File size: 87,277 Bytes
9d91dd3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 | ---
language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:242513
- loss:Contrastive
base_model: colbert-ir/colbertv2.0
datasets:
- reasonir/reasonir-data
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- accuracy
model-index:
- name: PyLate model based on colbert-ir/colbertv2.0
results:
- task:
type: col-berttriplet
name: Col BERTTriplet
dataset:
name: Unknown
type: unknown
metrics:
- type: accuracy
value: 0.92734694480896
name: Accuracy
---
# PyLate model based on colbert-ir/colbertv2.0
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) on the [reasonir-data](https://huggingface.co/datasets/reasonir/reasonir-data) dataset. 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:** [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) <!-- at revision c1e84128e85ef755c096a95bdb06b47793b13acf -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [reasonir-data](https://huggingface.co/datasets/reasonir/reasonir-data)
- **Language:** en
<!-- - **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': 768, '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=pylate_model_id,
)
# 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=pylate_model_id,
)
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** | **0.9273** |
<!--
## 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
#### reasonir-data
* Dataset: [reasonir-data](https://huggingface.co/datasets/reasonir/reasonir-data) at [0275f82](https://huggingface.co/datasets/reasonir/reasonir-data/tree/0275f825929b206d4ead23d34b4f8a50d4eddbc8)
* Size: 242,513 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 26.76 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.89 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.32 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Market analysis suggests that the ongoing trend of increased adoption of renewable energy sources will continue to drive the demand for solar panels in the coming years. According to various reports, the global solar panel market is projected to witness significant growth over the next decade, with some analysts predicting a compound annual growth rate (CAGR) of up to 20%. This growth is expected to be fueled by declining costs of production, government incentives, and growing environmental concerns. However, some experts also caution that the market may experience fluctuations due to trade policies, technological advancements, and changes in government regulations. Despite these challenges, the overall outlook for the solar panel market remains optimistic, with many companies investing heavily in research and development to improve efficiency and reduce costs. As the demand for renewable energy continues to rise, it is likely that the solar panel market will play a significant role in...</code> | <code>Contrary to the market analysis suggesting a compound annual growth rate (CAGR) of up to 20% for the global solar panel market, our financial reports indicate a more modest growth rate of 12% over the next decade. While we agree that declining production costs, government incentives, and growing environmental concerns will drive demand for solar panels, we also believe that trade policies, technological advancements, and changes in government regulations will have a more significant impact on the market than previously anticipated. Our projections suggest that the market will experience fluctuations, with some years experiencing higher growth rates than others. However, we do not anticipate the market to experience the same level of growth as predicted by other analysts. Our research indicates that the market will reach a saturation point, beyond which growth will slow down. Additionally, we believe that the impact of advancements in energy storage technologies on the solar panel marke...</code> | <code>The demand for solar panels has been on the rise in recent years, driven by an increase in environmental awareness and the need for sustainable energy sources. One of the key factors contributing to this growth is the decline in production costs. As technology advances, the cost of manufacturing solar panels has decreased, making them more affordable for consumers. Additionally, governments around the world have implemented policies and incentives to encourage the adoption of renewable energy sources, which has further boosted demand for solar panels. However, the solar panel market is not without its challenges. Trade policies and technological advancements can impact the market, and changes in government regulations can create uncertainty. Despite these challenges, the outlook for the solar panel market remains positive, with many companies investing heavily in research and development to improve efficiency and reduce costs. The development of new technologies, such as bifacial panel...</code> |
