File size: 37,436 Bytes
4190c18 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 |
---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5679484
- loss:Contrastive
base_model: nreimers/MiniLM-L6-H384-uncased
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- accuracy
model-index:
- name: PyLate model based on nreimers/MiniLM-L6-H384-uncased
results:
- task:
type: col-berttriplet
name: Col BERTTriplet
dataset:
name: Unknown
type: unknown
metrics:
- type: accuracy
value: 0.4773999750614166
name: Accuracy
---
# PyLate model based on nreimers/MiniLM-L6-H384-uncased
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) <!-- at revision 3276f0fac9d818781d7a1327b3ff818fc4e643c0 -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
#### Indexing documents
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path=ayushexel/colbert-MiniLM-L6-H384-uncased-3-neg-1-epoch-gooaq-1995000,
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path=ayushexel/colbert-MiniLM-L6-H384-uncased-3-neg-1-epoch-gooaq-1995000,
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### 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
#### Col BERTTriplet
* Evaluated with <code>pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator</code>
| Metric | Value |
|:-------------|:-----------|
| **accuracy** | **0.4774** |
<!--
## 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: 5,679,484 training samples
* Columns: <code>question</code>, <code>answer</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 12.97 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 31.84 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 31.64 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| question | answer | negative |
|:----------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>can i use bluetooth headphones for xbox one?</code> | <code>Headsets cannot be connected to any third party wireless controller. Headsets need to be connected to the Xbox one controller in order to function. The Xbox one console doesn't have a Bluetooth feature. Hence the headsets cannot be connected via Bluetooth.</code> | <code>You can connect Bluetooth headphones to a PS4, but only if they are compatible with the PS4. Most standard Bluetooth headphones are not compatible with the PS4, so you will need to make sure you have Bluetooth headphones that are specifically geared to the PS4.</code> |
| <code>can i use bluetooth headphones for xbox one?</code> | <code>Headsets cannot be connected to any third party wireless controller. Headsets need to be connected to the Xbox one controller in order to function. The Xbox one console doesn't have a Bluetooth feature. Hence the headsets cannot be connected via Bluetooth.</code> | <code>Summary – how to pair Sony Bluetooth headphones Tap and hold the Power button on the headphones for 7 seconds to put your Sony Bluetooth headphones into pairing mode. Tap the Settings icon on your iPhone. Select the Bluetooth option. Select your headphones from the list of devices, then wait for it to say “Connected.”</code> |
| <code>can i use bluetooth headphones for xbox one?</code> | <code>Headsets cannot be connected to any third party wireless controller. Headsets need to be connected to the Xbox one controller in order to function. The Xbox one console doesn't have a Bluetooth feature. Hence the headsets cannot be connected via Bluetooth.</code> | <code>You can only pair one Bluetooth headphone or soundbar and one other Bluetooth device to the TV at the same time, but not two Bluetooth headphones or soundbars at the same time.</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Evaluation Dataset
#### Unnamed Dataset
* Size: 5,000 evaluation samples
