`: None
|
295 |
+
- `gradient_accumulation_steps`: 8
|
296 |
+
- `eval_accumulation_steps`: None
|
297 |
+
- `torch_empty_cache_steps`: None
|
298 |
+
- `learning_rate`: 5e-05
|
299 |
+
- `weight_decay`: 0.0
|
300 |
+
- `adam_beta1`: 0.9
|
301 |
+
- `adam_beta2`: 0.999
|
302 |
+
- `adam_epsilon`: 1e-08
|
303 |
+
- `max_grad_norm`: 1.0
|
304 |
+
- `num_train_epochs`: 3.0
|
305 |
+
- `max_steps`: -1
|
306 |
+
- `lr_scheduler_type`: linear
|
307 |
+
- `lr_scheduler_kwargs`: {}
|
308 |
+
- `warmup_ratio`: 0.1
|
309 |
+
- `warmup_steps`: 0
|
310 |
+
- `log_level`: passive
|
311 |
+
- `log_level_replica`: warning
|
312 |
+
- `log_on_each_node`: True
|
313 |
+
- `logging_nan_inf_filter`: True
|
314 |
+
- `save_safetensors`: True
|
315 |
+
- `save_on_each_node`: False
|
316 |
+
- `save_only_model`: False
|
317 |
+
- `restore_callback_states_from_checkpoint`: False
|
318 |
+
- `no_cuda`: False
|
319 |
+
- `use_cpu`: False
|
320 |
+
- `use_mps_device`: False
|
321 |
+
- `seed`: 42
|
322 |
+
- `data_seed`: None
|
323 |
+
- `jit_mode_eval`: False
|
324 |
+
- `use_ipex`: False
|
325 |
+
- `bf16`: False
|
326 |
+
- `fp16`: False
|
327 |
+
- `fp16_opt_level`: O1
|
328 |
+
- `half_precision_backend`: auto
|
329 |
+
- `bf16_full_eval`: False
|
330 |
+
- `fp16_full_eval`: False
|
331 |
+
- `tf32`: None
|
332 |
+
- `local_rank`: 0
|
333 |
+
- `ddp_backend`: None
|
334 |
+
- `tpu_num_cores`: None
|
335 |
+
- `tpu_metrics_debug`: False
|
336 |
+
- `debug`: []
|
337 |
+
- `dataloader_drop_last`: True
|
338 |
+
- `dataloader_num_workers`: 0
|
339 |
+
- `dataloader_prefetch_factor`: None
|
340 |
+
- `past_index`: -1
|
341 |
+
- `disable_tqdm`: False
|
342 |
+
- `remove_unused_columns`: True
|
343 |
+
- `label_names`: None
|
344 |
+
- `load_best_model_at_end`: False
|
345 |
+
- `ignore_data_skip`: False
|
346 |
+
- `fsdp`: []
|
347 |
+
- `fsdp_min_num_params`: 0
|
348 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
349 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
350 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
351 |
+
- `deepspeed`: None
|
352 |
+
- `label_smoothing_factor`: 0.0
|
353 |
+
- `optim`: adamw_torch
|
354 |
+
- `optim_args`: None
|
355 |
+
- `adafactor`: False
|
356 |
+
- `group_by_length`: False
|
357 |
+
- `length_column_name`: length
|
358 |
+
- `ddp_find_unused_parameters`: None
|
359 |
+
- `ddp_bucket_cap_mb`: None
|
360 |
+
- `ddp_broadcast_buffers`: False
|
361 |
+
- `dataloader_pin_memory`: True
|
362 |
+
- `dataloader_persistent_workers`: False
|
363 |
+
- `skip_memory_metrics`: True
|
364 |
+
- `use_legacy_prediction_loop`: False
|
365 |
+
- `push_to_hub`: True
|
366 |
+
- `resume_from_checkpoint`: None
|
367 |
+
- `hub_model_id`: x2bee/sts_nli_tune_test
|
368 |
+
- `hub_strategy`: checkpoint
|
369 |
+
- `hub_private_repo`: None
|
370 |
+
- `hub_always_push`: False
|
371 |
+
- `gradient_checkpointing`: False
|
372 |
+
- `gradient_checkpointing_kwargs`: None
|
373 |
+
- `include_inputs_for_metrics`: False
|
374 |
+
- `include_for_metrics`: []
|
375 |
+
- `eval_do_concat_batches`: True
|
376 |
+
- `fp16_backend`: auto
|
377 |
+
- `push_to_hub_model_id`: None
|
378 |
+
- `push_to_hub_organization`: None
|
379 |
+
- `mp_parameters`:
|
380 |
+
- `auto_find_batch_size`: False
|
381 |
+
- `full_determinism`: False
|
382 |
+
- `torchdynamo`: None