| <code>As the sun set over the vast savannah, a sense of tranquility washed over the pride of lions. Their tawny coats glistened in the fading light, and the sound of crickets provided a soothing background hum. Nearby, a group of humans, armed with cameras and curiosity, observed the wild animals from a safe distance.</code> | <code>The lions lazed in the shade of a nearby tree, their tawny coats a blur as they basked in the warmth. The visitors watched in awe, clicking away at their cameras to capture the majesty of the wild animals. Crickets provided a constant, soothing background noise as the humans took care to keep a safe distance from the pride.</code> | <code>The city's tree planting initiative has been a huge success, providing a serene oasis in the midst of the bustling metropolis. The sounds of the city – car horns, chatter and crickets – blend together to create a symphony of noise. While many humans have been drawn to the tranquility of the park, others have raised concerns about the integration of urban wildlife.</code> |
| <code>Recent advancements in the field of artificial intelligence have led to significant breakthroughs in natural language processing. This has far-reaching implications for various industries, including education, where AI-powered chatbots can enhance student learning experiences by providing personalization and real-time feedback. Moreover, the integration of AI in educational settings can help address issues of accessibility and equity.</code> | <code>The rapid expansion of AI research has yielded substantial progress in natural language processing, allowing for the development of more sophisticated AI-powered tools. In the education sector, AI-driven chatbots can facilitate individualized learning and offer instantaneous feedback, thereby enriching the overall learning environment. However, it is crucial to address concerns surrounding the digital divide to ensure that these technological advancements are accessible to all.</code> | <code>One of the primary challenges facing archaeologists today is the authentication of ancient artifacts, which often involves meticulous analysis of relics and literary texts. The discovery of a previously unknown scroll, buried deep within the labyrinthine passages of an Egyptian tomb, shed new light on the role of language in ancient cultures. Interestingly, the sophisticated syntax and nuanced vocabulary of the ancient Egyptian language have some similarities with modern-day linguistic structures.</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Evaluation Dataset
#### reasonir-data
* Dataset: [reasonir-data](https://huggingface.co/datasets/reasonir/reasonir-data) at [0275f82](https://huggingface.co/datasets/reasonir/reasonir-data/tree/0275f825929b206d4ead23d34b4f8a50d4eddbc8)
* Size: 2,450 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 26.92 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.98 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.5 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>In the recent speech, the politician claimed that the current tax reform will benefit the middle class and lead to a significant increase in economic growth. The politician stated, 'Our plan is to cut taxes across the board, simplifying the tax code and making it fairer for all Americans. We're taking the money from the unfair and complex system and putting it back in the pockets of the hardworking people.' The politician also emphasized the effects of the cut taxes, 'When we cut taxes, we're putting more money in the hands of our small business owners and workers, who are the backbone of our economy. This means new jobs will be created, wages will rise, and the people who actually create the jobs, the entrepreneurs, will have the funds needed to invest more into their businesses.' Moreover, the politician asserted, 'Our country will witness a major boost in job creation and economic growth which in turn will positively affect local communities all around the country.' Furthermore, the...