Modelpublic

annazdr/nace-pl-v1

sourceHugging Faceupdated 2y agoView on Hugging Face

0likes19downloads

Model Card

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
—Maximum Sequence Length: 128 tokens
—Output Dimensionality: 384 tokens
—Similarity Function: Cosine Similarity

Model Sources

—Documentation: Sentence Transformers Documentation
—Repository: Sentence Transformers on GitHub
—Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("annazdr/nace-pl-v1")
# Run inference
sentences = [
    'dating and other speed networking activities',
    ' pressure, pushbutton, snap, tumbler switches)',
    ' dializy, chemioterapia, insulinoterapia, radioterapia',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 6,413 training samples
—Columns: <code>sentence_0</code> and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | sentence_0 | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | int | | details | <ul><li>min: 2 tokens</li><li>mean: 17.31 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~0.10%</li><li>1: ~0.30%</li><li>2: ~0.40%</li><li>3: ~0.20%</li><li>4: ~0.40%</li><li>5: ~0.10%</li><li>6: ~0.30%</li><li>8: ~0.30%</li><li>9: ~0.20%</li><li>10: ~0.10%</li><li>11: ~0.40%</li><li>12: ~0.30%</li><li>13: ~0.10%</li><li>14: ~0.10%</li><li>15: ~0.20%</li><li>16: ~0.10%</li><li>17: ~0.30%</li><li>18: ~0.20%</li><li>19: ~0.20%</li><li>20: ~0.40%</li><li>21: ~0.30%</li><li>22: ~0.10%</li><li>23: ~0.30%</li><li>24: ~0.20%</li><li>25: ~0.20%</li><li>26: ~0.10%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.20%</li><li>31: ~0.10%</li><li>34: ~0.20%</li><li>36: ~0.10%</li><li>37: ~0.30%</li><li>39: ~0.20%</li><li>40: ~0.60%</li><li>41: ~0.10%</li><li>42: ~0.30%</li><li>43: ~0.20%</li><li>44: ~0.60%</li><li>45: ~0.50%</li><li>46: ~0.20%</li><li>47: ~0.10%</li><li>48: ~0.10%</li><li>49: ~0.20%</li><li>50: ~0.20%</li><li>51: ~0.20%</li><li>52: ~0.20%</li><li>53: ~0.60%</li><li>54: ~0.10%</li><li>55: ~0.20%</li><li>57: ~0.20%</li><li>58: ~0.10%</li><li>59: ~0.10%</li><li>60: ~0.20%</li><li>62: ~0.10%</li><li>63: ~0.20%</li><li>64: ~0.80%</li><li>65: ~0.60%</li><li>66: ~0.70%</li><li>67: ~0.10%</li><li>68: ~0.20%</li><li>69: ~0.30%</li><li>70: ~0.70%</li><li>72: ~0.20%</li><li>73: ~0.90%</li><li>74: ~0.40%</li><li>75: ~0.10%</li><li>76: ~0.40%</li><li>77: ~0.10%</li><li>78: ~0.30%</li><li>79: ~0.20%</li><li>81: ~0.10%</li><li>82: ~0.60%</li><li>83: ~0.20%</li><li>85: ~0.20%</li><li>87: ~0.30%</li><li>88: ~0.20%</li><li>89: ~0.10%</li><li>90: ~0.50%</li><li>95: ~0.20%</li><li>96: ~0.10%</li><li>97: ~0.40%</li><li>98: ~0.30%</li><li>99: ~0.70%</li><li>100: ~0.60%</li><li>102: ~1.00%</li><li>103: ~0.30%</li><li>104: ~0.10%</li><li>106: ~0.20%</li><li>107: ~0.10%</li><li>108: ~0.20%</li><li>109: ~0.20%</li><li>110: ~0.30%</li><li>112: ~0.10%</li><li>115: ~0.10%</li><li>116: ~0.30%</li><li>120: ~0.40%</li><li>122: ~0.20%</li><li>123: ~0.20%</li><li>124: ~0.10%</li><li>125: ~0.30%</li><li>126: ~0.50%</li><li>127: ~0.40%</li><li>128: ~0.70%</li><li>130: ~0.10%</li><li>132: ~0.10%</li><li>135: ~0.20%</li><li>136: ~0.10%</li><li>140: ~0.10%</li><li>141: ~0.10%</li><li>143: ~0.10%</li><li>145: ~0.10%</li><li>148: ~0.30%</li><li>149: ~0.20%</li><li>150: ~0.10%</li><li>151: ~0.40%</li><li>152: ~0.40%</li><li>153: ~0.20%</li><li>154: ~0.50%</li><li>158: ~0.20%</li><li>159: ~0.10%</li><li>161: ~0.10%</li><li>163: ~0.10%</li><li>164: ~0.10%</li><li>167: ~0.10%</li><li>168: ~0.20%</li><li>169: ~0.10%</li><li>171: ~0.10%</li><li>172: ~0.10%</li><li>173: ~0.10%</li><li>179: ~0.10%</li><li>181: ~0.40%</li><li>182: ~0.50%</li><li>183: ~0.20%</li><li>184: ~0.10%</li><li>185: ~0.30%</li><li>186: ~0.20%</li><li>188: ~0.10%</li><li>189: ~0.40%</li><li>190: ~0.20%</li><li>191: ~0.20%</li><li>192: ~0.60%</li><li>193: ~0.20%</li><li>194: ~0.30%</li><li>195: ~0.40%</li><li>196: ~0.10%</li><li>198: ~0.10%</li><li>199: ~0.40%</li><li>200: ~0.20%</li><li>201: ~0.20%</li><li>202: ~0.30%</li><li>206: ~0.10%</li><li>209: ~0.10%</li><li>210: ~0.10%</li><li>211: ~0.20%</li><li>212: ~0.10%</li><li>213: ~0.10%</li><li>221: ~0.20%</li><li>222: ~0.10%</li><li>224: ~0.20%</li><li>227: ~0.10%</li><li>228: ~0.10%</li><li>229: ~0.40%</li><li>231: ~0.30%</li><li>233: ~0.10%</li><li>235: ~0.10%</li><li>236: ~0.40%</li><li>237: ~0.30%</li><li>238: ~0.10%</li><li>241: ~0.20%</li><li>242: ~0.30%</li><li>243: ~0.60%</li><li>244: ~0.30%</li><li>245: ~0.10%</li><li>246: ~0.20%</li><li>247: ~0.20%</li><li>248: ~0.10%</li><li>249: ~0.10%</li><li>250: ~0.20%</li><li>254: ~0.30%</li><li>255: ~0.10%</li><li>258: ~0.10%</li><li>259: ~0.10%</li><li>260: ~0.50%</li><li>261: ~0.10%</li><li>262: ~0.20%</li><li>264: ~0.20%</li><li>265: ~0.20%</li><li>270: ~0.20%</li><li>272: ~0.10%</li><li>273: ~0.10%</li><li>274: ~0.20%</li><li>276: ~0.10%</li><li>277: ~0.30%</li><li>279: ~0.10%</li><li>280: ~0.10%</li><li>283: ~0.10%</li><li>284: ~0.10%</li><li>285: ~0.40%</li><li>286: ~0.20%</li><li>287: ~0.20%</li><li>288: ~0.10%</li><li>289: ~0.40%</li><li>291: ~0.10%</li><li>292: ~0.40%</li><li>293: ~0.40%</li><li>294: ~0.10%</li><li>295: ~0.30%</li><li>296: ~0.30%</li><li>297: ~0.20%</li><li>298: ~0.20%</li><li>300: ~0.20%</li><li>302: ~0.20%</li><li>303: ~0.30%</li><li>304: ~0.20%</li><li>308: ~0.30%</li><li>310: ~0.30%</li><li>311: ~0.50%</li><li>312: ~0.20%</li><li>313: ~0.20%</li><li>314: ~0.30%</li><li>315: ~0.10%</li><li>316: ~0.20%</li><li>317: ~0.10%</li><li>319: ~0.20%</li><li>322: ~0.10%</li><li>323: ~0.10%</li><li>324: ~0.30%</li><li>325: ~0.30%</li><li>328: ~0.20%</li><li>329: ~0.30%</li><li>330: ~0.10%</li><li>332: ~0.20%</li><li>333: ~0.30%</li><li>335: ~0.20%</li><li>336: ~0.60%</li><li>337: ~0.40%</li><li>338: ~0.10%</li><li>339: ~0.10%</li><li>340: ~0.10%</li><li>341: ~0.10%</li><li>342: ~0.10%</li><li>344: ~0.20%</li><li>346: ~0.10%</li><li>347: ~0.30%</li><li>348: ~0.10%</li><li>349: ~0.30%</li><li>350: ~0.20%</li><li>351: ~0.10%</li><li>352: ~0.40%</li><li>353: ~0.30%</li><li>354: ~0.20%</li><li>356: ~0.20%</li><li>357: ~0.40%</li><li>358: ~0.40%</li><li>359: ~0.40%</li><li>360: ~0.20%</li><li>361: ~0.40%</li><li>362: ~0.20%</li><li>363: ~0.10%</li><li>366: ~0.10%</li><li>367: ~0.10%</li><li>368: ~0.70%</li><li>369: ~0.20%</li><li>370: ~0.30%</li><li>372: ~0.30%</li><li>373: ~0.20%</li><li>374: ~0.40%</li><li>375: ~0.10%</li><li>376: ~0.10%</li><li>377: ~0.10%</li><li>379: ~0.20%</li><li>381: ~0.30%</li><li>383: ~0.40%</li><li>384: ~0.20%</li><li>385: ~0.20%</li><li>386: ~0.20%</li><li>387: ~0.20%</li><li>389: ~0.10%</li><li>390: ~0.30%</li><li>391: ~0.20%</li><li>392: ~0.20%</li><li>393: ~0.20%</li><li>395: ~0.10%</li><li>397: ~0.40%</li><li>398: ~0.20%</li><li>399: ~0.30%</li><li>400: ~0.40%</li><li>402: ~0.10%</li><li>407: ~0.10%</li><li>408: ~0.20%</li><li>409: ~0.30%</li><li>411: ~0.20%</li><li>412: ~0.20%</li><li>414: ~0.20%</li><li>415: ~0.20%</li><li>416: ~0.10%</li><li>417: ~0.30%</li><li>418: ~0.10%</li><li>420: ~0.20%</li><li>422: ~0.50%</li><li>423: ~0.10%</li><li>425: ~0.40%</li><li>426: ~0.10%</li><li>427: ~0.10%</li><li>428: ~0.40%</li><li>429: ~0.20%</li><li>430: ~0.10%</li><li>431: ~0.10%</li><li>432: ~0.10%</li><li>433: ~0.20%</li><li>434: ~0.30%</li><li>435: ~0.20%</li><li>436: ~0.40%</li><li>437: ~0.10%</li><li>438: ~0.40%</li><li>440: ~0.80%</li><li>441: ~0.20%</li><li>442: ~0.50%</li><li>443: ~0.20%</li><li>444: ~0.30%</li><li>445: ~0.30%</li><li>446: ~0.10%</li><li>447: ~0.20%</li><li>450: ~0.30%</li><li>451: ~0.20%</li><li>452: ~0.20%</li><li>453: ~0.10%</li><li>454: ~0.20%</li><li>455: ~0.30%</li><li>456: ~0.10%</li><li>457: ~0.10%</li><li>458: ~0.20%</li><li>459: ~0.20%</li><li>460: ~0.10%</li><li>461: ~0.10%</li><li>462: ~0.10%</li><li>463: ~0.40%</li><li>464: ~0.30%</li><li>467: ~0.10%</li><li>469: ~0.10%</li><li>470: ~0.10%</li><li>472: ~0.10%</li><li>475: ~0.50%</li><li>476: ~0.30%</li><li>478: ~0.10%</li><li>479: ~0.20%</li><li>480: ~0.10%</li><li>482: ~0.30%</li><li>483: ~0.50%</li><li>484: ~0.30%</li><li>485: ~0.40%</li><li>486: ~0.20%</li><li>487: ~0.20%</li><li>489: ~0.10%</li><li>490: ~0.20%</li><li>491: ~0.10%</li><li>492: ~0.40%</li><li>493: ~0.40%</li><li>495: ~0.10%</li><li>497: ~0.10%</li><li>498: ~0.10%</li><li>499: ~0.30%</li><li>501: ~0.20%</li><li>502: ~0.20%</li><li>503: ~0.10%</li><li>504: ~0.30%</li><li>505: ~0.10%</li><li>506: ~0.10%</li><li>507: ~0.10%</li><li>508: ~0.20%</li><li>509: ~0.10%</li><li>510: ~0.10%</li><li>511: ~0.10%</li><li>512: ~0.50%</li><li>514: ~0.20%</li><li>517: ~0.40%</li><li>518: ~0.10%</li><li>519: ~0.60%</li><li>520: ~0.90%</li><li>521: ~0.60%</li><li>522: ~0.10%</li><li>523: ~0.10%</li><li>524: ~0.10%</li><li>525: ~0.10%</li><li>526: ~0.10%</li><li>527: ~0.10%</li><li>528: ~0.30%</li><li>529: ~0.40%</li><li>530: ~0.60%</li><li>531: ~0.20%</li><li>532: ~0.10%</li><li>533: ~0.30%</li><li>535: ~0.50%</li><li>537: ~0.20%</li><li>540: ~0.10%</li><li>541: ~0.10%</li><li>542: ~0.20%</li><li>543: ~0.30%</li><li>544: ~0.20%</li><li>545: ~0.50%</li><li>546: ~0.30%</li><li>547: ~0.10%</li><li>548: ~0.50%</li><li>549: ~0.10%</li><li>550: ~0.30%</li><li>551: ~0.30%</li><li>552: ~0.10%</li><li>554: ~0.60%</li><li>555: ~0.20%</li><li>556: ~0.10%</li><li>557: ~0.10%</li><li>560: ~0.20%</li><li>561: ~0.10%</li><li>563: ~0.20%</li><li>564: ~0.20%</li><li>565: ~0.20%</li><li>567: ~0.10%</li><li>568: ~0.20%</li><li>570: ~0.20%</li><li>571: ~0.10%</li><li>572: ~0.20%</li><li>573: ~0.20%</li><li>576: ~0.20%</li><li>579: ~0.10%</li></ul> |
—Samples: | sentence_0 | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | <code>retail sale of wooden, cork and wickerwork goods</code> | <code>202</code> | | <code>e</code> | <code>298</code> | | <code>produkcję maszyn do obróbki miękkiej gumy lub tworzyw sztucznych oraz wytwarzania wyrobów z tych materiałów: wytłaczarek, maszyn do formowania, maszyn do produkcji lub bieżnikowania opon pneumatycznych oraz pozostałych maszyn do produkcji wyrobów z gumy lub tworzyw sztucznych</code> | <code>79</code> |
—Loss: <code>BatchAllTripletLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 256
—per_device_eval_batch_size: 256
—num_train_epochs: 4
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

—overwrite_output_dir: False
—do_predict: False
—eval_strategy: no
—prediction_loss_only: True
—per_device_train_batch_size: 256
—per_device_eval_batch_size: 256
—per_gpu_train_batch_size: None
—per_gpu_eval_batch_size: None
—gradient_accumulation_steps: 1
—eval_accumulation_steps: None
—learning_rate: 5e-05
—weight_decay: 0.0
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—max_grad_norm: 1
—num_train_epochs: 4
—max_steps: -1
—lr_scheduler_type: linear
—lr_scheduler_kwargs: {}
—warmup_ratio: 0.0
—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: False
—ignore_data_skip: False
—fsdp: []
—fsdp_min_num_params: 0
—fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: 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
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin

</details>

Framework Versions

—Python: 3.10.12
—Sentence Transformers: 3.0.1
—Transformers: 4.41.2
—PyTorch: 2.3.0+cu121
—Accelerate: 0.31.0
—Datasets: 2.20.0
—Tokenizers: 0.19.1

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",
}

BatchAllTripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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