File size: 21,951 Bytes
df0a677 |
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 |
---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets:
- sentence-transformers/quora-duplicates
language:
- en
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:323432
- loss:OnlineContrastiveLoss
widget:
- source_sentence: How do I have a successful career in animation industry with all
distance mode of education (from schooling)?
sentences:
- The LINE app is blocked in China. I bought a VPN, but it's still not working.
Can someone help me?
- What is independent?
- How do I find all distance education schools in any city?
- source_sentence: How can I get the funding for my startup without revealing my idea?
sentences:
- How has demonetization affected big business people like Mukesh Ambani?
- How should I go about getting funding for my idea?
- What are the advantages and disadvantages of studying an MBBS in China?
- source_sentence: I am an okay looking young women but I am always feeling ugly since
I'm not extremely beautiful. How can I stop those thoughts?
sentences:
- Whenever I think about my failures in life, I always feel that I lack some qualities.
But which are those qualities, I am not able to find out. How can I find which
qualities I lack?
- What songs make you cry?
- What does histrionic personality disorder feel like physically to you?
- source_sentence: What do you think of Prime Minister Narendra Modi's decision to
introduce new INR 500 and INR 2000 currency notes?
sentences:
- What do you think of the decision by the Indian Government to replace 1000 notes
with 2000 notes?
- How do you find volume from density and mass?
- What are the consequences of having a blood sugar level over 300?
- source_sentence: Why do complementary angles have to be adjacent?
sentences:
- What is an AEG airsoft gun?
- How can I get rid of my bad habits?
- Can two adjacent angles be complementary?
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.8683618194860125
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7981455326080322
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.8292439905343131
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7598952651023865
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.7746589487768696
name: Cosine Precision
- type: cosine_recall
value: 0.8921046460992195
name: Cosine Recall
- type: cosine_ap
value: 0.8822291610822541
name: Cosine Ap
- type: dot_accuracy
value: 0.8359964382003018
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 17.112058639526367
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.7914425390403506
name: Dot F1
- type: dot_f1_threshold
value: 16.083341598510742
name: Dot F1 Threshold
- type: dot_precision
value: 0.7294350282485875
name: Dot Precision
- type: dot_recall
value: 0.8649716946370549
name: Dot Recall
- type: dot_ap
value: 0.8438654629805356
name: Dot Ap
- type: manhattan_accuracy
value: 0.8568230725469341
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 46.94310760498047
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.8144082547946494
name: Manhattan F1
- type: manhattan_f1_threshold
value: 50.51482391357422
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.7656268427880646
name: Manhattan Precision
- type: manhattan_recall
value: 0.8698288279234918
name: Manhattan Recall
- type: manhattan_ap
value: 0.8636170591577621
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.8568849093472507
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 3.0017127990722656
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.8143016129285076
name: Euclidean F1
- type: euclidean_f1_threshold
value: 3.2429399490356445
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.7652309686542541
name: Euclidean Precision
- type: euclidean_recall
value: 0.8700968076910194
name: Euclidean Recall
- type: euclidean_ap
value: 0.8637642883474006
name: Euclidean Ap
- type: max_accuracy
value: 0.8683618194860125
name: Max Accuracy
- type: max_accuracy_threshold
value: 46.94310760498047
name: Max Accuracy Threshold
- type: max_f1
value: 0.8292439905343131
name: Max F1
- type: max_f1_threshold
value: 50.51482391357422
name: Max F1 Threshold
- type: max_precision
value: 0.7746589487768696
name: Max Precision
- type: max_recall
value: 0.8921046460992195
name: Max Recall
- type: max_ap
value: 0.8822291610822541
name: Max Ap