</code> | <code>According to reputable sources in economics research, the tax reform that the administration is trying to implement closely resembles that of the 2001 and the 2003 cuts under former President George Bush and that of 1981 under President Ronald Reagan who reduced tax rates 23% and 25% respectively. Research carried out by a major university indicated that these reforms only yielded an estimated 10% increase in tax revenue, since the decrease in tax income could result in compensated revenues through economic stimulation. Some studies were actually pointing to the idea that no trickle-down economics apply as more funds were being placed in the already wealthy communities. This change could shift the economical inequalities to an extreme and showed a direct relationship between a tax reduction and a large national deficit increase. Employer demand for the borderline employee may not actually increase from the creation of the new jobs, and economists believed. The variation of wages for jo...</code> | <code>The concept of a universal basic income has been a topic of discussion among economists and policymakers in recent years. While some see it as a viable solution to poverty and economic inequality, others argue that it is not feasible due to the financial constraints it would impose on governments. One of the main concerns is that implementing a universal basic income would require significant funding, which would likely come from increased taxes or redistribution of existing social welfare funds. Critics argue that this could lead to a decrease in economic growth, as people may be less incentivized to work if they are receiving a guaranteed income. On the other hand, proponents argue that a universal basic income would provide a safety net for the most vulnerable members of society and allow people to pursue meaningful work rather than just taking any job for the sake of a paycheck. Some countries have experimented with universal basic income pilots, but the results have been mixed. Fi...</code> |
| <code>Recent advances in super-resolution techniques have led to a greater understanding of many sub-cellular structures and have opened up new avenues for exploring cellular behavior at the nanoscale. Fluorescence imaging, in particular, has greatly benefited from these advances and has enabled researchers to visualize the distribution and dynamics of proteins in real time. However, further developments in fluorescence imaging rely on a better comprehension of the interactions between imaging probes and their molecular environment. A crucial factor in these interactions is the size and shape of the probes, which must be optimized to minimize disruption of the native dynamics of the system while also achieving high fluorescence yields. The DNA-based probes have emerged as a promising solution, offering the opportunity to tune the size and shape of the probes to optimize performance.</code> | <code>Microscopy</code> | <code>Biophysics</code> |
| <code>I recently purchased this top-of-the-line smartwatch for my birthday, and I must say that it has been a revelation in terms of keeping track of my vital signs and daily activity levels. The watch has an elegant design that doesn't clash with my other wearable accessories, and I love how the touchscreen display lets me access a wealth of health metrics at a glance. Although I've encountered several instances where the heart rate monitoring system was delayed in capturing accurate readings, this minor shortcoming hardly detracts from my overall satisfaction with the product. The value proposition it presents in terms of quality, accuracy, and ascendancy over competing offerings makes it a compelling option in this class of devices. Despite never having owned one before, I found the smartwatch straightforward to use, and the companion app did an excellent job of simplifying the tracking and analysis of my fitness journey. Nothing in particular distinguishes this product's methodology in c...</code> | <code>authentic</code> | <code>fake</code> |
* Loss: <code>pylate.losses.contrastive.Contrastive</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `warmup_steps`: 100
- `fp16`: True
- `remove_unused_columns`: False
#### 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`: 16
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 100
