sentence-transformers safetensors modernbert sentence-similarity feature-extraction generated_from_trainer dataset_size:5749 loss:CosineSimilarityLoss arxiv:1908.10084 base_model:CocoRoF/mobert_retry_SimCSE_test base_model:finetune:CocoRoF/mobert_retry_SimCSE_test model-index autotrain_compatible endpoints_compatible region:us
---
base_model: CocoRoF/mobert_retry_SimCSE_test
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_euclidean
- spearman_euclidean
- pearson_manhattan
- spearman_manhattan
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5749
- loss:CosineSimilarityLoss
widget:
- source_sentence: 우리는 움직이는 동행 우주 정지 좌표계에 비례하여 이동하고 있습니다 ... 약 371km / s에서 별자리 leo
    쪽으로. "
  sentences:
  - 두 마리의 독수리가 가지에 앉는다.
  - 다른 물체와는 관련이 없는 '정지'는 없다.
  - 소녀는 버스의 열린 문 앞에 서 있다.
- source_sentence: 숲에는 개들이 있다.
  sentences:
  - 양을 보는 아이들.
  - 여왕의 배우자를 "왕"이라고 부르지 않는 것은 아주 좋은 이유가 있다. 왜냐하면 그들은 왕이 아니기 때문이다.
  - 개들은 숲속에 혼자 있다.
- source_sentence: '첫째, 두 가지 다른 종류의 대시가 있다는 것을 알아야 합니다 : en 대시와 em 대시.'
  sentences:
  - 그들은 그 물건들을 집 주변에 두고 가거나 집의 정리를 해칠 의도가 없다.
  - 세미콜론은 혼자 있을 수 있는 문장에 참여하는데 사용되지만, 그들의 관계를 강조하기 위해 결합됩니다.
  - 그의 남동생이 지켜보는 동안 집 앞에서 트럼펫을 연주하는 금발의 아이.
- source_sentence: 한 여성이 생선 껍질을 벗기고 있다.
  sentences:
  - 한 남자가 수영장으로 뛰어들었다.
  - 한 여성이 프라이팬에 노란 혼합물을 부어 넣고 있다.
  - 두 마리의 갈색 개가 눈 속에서 서로 놀고 있다.
- source_sentence: 버스가 바쁜 길을 따라 운전한다.
  sentences:
  - 우리와 같은 태양계가 은하계 밖에서 존재할 수도 있을 것입니다.
  - 그 여자는 데이트하러 가는 중이다.
  - 녹색 버스가 도로를 따라 내려간다.
model-index:
- name: SentenceTransformer based on CocoRoF/mobert_retry_SimCSE_test
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts_dev
    metrics:
    - type: pearson_cosine
      value: 0.7885728442437165
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7890106880187878
      name: Spearman Cosine
    - type: pearson_euclidean
      value: 0.7209624590910948
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7132906703480484
      name: Spearman Euclidean
    - type: pearson_manhattan
      value: 0.7228003273015342
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7161151111265872
      name: Spearman Manhattan
    - type: pearson_dot
      value: 0.7119673656141701
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7059066541365785
      name: Spearman Dot
    - type: pearson_max
      value: 0.7885728442437165
      name: Pearson Max
    - type: spearman_max
      value: 0.7890106880187878
      name: Spearman Max
---

# SentenceTransformer based on CocoRoF/mobert_retry_SimCSE_test

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [CocoRoF/mobert_retry_SimCSE_test](https://huggingface.co/CocoRoF/mobert_retry_SimCSE_test). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [CocoRoF/mobert_retry_SimCSE_test](https://huggingface.co/CocoRoF/mobert_retry_SimCSE_test) <!-- at revision 94f4e00947539b6741c4a31b977a66220298317d -->
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (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})
  (2): Dense({'in_features': 768, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```

## 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("CocoRoF/ModernBERT-SimCSE-multitask_v03-retry")
# Run inference
sentences = [
    '버스가 바쁜 길을 따라 운전한다.',
    '녹색 버스가 도로를 따라 내려간다.',
    '그 여자는 데이트하러 가는 중이다.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Semantic Similarity

* Dataset: `sts_dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value     |
|:-------------------|:----------|
| pearson_cosine     | 0.7886    |
| spearman_cosine    | 0.789     |
| pearson_euclidean  | 0.721     |
| spearman_euclidean | 0.7133    |
| pearson_manhattan  | 0.7228    |
| spearman_manhattan | 0.7161    |
| pearson_dot        | 0.712     |
| spearman_dot       | 0.7059    |
| pearson_max        | 0.7886    |
| **spearman_max**   | **0.789** |