* Columns: <code>question</code>, <code>answer</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
| | question | answer | negative_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 12.83 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 31.71 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 31.37 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| question | answer | negative_1 |
|:------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is controlled by the peripheral nervous system?</code> | <code>The efferent nerves of the somatic nervous system of the PNS is responsible for voluntary, conscious control of skeletal muscles (effector organ) using motor (efferent) nerves. The efferent nerves of the autonomic (visceral) nervous system control the visceral functions of the body.</code> | <code>Which of the following is not a part of peripheral nervous system? Explanation: Peripheral nervous system lies outside the brain and spinal cord. Spinal cord is not a part of peripheral nervous system.</code> |
| <code>is cold water good to drink in the morning?</code> | <code>This is probably because drinking cold water makes it easier for your body to maintain a lower core temperature. Drinking plain water, no matter the temperature, has been proven to give your body more energy throughout the day.</code> | <code>What Are Benefits of It Cold? Drinking water cold is beneficial because it tastes better and you are more likely to drink more of it. Cold lemon water tastes delicious and so you are more likely to drink more of it.</code> |
| <code>how to get rid of fungal nail quickly?</code> | <code>According to a 2016 review, thymol has antifungal and antibacterial properties. To treat toenail fungus, apply oregano oil to the affected nail twice daily with a cotton swab. Some people use oregano oil and tea tree oil together.</code> | <code>With treatment, many people can get rid of nail fungus. Even when the fungus clears, your nail(s) may look unhealthy until the infected nail grows out. A fingernail grows out in 4 to 6 months and a toenail in 12 to 18 months.</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 3e-06
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `dataloader_num_workers`: 12
- `load_best_model_at_end`: 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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 3e-06
- `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`: 12
- `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`: 12
- `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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | accuracy |
|:----------:|:---------:|:-------------:|:---------------:|:--------:|
| 0 | 0 | - | - | 0.3568 |
| 0.0000 | 1 | 10.0066 | - | - |
| 0.0045 | 200 | 9.562 | - | - |
| 0.0090 | 400 | 8.6749 | - | - |
| 0.0135 | 600 | 6.7475 | - | - |
| 0.0180 | 800 | 4.9203 | - | - |
| 0.0225 | 1000 | 3.4444 | - | - |
| 0.0270 | 1200 | 2.5604 | - | - |
| 0.0316 | 1400 | 2.1878 | - | - |
| 0.0361 | 1600 | 1.9166 | - | - |
| 0.0406 | 1800 | 1.7376 | - | - |
| 0.0451 | 2000 | 1.5786 | - | - |
| 0.0496 | 2200 | 1.4304 | - | - |
| 0.0541 | 2400 | 1.3307 | - | - |
| 0.0586 | 2600 | 1.2409 | - | - |
| 0.0631 | 2800 | 1.1913 | - | - |
| 0.0676 | 3000 | 1.0885 | - | - |
| 0.0721 | 3200 | 1.0439 | - | - |
| 0.0766 | 3400 | 0.9721 | - | - |
| 0.0811 | 3600 | 0.918 | - | - |
| 0.0856 | 3800 | 0.8688 | - | - |
| 0.0901 | 4000 | 0.8269 | - | - |
| 0.0947 | 4200 | 0.7815 | - | - |
| 0.0992 | 4400 | 0.7577 | - | - |
| 0.1037 | 4600 | 0.714 | - | - |
| 0.1082 | 4800 | 0.6923 | - | - |
| 0.1127 | 5000 | 0.6619 | - | - |
| 0.1172 | 5200 | 0.6409 | - | - |
| 0.1217 | 5400 | 0.6142 | - | - |
| 0.1262 | 5600 | 0.6163 | - | - |
| 0.1307 | 5800 | 0.5821 | - | - |
| 0.1352 | 6000 | 0.5822 | - | - |
| 0.1397 | 6200 | 0.5572 | - | - |
| 0.1442 | 6400 | 0.555 | - | - |
| 0.1487 | 6600 | 0.5392 | - | - |
| 0.1533 | 6800 | 0.5326 | - | - |
| 0.1578 | 7000 | 0.5185 | - | - |
| 0.1623 | 7200 | 0.507 | - | - |
| 0.1668 | 7400 | 0.4943 | - | - |
| 0.1713 | 7600 | 0.4915 | - | - |
| 0.1758 | 7800 | 0.4951 | - | - |
| 0.1803 | 8000 | 0.4806 | - | - |
| 0.1848 | 8200 | 0.4782 | - | - |
| 0.1893 | 8400 | 0.4719 | - | - |
| 0.1938 | 8600 | 0.4628 | - | - |
| 0.1983 | 8800 | 0.4615 | - | - |
| 0.2028 | 9000 | 0.4624 | - | - |
| 0.2073 | 9200 | 0.4462 | - | - |
| 0.2119 | 9400 | 0.4571 | - | - |
| 0.2164 | 9600 | 0.452 | - | - |
| 0.2209 | 9800 | 0.4454 | - | - |
| 0.2254 | 10000 | 0.4387 | - | - |
| 0.2299 | 10200 | 0.4247 | - | - |
| 0.2344 | 10400 | 0.4221 | - | - |
| 0.2389 | 10600 | 0.4242 | - | - |
| 0.2434 | 10800 | 0.422 | - | - |
| 0.2479 | 11000 | 0.4252 | - | - |
| 0.2524 | 11200 | 0.416 | - | - |
| 0.2569 | 11400 | 0.4138 | - | - |
| 0.2614 | 11600 | 0.4139 | - | - |
| 0.2659 | 11800 | 0.4168 | - | - |
| 0.2704 | 12000 | 0.4008 | - | - |
| 0.2750 | 12200 | 0.3994 | - | - |
| 0.2795 | 12400 | 0.3973 | - | - |
| 0.2840 | 12600 | 0.393 | - | - |
| 0.2885 | 12800 | 0.3863 | - | - |
| 0.2930 | 13000 | 0.3914 | - | - |
| 0.2975 | 13200 | 0.38 | - | - |
| 0.3020 | 13400 | 0.3805 | - | - |
| 0.3065 | 13600 | 0.3749 | - | - |
| 0.3110 | 13800 | 0.3814 | - | - |
| 0.3155 | 14000 | 0.3783 | - | - |
| 0.3200 | 14200 | 0.3733 | - | - |
| 0.3245 | 14400 | 0.3762 | - | - |
| 0.3290 | 14600 | 0.3797 | - | - |
| 0.3336 | 14800 | 0.3727 | - | - |
| 0.3381 | 15000 | 0.3658 | - | - |
| 0.3426 | 15200 | 0.3655 | - | - |
| 0.3471 | 15400 | 0.3619 | - | - |
| 0.3516 | 15600 | 0.3685 | - | - |
| 0.3561 | 15800 | 0.3608 | - | - |
| 0.3606 | 16000 | 0.3631 | - | - |
| 0.3651 | 16200 | 0.3587 | - | - |
| 0.3696 | 16400 | 0.3536 | - | - |
| 0.3741 | 16600 | 0.3477 | - | - |
| 0.3786 | 16800 | 0.3595 | - | - |
| 0.3831 | 17000 | 0.3558 | - | - |
| 0.3876 | 17200 | 0.3518 | - | - |
| 0.3921 | 17400 | 0.353 | - | - |
| 0.3967 | 17600 | 0.354 | - | - |
| 0.4012 | 17800 | 0.3477 | - | - |
| 0.4057 | 18000 | 0.3457 | - | - |
| 0.4102 | 18200 | 0.346 | - | - |
| 0.4147 | 18400 | 0.3451 | - | - |
| 0.4192 | 18600 | 0.3437 | - | - |
| 0.4237 | 18800 | 0.3401 | - | - |
| 0.4282 | 19000 | 0.342 | - | - |
| 0.4327 | 19200 | 0.3416 | - | - |
| 0.4372 | 19400 | 0.3405 | - | - |
| 0.4417 | 19600 | 0.3331 | - | - |
| 0.4462 | 19800 | 0.3319 | - | - |
| 0.4507 | 20000 | 0.3264 | - | - |
| 0 | 0 | - | - | 0.4590 |
| 0.4507 | 20000 | - | 1.2902 | - |
| 0.4553 | 20200 | 0.3312 | - | - |
| 0.4598 | 20400 | 0.3363 | - | - |
| 0.4643 | 20600 | 0.333 | - | - |
| 0.4688 | 20800 | 0.3341 | - | - |
| 0.4733 | 21000 | 0.3287 | - | - |
| 0.4778 | 21200 | 0.3357 | - | - |
| 0.4823 | 21400 | 0.3325 | - | - |
| 0.4868 | 21600 | 0.3323 | - | - |
| 0.4913 | 21800 | 0.3385 | - | - |
| 0.4958 | 22000 | 0.3244 | - | - |
| 0.5003 | 22200 | 0.3281 | - | - |
| 0.5048 | 22400 | 0.3251 | - | - |
| 0.5093 | 22600 | 0.3271 | - | - |
| 0.5138 | 22800 | 0.3271 | - | - |
| 0.5184 | 23000 | 0.3245 | - | - |
| 0.5229 | 23200 | 0.3185 | - | - |
| 0.5274 | 23400 | 0.3212 | - | - |
| 0.5319 | 23600 | 0.3211 | - | - |
| 0.5364 | 23800 | 0.3205 | - | - |
| 0.5409 | 24000 | 0.3104 | - | - |
| 0.5454 | 24200 | 0.3208 | - | - |
| 0.5499 | 24400 | 0.3218 | - | - |
| 0.5544 | 24600 | 0.3183 | - | - |
| 0.5589 | 24800 | 0.3208 | - | - |
| 0.5634 | 25000 | 0.3151 | - | - |
| 0.5679 | 25200 | 0.3138 | - | - |
| 0.5724 | 25400 | 0.3155 | - | - |
| 0.5770 | 25600 | 0.3201 | - | - |
