File size: 24,741 Bytes
e5d453c |
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 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:178
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: Where can investors find more information about NVIDIA's financial
information and company updates?
sentences:
- ' The potential risks include restrictions on sales of products containing certain
components made by Micron, restrictions on receiving supply of components, parts,
or services from Taiwan, increased scrutiny from shareholders, regulators, and
others regarding corporate sustainability practices, and failure to meet evolving
shareholder, regulator, or other industry stakeholder expectations, which could
result in additional costs, reputational harm, and loss of customers and suppliers.'
- ' Investors and others can find more information about NVIDIA''s financial information
and company updates on the company''s investor relations website, through press
releases, SEC filings, public conference calls and webcasts, as well as on the
company''s social media channels, including Twitter, the NVIDIA Corporate Blog,
Facebook, LinkedIn, Instagram, and YouTube.'
- ' The text mentions the following forms and agreements: Officers'' Certificate,
Form of Note (with various years), Form of Indemnity Agreement, Amended and Restated
2007 Equity Incentive Plan, Non-Employee Director Deferred Restricted Stock Unit
Grant Notice and Deferred Restricted Stock Unit Agreement, Non-Employee Director
Restricted Stock Unit Grant Notice and Restricted Stock Unit Agreement, Global
Performance-Based Restricted Stock Unit Grant Notice and Performance-Based Restricted
Stock Unit Agreement, Global Restricted Stock Unit Grant Notice and Global Restricted
Stock Unit Agreement, and various Schedules and Exhibits (such as 2.1, 3.1, 4.1,
4.2, 10.1, 10.2, 10.26, and 10.27).'
- source_sentence: What are the potential consequences if regulators in China conclude
that NVIDIA has failed to fulfill its commitments or has violated applicable law
in China?
sentences:
- ' The company''s share repurchase program aims to offset dilution from shares
issued to employees.'
- ' Ms. Shoquist served as Senior Vice President and General Manager of the Electro-Optics
business at Coherent, Inc., and previously worked at Quantum Corp. as President
of the Personal Computer Hard Disk Drive Division, and at Hewlett-Packard.'
- ' If regulators in China conclude that NVIDIA has failed to fulfill its commitments
or has violated applicable law in China, the company could be subject to various
penalties or restrictions on its ability to conduct its business, which could
have a material and adverse impact on its business, operating results, and financial
condition.'
- source_sentence: What percentage of the company's revenue was attributed to sales
to customers outside of the United States in fiscal year 2024?
sentences:
- ' NVIDIA reports its business results in two segments: the Compute & Networking
segment and the Graphics segment.'
- ' The company expects to use its existing cash, cash equivalents, and marketable
securities, as well as the cash generated by its operations, to fund its capital
investments of approximately $3.5 billion to $4.0 billion related to property
and equipment during fiscal year 2025.'
- ' 56% of the company''s total revenue in fiscal year 2024 was attributed to sales
to customers outside of the United States.'
- source_sentence: What is the net income per share of NVIDIA Corporation for the
year ended January 29, 2023?
sentences:
- ' 6% of the company''s workforce in the United States is composed of Black or
African American employees.'
- ' The net income per share of NVIDIA Corporation for the year ended January 29,
2023 is $12.05 for basic and $11.93 for diluted.'
- ' The company may face potential risks and challenges such as increased expenses,
substantial expenditures and time spent to fully resume operations, disruption
to product development or operations due to employees being called-up for active
military duty, and potential impact on future product development, operations,
and revenue. Additionally, the company may also experience interruptions or delays
in services from third-party providers, which could impair its ability to provide
its products and services and harm its business.'
- source_sentence: What percentage of the company's accounts receivable balance as
of January 28, 2024, was accounted for by two customers?
sentences:
- ' The change in equipment and assembly and test equipment resulted in a benefit
of $135 million in operating income and $114 million in net income, or $0.05 per
both basic and diluted share, for the fiscal year ended January 28, 2024.'
- ' The estimates of deferred tax assets and liabilities may change based on added
certainty or finality to an anticipated outcome, changes in accounting standards
or tax laws in the U.S. or foreign jurisdictions where the company operates, or
changes in other facts or circumstances.'