- `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`: False
- `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`: 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 |
|:------:|:-----:|:-------------:|:---------------:|:--------:|
| 0.0066 | 50 | 3.9255 | - | - |
| 0.0132 | 100 | 1.7945 | - | - |
| 0.0198 | 150 | 1.5522 | - | - |
| 0.0264 | 200 | 1.6267 | - | - |
| 0.0330 | 250 | 1.5434 | - | - |
| 0 | 0 | - | - | 0.8714 |
| 0.0330 | 250 | - | 0.8547 | - |
| 0.0396 | 300 | 1.3113 | - | - |
| 0.0462 | 350 | 1.3674 | - | - |
| 0.0528 | 400 | 1.3417 | - | - |
| 0.0594 | 450 | 1.2831 | - | - |
| 0.0660 | 500 | 1.2243 | - | - |
| 0 | 0 | - | - | 0.8820 |
| 0.0660 | 500 | - | 0.7873 | - |
| 0.0726 | 550 | 1.2276 | - | - |
| 0.0792 | 600 | 1.2502 | - | - |
| 0.0858 | 650 | 1.2247 | - | - |
| 0.0924 | 700 | 1.178 | - | - |
| 0.0990 | 750 | 1.2379 | - | - |
| 0 | 0 | - | - | 0.8931 |
| 0.0990 | 750 | - | 0.7503 | - |
| 0.1056 | 800 | 1.3893 | - | - |
| 0.1122 | 850 | 1.1852 | - | - |
| 0.1187 | 900 | 1.1082 | - | - |
| 0.1253 | 950 | 0.9946 | - | - |
| 0.1319 | 1000 | 1.1834 | - | - |
| 0 | 0 | - | - | 0.8996 |
| 0.1319 | 1000 | - | 0.7309 | - |
| 0.1385 | 1050 | 1.1556 | - | - |
| 0.1451 | 1100 | 1.0251 | - | - |
| 0.1517 | 1150 | 1.1943 | - | - |
| 0.1583 | 1200 | 1.086 | - | - |
| 0.1649 | 1250 | 1.1236 | - | - |
| 0 | 0 | - | - | 0.9008 |
| 0.1649 | 1250 | - | 0.6946 | - |
| 0.1715 | 1300 | 1.0485 | - | - |
| 0.1781 | 1350 | 0.9481 | - | - |
| 0.1847 | 1400 | 1.0898 | - | - |
| 0.1913 | 1450 | 1.0863 | - | - |
| 0.1979 | 1500 | 1.0756 | - | - |
| 0 | 0 | - | - | 0.9037 |
| 0.1979 | 1500 | - | 0.6747 | - |
| 0.2045 | 1550 | 0.9973 | - | - |
| 0.2111 | 1600 | 1.1098 | - | - |
| 0.2177 | 1650 | 1.1745 | - | - |
| 0.2243 | 1700 | 0.9654 | - | - |
| 0.2309 | 1750 | 1.0919 | - | - |
| 0 | 0 | - | - | 0.9094 |
| 0.2309 | 1750 | - | 0.6499 | - |
| 0.2375 | 1800 | 1.0249 | - | - |
| 0.2441 | 1850 | 0.9863 | - | - |
| 0.2507 | 1900 | 1.1091 | - | - |
| 0.2573 | 1950 | 1.0989 | - | - |
| 0.2639 | 2000 | 1.0662 | - | - |
| 0 | 0 | - | - | 0.9065 |
| 0.2639 | 2000 | - | 0.6661 | - |
| 0.2705 | 2050 | 1.0456 | - | - |
| 0.2771 | 2100 | 1.1349 | - | - |
| 0.2837 | 2150 | 1.0111 | - | - |
| 0.2903 | 2200 | 1.026 | - | - |
| 0.2969 | 2250 | 0.9415 | - | - |
| 0 | 0 | - | - | 0.9073 |
| 0.2969 | 2250 | - | 0.6390 | - |
| 0.3035 | 2300 | 0.9761 | - | - |
| 0.3101 | 2350 | 0.9748 | - | - |
| 0.3167 | 2400 | 1.0238 | - | - |
| 0.3233 | 2450 | 1.0456 | - | - |
| 0.3299 | 2500 | 0.9895 | - | - |
| 0 | 0 | - | - | 0.9110 |
| 0.3299 | 2500 | - | 0.6435 | - |
| 0.3365 | 2550 | 0.8796 | - | - |
| 0.3431 | 2600 | 1.0172 | - | - |
| 0.3497 | 2650 | 1.014 | - | - |
| 0.3562 | 2700 | 0.9748 | - | - |
| 0.3628 | 2750 | 0.9273 | - | - |
| 0 | 0 | - | - | 0.9082 |
| 0.3628 | 2750 | - | 0.6303 | - |
| 0.3694 | 2800 | 1.0122 | - | - |
| 0.3760 | 2850 | 1.0054 | - | - |
| 0.3826 | 2900 | 0.8974 | - | - |
| 0.3892 | 2950 | 0.9396 | - | - |
| 0.3958 | 3000 | 0.8734 | - | - |
| 0 | 0 | - | - | 0.9049 |
| 0.3958 | 3000 | - | 0.6238 | - |
| 0.4024 | 3050 | 1.0048 | - | - |
| 0.4090 | 3100 | 0.9701 | - | - |
| 0.4156 | 3150 | 0.9924 | - | - |
| 0.4222 | 3200 | 0.9349 | - | - |
| 0.4288 | 3250 | 0.974 | - | - |
| 0 | 0 | - | - | 0.9118 |
| 0.4288 | 3250 | - | 0.6216 | - |
| 0.4354 | 3300 | 1.0539 | - | - |
| 0.4420 | 3350 | 0.9389 | - | - |
| 0.4486 | 3400 | 0.9171 | - | - |
| 0.4552 | 3450 | 0.9706 | - | - |
| 0.4618 | 3500 | 1.0124 | - | - |
| 0 | 0 | - | - | 0.9065 |
| 0.4618 | 3500 | - | 0.6126 | - |
| 0.4684 | 3550 | 0.9215 | - | - |
| 0.4750 | 3600 | 0.8563 | - | - |
| 0.4816 | 3650 | 0.8249 | - | - |
| 0.4882 | 3700 | 0.8794 | - | - |
| 0.4948 | 3750 | 1.0013 | - | - |
| 0 | 0 | - | - | 0.9078 |
| 0.4948 | 3750 | - | 0.6022 | - |
| 0.5014 | 3800 | 0.922 | - | - |
| 0.5080 | 3850 | 0.9168 | - | - |
| 0.5146 | 3900 | 0.9315 | - | - |
| 0.5212 | 3950 | 0.9265 | - | - |
| 0.5278 | 4000 | 0.9453 | - | - |
| 0 | 0 | - | - | 0.9078 |
| 0.5278 | 4000 | - | 0.6083 | - |
| 0.5344 | 4050 | 0.9585 | - | - |
| 0.5410 | 4100 | 0.9886 | - | - |
| 0.5476 | 4150 | 0.9081 | - | - |
| 0.5542 | 4200 | 0.8181 | - | - |
| 0.5608 | 4250 | 0.8806 | - | - |
| 0 | 0 | - | - | 0.9118 |
| 0.5608 | 4250 | - | 0.5918 | - |
| 0.5674 | 4300 | 0.858 | - | - |
| 0.5740 | 4350 | 0.8781 | - | - |
| 0.5806 | 4400 | 0.9059 | - | - |
| 0.5871 | 4450 | 0.8475 | - | - |
| 0.5937 | 4500 | 0.9649 | - | - |
| 0 | 0 | - | - | 0.9057 |
| 0.5937 | 4500 | - | 0.5951 | - |
| 0.6003 | 4550 | 0.969 | - | - |
| 0.6069 | 4600 | 0.8685 | - | - |
| 0.6135 | 4650 | 0.9555 | - | - |
| 0.6201 | 4700 | 0.9166 | - | - |
| 0.6267 | 4750 | 0.877 | - | - |
| 0 | 0 | - | - | 0.9073 |
| 0.6267 | 4750 | - | 0.5858 | - |
| 0.6333 | 4800 | 0.938 | - | - |
| 0.6399 | 4850 | 0.9211 | - | - |
| 0.6465 | 4900 | 0.9699 | - | - |
| 0.6531 | 4950 | 0.8818 | - | - |
| 0.6597 | 5000 | 0.9814 | - | - |
| 0 | 0 | - | - | 0.9176 |
| 0.6597 | 5000 | - | 0.5705 | - |
| 0.6663 | 5050 | 0.8588 | - | - |
| 0.6729 | 5100 | 0.8922 | - | - |
| 0.6795 | 5150 | 1.0096 | - | - |
| 0.6861 | 5200 | 0.9217 | - | - |
| 0.6927 | 5250 | 0.9446 | - | - |
| 0 | 0 | - | - | 0.9147 |
| 0.6927 | 5250 | - | 0.5740 | - |
| 0.6993 | 5300 | 0.9301 | - | - |
| 0.7059 | 5350 | 0.8436 | - | - |
| 0.7125 | 5400 | 0.8547 | - | - |
| 0.7191 | 5450 | 0.9552 | - | - |
| 0.7257 | 5500 | 0.9227 | - | - |
| 0 | 0 | - | - | 0.9135 |
| 0.7257 | 5500 | - | 0.5913 | - |
| 0.7323 | 5550 | 0.8813 | - | - |
| 0.7389 | 5600 | 0.8519 | - | - |
| 0.7455 | 5650 | 0.8223 | - | - |
| 0.7521 | 5700 | 0.8603 | - | - |
| 0.7587 | 5750 | 0.8208 | - | - |
| 0 | 0 | - | - | 0.9151 |
| 0.7587 | 5750 | - | 0.5698 | - |
| 0.7653 | 5800 | 0.8542 | - | - |
| 0.7719 | 5850 | 0.7924 | - | - |
| 0.7785 | 5900 | 0.9238 | - | - |
| 0.7851 | 5950 | 0.8303 | - | - |
| 0.7917 | 6000 | 0.8254 | - | - |
| 0 | 0 | - | - | 0.9159 |
| 0.7917 | 6000 | - | 0.5643 | - |
| 0.7983 | 6050 | 0.8556 | - | - |
| 0.8049 | 6100 | 0.9286 | - | - |
| 0.8115 | 6150 | 0.8776 | - | - |
| 0.8180 | 6200 | 0.8146 | - | - |
| 0.8246 | 6250 | 0.8469 | - | - |
| 0 | 0 | - | - | 0.9127 |
| 0.8246 | 6250 | - | 0.5627 | - |
| 0.8312 | 6300 | 0.9719 | - | - |
| 0.8378 | 6350 | 0.9297 | - | - |
| 0.8444 | 6400 | 0.896 | - | - |
| 0.8510 | 6450 | 0.8709 | - | - |
| 0.8576 | 6500 | 0.9436 | - | - |
| 0 | 0 | - | - | 0.9159 |
| 0.8576 | 6500 | - | 0.5638 | - |
| 0.8642 | 6550 | 0.8938 | - | - |
| 0.8708 | 6600 | 0.8065 | - | - |
| 0.8774 | 6650 | 0.8281 | - | - |
| 0.8840 | 6700 | 0.8449 | - | - |
| 0.8906 | 6750 | 0.813 | - | - |
| 0 | 0 | - | - | 0.9167 |
| 0.8906 | 6750 | - | 0.5694 | - |
| 0.8972 | 6800 | 0.9052 | - | - |
| 0.9038 | 6850 | 0.9501 | - | - |
| 0.9104 | 6900 | 0.9612 | - | - |
| 0.9170 | 6950 | 0.8649 | - | - |
| 0.9236 | 7000 | 0.7366 | - | - |
| 0 | 0 | - | - | 0.9188 |
| 0.9236 | 7000 | - | 0.5691 | - |
| 0.9302 | 7050 | 0.9621 | - | - |
| 0.9368 | 7100 | 0.9154 | - | - |
| 0.9434 | 7150 | 0.8617 | - | - |
| 0.9500 | 7200 | 0.8629 | - | - |
| 0.9566 | 7250 | 0.899 | - | - |
| 0 | 0 | - | - | 0.9159 |
| 0.9566 | 7250 | - | 0.5559 | - |
| 0.9632 | 7300 | 0.7766 | - | - |
| 0.9698 | 7350 | 0.8968 | - | - |
| 0.9764 | 7400 | 0.8462 | - | - |
| 0.9830 | 7450 | 0.8478 | - | - |
| 0.9896 | 7500 | 0.8184 | - | - |
| 0 | 0 | - | - | 0.9163 |
| 0.9896 | 7500 | - | 0.5564 | - |
| 0.9962 | 7550 | 0.8445 | - | - |
| 1.0028 | 7600 | 0.7305 | - | - |
| 1.0094 | 7650 | 0.695 | - | - |
| 1.0160 | 7700 | 0.779 | - | - |
| 1.0226 | 7750 | 0.5876 | - | - |
| 0 | 0 | - | - | 0.9184 |
| 1.0226 | 7750 | - | 0.5776 | - |
| 1.0292 | 7800 | 0.6372 | - | - |
| 1.0358 | 7850 | 0.7066 | - | - |
| 1.0424 | 7900 | 0.6561 | - | - |
| 1.0490 | 7950 | 0.6854 | - | - |
| 1.0555 | 8000 | 0.7083 | - | - |
| 0 | 0 | - | - | 0.9212 |
| 1.0555 | 8000 | - | 0.5645 | - |
| 1.0621 | 8050 | 0.6618 | - | - |
| 1.0687 | 8100 | 0.6602 | - | - |
| 1.0753 | 8150 | 0.7141 | - | - |
| 1.0819 | 8200 | 0.7599 | - | - |
| 1.0885 | 8250 | 0.6307 | - | - |
| 0 | 0 | - | - | 0.9159 |
| 1.0885 | 8250 | - | 0.5608 | - |
| 1.0951 | 8300 | 0.6611 | - | - |
| 1.1017 | 8350 | 0.6308 | - | - |
| 1.1083 | 8400 | 0.7035 | - | - |
| 1.1149 | 8450 | 0.683 | - | - |
| 1.1215 | 8500 | 0.7077 | - | - |
| 0 | 0 | - | - | 0.9184 |
| 1.1215 | 8500 | - | 0.5556 | - |
| 1.1281 | 8550 | 0.7153 | - | - |
| 1.1347 | 8600 | 0.6186 | - | - |
| 1.1413 | 8650 | 0.6289 | - | - |
| 1.1479 | 8700 | 0.5718 | - | - |
| 1.1545 | 8750 | 0.5749 | - | - |
| 0 | 0 | - | - | 0.9167 |
| 1.1545 | 8750 | - | 0.5695 | - |
| 1.1611 | 8800 | 0.6788 | - | - |
| 1.1677 | 8850 | 0.7731 | - | - |
| 1.1743 | 8900 | 0.6954 | - | - |
| 1.1809 | 8950 | 0.7404 | - | - |
| 1.1875 | 9000 | 0.6871 | - | - |
| 0 | 0 | - | - | 0.9208 |
| 1.1875 | 9000 | - | 0.5666 | - |
| 1.1941 | 9050 | 0.6415 | - | - |
| 1.2007 | 9100 | 0.6517 | - | - |
| 1.2073 | 9150 | 0.7354 | - | - |
| 1.2139 | 9200 | 0.7325 | - | - |
