File size: 32,354 Bytes
4875efc |
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 |
---
language:
- en
tags:
- sentence-transformers
- cross-encoder
- generated_from_trainer
- dataset_size:78704
- loss:PListMLELoss
base_model: microsoft/MiniLM-L12-H384-uncased
datasets:
- microsoft/ms_marco
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
co2_eq_emissions:
emissions: 91.76522350814476
energy_consumed: 0.2360811714581987
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.882
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CrossEncoder based on microsoft/MiniLM-L12-H384-uncased
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R100
type: NanoMSMARCO_R100
metrics:
- type: map
value: 0.5177
name: Map
- type: mrr@10
value: 0.5097
name: Mrr@10
- type: ndcg@10
value: 0.5896
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNFCorpus R100
type: NanoNFCorpus_R100
metrics:
- type: map
value: 0.3178
name: Map
- type: mrr@10
value: 0.5947
name: Mrr@10
- type: ndcg@10
value: 0.3558
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R100
type: NanoNQ_R100
metrics:
- type: map
value: 0.6038
name: Map
- type: mrr@10
value: 0.6118
name: Mrr@10
- type: ndcg@10
value: 0.6704
name: Ndcg@10
- task:
type: cross-encoder-nano-beir
name: Cross Encoder Nano BEIR
dataset:
name: NanoBEIR R100 mean
type: NanoBEIR_R100_mean
metrics:
- type: map
value: 0.4797
name: Map
- type: mrr@10
value: 0.5721
name: Mrr@10
- type: ndcg@10
value: 0.5386
name: Ndcg@10
---
# CrossEncoder based on microsoft/MiniLM-L12-H384-uncased
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) <!-- at revision 44acabbec0ef496f6dbc93adadea57f376b7c0ec -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
- [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## 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 CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("tomaarsen/reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-plistmle-softmax")
# Get scores for pairs of texts
pairs = [
['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (3,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'How many calories in an egg',
[
'There are on average between 55 and 80 calories in an egg depending on its size.',
'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
'Most of the calories in an egg come from the yellow yolk in the center.',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### 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
#### Cross Encoder Reranking
* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
|:------------|:---------------------|:---------------------|:---------------------|
| map | 0.5177 (+0.0281) | 0.3178 (+0.0568) | 0.6038 (+0.1842) |
| mrr@10 | 0.5097 (+0.0322) | 0.5947 (+0.0949) | 0.6118 (+0.1851) |
| **ndcg@10** | **0.5896 (+0.0492)** | **0.3558 (+0.0308)** | **0.6704 (+0.1697)** |
#### Cross Encoder Nano BEIR
* Dataset: `NanoBEIR_R100_mean`
* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"rerank_k": 100,
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.4797 (+0.0897) |
| mrr@10 | 0.5721 (+0.1040) |
| **ndcg@10** | **0.5386 (+0.0832)** |
<!--
## 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
#### ms_marco
* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
* Size: 78,704 training samples
* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
| | query | docs | labels |
|:--------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 10 characters</li><li>mean: 34.08 characters</li><li>max: 109 characters</li></ul> | <ul><li>min: 3 elements</li><li>mean: 6.50 elements</li><li>max: 10 elements</li></ul> | <ul><li>min: 3 elements</li><li>mean: 6.50 elements</li><li>max: 10 elements</li></ul> |
* Samples:
| query | docs | labels |
|:-----------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
| <code>what is ha</code> | <code>['1 ha. interjection \\ˈhae\\. ˈhä. —tweet used especially to express surprise or a feeling of pleasure that you have when you do something or find out about. something Full Definition of HA. —used especially to express surprise, joy, or triumph. See ha defined for English-language learners.', 'Ha-ha protecting the lawn at Heaton Hall, Manchester. Note how the wall disappears as it curves away to the right of the picture. A ha-ha is a recessed landscape design element that creates a vertical barrier whilst preserving an uninterrupted view of the landscape beyond. The design includes a turfed incline which slopes downward to a sharply vertical face, typically a masonry retaining wall. Ha-has are used in landscape design to prevent access to a garden, for example by grazing livestock, without obstructing views. In security design, the element is used to deter vehicular access to a site while minimizing visual obstruction', 'The doctors said they are afraid of HA-MRSA. Why is it so scary?...