File size: 27,114 Bytes
b482253 |
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 |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- cross-encoder
- generated_from_trainer
- dataset_size:36728
- loss:BinaryCrossEntropyLoss
base_model: cross-encoder/ms-marco-MiniLM-L6-v2
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- pearson
- spearman
- map
- mrr@10
- ndcg@10
model-index:
- name: ms-marco-MiniLM-L-6-v2 Finetuned on PV211 HomeWork
results:
- task:
type: cross-encoder-correlation
name: Cross Encoder Correlation
dataset:
name: sts dev
type: sts_dev
metrics:
- type: pearson
value: 0.8857946136871967
name: Pearson
- type: spearman
value: 0.8182465826410324
name: Spearman
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R100
type: NanoMSMARCO_R100
metrics:
- type: map
value: 0.6048
name: Map
- type: mrr@10
value: 0.5974
name: Mrr@10
- type: ndcg@10
value: 0.6644
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNFCorpus R100
type: NanoNFCorpus_R100
metrics:
- type: map
value: 0.3633
name: Map
- type: mrr@10
value: 0.5961
name: Mrr@10
- type: ndcg@10
value: 0.4082
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R100
type: NanoNQ_R100
metrics:
- type: map
value: 0.6871
name: Map
- type: mrr@10
value: 0.7117
name: Mrr@10
- type: ndcg@10
value: 0.7413
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: 0.5517
name: Map
- type: mrr@10
value: 0.635
name: Mrr@10
- type: ndcg@10
value: 0.6046
name: Ndcg@10
---
# ms-marco-MiniLM-L-6-v2 Finetuned on PV211 HomeWork
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) 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:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) <!-- at revision ce0834f22110de6d9222af7a7a03628121708969 -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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("maennyn/pv211_beir_cqadupstack_crossencoder2")
# Get scores for pairs of texts
pairs = [
['Increase the X length of a tikzpicture', "In recent years I've developed a habit of formatting SQL `SELECT` queries like so: SELECT fieldNames FROM sources JOIN tableSource ON col1 = col2 JOIN ( SELECT fieldNames FROM otherSources ) AS subQuery ON subQuery.foo = col2 WHERE someField = somePredicate So you see my pattern: each keyword is on its own line and that keyword's fields are indented by 1 tab-stop and the pattern is used recursively for sub- queries. This works well for all of my `SELECT` queries, as it maximizes readability though at the cost of vertical space; but it doesn't work for things like `INSERT` and `UPDATE` which have radically different syntax. INSERT INTO tableName ( col1, col2, col3, col4, col5, col6, col7, col8 ) VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' ), VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' ) UPDATE tableName SET col1 = 'col1', col2 = 'col2', col3 = 'col3', // etc WHERE someField = somePredicate As you can see, they aren't as pretty, and when you're dealing with tables with a lot of columns they quickly become unweildly. Is there a better way to format `INSERT` and `UPDATE`? And what about `CREATE` statements and other operations?"],
['Fillable form: checkbox linked to hide/unhide sections; pushbutton to add/delete rows', "I'd like to create a LaTeX document that when rendered into PDF, has forms that can be filled out using Adobe Reader or other such programs. Then I'd like to be able to extract the data. I deliberately would like to avoid using Acrobat for all the usual reasons (non-free, need different versions for different platforms etc). Can this be done ?"],
['Is there any way to get something like pmatrix with customizable grid lines between cells?', "> **Possible Duplicate:** > Highlight elements in the matrix i have a matrix: \\begin{equation} \\begin{bmatrix} 1 & 5 & 4 & 2 & 1 \\\\ 1 & 5 & 4 & 2 & 1 \\\\ 1 & 5 & 4 & 2 & 1 \\\\ \\end{bmatrix} \\label{e:crop1} \\end{equation} and i would like to draw a box around a few of the values to highlight a selection & label it, how would i go about this? I've looked at nodes but havent got a clue. thanks"],