| 1.2205 | 9250 | 0.6272 | - | - |
| 0 | 0 | - | - | 0.9147 |
| 1.2205 | 9250 | - | 0.5714 | - |
| 1.2271 | 9300 | 0.7292 | - | - |
| 1.2337 | 9350 | 0.6325 | - | - |
| 1.2403 | 9400 | 0.6344 | - | - |
| 1.2469 | 9450 | 0.7218 | - | - |
| 1.2535 | 9500 | 0.6815 | - | - |
| 0 | 0 | - | - | 0.9176 |
| 1.2535 | 9500 | - | 0.5651 | - |
| 1.2601 | 9550 | 0.7186 | - | - |
| 1.2667 | 9600 | 0.6145 | - | - |
| 1.2733 | 9650 | 0.7095 | - | - |
| 1.2799 | 9700 | 0.674 | - | - |
| 1.2864 | 9750 | 0.7405 | - | - |
| 0 | 0 | - | - | 0.9200 |
| 1.2864 | 9750 | - | 0.5666 | - |
| 1.2930 | 9800 | 0.7186 | - | - |
| 1.2996 | 9850 | 0.6352 | - | - |
| 1.3062 | 9900 | 0.7077 | - | - |
| 1.3128 | 9950 | 0.6873 | - | - |
| 1.3194 | 10000 | 0.5939 | - | - |
| 0 | 0 | - | - | 0.9204 |
| 1.3194 | 10000 | - | 0.5752 | - |
| 1.3260 | 10050 | 0.7171 | - | - |
| 1.3326 | 10100 | 0.6592 | - | - |
| 1.3392 | 10150 | 0.6631 | - | - |
| 1.3458 | 10200 | 0.7658 | - | - |
| 1.3524 | 10250 | 0.6213 | - | - |
| 0 | 0 | - | - | 0.9180 |
| 1.3524 | 10250 | - | 0.5678 | - |
| 1.3590 | 10300 | 0.6486 | - | - |
| 1.3656 | 10350 | 0.662 | - | - |
| 1.3722 | 10400 | 0.6924 | - | - |
| 1.3788 | 10450 | 0.7106 | - | - |
| 1.3854 | 10500 | 0.7239 | - | - |
| 0 | 0 | - | - | 0.9184 |
| 1.3854 | 10500 | - | 0.5687 | - |
| 1.3920 | 10550 | 0.735 | - | - |
| 1.3986 | 10600 | 0.6784 | - | - |
| 1.4052 | 10650 | 0.6886 | - | - |
| 1.4118 | 10700 | 0.649 | - | - |
| 1.4184 | 10750 | 0.6133 | - | - |
| 0 | 0 | - | - | 0.9200 |
| 1.4184 | 10750 | - | 0.5683 | - |
| 1.4250 | 10800 | 0.6635 | - | - |
| 1.4316 | 10850 | 0.6803 | - | - |
| 1.4382 | 10900 | 0.6497 | - | - |
| 1.4448 | 10950 | 0.6812 | - | - |
| 1.4514 | 11000 | 0.7493 | - | - |
| 0 | 0 | - | - | 0.9220 |
| 1.4514 | 11000 | - | 0.5587 | - |
| 1.4580 | 11050 | 0.6694 | - | - |
| 1.4646 | 11100 | 0.6782 | - | - |
| 1.4712 | 11150 | 0.6839 | - | - |
| 1.4778 | 11200 | 0.671 | - | - |
| 1.4844 | 11250 | 0.7648 | - | - |
| 0 | 0 | - | - | 0.9208 |
| 1.4844 | 11250 | - | 0.5466 | - |
| 1.4910 | 11300 | 0.7448 | - | - |
| 1.4976 | 11350 | 0.6811 | - | - |
| 1.5042 | 11400 | 0.6984 | - | - |
| 1.5108 | 11450 | 0.6676 | - | - |
| 1.5174 | 11500 | 0.7054 | - | - |
| 0 | 0 | - | - | 0.9204 |
| 1.5174 | 11500 | - | 0.5569 | - |
| 1.5239 | 11550 | 0.6109 | - | - |
| 1.5305 | 11600 | 0.7581 | - | - |
| 1.5371 | 11650 | 0.7035 | - | - |
| 1.5437 | 11700 | 0.6943 | - | - |
| 1.5503 | 11750 | 0.6225 | - | - |
| 0 | 0 | - | - | 0.9224 |
| 1.5503 | 11750 | - | 0.5571 | - |
| 1.5569 | 11800 | 0.661 | - | - |
| 1.5635 | 11850 | 0.635 | - | - |
| 1.5701 | 11900 | 0.613 | - | - |
| 1.5767 | 11950 | 0.6502 | - | - |
| 1.5833 | 12000 | 0.6935 | - | - |
| 0 | 0 | - | - | 0.9200 |
| 1.5833 | 12000 | - | 0.5579 | - |
| 1.5899 | 12050 | 0.6147 | - | - |
| 1.5965 | 12100 | 0.6575 | - | - |
| 1.6031 | 12150 | 0.6837 | - | - |
| 1.6097 | 12200 | 0.7437 | - | - |
| 1.6163 | 12250 | 0.6808 | - | - |
| 0 | 0 | - | - | 0.9204 |
| 1.6163 | 12250 | - | 0.5507 | - |
| 1.6229 | 12300 | 0.6698 | - | - |
| 1.6295 | 12350 | 0.6803 | - | - |
| 1.6361 | 12400 | 0.676 | - | - |
| 1.6427 | 12450 | 0.6418 | - | - |
| 1.6493 | 12500 | 0.6042 | - | - |
| 0 | 0 | - | - | 0.9188 |
| 1.6493 | 12500 | - | 0.5563 | - |
| 1.6559 | 12550 | 0.7139 | - | - |
| 1.6625 | 12600 | 0.6995 | - | - |
| 1.6691 | 12650 | 0.6097 | - | - |
| 1.6757 | 12700 | 0.6407 | - | - |
| 1.6823 | 12750 | 0.5994 | - | - |
| 0 | 0 | - | - | 0.9249 |
| 1.6823 | 12750 | - | 0.5621 | - |
| 1.6889 | 12800 | 0.6642 | - | - |
| 1.6955 | 12850 | 0.6198 | - | - |
| 1.7021 | 12900 | 0.6648 | - | - |
| 1.7087 | 12950 | 0.5644 | - | - |
| 1.7153 | 13000 | 0.6531 | - | - |
| 0 | 0 | - | - | 0.9241 |
| 1.7153 | 13000 | - | 0.5617 | - |
| 1.7219 | 13050 | 0.6159 | - | - |
| 1.7285 | 13100 | 0.7855 | - | - |
| 1.7351 | 13150 | 0.6307 | - | - |
| 1.7417 | 13200 | 0.61 | - | - |
| 1.7483 | 13250 | 0.6672 | - | - |
| 0 | 0 | - | - | 0.9237 |
| 1.7483 | 13250 | - | 0.5589 | - |
| 1.7548 | 13300 | 0.6002 | - | - |
| 1.7614 | 13350 | 0.6638 | - | - |
| 1.7680 | 13400 | 0.6112 | - | - |
| 1.7746 | 13450 | 0.6236 | - | - |
| 1.7812 | 13500 | 0.6245 | - | - |
| 0 | 0 | - | - | 0.9220 |
| 1.7812 | 13500 | - | 0.5580 | - |
| 1.7878 | 13550 | 0.7146 | - | - |
| 1.7944 | 13600 | 0.5969 | - | - |
| 1.8010 | 13650 | 0.7246 | - | - |
| 1.8076 | 13700 | 0.65 | - | - |
| 1.8142 | 13750 | 0.7136 | - | - |
| 0 | 0 | - | - | 0.9204 |
| 1.8142 | 13750 | - | 0.5533 | - |