- ' 24% and 11%, which is a total of 35%.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: bge base en
type: bge-base-en
metrics:
- type: cosine_accuracy@1
value: 0.9269662921348315
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9831460674157303
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9943820224719101
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9269662921348315
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3277153558052434
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.198876404494382
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9269662921348315
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9831460674157303
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9943820224719101
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9682702490705566
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9575842696629214
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9575842696629213
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.9269662921348315
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9831460674157303
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9943820224719101
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.9269662921348315
name: Dot Precision@1
- type: dot_precision@3
value: 0.3277153558052434
name: Dot Precision@3
- type: dot_precision@5
value: 0.198876404494382
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.9269662921348315
name: Dot Recall@1
- type: dot_recall@3
value: 0.9831460674157303
name: Dot Recall@3
- type: dot_recall@5
value: 0.9943820224719101
name: Dot Recall@5
- type: dot_recall@10
value: 1.0
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9682702490705566
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9575842696629214
name: Dot Mrr@10
- type: dot_map@100
value: 0.9575842696629213
name: Dot Map@100
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the train dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- train
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("rezarahim/bge-finetuned-detail")
# Run inference
sentences = [
"What percentage of the company's accounts receivable balance as of January 28, 2024, was accounted for by two customers?",
' 24% and 11%, which is a total of 35%.',
' The change in equipment and assembly and test equipment resulted in a benefit of $135 million in operating income and $114 million in net income, or $0.05 per both basic and diluted share, for the fiscal year ended January 28, 2024.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `bge-base-en`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.927 |
| cosine_accuracy@3 | 0.9831 |
| cosine_accuracy@5 | 0.9944 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.927 |
| cosine_precision@3 | 0.3277 |
| cosine_precision@5 | 0.1989 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.927 |
| cosine_recall@3 | 0.9831 |
| cosine_recall@5 | 0.9944 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.9683 |
| cosine_mrr@10 | 0.9576 |
| **cosine_map@100** | **0.9576** |
| dot_accuracy@1 | 0.927 |
| dot_accuracy@3 | 0.9831 |
| dot_accuracy@5 | 0.9944 |
| dot_accuracy@10 | 1.0 |
| dot_precision@1 | 0.927 |
| dot_precision@3 | 0.3277 |
| dot_precision@5 | 0.1989 |
| dot_precision@10 | 0.1 |
| dot_recall@1 | 0.927 |
| dot_recall@3 | 0.9831 |
| dot_recall@5 | 0.9944 |
| dot_recall@10 | 1.0 |
| dot_ndcg@10 | 0.9683 |
| dot_mrr@10 | 0.9576 |
| dot_map@100 | 0.9576 |
<!--
## 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
#### train
* Dataset: train
* Size: 178 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 178 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 23.63 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 66.67 tokens</li><li>max: 313 tokens</li></ul> |
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the publication date of the NVIDIA Corporation Annual Report 2024?</code> | <code> The publication date of the NVIDIA Corporation Annual Report 2024 is February 21st, 2024.</code> |
| <code>What is the filing date of the 10-K report for NVIDIA Corporation in 2004?</code> | <code> The filing dates of the 10-K reports for NVIDIA Corporation in 2004 are May 20th, March 29th, and April 25th.</code> |
| <code>What is the purpose of the section of the filing that requires the registrant to indicate whether it has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T?</code> | <code> The purpose of this section is to require the registrant to disclose whether it has submitted all required Interactive Data Files electronically, as mandated by Rule 405 of Regulation S-T, during the preceding 12 months or for the shorter period that the registrant was required to submit such files.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 25
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### 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`: 4
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: 25
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: 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_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | bge-base-en_cosine_map@100 |
|:-----------:|:------:|:-------------:|:--------------------------:|
| 0 | 0 | - | 0.8574 |
| 0.7111 | 2 | - | 0.8591 |
| 1.7778 | 5 | - | 0.8757 |
| 2.8444 | 8 | - | 0.9012 |
| 3.5556 | 10 | 0.2885 | - |
| 3.9111 | 11 | - | 0.9134 |
| 4.9778 | 14 | - | 0.9277 |
| 5.6889 | 16 | - | 0.9391 |
| 6.7556 | 19 | - | 0.9463 |
| 7.1111 | 20 | 0.0644 | - |
| 7.8222 | 22 | - | 0.9506 |
| 8.8889 | 25 | - | 0.9515 |
| 9.9556 | 28 | - | 0.9555 |
| 10.6667 | 30 | 0.0333 | 0.9560 |
| 11.7333 | 33 | - | 0.9551 |
| 12.8 | 36 | - | 0.9569 |
| **13.8667** | **39** | **-** | **0.9579** |
| 14.2222 | 40 | 0.0157 | - |
| 14.9333 | 42 | - | 0.9576 |
| 16.0 | 45 | - | 0.9576 |
| 16.7111 | 47 | - | 0.9576 |
| 17.7778 | 50 | 0.0124 | 0.9576 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## 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.*
--> |