File size: 20,126 Bytes
61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c 9846683 61a137c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5749
- loss:CosineSimilarityLoss
base_model: CocoRoF/ModernBERT-SimCSE_v02
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.
pipeline_tag: sentence-similarity
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
model-index:
- name: SentenceTransformer based on CocoRoF/ModernBERT-SimCSE_v02
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts_dev
metrics:
- type: pearson_cosine
value: 0.8223949445074785
name: Pearson Cosine
- type: spearman_cosine
value: 0.8220107207834706
name: Spearman Cosine
- type: pearson_euclidean
value: 0.7785831525283676
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7815628643916452
name: Spearman Euclidean
- type: pearson_manhattan
value: 0.7809119630672191
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7846536514745763
name: Spearman Manhattan
- type: pearson_dot
value: 0.7543765794886113
name: Pearson Dot
- type: spearman_dot
value: 0.7434525191412167
name: Spearman Dot
- type: pearson_max
value: 0.8223949445074785
name: Pearson Max
- type: spearman_max
value: 0.8220107207834706
name: Spearman Max
---
# SentenceTransformer based on CocoRoF/ModernBERT-SimCSE_v02
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.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [CocoRoF/ModernBERT-SimCSE_v02](https://huggingface.co/CocoRoF/ModernBERT-SimCSE_v02) <!-- at revision de4148c764893843e15a4e0b241fe308147a9aaa -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 512, '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': 768, '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")
# 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, 768]
# 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 | 0.8224 |
| spearman_cosine | 0.822 |
| pearson_euclidean | 0.7786 |
| spearman_euclidean | 0.7816 |
| pearson_manhattan | 0.7809 |
| spearman_manhattan | 0.7847 |
| pearson_dot | 0.7544 |
| spearman_dot | 0.7435 |
| pearson_max | 0.8224 |
| **spearman_max** | **0.822** |
<!--
## 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`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 1e-05
- `num_train_epochs`: 10.0
- `warmup_ratio`: 0.1
- `push_to_hub`: True
- `hub_model_id`: CocoRoF/ModernBERT-SimCSE-multitask_v03
- `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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-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`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: 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
- `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 |
|:------:|:----:|:-------------:|:---------------:|:--------------------:|
| 0.2228 | 10 | 0.0283 | - | - |
| 0.4457 | 20 | 0.0344 | - | - |
| 0.6685 | 30 | 0.0305 | 0.0310 | 0.7939 |
| 0.8914 | 40 | 0.0489 | - | - |
| 1.1337 | 50 | 0.0382 | - | - |
| 1.3565 | 60 | 0.0271 | 0.0293 | 0.7994 |
| 1.5794 | 70 | 0.0344 | - | - |
| 1.8022 | 80 | 0.0382 | - | - |
| 2.0446 | 90 | 0.0419 | 0.0280 | 0.8059 |
| 2.2674 | 100 | 0.0244 | - | - |
| 2.4903 | 110 | 0.0307 | - | - |
| 2.7131 | 120 | 0.0291 | 0.0269 | 0.8108 |
| 2.9359 | 130 | 0.038 | - | - |
| 3.1783 | 140 | 0.0269 | - | - |
| 3.4011 | 150 | 0.0268 | 0.0262 | 0.8155 |
| 3.6240 | 160 | 0.0246 | - | - |
| 3.8468 | 170 | 0.0313 | - | - |
| 4.0891 | 180 | 0.0303 | 0.0259 | 0.8185 |
| 4.3120 | 190 | 0.0198 | - | - |
| 4.5348 | 200 | 0.0257 | - | - |
| 4.7577 | 210 | 0.0242 | 0.0255 | 0.8202 |
| 4.9805 | 220 | 0.0293 | - | - |
| 5.2228 | 230 | 0.0193 | - | - |
| 5.4457 | 240 | 0.0222 | 0.0254 | 0.8222 |
| 5.6685 | 250 | 0.0184 | - | - |
| 5.8914 | 260 | 0.0243 | - | - |
| 6.1337 | 270 | 0.0204 | 0.0254 | 0.8235 |
| 6.3565 | 280 | 0.0147 | - | - |
| 6.5794 | 290 | 0.0196 | - | - |
| 6.8022 | 300 | 0.0176 | 0.0253 | 0.8227 |
| 7.0446 | 310 | 0.0202 | - | - |
| 7.2674 | 320 | 0.0123 | - | - |
| 7.4903 | 330 | 0.0151 | 0.0254 | 0.8236 |
| 7.7131 | 340 | 0.0132 | - | - |
| 7.9359 | 350 | 0.0158 | - | - |
| 8.1783 | 360 | 0.0118 | 0.0256 | 0.8240 |
| 8.4011 | 370 | 0.0115 | - | - |
| 8.6240 | 380 | 0.0105 | - | - |
| 8.8468 | 390 | 0.0111 | 0.0256 | 0.8215 |
| 9.0891 | 400 | 0.011 | - | - |
| 9.3120 | 410 | 0.0076 | - | - |
| 9.5348 | 420 | 0.0091 | 0.0256 | 0.8220 |
| 9.7577 | 430 | 0.0075 | - | - |
| 9.9805 | 440 | 0.0093 | - | - |
### 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
## 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",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |