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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:5749 |
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- loss:CosineSimilarityLoss |
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base_model: CocoRoF/ModernBERT-SimCSE_v02 |
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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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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 CocoRoF/ModernBERT-SimCSE_v02 |
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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: 0.805539118357127 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8061033061285413 |
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name: Spearman Cosine |
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- type: pearson_euclidean |
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value: 0.7633523638911596 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.7628951831481233 |
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name: Spearman Euclidean |
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- type: pearson_manhattan |
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value: 0.7652880535446602 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.7657560923304267 |
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name: Spearman Manhattan |
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- type: pearson_dot |
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value: 0.7133686434266335 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.7015065203951969 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.805539118357127 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.8061033061285413 |
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name: Spearman Max |
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--- |
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|
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# SentenceTransformer based on CocoRoF/ModernBERT-SimCSE_v02 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [CocoRoF/ModernBERT-SimCSE_v02](https://huggingface.co/CocoRoF/ModernBERT-SimCSE_v02). 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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|
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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:** [CocoRoF/ModernBERT-SimCSE_v02](https://huggingface.co/CocoRoF/ModernBERT-SimCSE_v02) <!-- at revision de4148c764893843e15a4e0b241fe308147a9aaa --> |
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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:** Unknown --> |
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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': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): 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("CocoRoF/ModernBERT-SimCSE-multitask_v02") |
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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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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### 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 | 0.8055 | |
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| spearman_cosine | 0.8061 | |
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| pearson_euclidean | 0.7634 | |
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| spearman_euclidean | 0.7629 | |
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| pearson_manhattan | 0.7653 | |
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| spearman_manhattan | 0.7658 | |
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| pearson_dot | 0.7134 | |
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| spearman_dot | 0.7015 | |
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| pearson_max | 0.8055 | |
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| **spearman_max** | **0.8061** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 5,749 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: |
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| | sentence1 | sentence2 | score | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 13.52 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.41 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:------------------------------------|:------------------------------------------|:------------------| |
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| <code>비행기가 이륙하고 있다.</code> | <code>비행기가 이륙하고 있다.</code> | <code>1.0</code> | |
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| <code>한 남자가 큰 플루트를 연주하고 있다.</code> | <code>남자가 플루트를 연주하고 있다.</code> | <code>0.76</code> | |
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| <code>한 남자가 피자에 치즈를 뿌려놓고 있다.</code> | <code>한 남자가 구운 피자에 치즈 조각을 뿌려놓고 있다.</code> | <code>0.76</code> | |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
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```json |
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{ |
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"loss_fct": "torch.nn.modules.loss.MSELoss" |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 1,500 evaluation 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: |
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| | sentence1 | sentence2 | score | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| 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> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:-------------------------------------|:------------------------------------|:------------------| |
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| <code>안전모를 가진 한 남자가 춤을 추고 있다.</code> | <code>안전모를 쓴 한 남자가 춤을 추고 있다.</code> | <code>1.0</code> | |
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| <code>어린아이가 말을 타고 있다.</code> | <code>아이가 말을 타고 있다.</code> | <code>0.95</code> | |
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| <code>한 남자가 뱀에게 쥐를 먹이고 있다.</code> | <code>남자가 뱀에게 쥐를 먹이고 있다.</code> | <code>1.0</code> | |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
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```json |
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{ |
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"loss_fct": "torch.nn.modules.loss.MSELoss" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `overwrite_output_dir`: True |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 8 |
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- `learning_rate`: 5e-07 |
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- `num_train_epochs`: 10.0 |
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- `warmup_ratio`: 0.1 |
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- `push_to_hub`: True |
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- `hub_model_id`: CocoRoF/ModernBERT-SimCSE-multitask_v02 |
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- `hub_strategy`: checkpoint |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: True |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 8 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-07 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 10.0 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: True |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: True |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: CocoRoF/ModernBERT-SimCSE-multitask_v02 |
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- `hub_strategy`: checkpoint |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
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</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | sts_dev_spearman_max | |
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|:------:|:----:|:-------------:|:---------------:|:--------------------:| |
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| 0.2228 | 10 | 0.0284 | - | - | |
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| 0.4457 | 20 | 0.0346 | - | - | |
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| 0.6685 | 30 | 0.0305 | 0.0317 | 0.7927 | |
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| 0.8914 | 40 | 0.0495 | - | - | |
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| 1.1337 | 50 | 0.04 | - | - | |
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| 1.3565 | 60 | 0.0295 | 0.0316 | 0.7930 | |
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| 1.5794 | 70 | 0.0352 | - | - | |
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| 1.8022 | 80 | 0.042 | - | - | |
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| 2.0446 | 90 | 0.0476 | 0.0314 | 0.7933 | |
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| 2.2674 | 100 | 0.0289 | - | - | |
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| 2.4903 | 110 | 0.0345 | - | - | |
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| 2.7131 | 120 | 0.0339 | 0.0311 | 0.7940 | |
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| 2.9359 | 130 | 0.0493 | - | - | |
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| 3.1783 | 140 | 0.0341 | - | - | |
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| 3.4011 | 150 | 0.0332 | 0.0308 | 0.7952 | |
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| 3.6240 | 160 | 0.0303 | - | - | |
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| 3.8468 | 170 | 0.045 | - | - | |
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| 4.0891 | 180 | 0.0422 | 0.0305 | 0.7961 | |
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| 4.3120 | 190 | 0.0278 | - | - | |
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| 4.5348 | 200 | 0.0338 | - | - | |
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| 4.7577 | 210 | 0.0372 | 0.0302 | 0.7968 | |
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| 4.9805 | 220 | 0.0469 | - | - | |
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| 5.2228 | 230 | 0.0303 | - | - | |
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| 5.4457 | 240 | 0.0328 | 0.0297 | 0.7982 | |
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| 5.6685 | 250 | 0.0295 | - | - | |
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| 5.8914 | 260 | 0.0458 | - | - | |
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| 6.1337 | 270 | 0.0363 | 0.0295 | 0.7997 | |
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| 6.3565 | 280 | 0.0265 | - | - | |
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| 6.5794 | 290 | 0.0341 | - | - | |
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| 6.8022 | 300 | 0.0384 | 0.0291 | 0.8007 | |
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| 7.0446 | 310 | 0.0431 | - | - | |
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| 7.2674 | 320 | 0.0256 | - | - | |
|
| 7.4903 | 330 | 0.0321 | 0.0287 | 0.8022 | |
|
| 7.7131 | 340 | 0.0315 | - | - | |
|
| 7.9359 | 350 | 0.0438 | - | - | |
|
| 8.1783 | 360 | 0.0301 | 0.0284 | 0.8038 | |
|
| 8.4011 | 370 | 0.0301 | - | - | |
|
| 8.6240 | 380 | 0.0285 | - | - | |
|
| 8.8468 | 390 | 0.0394 | 0.0282 | 0.8049 | |
|
| 9.0891 | 400 | 0.0374 | - | - | |
|
| 9.3120 | 410 | 0.0245 | - | - | |
|
| 9.5348 | 420 | 0.0316 | 0.0279 | 0.8061 | |
|
| 9.7577 | 430 | 0.0331 | - | - | |
|
| 9.9805 | 440 | 0.0411 | - | - | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.11.10 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.48.0.dev0 |
|
- PyTorch: 2.5.1+cu121 |
|
- Accelerate: 1.1.0 |
|
- Datasets: 3.1.0 |
|
- Tokenizers: 0.21.0 |
|
|
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## Citation |
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|
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### BibTeX |
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|
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
|
publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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