<!--
## 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: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | 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> |
* Samples:
  | sentence1                           | sentence2                                 | score             |
  |:------------------------------------|:------------------------------------------|:------------------|
  | <code>비행기가 이륙하고 있다.</code>          | <code>비행기가 이륙하고 있다.</code>                | <code>1.0</code>  |
  | <code>한 남자가 큰 플루트를 연주하고 있다.</code>  | <code>남자가 플루트를 연주하고 있다.</code>            | <code>0.76</code> |
  | <code>한 남자가 피자에 치즈를 뿌려놓고 있다.</code> | <code>한 남자가 구운 피자에 치즈 조각을 뿌려놓고 있다.</code> | <code>0.76</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | 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> |
* Samples:
  | sentence1                            | sentence2                           | score             |
  |:-------------------------------------|:------------------------------------|:------------------|
  | <code>안전모를 가진 한 남자가 춤을 추고 있다.</code> | <code>안전모를 쓴 한 남자가 춤을 추고 있다.</code> | <code>1.0</code>  |
  | <code>어린아이가 말을 타고 있다.</code>         | <code>아이가 말을 타고 있다.</code>          | <code>0.95</code> |
  | <code>한 남자가 뱀에게 쥐를 먹이고 있다.</code>    | <code>남자가 뱀에게 쥐를 먹이고 있다.</code>     | <code>1.0</code>  |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `overwrite_output_dir`: True
- `eval_strategy`: steps
- `per_device_train_batch_size`: 1
- `per_device_eval_batch_size`: 1
- `gradient_accumulation_steps`: 16
- `learning_rate`: 8e-05
- `num_train_epochs`: 10.0
- `warmup_ratio`: 0.2
- `push_to_hub`: True
- `hub_model_id`: CocoRoF/ModernBERT-SimCSE-multitask_v03-retry
- `hub_strategy`: checkpoint
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: True
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 1
- `per_device_eval_batch_size`: 1
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 8e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10.0
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: CocoRoF/ModernBERT-SimCSE-multitask_v03-retry
- `hub_strategy`: checkpoint
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | sts_dev_spearman_max |
|:------:|:----:|:-------------:|:---------------:|:--------------------:|
| 0.1114 | 5    | -             | 0.0377          | 0.7471               |
| 0.2228 | 10   | 0.6923        | 0.0377          | 0.7471               |
| 0.3343 | 15   | -             | 0.0376          | 0.7473               |
| 0.4457 | 20   | 0.6832        | 0.0376          | 0.7475               |
| 0.5571 | 25   | -             | 0.0375          | 0.7479               |
| 0.6685 | 30   | 0.6787        | 0.0375          | 0.7484               |
| 0.7799 | 35   | -             | 0.0374          | 0.7488               |
| 0.8914 | 40   | 0.6154        | 0.0373          | 0.7494               |
| 1.0223 | 45   | -             | 0.0372          | 0.7500               |
| 1.1337 | 50   | 0.6231        | 0.0371          | 0.7506               |
| 1.2451 | 55   | -             | 0.0370          | 0.7512               |
| 1.3565 | 60   | 0.6562        | 0.0369          | 0.7519               |
| 1.4680 | 65   | -             | 0.0368          | 0.7526               |
| 1.5794 | 70   | 0.6578        | 0.0366          | 0.7534               |
| 1.6908 | 75   | -             | 0.0365          | 0.7541               |
| 1.8022 | 80   | 0.6669        | 0.0364          | 0.7549               |
| 1.9136 | 85   | -             | 0.0363          | 0.7559               |
| 2.0446 | 90   | 0.6428        | 0.0361          | 0.7568               |
| 2.1560 | 95   | -             | 0.0360          | 0.7577               |
| 2.2674 | 100  | 0.5854        | 0.0358          | 0.7586               |
| 2.3788 | 105  | -             | 0.0357          | 0.7597               |
| 2.4903 | 110  | 0.6027        | 0.0356          | 0.7607               |
| 2.6017 | 115  | -             | 0.0354          | 0.7618               |
| 2.7131 | 120  | 0.6375        | 0.0353          | 0.7627               |
| 2.8245 | 125  | -             | 0.0351          | 0.7635               |
| 2.9359 | 130  | 0.6204        | 0.0350          | 0.7643               |
| 3.0669 | 135  | -             | 0.0348          | 0.7653               |
| 3.1783 | 140  | 0.6077        | 0.0347          | 0.7663               |
| 3.2897 | 145  | -             | 0.0346          | 0.7672               |
| 3.4011 | 150  | 0.5772        | 0.0344          | 0.7681               |
