File size: 19,581 Bytes
f8ff613 957b956 f8ff613 | 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 | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1441905
- loss:CachedMultipleNegativesRankingLoss
widget:
- source_sentence: Treponema caused disease or disorder
sentences:
- bejel
- tumor of ureter
- debrisoquine, ultrarapid metabolism of
- source_sentence: B cell (antibody) deficiencies
sentences:
- distal phalanx of digit IV
- well-differentiated fetal adenocarcinoma of the lung
- deficiency of humoral immunity
- source_sentence: Elevated AdoHcy concentration
sentences:
- gepulste Abgabe
- Elevated circulating S-adenosyl-L-homocysteine concentration
- Frequently cries for no reason
- source_sentence: Isoelectric focusing of serum transferrin consistent with CDG type
II
sentences:
- Amblyomma aureolatum
- squamous cell carcinoma of the bile duct
- Abnormal isoelectric focusing of serum transferrin, type 2 pattern
- source_sentence: Light-chain amyloidosis
sentences:
- partial deletion of the long arm of chromosome X
- Teneria teneriensis
- amyloidosis primary systemic
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: owl ontology eval
type: owl_ontology_eval
metrics:
- type: cosine_accuracy@1
value: 0.6302799165287473
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8147801683816651
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8775275239260272
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9268187378570915
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6302799165287473
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27634261591230724
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17979420018709072
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09566812981218968
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6216929313281044
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8081120625554675
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8723585426111152
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9241442997289582
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7796907170635903
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7342337217921898
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.734065731352359
name: Cosine Map@100
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the json dataset. It maps sentences & paragraphs to a 384-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
<!-- - **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': 1024, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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("pankajrajdeo/bond-embed-v1-fp16")
# Run inference
sentences = [
'Light-chain amyloidosis',
'amyloidosis primary systemic',
'partial deletion of the long arm of chromosome X',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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
#### Information Retrieval
* Dataset: `owl_ontology_eval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.6303 |
| cosine_accuracy@3 | 0.8148 |
| cosine_accuracy@5 | 0.8775 |
| cosine_accuracy@10 | 0.9268 |
| cosine_precision@1 | 0.6303 |
| cosine_precision@3 | 0.2763 |
| cosine_precision@5 | 0.1798 |
| cosine_precision@10 | 0.0957 |
| cosine_recall@1 | 0.6217 |
| cosine_recall@3 | 0.8081 |
| cosine_recall@5 | 0.8724 |
| cosine_recall@10 | 0.9241 |
| **cosine_ndcg@10** | **0.7797** |
| cosine_mrr@10 | 0.7342 |
| cosine_map@100 | 0.7341 |
<!--
## 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
#### json
* Dataset: json
* Size: 1,441,905 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 9.48 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.68 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------|:-------------------------------------|
| <code>Mangshan horned toad</code> | <code>Mangshan spadefoot toad</code> |
