File size: 19,035 Bytes
d7b2bbc |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 |
---
base_model: colorfulscoop/sbert-base-ja
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7464
- loss:CosineSimilarityLoss
widget:
- source_sentence: なにか思い出せることは？
sentences:
- なんて言った？
- なにが欲しい？
- いつ失くしたの？
- source_sentence: 木にスカーフがひっかかってる?
sentences:
- 物の姿を変えられる魔法
- 木にスカーフがひっかかってる?
- あの木の上のやつ、スカーフ？
- source_sentence: どんなスカーフ？
sentences:
- お気に入りの食べ物は？
- くさい
- もしもし
- source_sentence: どうやったら猫が話せる？
sentences:
- どっちがいいと思う？
- ワゴンにスカーフはあった？
- 井戸へ行ったことある？
- source_sentence: なにか思い出せることは？
sentences:
- 村人はどんな呪文が使えるの？
- 町って？
- キミならどっちがいい？
model-index:
- name: SentenceTransformer based on colorfulscoop/sbert-base-ja
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: custom arc semantics data jp
type: custom-arc-semantics-data-jp
metrics:
- type: cosine_accuracy
value: 0.9705409748259239
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.5118279457092285
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9102773246329527
name: Cosine F1
- type: cosine_f1_threshold
value: 0.45031607151031494
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.8857142857142857
name: Cosine Precision
- type: cosine_recall
value: 0.9362416107382551
name: Cosine Recall
- type: cosine_ap
value: 0.9236294738598163
name: Cosine Ap
- type: dot_accuracy
value: 0.9694697375468666
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 251.2455596923828
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.9060955518945634
name: Dot F1
- type: dot_f1_threshold
value: 246.36648559570312
name: Dot F1 Threshold
- type: dot_precision
value: 0.889967637540453
name: Dot Precision
- type: dot_recall
value: 0.9228187919463087
name: Dot Recall
- type: dot_ap
value: 0.9196731890884118
name: Dot Ap
- type: manhattan_accuracy
value: 0.9716122121049813
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 514.571533203125
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.9132569558101473
name: Manhattan F1
- type: manhattan_f1_threshold
value: 514.571533203125
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.8913738019169329
name: Manhattan Precision
- type: manhattan_recall
value: 0.9362416107382551
name: Manhattan Recall
- type: manhattan_ap
value: 0.9255015709844487
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9721478307445099
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 23.195274353027344
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.9147540983606558
name: Euclidean F1
- type: euclidean_f1_threshold
value: 23.195274353027344
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.8942307692307693
name: Euclidean Precision
- type: euclidean_recall
value: 0.9362416107382551
name: Euclidean Recall
- type: euclidean_ap
value: 0.9259440018381992
name: Euclidean Ap
- type: max_accuracy
value: 0.9721478307445099
name: Max Accuracy
- type: max_accuracy_threshold
value: 514.571533203125
name: Max Accuracy Threshold
- type: max_f1
value: 0.9147540983606558
name: Max F1
- type: max_f1_threshold
value: 514.571533203125
name: Max F1 Threshold
- type: max_precision
value: 0.8942307692307693
name: Max Precision
- type: max_recall
value: 0.9362416107382551
name: Max Recall
- type: max_ap
value: 0.9259440018381992
name: Max Ap
---
# SentenceTransformer based on colorfulscoop/sbert-base-ja
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja). 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:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **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: BertModel
(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})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'なにか思い出せることは？',
'村人はどんな呪文が使えるの？',
'キミならどっちがいい？',
]
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
#### Binary Classification
* Dataset: `custom-arc-semantics-data-jp`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9705 |
| cosine_accuracy_threshold | 0.5118 |
| cosine_f1 | 0.9103 |
| cosine_f1_threshold | 0.4503 |
| cosine_precision | 0.8857 |
| cosine_recall | 0.9362 |
| cosine_ap | 0.9236 |
| dot_accuracy | 0.9695 |
| dot_accuracy_threshold | 251.2456 |
| dot_f1 | 0.9061 |
| dot_f1_threshold | 246.3665 |
| dot_precision | 0.89 |
| dot_recall | 0.9228 |
| dot_ap | 0.9197 |
| manhattan_accuracy | 0.9716 |
| manhattan_accuracy_threshold | 514.5715 |
| manhattan_f1 | 0.9133 |
| manhattan_f1_threshold | 514.5715 |
| manhattan_precision | 0.8914 |
| manhattan_recall | 0.9362 |
| manhattan_ap | 0.9255 |
| euclidean_accuracy | 0.9721 |
| euclidean_accuracy_threshold | 23.1953 |
| euclidean_f1 | 0.9148 |
| euclidean_f1_threshold | 23.1953 |
| euclidean_precision | 0.8942 |
| euclidean_recall | 0.9362 |
| euclidean_ap | 0.9259 |
| max_accuracy | 0.9721 |
| max_accuracy_threshold | 514.5715 |
| max_f1 | 0.9148 |
| max_f1_threshold | 514.5715 |
| max_precision | 0.8942 |
| max_recall | 0.9362 |
| **max_ap** | **0.9259** |
<!--
## 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: 7,464 training samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.93 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~86.30%</li><li>1: ~13.70%</li></ul> |
* Samples:
| text1 | text2 | label |
|:------------------------------|:-------------------------|:---------------|
| <code>昨日夕飯にチキンヌードル食べた？</code> | <code>何か企んでる？</code> | <code>0</code> |
| <code>どっちも欲しくない</code> | <code>お気に入りの食べ物は？</code> | <code>0</code> |
| <code>見た目を変える魔法</code> | <code>物の姿を変えられる魔法</code> | <code>1</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: 1,867 evaluation samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.24 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.1 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~83.40%</li><li>1: ~16.60%</li></ul> |
* Samples:
| text1 | text2 | label |
|:------------------------------|:-------------------------|:---------------|
| <code>例えば？</code> | <code>どうも</code> | <code>0</code> |
| <code>何を作ったの？</code> | <code>君は何でここにいるの？</code> | <code>0</code> |
| <code>昨日夕飯にビーフシチュー食べた？</code> | <code>屋根裏って？</code> | <code>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
- `eval_strategy`: epoch
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.4
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.4
- `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`: True
- `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`: False
- `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`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
| 1.0 | 933 | 0.0601 | 0.0303 | 0.9259 |
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## 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.*
--> |