| 1.8208 | 13800 | 0.7062 | - | - |
| 1.8274 | 13850 | 0.6987 | - | - |
| 1.8340 | 13900 | 0.6642 | - | - |
| 1.8406 | 13950 | 0.6761 | - | - |
| 1.8472 | 14000 | 0.6766 | - | - |
| 0 | 0 | - | - | 0.9212 |
| 1.8472 | 14000 | - | 0.5655 | - |
| 1.8538 | 14050 | 0.5758 | - | - |
| 1.8604 | 14100 | 0.6594 | - | - |
| 1.8670 | 14150 | 0.7866 | - | - |
| 1.8736 | 14200 | 0.5798 | - | - |
| 1.8802 | 14250 | 0.6472 | - | - |
| 0 | 0 | - | - | 0.9212 |
| 1.8802 | 14250 | - | 0.5509 | - |
| 1.8868 | 14300 | 0.7387 | - | - |
| 1.8934 | 14350 | 0.6677 | - | - |
| 1.9000 | 14400 | 0.6697 | - | - |
| 1.9066 | 14450 | 0.6711 | - | - |
| 1.9132 | 14500 | 0.6988 | - | - |
| 0 | 0 | - | - | 0.9229 |
| 1.9132 | 14500 | - | 0.5528 | - |
| 1.9198 | 14550 | 0.6301 | - | - |
| 1.9264 | 14600 | 0.6259 | - | - |
| 1.9330 | 14650 | 0.6223 | - | - |
| 1.9396 | 14700 | 0.5702 | - | - |
| 1.9462 | 14750 | 0.6324 | - | - |
| 0 | 0 | - | - | 0.9253 |
| 1.9462 | 14750 | - | 0.5508 | - |
| 1.9528 | 14800 | 0.6409 | - | - |
| 1.9594 | 14850 | 0.6609 | - | - |
| 1.9660 | 14900 | 0.6581 | - | - |
| 1.9726 | 14950 | 0.6313 | - | - |
| 1.9792 | 15000 | 0.6191 | - | - |
| 0 | 0 | - | - | 0.9216 |
| 1.9792 | 15000 | - | 0.5452 | - |
| 1.9858 | 15050 | 0.6665 | - | - |
| 1.9923 | 15100 | 0.5907 | - | - |
| 1.9989 | 15150 | 0.6586 | - | - |
| 2.0055 | 15200 | 0.5673 | - | - |
| 2.0121 | 15250 | 0.5516 | - | - |
| 0 | 0 | - | - | 0.9233 |
| 2.0121 | 15250 | - | 0.5589 | - |
| 2.0187 | 15300 | 0.5012 | - | - |
| 2.0253 | 15350 | 0.5227 | - | - |
| 2.0319 | 15400 | 0.4449 | - | - |
| 2.0385 | 15450 | 0.4862 | - | - |
| 2.0451 | 15500 | 0.5413 | - | - |
| 0 | 0 | - | - | 0.9233 |
| 2.0451 | 15500 | - | 0.5642 | - |
| 2.0517 | 15550 | 0.5462 | - | - |
| 2.0583 | 15600 | 0.5318 | - | - |
| 2.0649 | 15650 | 0.5706 | - | - |
| 2.0715 | 15700 | 0.5055 | - | - |
| 2.0781 | 15750 | 0.6141 | - | - |
| 0 | 0 | - | - | 0.9233 |
| 2.0781 | 15750 | - | 0.5611 | - |
| 2.0847 | 15800 | 0.5247 | - | - |
| 2.0913 | 15850 | 0.4817 | - | - |
| 2.0979 | 15900 | 0.4599 | - | - |
| 2.1045 | 15950 | 0.5676 | - | - |
| 2.1111 | 16000 | 0.3992 | - | - |
| 0 | 0 | - | - | 0.9237 |
| 2.1111 | 16000 | - | 0.5720 | - |
| 2.1177 | 16050 | 0.5337 | - | - |
| 2.1243 | 16100 | 0.4641 | - | - |
| 2.1309 | 16150 | 0.5636 | - | - |
| 2.1375 | 16200 | 0.4811 | - | - |
| 2.1441 | 16250 | 0.499 | - | - |
| 0 | 0 | - | - | 0.9216 |
| 2.1441 | 16250 | - | 0.5673 | - |
| 2.1507 | 16300 | 0.5822 | - | - |
| 2.1573 | 16350 | 0.5935 | - | - |
| 2.1639 | 16400 | 0.5028 | - | - |
| 2.1705 | 16450 | 0.5118 | - | - |
| 2.1771 | 16500 | 0.5623 | - | - |
| 0 | 0 | - | - | 0.9261 |
| 2.1771 | 16500 | - | 0.5656 | - |
| 2.1837 | 16550 | 0.481 | - | - |
| 2.1903 | 16600 | 0.5461 | - | - |
| 2.1969 | 16650 | 0.5802 | - | - |
| 2.2035 | 16700 | 0.5269 | - | - |
| 2.2101 | 16750 | 0.5022 | - | - |
| 0 | 0 | - | - | 0.9220 |
| 2.2101 | 16750 | - | 0.5671 | - |
| 2.2167 | 16800 | 0.5203 | - | - |
| 2.2232 | 16850 | 0.5461 | - | - |
| 2.2298 | 16900 | 0.5711 | - | - |
| 2.2364 | 16950 | 0.5615 | - | - |
| 2.2430 | 17000 | 0.5748 | - | - |
| 0 | 0 | - | - | 0.9257 |
| 2.2430 | 17000 | - | 0.5605 | - |
| 2.2496 | 17050 | 0.5272 | - | - |
| 2.2562 | 17100 | 0.4401 | - | - |
| 2.2628 | 17150 | 0.5158 | - | - |
| 2.2694 | 17200 | 0.5163 | - | - |
| 2.2760 | 17250 | 0.5195 | - | - |
| 0 | 0 | - | - | 0.9237 |
| 2.2760 | 17250 | - | 0.5647 | - |
| 2.2826 | 17300 | 0.5235 | - | - |
| 2.2892 | 17350 | 0.5335 | - | - |
| 2.2958 | 17400 | 0.4915 | - | - |
| 2.3024 | 17450 | 0.4915 | - | - |
| 2.3090 | 17500 | 0.4959 | - | - |
| 0 | 0 | - | - | 0.9233 |
| 2.3090 | 17500 | - | 0.5675 | - |
| 2.3156 | 17550 | 0.5161 | - | - |
| 2.3222 | 17600 | 0.4944 | - | - |
| 2.3288 | 17650 | 0.5052 | - | - |
| 2.3354 | 17700 | 0.4937 | - | - |
| 2.3420 | 17750 | 0.4695 | - | - |
| 0 | 0 | - | - | 0.9253 |
| 2.3420 | 17750 | - | 0.5615 | - |
| 2.3486 | 17800 | 0.5159 | - | - |
| 2.3552 | 17850 | 0.4992 | - | - |
| 2.3618 | 17900 | 0.5288 | - | - |
| 2.3684 | 17950 | 0.5247 | - | - |
| 2.3750 | 18000 | 0.5491 | - | - |
| 0 | 0 | - | - | 0.9257 |
| 2.3750 | 18000 | - | 0.5594 | - |
| 2.3816 | 18050 | 0.5332 | - | - |
| 2.3882 | 18100 | 0.529 | - | - |
| 2.3948 | 18150 | 0.5534 | - | - |
| 2.4014 | 18200 | 0.5595 | - | - |
| 2.4080 | 18250 | 0.573 | - | - |
| 0 | 0 | - | - | 0.9261 |
| 2.4080 | 18250 | - | 0.5610 | - |
| 2.4146 | 18300 | 0.4859 | - | - |
| 2.4212 | 18350 | 0.5019 | - | - |