| 0.5815 | 25800 | 0.3135 | - | - |
| 0.5860 | 26000 | 0.3157 | - | - |
| 0.5905 | 26200 | 0.3051 | - | - |
| 0.5950 | 26400 | 0.3121 | - | - |
| 0.5995 | 26600 | 0.3109 | - | - |
| 0.6040 | 26800 | 0.3103 | - | - |
| 0.6085 | 27000 | 0.316 | - | - |
| 0.6130 | 27200 | 0.3119 | - | - |
| 0.6175 | 27400 | 0.3135 | - | - |
| 0.6220 | 27600 | 0.3007 | - | - |
| 0.6265 | 27800 | 0.304 | - | - |
| 0.6310 | 28000 | 0.3014 | - | - |
| 0.6356 | 28200 | 0.3075 | - | - |
| 0.6401 | 28400 | 0.3074 | - | - |
| 0.6446 | 28600 | 0.3072 | - | - |
| 0.6491 | 28800 | 0.3043 | - | - |
| 0.6536 | 29000 | 0.3059 | - | - |
| 0.6581 | 29200 | 0.3054 | - | - |
| 0.6626 | 29400 | 0.3019 | - | - |
| 0.6671 | 29600 | 0.3108 | - | - |
| 0.6716 | 29800 | 0.3032 | - | - |
| 0.6761 | 30000 | 0.3054 | - | - |
| 0.6806 | 30200 | 0.3034 | - | - |
| 0.6851 | 30400 | 0.3008 | - | - |
| 0.6896 | 30600 | 0.3 | - | - |
| 0.6941 | 30800 | 0.3042 | - | - |
| 0.6987 | 31000 | 0.3018 | - | - |
| 0.7032 | 31200 | 0.3162 | - | - |
| 0.7077 | 31400 | 0.2998 | - | - |
| 0.7122 | 31600 | 0.2975 | - | - |
| 0.7167 | 31800 | 0.3015 | - | - |
| 0.7212 | 32000 | 0.3005 | - | - |
| 0.7257 | 32200 | 0.3028 | - | - |
| 0.7302 | 32400 | 0.3029 | - | - |
| 0.7347 | 32600 | 0.2968 | - | - |
| 0.7392 | 32800 | 0.3066 | - | - |
| 0.7437 | 33000 | 0.2958 | - | - |
| 0.7482 | 33200 | 0.2968 | - | - |
| 0.7527 | 33400 | 0.2963 | - | - |
| 0.7573 | 33600 | 0.3026 | - | - |
| 0.7618 | 33800 | 0.2891 | - | - |
| 0.7663 | 34000 | 0.2991 | - | - |
| 0.7708 | 34200 | 0.2939 | - | - |
| 0.7753 | 34400 | 0.2923 | - | - |
| 0.7798 | 34600 | 0.295 | - | - |
| 0.7843 | 34800 | 0.2901 | - | - |
| 0.7888 | 35000 | 0.294 | - | - |
| 0.7933 | 35200 | 0.2945 | - | - |
| 0.7978 | 35400 | 0.299 | - | - |
| 0.8023 | 35600 | 0.297 | - | - |
| 0.8068 | 35800 | 0.2881 | - | - |
| 0.8113 | 36000 | 0.298 | - | - |
| 0.8158 | 36200 | 0.2925 | - | - |
| 0.8204 | 36400 | 0.2978 | - | - |
| 0.8249 | 36600 | 0.2989 | - | - |
| 0.8294 | 36800 | 0.2914 | - | - |
| 0.8339 | 37000 | 0.2913 | - | - |
| 0.8384 | 37200 | 0.2925 | - | - |
| 0.8429 | 37400 | 0.2991 | - | - |
| 0.8474 | 37600 | 0.291 | - | - |
| 0.8519 | 37800 | 0.2937 | - | - |
| 0.8564 | 38000 | 0.2989 | - | - |
| 0.8609 | 38200 | 0.2854 | - | - |
| 0.8654 | 38400 | 0.2878 | - | - |
| 0.8699 | 38600 | 0.2905 | - | - |
| 0.8744 | 38800 | 0.287 | - | - |
| 0.8790 | 39000 | 0.2869 | - | - |
| 0.8835 | 39200 | 0.2927 | - | - |
| 0.8880 | 39400 | 0.2889 | - | - |
| 0.8925 | 39600 | 0.2912 | - | - |
| 0.8970 | 39800 | 0.2927 | - | - |
| **0.9015** | **40000** | **0.2952** | **-** | **-** |
| 0 | 0 | - | - | 0.4774 |
| **0.9015** | **40000** | **-** | **1.2227** | **-** |
| 0.9060 | 40200 | 0.29 | - | - |
| 0.9105 | 40400 | 0.2878 | - | - |
| 0.9150 | 40600 | 0.2924 | - | - |
| 0.9195 | 40800 | 0.2877 | - | - |
| 0.9240 | 41000 | 0.2844 | - | - |
| 0.9285 | 41200 | 0.2951 | - | - |
| 0.9330 | 41400 | 0.291 | - | - |
| 0.9375 | 41600 | 0.292 | - | - |
| 0.9421 | 41800 | 0.2902 | - | - |
| 0.9466 | 42000 | 0.2815 | - | - |
| 0.9511 | 42200 | 0.29 | - | - |
| 0.9556 | 42400 | 0.2872 | - | - |
| 0.9601 | 42600 | 0.2759 | - | - |
| 0.9646 | 42800 | 0.2832 | - | - |
| 0.9691 | 43000 | 0.2886 | - | - |
| 0.9736 | 43200 | 0.2908 | - | - |
| 0.9781 | 43400 | 0.2857 | - | - |
| 0.9826 | 43600 | 0.2833 | - | - |
| 0.9871 | 43800 | 0.2837 | - | - |
| 0.9916 | 44000 | 0.2882 | - | - |
| 0.9961 | 44200 | 0.2919 | - | - |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.11.0
- Sentence Transformers: 4.0.1
- PyLate: 1.1.7
- Transformers: 4.48.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
<!--
## 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.*
--> |