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **Language:** en
<!-- - **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': 128, '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("DashReza7/paraphrase-multilingual-MiniLM-L12-v2_QuoraDuplicateDetection_FINETUNED")
# Run inference
sentences = [
'Why do complementary angles have to be adjacent?',
'Can two adjacent angles be complementary?',
'How can I get rid of my bad habits?',
]
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
#### Binary Classification
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.8684 |
| cosine_accuracy_threshold | 0.7981 |
| cosine_f1 | 0.8292 |
| cosine_f1_threshold | 0.7599 |
| cosine_precision | 0.7747 |
| cosine_recall | 0.8921 |
| cosine_ap | 0.8822 |
| dot_accuracy | 0.836 |
| dot_accuracy_threshold | 17.1121 |
| dot_f1 | 0.7914 |
| dot_f1_threshold | 16.0833 |
| dot_precision | 0.7294 |
| dot_recall | 0.865 |
| dot_ap | 0.8439 |
| manhattan_accuracy | 0.8568 |
| manhattan_accuracy_threshold | 46.9431 |
| manhattan_f1 | 0.8144 |
| manhattan_f1_threshold | 50.5148 |
| manhattan_precision | 0.7656 |
| manhattan_recall | 0.8698 |
| manhattan_ap | 0.8636 |
| euclidean_accuracy | 0.8569 |
| euclidean_accuracy_threshold | 3.0017 |
| euclidean_f1 | 0.8143 |
| euclidean_f1_threshold | 3.2429 |
| euclidean_precision | 0.7652 |
| euclidean_recall | 0.8701 |
| euclidean_ap | 0.8638 |
| max_accuracy | 0.8684 |
| max_accuracy_threshold | 46.9431 |
| max_f1 | 0.8292 |
| max_f1_threshold | 50.5148 |
| max_precision | 0.7747 |
| max_recall | 0.8921 |
| **max_ap** | **0.8822** |
<!--
## 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
#### sentence-transformers/quora-duplicates
* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 323,432 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.39 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.2 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>0: ~62.10%</li><li>1: ~37.90%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------|:-----------------------------------------------------------------------|:---------------|
| <code>Which are the best compilers for C language (for Windows 10)?</code> | <code>Which is the best open source C/C++ compiler for Windows?</code> | <code>0</code> |
| <code>How much does YouTube pay per 1000 views in India?</code> | <code>How much does youtube pay per 1000 views?</code> | <code>0</code> |
| <code>What parts do I need to build my own PC?</code> | <code>I want to build a new computer. What parts do I need?</code> | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### sentence-transformers/quora-duplicates
* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 80,858 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.48 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.76 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>0: ~63.90%</li><li>1: ~36.10%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------|
| <code>How many stories got busted on Quora while being anonymous?</code> | <code>Can what I say on Quora anonymously be used against me legally?</code> | <code>0</code> |
| <code>What are innovative mechanical component designs?</code> | <code>What is the Innovation design?</code> | <code>0</code> |
| <code>What is the best way to learn phrasal verbs?</code> | <code>Why should I learn phrasal verbs?</code> | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: 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`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 1
- `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`: 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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | max_ap |
|:------:|:----:|:-------------:|:------:|:------:|
| 0.0791 | 100 | - | 8.0607 | 0.8164 |
| 0.1582 | 200 | - | 7.3012 | 0.8445 |
| 0.2373 | 300 | - | 6.9626 | 0.8582 |
| 0.3165 | 400 | - | 6.7901 | 0.8639 |
| 0.3956 | 500 | 7.5229 | 6.6498 | 0.8694 |
| 0.4747 | 600 | - | 6.5315 | 0.8736 |
| 0.5538 | 700 | - | 6.4686 | 0.8766 |
| 0.6329 | 800 | - | 6.4027 | 0.8787 |
| 0.7120 | 900 | - | 6.3108 | 0.8797 |
| 0.7911 | 1000 | 6.4636 | 6.2862 | 0.8812 |
| 0.8703 | 1100 | - | 6.2449 | 0.8818 |
| 0.9494 | 1200 | - | 6.2344 | 0.8822 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.21.0
- 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.*
--> |