|
383 |
+
- `ray_scope`: last
|
384 |
+
- `ddp_timeout`: 1800
|
385 |
+
- `torch_compile`: False
|
386 |
+
- `torch_compile_backend`: None
|
387 |
+
- `torch_compile_mode`: None
|
388 |
+
- `dispatch_batches`: None
|
389 |
+
- `split_batches`: None
|
390 |
+
- `include_tokens_per_second`: False
|
391 |
+
- `include_num_input_tokens_seen`: False
|
392 |
+
- `neftune_noise_alpha`: None
|
393 |
+
- `optim_target_modules`: None
|
394 |
+
- `batch_eval_metrics`: False
|
395 |
+
- `eval_on_start`: False
|
396 |
+
- `use_liger_kernel`: False
|
397 |
+
- `eval_use_gather_object`: False
|
398 |
+
- `average_tokens_across_devices`: False
|
399 |
+
- `prompts`: None
|
400 |
+
- `batch_sampler`: no_duplicates
|
401 |
+
- `multi_dataset_batch_sampler`: proportional
|
402 |
+
|
403 |
+
</details>
|
404 |
+
|
405 |
+
### Training Logs
|
406 |
+
| Epoch | Step | Training Loss | Validation Loss | sts_dev_spearman_max |
|
407 |
+
|:------:|:----:|:-------------:|:---------------:|:--------------------:|
|
408 |
+
| 0.0326 | 25 | 0.3733 | - | - |
|
409 |
+
| 0.0652 | 50 | 0.362 | - | - |
|
410 |
+
| 0.0978 | 75 | 0.3543 | - | - |
|
411 |
+
| 0.1304 | 100 | 0.3431 | - | - |
|
412 |
+
| 0.1630 | 125 | 0.3273 | - | - |
|
413 |
+
| 0.1956 | 150 | 0.2745 | - | - |
|
414 |
+
| 0.2282 | 175 | 0.2061 | - | - |
|
415 |
+
| 0.2608 | 200 | 0.1814 | - | - |
|
416 |
+
| 0.2934 | 225 | 0.1658 | - | - |
|
417 |
+
| 0.3260 | 250 | 0.1637 | - | - |
|
418 |
+
| 0.3586 | 275 | 0.1542 | - | - |
|
419 |
+
| 0.3912 | 300 | 0.147 | - | - |
|
420 |
+
| 0.4238 | 325 | 0.1392 | - | - |
|
421 |
+
| 0.4564 | 350 | 0.1329 | - | - |
|
422 |
+
| 0.4890 | 375 | 0.131 | - | - |
|
423 |
+
| 0.5216 | 400 | 0.1294 | - | - |
|
424 |
+
| 0.5542 | 425 | 0.1245 | - | - |
|
425 |
+
| 0.5868 | 450 | 0.1243 | - | - |
|
426 |
+
| 0.6194 | 475 | 0.1237 | - | - |
|
427 |
+
| 0.6520 | 500 | 0.1236 | 0.0956 | 0.5284 |
|
428 |
+
| 0.6846 | 525 | 0.1183 | - | - |
|
429 |
+
| 0.7172 | 550 | 0.1166 | - | - |
|
430 |
+
| 0.7498 | 575 | 0.1176 | - | - |
|
431 |
+
| 0.7824 | 600 | 0.1144 | - | - |
|
432 |
+
| 0.8150 | 625 | 0.1141 | - | - |
|
433 |
+
| 0.8476 | 650 | 0.1093 | - | - |
|
434 |
+
| 0.8802 | 675 | 0.1081 | - | - |
|
435 |
+
| 0.9128 | 700 | 0.1082 | - | - |
|
436 |
+
| 0.9454 | 725 | 0.1078 | - | - |
|
437 |
+
| 0.9780 | 750 | 0.1039 | - | - |
|
438 |
+
| 1.0117 | 775 | 0.1106 | - | - |
|
439 |
+
| 1.0443 | 800 | 0.1113 | - | - |
|
440 |
+
| 1.0769 | 825 | 0.1113 | - | - |
|
441 |
+
| 1.1095 | 850 | 0.1103 | - | - |
|
442 |
+
| 1.1421 | 875 | 0.1098 | - | - |
|
443 |
+
| 1.1747 | 900 | 0.1118 | - | - |
|
444 |
+
| 1.2073 | 925 | 0.1085 | - | - |
|
445 |
+
| 1.2399 | 950 | 0.1057 | - | - |
|
446 |
+
| 1.2725 | 975 | 0.1081 | - | - |
|
447 |
+
| 1.3051 | 1000 | 0.1052 | 0.0930 | 0.5830 |
|
448 |
+
| 1.3377 | 1025 | 0.1087 | - | - |
|
449 |
+
| 1.3703 | 1050 | 0.1046 | - | - |
|
450 |
+
| 1.4029 | 1075 | 0.1032 | - | - |
|
451 |
+
| 1.4355 | 1100 | 0.1037 | - | - |
|
452 |
+
| 1.4681 | 1125 | 0.1026 | - | - |
|
453 |
+
| 1.5007 | 1150 | 0.1036 | - | - |
|
454 |
+
| 1.5333 | 1175 | 0.102 | - | - |
|
455 |
+
| 1.5659 | 1200 | 0.101 | - | - |
|
456 |
+
| 1.5985 | 1225 | 0.1014 | - | - |
|
457 |
+
| 1.6311 | 1250 | 0.1024 | - | - |
|
458 |
+
| 1.6637 | 1275 | 0.1005 | - | - |
|
459 |
+
| 1.6963 | 1300 | 0.0993 | - | - |
|
