File size: 20,803 Bytes
488d0c8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 |
---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1625
- loss:CosineSimilarityLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Boron Steel
sentences:
- Rock Bit
- Spalling Test
- Excavator Bucket
- source_sentence: Friction Wear
sentences:
- Tool Steel
- Medium Carbon Steel
- Diffusion Bonding
- source_sentence: Delamination
sentences:
- Subsea Christmas Tree
- Low Alloyed Steel
- Screw Conveyors
- source_sentence: Nitriding
sentences:
- Subsea Manifold
- Trencher Chain
- Cylinder
- source_sentence: Corrosion Resistant Coatings
sentences:
- Mower Blade
- Gas Metal Arc Welding (GMAW)
- Corrosion Resistant Coatings
pipeline_tag: sentence-similarity
model-index:
- name: BGE base Financial Matryoshka
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 768
type: dim_768
metrics:
- type: pearson_cosine
value: 0.9548051644723275
name: Pearson Cosine
- type: spearman_cosine
value: 0.6620048542679903
name: Spearman Cosine
- type: pearson_manhattan
value: 0.985909077336812
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6620048542679903
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9863519709955113
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6620048542679903
name: Spearman Euclidean
- type: pearson_dot
value: 0.9548051701614557
name: Pearson Dot
- type: spearman_dot
value: 0.6610658947764548
name: Spearman Dot
- type: pearson_max
value: 0.9863519709955113
name: Pearson Max
- type: spearman_max
value: 0.6620048542679903
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 512
type: dim_512
metrics:
- type: pearson_cosine
value: 0.9544417196413574
name: Pearson Cosine
- type: spearman_cosine
value: 0.6620048542679903
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9855825558550574
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6620048542679903
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9862004412296757
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6620048542679903
name: Spearman Euclidean
- type: pearson_dot
value: 0.9501184326722917
name: Pearson Dot
- type: spearman_dot
value: 0.6607798700248341
name: Spearman Dot
- type: pearson_max
value: 0.9862004412296757
name: Pearson Max
- type: spearman_max
value: 0.6620048542679903
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 256
type: dim_256
metrics:
- type: pearson_cosine
value: 0.9494511778471465
name: Pearson Cosine
- type: spearman_cosine
value: 0.6620048542679903
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9830259644213172
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6620048542679903
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9835562939431381
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6620048542679903
name: Spearman Euclidean
- type: pearson_dot
value: 0.9469313992827345
name: Pearson Dot
- type: spearman_dot
value: 0.6607798700248341
name: Spearman Dot
- type: pearson_max
value: 0.9835562939431381
name: Pearson Max
- type: spearman_max
value: 0.6620048542679903
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 128
type: dim_128
metrics:
- type: pearson_cosine
value: 0.9397052405386266
name: Pearson Cosine
- type: spearman_cosine
value: 0.6620048542679903
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9762184586055923
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6620048542679903
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9781975526221939
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6620048542679903
name: Spearman Euclidean
- type: pearson_dot
value: 0.9271211389022183
name: Pearson Dot
- type: spearman_dot
value: 0.6607798700248341
name: Spearman Dot
- type: pearson_max
value: 0.9781975526221939
name: Pearson Max
- type: spearman_max
value: 0.6620048542679903
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: dim 64
type: dim_64
metrics:
- type: pearson_cosine
value: 0.9149032642312528
name: Pearson Cosine
- type: spearman_cosine
value: 0.6620048542679903
name: Spearman Cosine
- type: pearson_manhattan
value: 0.968215524939354
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6620048542679903
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9708485057392984
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6620048542679903
