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news_v3_graph_edges_embeddings_setence_paragraph

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:104022
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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VANTIGE_NEWS_v3_EDGE_DETECTION

This is a sentence-transformers model finetuned from BAAI/bge-large-en-v1.5. It maps sentences & paragraphs to a 1024-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-large-en-v1.5
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 1024 tokens
  • Similarity Function: Cosine Similarity
  • Language: en
  • License: other

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("dustyatx/news_v3_graph_edges_embeddings_setence_paragraph")
# Run inference
sentences = [
    "Scope Control provides a digital ledger of inspected lines, creating a credible line history that underscores Custom Truck One Source's commitment to operational safety.",
    'Scope Control utilizes advanced Computer Vision and Deep Learning technologies to accurately assess line health and categorize it as new, used, or bad based on safety standards and residual break strength.',
    'China has implemented measures to address hidden debt, including extending debt maturities, selling assets to repay debts, and replacing short-term local government financial vehicle debts with longer-term, lower-cost refinancing bonds.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.828
cosine_accuracy@3 0.978
cosine_accuracy@5 0.986
cosine_accuracy@10 0.992
cosine_precision@1 0.828
cosine_precision@3 0.326
cosine_precision@5 0.1972
cosine_precision@10 0.0992
cosine_recall@1 0.828
cosine_recall@3 0.978
cosine_recall@5 0.986
cosine_recall@10 0.992
cosine_ndcg@10 0.9262
cosine_mrr@10 0.9035
cosine_map@100 0.9039

Information Retrieval

Metric Value
cosine_accuracy@1 0.83
cosine_accuracy@3 0.978
cosine_accuracy@5 0.986
cosine_accuracy@10 0.99
cosine_precision@1 0.83
cosine_precision@3 0.326
cosine_precision@5 0.1972
cosine_precision@10 0.099
cosine_recall@1 0.83
cosine_recall@3 0.978
cosine_recall@5 0.986
cosine_recall@10 0.99
cosine_ndcg@10 0.9265
cosine_mrr@10 0.9044
cosine_map@100 0.905

Information Retrieval

Metric Value
cosine_accuracy@1 0.83
cosine_accuracy@3 0.978
cosine_accuracy@5 0.988
cosine_accuracy@10 0.99
cosine_precision@1 0.83
cosine_precision@3 0.326
cosine_precision@5 0.1976
cosine_precision@10 0.099
cosine_recall@1 0.83
cosine_recall@3 0.978
cosine_recall@5 0.988
cosine_recall@10 0.99
cosine_ndcg@10 0.9262
cosine_mrr@10 0.9041
cosine_map@100 0.9046

Information Retrieval

Metric Value
cosine_accuracy@1 0.828
cosine_accuracy@3 0.978
cosine_accuracy@5 0.984
cosine_accuracy@10 0.99
cosine_precision@1 0.828
cosine_precision@3 0.326
cosine_precision@5 0.1968
cosine_precision@10 0.099
cosine_recall@1 0.828
cosine_recall@3 0.978
cosine_recall@5 0.984
cosine_recall@10 0.99
cosine_ndcg@10 0.9251
cosine_mrr@10 0.9026
cosine_map@100 0.9032

Information Retrieval

Metric Value
cosine_accuracy@1 0.832
cosine_accuracy@3 0.978
cosine_accuracy@5 0.986
cosine_accuracy@10 0.992
cosine_precision@1 0.832
cosine_precision@3 0.326
cosine_precision@5 0.1972
cosine_precision@10 0.0992
cosine_recall@1 0.832
cosine_recall@3 0.978
cosine_recall@5 0.986
cosine_recall@10 0.992
cosine_ndcg@10 0.9276
cosine_mrr@10 0.9054
cosine_map@100 0.9059

Information Retrieval

Metric Value
cosine_accuracy@1 0.822
cosine_accuracy@3 0.978
cosine_accuracy@5 0.986
cosine_accuracy@10 0.99
cosine_precision@1 0.822
cosine_precision@3 0.326
cosine_precision@5 0.1972
cosine_precision@10 0.099
cosine_recall@1 0.822
cosine_recall@3 0.978
cosine_recall@5 0.986
cosine_recall@10 0.99
cosine_ndcg@10 0.9224
cosine_mrr@10 0.899
cosine_map@100 0.8995