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>salary for kinesiology degree</code> | <code>['Coaches. Coaching is another common career option for those holding a degree in kinesiology. Athletic coaches made an average annual salary of $35,950, as of May 2010, according to the bureau, while the middle 50 percent made between $18,800 and $43,930 annually.', "Athletic Trainers. One common career option for those holding a bachelor's degree in kinesiology is a career in the athletic training field. Athletic trainers made an average annual salary of $44,030, as of May 2010, according to the U.S. Bureau of Labor Statistics.", 'Kinesiologist salary. A newly graduated kinesiologist may receive a yearly minimum pay of around $20,000. This average will increase significantly after a year. As a matter of fact a kinesiologist can receive up to around $80,000 yearly. The median average salary of all kinesiologists is around $46,000 per annum.', 'Salary of starting kinesiologist in Georgia ranges from $29,592 – $44,387. The average kinesiology wage ranges from $35,933 – $53,899 and the t...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>average temperatures owensboro ky</code> | <code>['2.8. The highest average temperature in Owensboro is August at 79.5 degrees. The coldest average temperature in Owensboro is February at 37 degrees. The most monthly precipitation in Owensboro occurs in September with 4 inches. The Owensboro weather information is based on the average of the previous 3-7 years of data. Loading...', 'Most / Least Educated Cities in KY. The average temperature of Owensboro is 56.97°F, which is higher than the Kentucky average temperature of 55.62°F and is higher than the national average temperature of 54.45°F.', 'Owensboro, Kentucky, gets 45 inches of rain per year. The US average is 37. Snowfall is 10 inches. The average US city gets 25 inches of snow per year. The number of days with any measurable precipitation is 103. On average, there are 202 sunny days per year in Owensboro, Kentucky. The July high is around 90 degrees. The January low is 25.']</code> | <code>[1, 0, 0]</code> |
* Loss: [<code>PListMLELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#plistmleloss) with these parameters:
```json
{
"lambda_weight": "sentence_transformers.cross_encoder.losses.PListMLELoss.PListMLELambdaWeight",
"activation_fct": "torch.nn.modules.linear.Identity",
"mini_batch_size": 16,
"respect_input_order": true
}
```
### Evaluation Dataset
#### ms_marco
* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
* Size: 1,000 evaluation samples
* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
| | query | docs | labels |
|:--------|:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 8 characters</li><li>mean: 33.76 characters</li><li>max: 90 characters</li></ul> | <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> | <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> |
* Samples:
| query | docs | labels |
|:--------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
| <code>meaning of regional value content</code> | <code>['Regional Value Content Some specific rules of origin require that a good have a minimum regional value content, meaning that a certain percentage of the value of the goods must be from North America. There are two formulas for calculating the regional value content. TV. Where RVC is the regional value content, expressed as a percentage; TV is the transaction value of the good adjusted to an FOB basis; and VNM is the value of non-originating material used by the producer in the production of the good.', 'Regional Value Content: For goods subject to a regional value content rule of origin, please refer to Regional Value content (Explanatory Material and Article 402), for an explanation of regional value content rules of origin. In the two above situations, no tariff shift is possible because of how the goods were classified. Goods in this situation may obtain NAFTA tariff preference if they have 50 or 60 percent North American value content, depending on method used. Refer to Regional ...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>how long does it take to get an irs refund</code> | <code>['Essentially, they’re saying most people will get their refunds in less than 21 days, but there won’t be any schedules they will follow this year. The IRS provides these tips to tax preparers to get the fastest tax refund: 1 File an accurate tax return. 