460 |
+
| 1.7289 | 1325 | 0.0982 | - | - |
|
461 |
+
| 1.7615 | 1350 | 0.0988 | - | - |
|
462 |
+
| 1.7941 | 1375 | 0.0965 | - | - |
|
463 |
+
| 1.8267 | 1400 | 0.0984 | - | - |
|
464 |
+
| 1.8593 | 1425 | 0.0936 | - | - |
|
465 |
+
| 1.8919 | 1450 | 0.0924 | - | - |
|
466 |
+
| 1.9245 | 1475 | 0.0956 | - | - |
|
467 |
+
| 1.9571 | 1500 | 0.0927 | 0.0732 | 0.6470 |
|
468 |
+
| 1.9897 | 1525 | 0.0915 | - | - |
|
469 |
+
| 2.0235 | 1550 | 0.0991 | - | - |
|
470 |
+
| 2.0561 | 1575 | 0.097 | - | - |
|
471 |
+
| 2.0887 | 1600 | 0.0957 | - | - |
|
472 |
+
| 2.1213 | 1625 | 0.0968 | - | - |
|
473 |
+
| 2.1539 | 1650 | 0.0968 | - | - |
|
474 |
+
| 2.1865 | 1675 | 0.0973 | - | - |
|
475 |
+
| 2.2191 | 1700 | 0.0936 | - | - |
|
476 |
+
| 2.2517 | 1725 | 0.0955 | - | - |
|
477 |
+
| 2.2843 | 1750 | 0.0942 | - | - |
|
478 |
+
| 2.3169 | 1775 | 0.0939 | - | - |
|
479 |
+
| 2.3495 | 1800 | 0.0947 | - | - |
|
480 |
+
| 2.3821 | 1825 | 0.0934 | - | - |
|
481 |
+
| 2.4147 | 1850 | 0.0919 | - | - |
|
482 |
+
| 2.4473 | 1875 | 0.0919 | - | - |
|
483 |
+
| 2.4799 | 1900 | 0.0928 | - | - |
|
484 |
+
| 2.5125 | 1925 | 0.0927 | - | - |
|
485 |
+
| 2.5451 | 1950 | 0.0899 | - | - |
|
486 |
+
| 2.5777 | 1975 | 0.0911 | - | - |
|
487 |
+
| 2.6103 | 2000 | 0.0915 | 0.0671 | 0.6687 |
|
488 |
+
| 2.6429 | 2025 | 0.0905 | - | - |
|
489 |
+
| 2.6755 | 2050 | 0.0894 | - | - |
|
490 |
+
| 2.7081 | 2075 | 0.0887 | - | - |
|
491 |
+
| 2.7407 | 2100 | 0.0903 | - | - |
|
492 |
+
| 2.7733 | 2125 | 0.0887 | - | - |
|
493 |
+
| 2.8059 | 2150 | 0.0869 | - | - |
|
494 |
+
| 2.8385 | 2175 | 0.0871 | - | - |
|
495 |
+
| 2.8711 | 2200 | 0.0843 | - | - |
|
496 |
+
| 2.9037 | 2225 | 0.0838 | - | - |
|
497 |
+
| 2.9363 | 2250 | 0.0864 | - | - |
|
498 |
+
| 2.9689 | 2275 | 0.0831 | - | - |
|
499 |
+
|
500 |
+
|
501 |
+
### Framework Versions
|
502 |
+
- Python: 3.11.10
|
503 |
+
- Sentence Transformers: 3.3.1
|
504 |
+
- Transformers: 4.48.0
|
505 |
+
- PyTorch: 2.5.1+cu124
|
506 |
+
- Accelerate: 1.2.1
|
507 |
+
- Datasets: 3.2.0
|
508 |
+
- Tokenizers: 0.21.0
|
509 |
+
|
510 |
+
## Citation
|
511 |
+
|
512 |
+
### BibTeX
|
513 |
+
|
514 |
+
#### Sentence Transformers
|
515 |
+
```bibtex
|
516 |
+
@inproceedings{reimers-2019-sentence-bert,
|
517 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
518 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
519 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
520 |
+
month = "11",
|
521 |
+
year = "2019",
|
522 |
+
publisher = "Association for Computational Linguistics",
|
523 |
+
url = "https://arxiv.org/abs/1908.10084",
|
524 |
+
}
|
525 |
+
```
|
526 |
+
|
527 |
+
<!--
|
528 |
+
## Glossary
|
529 |
+
|
530 |
+
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|
531 |
+
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|
532 |
+
|
533 |
+
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|
534 |
+
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|
535 |
+
|
536 |
+
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|
537 |
+
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|
538 |
+
|
539 |
+
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|
540 |
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|
541 |
|
542 |
+
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|
543 |
+
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