| 2.4278 | 18400 | 0.4771 | - | - |
| 2.4344 | 18450 | 0.5062 | - | - |
| 2.4410 | 18500 | 0.5342 | - | - |
| 0 | 0 | - | - | 0.9229 |
| 2.4410 | 18500 | - | 0.5617 | - |
| 2.4476 | 18550 | 0.5275 | - | - |
| 2.4541 | 18600 | 0.576 | - | - |
| 2.4607 | 18650 | 0.5172 | - | - |
| 2.4673 | 18700 | 0.5127 | - | - |
| 2.4739 | 18750 | 0.4728 | - | - |
| 0 | 0 | - | - | 0.9249 |
| 2.4739 | 18750 | - | 0.5651 | - |
| 2.4805 | 18800 | 0.4256 | - | - |
| 2.4871 | 18850 | 0.4493 | - | - |
| 2.4937 | 18900 | 0.4881 | - | - |
| 2.5003 | 18950 | 0.4843 | - | - |
| 2.5069 | 19000 | 0.517 | - | - |
| 0 | 0 | - | - | 0.9249 |
| 2.5069 | 19000 | - | 0.5626 | - |
| 2.5135 | 19050 | 0.5927 | - | - |
| 2.5201 | 19100 | 0.5687 | - | - |
| 2.5267 | 19150 | 0.5261 | - | - |
| 2.5333 | 19200 | 0.5698 | - | - |
| 2.5399 | 19250 | 0.5593 | - | - |
| 0 | 0 | - | - | 0.9269 |
| 2.5399 | 19250 | - | 0.5581 | - |
| 2.5465 | 19300 | 0.571 | - | - |
| 2.5531 | 19350 | 0.5606 | - | - |
| 2.5597 | 19400 | 0.4912 | - | - |
| 2.5663 | 19450 | 0.4805 | - | - |
| 2.5729 | 19500 | 0.5324 | - | - |
| 0 | 0 | - | - | 0.9282 |
| 2.5729 | 19500 | - | 0.5537 | - |
| 2.5795 | 19550 | 0.5584 | - | - |
| 2.5861 | 19600 | 0.508 | - | - |
| 2.5927 | 19650 | 0.5231 | - | - |
| 2.5993 | 19700 | 0.557 | - | - |
| 2.6059 | 19750 | 0.5338 | - | - |
| 0 | 0 | - | - | 0.9257 |
| 2.6059 | 19750 | - | 0.5518 | - |
| 2.6125 | 19800 | 0.5037 | - | - |
| 2.6191 | 19850 | 0.6057 | - | - |
| 2.6257 | 19900 | 0.5571 | - | - |
| 2.6323 | 19950 | 0.5177 | - | - |
| 2.6389 | 20000 | 0.4946 | - | - |
| 0 | 0 | - | - | 0.9253 |
| 2.6389 | 20000 | - | 0.5548 | - |
| 2.6455 | 20050 | 0.5256 | - | - |
| 2.6521 | 20100 | 0.5107 | - | - |
| 2.6587 | 20150 | 0.5988 | - | - |
| 2.6653 | 20200 | 0.4907 | - | - |
| 2.6719 | 20250 | 0.4697 | - | - |
| 0 | 0 | - | - | 0.9269 |
| 2.6719 | 20250 | - | 0.5566 | - |
| 2.6785 | 20300 | 0.4897 | - | - |
| 2.6851 | 20350 | 0.5088 | - | - |
| 2.6916 | 20400 | 0.5442 | - | - |
| 2.6982 | 20450 | 0.536 | - | - |
| 2.7048 | 20500 | 0.551 | - | - |
| 0 | 0 | - | - | 0.9269 |
| 2.7048 | 20500 | - | 0.5562 | - |
| 2.7114 | 20550 | 0.5038 | - | - |
| 2.7180 | 20600 | 0.502 | - | - |
| 2.7246 | 20650 | 0.5021 | - | - |
| 2.7312 | 20700 | 0.5441 | - | - |
| 2.7378 | 20750 | 0.4818 | - | - |
| 0 | 0 | - | - | 0.9286 |
| 2.7378 | 20750 | - | 0.5548 | - |
| 2.7444 | 20800 | 0.5012 | - | - |
| 2.7510 | 20850 | 0.5294 | - | - |
| 2.7576 | 20900 | 0.4674 | - | - |
| 2.7642 | 20950 | 0.5436 | - | - |
| 2.7708 | 21000 | 0.4609 | - | - |
| 0 | 0 | - | - | 0.9269 |
| 2.7708 | 21000 | - | 0.5538 | - |
| 2.7774 | 21050 | 0.5015 | - | - |
| 2.7840 | 21100 | 0.5299 | - | - |
| 2.7906 | 21150 | 0.4363 | - | - |
| 2.7972 | 21200 | 0.5018 | - | - |
| 2.8038 | 21250 | 0.5079 | - | - |
| 0 | 0 | - | - | 0.9265 |
| 2.8038 | 21250 | - | 0.5549 | - |
| 2.8104 | 21300 | 0.4467 | - | - |
| 2.8170 | 21350 | 0.5769 | - | - |
| 2.8236 | 21400 | 0.5323 | - | - |
| 2.8302 | 21450 | 0.4714 | - | - |
| 2.8368 | 21500 | 0.4491 | - | - |
| 0 | 0 | - | - | 0.9257 |
| 2.8368 | 21500 | - | 0.5538 | - |
| 2.8434 | 21550 | 0.4801 | - | - |
| 2.8500 | 21600 | 0.5132 | - | - |
| 2.8566 | 21650 | 0.4542 | - | - |
| 2.8632 | 21700 | 0.5015 | - | - |
| 2.8698 | 21750 | 0.4818 | - | - |
| 0 | 0 | - | - | 0.9278 |
| 2.8698 | 21750 | - | 0.5554 | - |
| 2.8764 | 21800 | 0.5078 | - | - |
| 2.8830 | 21850 | 0.508 | - | - |
| 2.8896 | 21900 | 0.5331 | - | - |
| 2.8962 | 21950 | 0.5185 | - | - |
| 2.9028 | 22000 | 0.4469 | - | - |
| 0 | 0 | - | - | 0.9265 |
| 2.9028 | 22000 | - | 0.5551 | - |
| 2.9094 | 22050 | 0.4762 | - | - |
| 2.9160 | 22100 | 0.5799 | - | - |
| 2.9225 | 22150 | 0.4978 | - | - |
| 2.9291 | 22200 | 0.566 | - | - |
| 2.9357 | 22250 | 0.5837 | - | - |
| 0 | 0 | - | - | 0.9269 |
| 2.9357 | 22250 | - | 0.5532 | - |
| 2.9423 | 22300 | 0.5401 | - | - |
| 2.9489 | 22350 | 0.523 | - | - |
| 2.9555 | 22400 | 0.5913 | - | - |
| 2.9621 | 22450 | 0.4701 | - | - |
| 2.9687 | 22500 | 0.5568 | - | - |
| 0 | 0 | - | - | 0.9273 |
| 2.9687 | 22500 | - | 0.5529 | - |
| 2.9753 | 22550 | 0.5266 | - | - |
| 2.9819 | 22600 | 0.4969 | - | - |
| 2.9885 | 22650 | 0.4917 | - | - |
| 2.9951 | 22700 | 0.5128 | - | - |
</details>
### Framework Versions
- Python: 3.12.4
- Sentence Transformers: 4.0.2
- PyLate: 1.2.0
- Transformers: 4.48.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- 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.*
--> |