| 3.5125 | 155  | -             | 0.0343          | 0.7690               |
| 3.6240 | 160  | 0.5793        | 0.0341          | 0.7698               |
| 3.7354 | 165  | -             | 0.0340          | 0.7705               |
| 3.8468 | 170  | 0.5807        | 0.0338          | 0.7712               |
| 3.9582 | 175  | -             | 0.0337          | 0.7721               |
| 4.0891 | 180  | 0.5576        | 0.0336          | 0.7729               |
| 4.2006 | 185  | -             | 0.0334          | 0.7734               |
| 4.3120 | 190  | 0.5244        | 0.0333          | 0.7740               |
| 4.4234 | 195  | -             | 0.0332          | 0.7748               |
| 4.5348 | 200  | 0.539         | 0.0331          | 0.7754               |
| 4.6462 | 205  | -             | 0.0330          | 0.7760               |
| 4.7577 | 210  | 0.5517        | 0.0329          | 0.7765               |
| 4.8691 | 215  | -             | 0.0328          | 0.7769               |
| 4.9805 | 220  | 0.5265        | 0.0327          | 0.7776               |
| 5.1114 | 225  | -             | 0.0326          | 0.7780               |
| 5.2228 | 230  | 0.5285        | 0.0325          | 0.7783               |
| 5.3343 | 235  | -             | 0.0324          | 0.7789               |
| 5.4457 | 240  | 0.4697        | 0.0323          | 0.7793               |
| 5.5571 | 245  | -             | 0.0323          | 0.7798               |
| 5.6685 | 250  | 0.4913        | 0.0322          | 0.7804               |
| 5.7799 | 255  | -             | 0.0321          | 0.7809               |
| 5.8914 | 260  | 0.5253        | 0.0320          | 0.7813               |
| 6.0223 | 265  | -             | 0.0320          | 0.7817               |
| 6.1337 | 270  | 0.4924        | 0.0319          | 0.7819               |
| 6.2451 | 275  | -             | 0.0318          | 0.7820               |
| 6.3565 | 280  | 0.4844        | 0.0317          | 0.7822               |
| 6.4680 | 285  | -             | 0.0317          | 0.7825               |
| 6.5794 | 290  | 0.442         | 0.0316          | 0.7827               |
| 6.6908 | 295  | -             | 0.0315          | 0.7830               |
| 6.8022 | 300  | 0.4665        | 0.0314          | 0.7834               |
| 6.9136 | 305  | -             | 0.0314          | 0.7839               |
| 7.0446 | 310  | 0.4672        | 0.0314          | 0.7843               |
| 7.1560 | 315  | -             | 0.0314          | 0.7851               |
| 7.2674 | 320  | 0.4131        | 0.0314          | 0.7850               |
| 7.3788 | 325  | -             | 0.0313          | 0.7849               |
| 7.4903 | 330  | 0.4221        | 0.0312          | 0.7848               |
| 7.6017 | 335  | -             | 0.0311          | 0.7854               |
| 7.7131 | 340  | 0.4268        | 0.0310          | 0.7857               |
| 7.8245 | 345  | -             | 0.0309          | 0.7861               |
| 7.9359 | 350  | 0.4316        | 0.0309          | 0.7866               |
| 8.0669 | 355  | -             | 0.0309          | 0.7872               |
| 8.1783 | 360  | 0.4277        | 0.0309          | 0.7873               |
| 8.2897 | 365  | -             | 0.0308          | 0.7870               |
| 8.4011 | 370  | 0.3925        | 0.0308          | 0.7868               |
| 8.5125 | 375  | -             | 0.0308          | 0.7866               |
| 8.6240 | 380  | 0.4049        | 0.0308          | 0.7869               |
| 8.7354 | 385  | -             | 0.0308          | 0.7875               |
| 8.8468 | 390  | 0.3742        | 0.0308          | 0.7883               |
| 8.9582 | 395  | -             | 0.0307          | 0.7885               |
| 9.0891 | 400  | 0.3498        | 0.0307          | 0.7886               |
| 9.2006 | 405  | -             | 0.0307          | 0.7881               |
| 9.3120 | 410  | 0.3569        | 0.0307          | 0.7878               |
| 9.4234 | 415  | -             | 0.0307          | 0.7876               |
| 9.5348 | 420  | 0.3312        | 0.0306          | 0.7877               |
| 9.6462 | 425  | -             | 0.0305          | 0.7881               |
| 9.7577 | 430  | 0.3848        | 0.0304          | 0.7885               |
| 9.8691 | 435  | -             | 0.0304          | 0.7889               |
| 9.9805 | 440  | 0.332         | 0.0305          | 0.7890               |


### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.0
- Tokenizers: 0.21.0

## 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",
}
```

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