name: Spearman Euclidean
- type: pearson_dot
value: 0.8940456314300972
name: Pearson Dot
- type: spearman_dot
value: 0.6602255244962898
name: Spearman Dot
- type: pearson_max
value: 0.9708485057392984
name: Pearson Max
- type: spearman_max
value: 0.6620048542679903
name: Spearman Max
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Normalize()
)
```
## 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("thetayne/finetuned_model_0613")
# Run inference
sentences = [
'Corrosion Resistant Coatings',
'Corrosion Resistant Coatings',
'Mower Blade',
]
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: `dim_768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.9548 |
| **spearman_cosine** | **0.662** |
| pearson_manhattan | 0.9859 |
| spearman_manhattan | 0.662 |
| pearson_euclidean | 0.9864 |
| spearman_euclidean | 0.662 |
| pearson_dot | 0.9548 |
| spearman_dot | 0.6611 |
| pearson_max | 0.9864 |
| spearman_max | 0.662 |
#### Semantic Similarity
* Dataset: `dim_512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.9544 |
| **spearman_cosine** | **0.662** |
| pearson_manhattan | 0.9856 |
| spearman_manhattan | 0.662 |
| pearson_euclidean | 0.9862 |
| spearman_euclidean | 0.662 |
| pearson_dot | 0.9501 |
| spearman_dot | 0.6608 |
| pearson_max | 0.9862 |
| spearman_max | 0.662 |
#### Semantic Similarity
* Dataset: `dim_256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.9495 |
| **spearman_cosine** | **0.662** |
| pearson_manhattan | 0.983 |
| spearman_manhattan | 0.662 |
| pearson_euclidean | 0.9836 |
| spearman_euclidean | 0.662 |
| pearson_dot | 0.9469 |
| spearman_dot | 0.6608 |
| pearson_max | 0.9836 |
| spearman_max | 0.662 |
#### Semantic Similarity
* Dataset: `dim_128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.9397 |
| **spearman_cosine** | **0.662** |
| pearson_manhattan | 0.9762 |
| spearman_manhattan | 0.662 |
| pearson_euclidean | 0.9782 |
| spearman_euclidean | 0.662 |
| pearson_dot | 0.9271 |
| spearman_dot | 0.6608 |
| pearson_max | 0.9782 |
| spearman_max | 0.662 |
#### Semantic Similarity
* Dataset: `dim_64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.9149 |
| **spearman_cosine** | **0.662** |
| pearson_manhattan | 0.9682 |
| spearman_manhattan | 0.662 |
| pearson_euclidean | 0.9708 |
| spearman_euclidean | 0.662 |
| pearson_dot | 0.894 |
| spearman_dot | 0.6602 |
| pearson_max | 0.9708 |
| spearman_max | 0.662 |
<!--
## 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: 1,625 training samples
* Columns: <code>sentence_A</code>, <code>sentence_B</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_A | sentence_B | score |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.73 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>0: ~83.30%</li><li>1: ~16.70%</li></ul> |
* Samples:
| sentence_A | sentence_B | score |
|:-----------------------------------|:--------------------------------------|:---------------|
| <code>Thermal Fatigue</code> | <code>Ferritic Stainless Steel</code> | <code>0</code> |
| <code>High Temperature Wear</code> | <code>Drill String</code> | <code>0</code> |
| <code>Carbide Coatings</code> | <code>Carbide Coatings</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"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `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`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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`: True
- `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_fused
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_128_spearman_cosine | dim_256_spearman_cosine | dim_512_spearman_cosine | dim_64_spearman_cosine | dim_768_spearman_cosine |
|:----------:|:------:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:-----------------------:|
| 0 | 0 | - | 0.6626 | 0.6626 | 0.6626 | 0.6626 | 0.6626 |
| 0.9412 | 3 | - | 0.6620 | 0.6620 | 0.6620 | 0.6620 | 0.6620 |
| 1.8627 | 6 | - | 0.6620 | 0.6620 | 0.6620 | 0.6620 | 0.6620 |
| 2.7843 | 9 | - | 0.6620 | 0.6620 | 0.6620 | 0.6620 | 0.6620 |
| 3.0784 | 10 | 0.156 | - | - | - | - | - |
| **3.7059** | **12** | **-** | **0.662** | **0.662** | **0.662** | **0.662** | **0.662** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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.*
--> |