2 Efile your tax return. The IRS eliminated the refund schedules that were used in prior years for both direct deposit and mailed refunds. The guideline the IRS is using in 2015 for filing your 2014 tax return is: Don’t count on getting your refund by a certain date to make major purchases or pay other financial obligations. Even though the IRS issues most refunds in less than 21 days, it’s possible your tax return may require additional review and take longer', 'If you file a paper return, the IRS says you should allow about six weeks to receive your refund. If you file Form 8379, Injured Spouse Allocation, it could take up to 14 weeks to process your tax return. ', 'Tax refunds are normally issued within approximately 21 days if yo...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>calculate margin on cost to retail</code> | <code>['The formula for calculating retail margin is the sales price of an item minus COGS, divided by the sales price, multiplied by 100. If you sell an item at $20 and paid $10 to acquire it and sell it, your retail margin is $10 divided by $20, or 50 percent. You subtract your COGS from your desired sales price in the same way, but then divide that amount by your COGS. If your target price is $20 and your COGS are $10, your markup is $10 divided by $10, or 100 percent. Thus, to achieve a 50 percent margin on an item that costs you $10, you need a 100 percent markup.', 'This gives you a 70 percent cost-to-retail ratio. For the year, you book $336,000 in net sales. Your cost of goods sold under the retail method is 70 percent of net sales, or $235,200. Your gross profit is $100,800, which is 30 percent of net sales. You can shortcut the calculation of gross profit margin by subtracting your cost-to-retail percentage of 70 percent from 100 percent. While easy enough to calculate, the gross m...</code> | <code>[1, 1, 0, 0, 0, ...]</code> |
* Loss: [<code>PListMLELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#plistmleloss) with these parameters:
```json
{
"lambda_weight": "sentence_transformers.cross_encoder.losses.PListMLELoss.PListMLELambdaWeight",
"activation_fct": "torch.nn.modules.linear.Identity",
"mini_batch_size": 16,
"respect_input_order": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 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`: 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`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `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
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
|:----------:|:--------:|:-------------:|:---------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
| -1 | -1 | - | - | 0.0785 (-0.4620) | 0.2441 (-0.0809) | 0.0473 (-0.4533) | 0.1233 (-0.3321) |
| 0.0002 | 1 | 2.2881 | - | - | - | - | - |
| 0.0508 | 250 | 2.2177 | - | - | - | - | - |
| 0.1016 | 500 | 2.0172 | 1.8991 | 0.3929 (-0.1475) | 0.2715 (-0.0535) | 0.3910 (-0.1096) | 0.3518 (-0.1036) |
| 0.1525 | 750 | 1.8769 | - | - | - | - | - |
| 0.2033 | 1000 | 1.8053 | 1.7602 | 0.5109 (-0.0296) | 0.3391 (+0.0140) | 0.6059 (+0.1052) | 0.4853 (+0.0299) |
| 0.2541 | 1250 | 1.7566 | - | - | - | - | - |
| 0.3049 | 1500 | 1.7298 | 1.7028 | 0.5152 (-0.0253) | 0.3334 (+0.0084) | 0.6332 (+0.1326) | 0.4939 (+0.0385) |
| 0.3558 | 1750 | 1.7261 | - | - | - | - | - |
| 0.4066 | 2000 | 1.6907 | 1.7166 | 0.5441 (+0.0037) | 0.3571 (+0.0321) | 0.6231 (+0.1224) | 0.5081 (+0.0527) |
| 0.4574 | 2250 | 1.6579 | - | - | - | - | - |
| 0.5082 | 2500 | 1.6599 | 1.6482 | 0.5434 (+0.0030) | 0.3284 (+0.0034) | 0.6008 (+0.1001) | 0.4909 (+0.0355) |
| 0.5591 | 2750 | 1.6467 | - | - | - | - | - |
| 0.6099 | 3000 | 1.6489 | 1.6266 | 0.5541 (+0.0137) | 0.3514 (+0.0264) | 0.6354 (+0.1347) | 0.5136 (+0.0583) |
| 0.6607 | 3250 | 1.6538 | - | - | - | - | - |
| **0.7115** | **3500** | **1.6246** | **1.6389** | **0.5896 (+0.0492)** | **0.3558 (+0.0308)** | **0.6704 (+0.1697)** | **0.5386 (+0.0832)** |
| 0.7624 | 3750 | 1.6164 | - | - | - | - | - |
| 0.8132 | 4000 | 1.6139 | 1.6174 | 0.5639 (+0.0235) | 0.3358 (+0.0108) | 0.5988 (+0.0981) | 0.4995 (+0.0441) |
| 0.8640 | 4250 | 1.6283 | - | - | - | - | - |
| 0.9148 | 4500 | 1.6265 | 1.5999 | 0.5737 (+0.0333) | 0.3412 (+0.0161) | 0.6161 (+0.1154) | 0.5103 (+0.0550) |
| 0.9656 | 4750 | 1.5755 | - | - | - | - | - |
| -1 | -1 | - | - | 0.5896 (+0.0492) | 0.3558 (+0.0308) | 0.6704 (+0.1697) | 0.5386 (+0.0832) |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.236 kWh
- **Carbon Emitted**: 0.092 kg of CO2
- **Hours Used**: 0.882 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### PListMLELoss
```bibtex
@inproceedings{lan2014position,
title={Position-Aware ListMLE: A Sequential Learning Process for Ranking.},
author={Lan, Yanyan and Zhu, Yadong and Guo, Jiafeng and Niu, Shuzi and Cheng, Xueqi},
booktitle={UAI},
volume={14},
pages={449--458},
year={2014}
}
```
<!--
## 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.*
--> |