| <code>Leuconotopicos borealis</code> | <code>Picoides borealis</code> |
| <code>Cylindrella teneriensis</code> | <code>Teneria teneriensis</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `learning_rate`: 1.5e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.05
- `bf16`: True
- `dataloader_num_workers`: 32
- `load_best_model_at_end`: True
- `gradient_checkpointing`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 1024
- `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`: 1.5e-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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `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`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 32
- `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
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: True
- `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
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | owl_ontology_eval_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:--------------------------------:|
| 0.0717 | 100 | 1.3232 | - |
| 0.1434 | 200 | 1.021 | - |
| 0.2151 | 300 | 0.9633 | - |
| 0.2867 | 400 | 0.9068 | - |
| 0.3297 | 460 | - | 0.7207 |
| 0.3584 | 500 | 0.8723 | - |
| 0.4301 | 600 | 0.852 | - |
| 0.5018 | 700 | 0.8161 | - |
| 0.5735 | 800 | 0.7939 | - |
| 0.6452 | 900 | 0.7935 | - |
| 0.6595 | 920 | - | 0.7364 |
| 0.7168 | 1000 | 0.7646 | - |
| 0.7885 | 1100 | 0.7464 | - |
| 0.8602 | 1200 | 0.7376 | - |
| 0.9319 | 1300 | 0.7313 | - |
| 0.9892 | 1380 | - | 0.7468 |
| 1.0036 | 1400 | 0.7099 | - |
| 1.0753 | 1500 | 0.6884 | - |
| 1.1470 | 1600 | 0.6776 | - |
| 1.2186 | 1700 | 0.6694 | - |
| 1.2903 | 1800 | 0.6641 | - |
| 1.3190 | 1840 | - | 0.7561 |
| 1.3620 | 1900 | 0.6526 | - |
| 1.4337 | 2000 | 0.6524 | - |
| 1.5054 | 2100 | 0.6364 | - |
| 1.5771 | 2200 | 0.6339 | - |
| 1.6487 | 2300 | 0.626 | 0.7614 |
| 1.7204 | 2400 | 0.6197 | - |
| 1.7921 | 2500 | 0.6193 | - |
| 1.8638 | 2600 | 0.6155 | - |
| 1.9355 | 2700 | 0.6142 | - |
| 1.9785 | 2760 | - | 0.7662 |
| 2.0072 | 2800 | 0.5853 | - |
| 2.0789 | 2900 | 0.5824 | - |
| 2.1505 | 3000 | 0.5769 | - |
| 2.2222 | 3100 | 0.5765 | - |
| 2.2939 | 3200 | 0.5608 | - |
| 2.3082 | 3220 | - | 0.7698 |
| 2.3656 | 3300 | 0.5695 | - |
| 2.4373 | 3400 | 0.5641 | - |
| 2.5090 | 3500 | 0.5638 | - |
| 2.5806 | 3600 | 0.554 | - |
| 2.6380 | 3680 | - | 0.7735 |
| 2.6523 | 3700 | 0.5539 | - |
| 2.7240 | 3800 | 0.5495 | - |
| 2.7957 | 3900 | 0.5556 | - |
| 2.8674 | 4000 | 0.5397 | - |
| 2.9391 | 4100 | 0.5447 | - |
| 2.9677 | 4140 | - | 0.7757 |
| 3.0108 | 4200 | 0.5331 | - |
| 3.0824 | 4300 | 0.5336 | - |
| 3.1541 | 4400 | 0.5346 | - |
| 3.2258 | 4500 | 0.5247 | - |
| 3.2975 | 4600 | 0.5241 | 0.7775 |
| 3.3692 | 4700 | 0.5257 | - |
| 3.4409 | 4800 | 0.5241 | - |
| 3.5125 | 4900 | 0.5171 | - |
| 3.5842 | 5000 | 0.5215 | - |
| 3.6272 | 5060 | - | 0.7787 |
| 3.6559 | 5100 | 0.5203 | - |
| 3.7276 | 5200 | 0.5214 | - |
| 3.7993 | 5300 | 0.5266 | - |
| 3.8710 | 5400 | 0.5127 | - |
| 3.9427 | 5500 | 0.5062 | - |
| 3.9570 | 5520 | - | 0.7790 |
| 4.0143 | 5600 | 0.5104 | - |
| 4.0860 | 5700 | 0.5155 | - |
| 4.1577 | 5800 | 0.5042 | - |
| 4.2294 | 5900 | 0.5174 | - |
| 4.2867 | 5980 | - | 0.7797 |
| 4.3011 | 6000 | 0.509 | - |
| 4.3728 | 6100 | 0.5106 | - |
| 4.4444 | 6200 | 0.5076 | - |
| 4.5161 | 6300 | 0.5046 | - |
| 4.5878 | 6400 | 0.5077 | - |
| 4.6165 | 6440 | - | 0.7795 |
| 4.6595 | 6500 | 0.5114 | - |
| 4.7312 | 6600 | 0.5103 | - |
| 4.8029 | 6700 | 0.5106 | - |
| 4.8746 | 6800 | 0.5102 | - |
| 4.9462 | 6900 | 0.5076 | 0.7797 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.53.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.2.0
- 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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
## 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.*
--> |