["Difference between 'all' and 'all the'", 'I am not confident about my judgement as to whether or not "the" is required if a relative clause is used in a sentence. For example, > The data can be collected on all the computers on which the software is > installed. I think it must be "all the computers " and not be "all computers" because "computers" is specified by "on which the software is installed". Please help me confirm that I am right.'],
['Understanding the exclamation mark (!) in bash', "I'm following through a tutorial and it mentions to run this command: sudo chmod 700 !$ I'm not familiar with `!$`. What does it mean?"],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'Increase the X length of a tikzpicture',
[
"In recent years I've developed a habit of formatting SQL `SELECT` queries like so: SELECT fieldNames FROM sources JOIN tableSource ON col1 = col2 JOIN ( SELECT fieldNames FROM otherSources ) AS subQuery ON subQuery.foo = col2 WHERE someField = somePredicate So you see my pattern: each keyword is on its own line and that keyword's fields are indented by 1 tab-stop and the pattern is used recursively for sub- queries. This works well for all of my `SELECT` queries, as it maximizes readability though at the cost of vertical space; but it doesn't work for things like `INSERT` and `UPDATE` which have radically different syntax. INSERT INTO tableName ( col1, col2, col3, col4, col5, col6, col7, col8 ) VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' ), VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' ) UPDATE tableName SET col1 = 'col1', col2 = 'col2', col3 = 'col3', // etc WHERE someField = somePredicate As you can see, they aren't as pretty, and when you're dealing with tables with a lot of columns they quickly become unweildly. Is there a better way to format `INSERT` and `UPDATE`? And what about `CREATE` statements and other operations?",
"I'd like to create a LaTeX document that when rendered into PDF, has forms that can be filled out using Adobe Reader or other such programs. Then I'd like to be able to extract the data. I deliberately would like to avoid using Acrobat for all the usual reasons (non-free, need different versions for different platforms etc). Can this be done ?",
"> **Possible Duplicate:** > Highlight elements in the matrix i have a matrix: \\begin{equation} \\begin{bmatrix} 1 & 5 & 4 & 2 & 1 \\\\ 1 & 5 & 4 & 2 & 1 \\\\ 1 & 5 & 4 & 2 & 1 \\\\ \\end{bmatrix} \\label{e:crop1} \\end{equation} and i would like to draw a box around a few of the values to highlight a selection & label it, how would i go about this? I've looked at nodes but havent got a clue. thanks",
'I am not confident about my judgement as to whether or not "the" is required if a relative clause is used in a sentence. For example, > The data can be collected on all the computers on which the software is > installed. I think it must be "all the computers " and not be "all computers" because "computers" is specified by "on which the software is installed". Please help me confirm that I am right.',
"I'm following through a tutorial and it mentions to run this command: sudo chmod 700 !$ I'm not familiar with `!$`. What does it mean?",
]
)
# [{'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 Correlation
* Dataset: `sts_dev`
* Evaluated with [<code>CrossEncoderCorrelationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderCorrelationEvaluator)
| Metric | Value |
|:-------------|:-----------|
| pearson | 0.8858 |
| **spearman** | **0.8182** |
#### 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 | 0.6048 (+0.1152) | 0.3633 (+0.1023) | 0.6871 (+0.2674) |
| mrr@10 | 0.5974 (+0.1199) | 0.5961 (+0.0962) | 0.7117 (+0.2850) |
| **ndcg@10** | **0.6644 (+0.1240)** | **0.4082 (+0.0832)** | **0.7413 (+0.2407)** |
#### 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 | 0.5517 (+0.1616) |
| mrr@10 | 0.6350 (+0.1670) |
| **ndcg@10** | **0.6046 (+0.1493)** |
<!--
## 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: 36,728 training samples
* Columns: <code>query</code>, <code>document</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | query | document | label |
|:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 15 characters</li><li>mean: 49.89 characters</li><li>max: 128 characters</li></ul> | <ul><li>min: 36 characters</li><li>mean: 718.8 characters</li><li>max: 17541 characters</li></ul> | <ul><li>0: ~48.90%</li><li>1: ~51.10%</li></ul> |
* Samples:
| query | document | label |
|:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Increase the X length of a tikzpicture</code> | <code>In recent years I've developed a habit of formatting SQL `SELECT` queries like so: SELECT fieldNames FROM sources JOIN tableSource ON col1 = col2 JOIN ( SELECT fieldNames FROM otherSources ) AS subQuery ON subQuery.foo = col2 WHERE someField = somePredicate So you see my pattern: each keyword is on its own line and that keyword's fields are indented by 1 tab-stop and the pattern is used recursively for sub- queries. This works well for all of my `SELECT` queries, as it maximizes readability though at the cost of vertical space; but it doesn't work for things like `INSERT` and `UPDATE` which have radically different syntax. INSERT INTO tableName ( col1, col2, col3, col4, col5, col6, col7, col8 ) VALUES ( 'col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8' ), VALUES ( 'col1', 'col2', 'col3', 'col4',...</code> | <code>0</code> |
| <code>Fillable form: checkbox linked to hide/unhide sections; pushbutton to add/delete rows</code> | <code>I'd like to create a LaTeX document that when rendered into PDF, has forms that can be filled out using Adobe Reader or other such programs. Then I'd like to be able to extract the data. I deliberately would like to avoid using Acrobat for all the usual reasons (non-free, need different versions for different platforms etc). Can this be done ?</code> | <code>1</code> |
| <code>Is there any way to get something like pmatrix with customizable grid lines between cells?</code> | <code>> **Possible Duplicate:** > Highlight elements in the matrix i have a matrix: \begin{equation} \begin{bmatrix} 1 & 5 & 4 & 2 & 1 \\ 1 & 5 & 4 & 2 & 1 \\ 1 & 5 & 4 & 2 & 1 \\ \end{bmatrix} \label{e:crop1} \end{equation} and i would like to draw a box around a few of the values to highlight a selection & label it, how would i go about this? I've looked at nodes but havent got a clue. thanks</code> | <code>1</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `save_only_model`: True
- `fp16`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `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`: 3
- `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`: True
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: 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}
- `tp_size`: 0
- `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
- `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 | sts_dev_spearman | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
|:-------:|:--------:|:-------------:|:----------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
| -1 | -1 | - | 0.7222 | 0.6686 (+0.1282) | 0.3930 (+0.0680) | 0.7599 (+0.2592) | 0.6072 (+0.1518) |
| 0.4355 | 1000 | 0.4163 | - | - | - | - | - |
| 0.8711 | 2000 | 0.1632 | - | - | - | - | - |
| **1.0** | **2296** | **-** | **0.8182** | **0.6644 (+0.1240)** | **0.4082 (+0.0832)** | **0.7413 (+0.2407)** | **0.6046 (+0.1493)** |
| 1.3066 | 3000 | 0.1227 | - | - | - | - | - |
| 1.7422 | 4000 | 0.1157 | - | - | - | - | - |
| 2.0 | 4592 | - | 0.8201 | 0.6266 (+0.0862) | 0.4096 (+0.0846) | 0.7032 (+0.2026) | 0.5798 (+0.1244) |
| 2.1777 | 5000 | 0.0964 | - | - | - | - | - |
| 2.6132 | 6000 | 0.081 | - | - | - | - | - |
| 3.0 | 6888 | - | 0.8203 | 0.6241 (+0.0837) | 0.4068 (+0.0817) | 0.6931 (+0.1924) | 0.5747 (+0.1193) |
| -1 | -1 | - | 0.8182 | 0.6644 (+0.1240) | 0.4082 (+0.0832) | 0.7413 (+0.2407) | 0.6046 (+0.1493) |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.8.0.dev20250319+cu128
- Accelerate: 1.6.0
- 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",
}
```
<!--
## 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.*
--> |