Training Details

Training Dataset

Unnamed Dataset

  • Size: 104,022 training samples
  • Columns: anchor and positive
  • Approximate statistics based on the first 1000 samples:
    anchor positive
    type string string
    details
    • min: 16 tokens
    • mean: 36.53 tokens
    • max: 102 tokens
    • min: 13 tokens
    • mean: 35.17 tokens
    • max: 117 tokens
  • Samples:
    anchor positive
    The general public, including retail investors, collectively own 11% of FINEOS Corporation Holdings' shares, representing a minority stake in the company. Private companies, with their 50% ownership stake, have substantial influence over FINEOS Corporation Holdings' management and governance decisions.
    A study by the Insurance Institute for Highway Safety (IIHS) found that SUVs and vans with hood heights exceeding 40 inches are approximately 45% more likely to cause pedestrian fatalities compared to vehicles with hood heights of 30 inches or less and a sloping profile. Vehicles with front ends exceeding 35 inches in height, particularly those lacking a sloping profile, are more likely to cause severe head, torso, and hip injuries to pedestrians.
    SpringWorks Therapeutics has a portfolio of small molecule targeted oncology product candidates and is conducting clinical trials for rare tumor types and genetically defined cancers. SpringWorks Therapeutics operates in the biopharmaceutical industry, specializing in precision medicine for underserved patient populations.
  • Loss: MatryoshkaLoss with these parameters:
    {
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 30
  • per_device_eval_batch_size: 20
  • gradient_accumulation_steps: 8
  • learning_rate: 3e-05
  • num_train_epochs: 2
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.2
  • bf16: True
  • tf32: True
  • dataloader_num_workers: 30
  • load_best_model_at_end: True
  • optim: adamw_torch_fused
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 30
  • per_device_eval_batch_size: 20
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 8
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 3e-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: 2
  • max_steps: -1
  • lr_scheduler_type: cosine
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.2
  • 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: True
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: True
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 30
  • 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
  • eval_use_gather_object: False
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss dim_1024_cosine_map@100 dim_128_cosine_map@100 dim_256_cosine_map@100 dim_512_cosine_map@100 dim_64_cosine_map@100 dim_768_cosine_map@100
0.0023 1 1.8313 - - - - - -
0.0046 2 1.9678 - - - - - -
0.0069 3 0.8038 - - - - - -
0.0092 4 0.7993 - - - - - -
0.0115 5 0.7926 - - - - - -
0.0138 6 0.9348 - - - - - -
0.0161 7 0.8707 - - - - - -
0.0185 8 0.7293 - - - - - -
0.0208 9 0.6618 - - - - - -
0.0231 10 0.846 - - - - - -
0.0254 11 0.6836 - - - - - -
0.0277 12 0.7034 - - - - - -
0.0300 13 0.7987 - - - - - -
0.0323 14 0.6443 - - - - - -
0.0346 15 0.5975 - - - - - -
0.0369 16 0.4471 - - - - - -
0.0392 17 0.4739 - - - - - -
0.0415 18 0.4136 - - - - - -
0.0438 19 0.3865 - - - - - -
0.0461 20 0.3421 - - - - - -
0.0484 21 0.5076 - - - - - -
0.0507 22 0.1878 - - - - - -
0.0531 23 0.3597 - - - - - -
0.0554 24 0.23 - - - - - -
0.0577 25 0.1331 - - - - - -
0.0600 26 0.1793 - - - - - -
0.0623 27 0.1309 - - - - - -
0.0646 28 0.1077 - - - - - -
0.0669 29 0.1681 - - - - - -
0.0692 30 0.055 - - - - - -
0.0715 31 0.1062 - - - - - -
0.0738 32 0.0672 - - - - - -
0.0761 33 0.067 - - - - - -
0.0784 34 0.0953 - - - - - -
0.0807 35 0.0602 - - - - - -
0.0830 36 0.1312 - - - - - -
0.0854 37 0.0356 - - - - - -
0.0877 38 0.0707 - - - - - -
0.0900 39 0.1525 - - - - - -
0.0923 40 0.0362 - - - - - -
0.0946 41 0.253 - - - - - -
0.0969 42 0.0572 - - - - - -
0.0992 43 0.1031 - - - - - -
0.1015 44 0.1023 - - - - - -
0.1038 45 0.052 - - - - - -
0.1061 46 0.0614 - - - - - -
0.1084 47 0.1256 - - - - - -
0.1107 48 0.1624 - - - - - -
0.1130 49 0.0363 - - - - - -
0.1153 50 0.2001 0.8949 0.8940 0.8947 0.8950 0.8864 0.8972
0.1176 51 0.0846 - - - - - -
0.1200 52 0.0338 - - - - - -
0.1223 53 0.0648 - - - - - -
0.1246 54 0.1232 - - - - - -
0.1269 55 0.0318 - - - - - -
0.1292 56 0.1148 - - - - - -
0.1315 57 0.0826 - - - - - -
0.1338 58 0.034 - - - - - -
0.1361 59 0.0492 - - - - - -
0.1384 60 0.0427 - - - - - -
0.1407 61 0.0709 - - - - - -
0.1430 62 0.0494 - - - - - -
0.1453 63 0.0554 - - - - - -
0.1476 64 0.061 - - - - - -
0.1499 65 0.1155 - - - - - -
0.1522 66 0.0419 - - - - - -
0.1546 67 0.0185 - - - - - -
0.1569 68 0.0559 - - - - - -
0.1592 69 0.0219 - - - - - -
0.1615 70 0.0302 - - - - - -
0.1638 71 0.0322 - - - - - -
0.1661 72 0.0604 - - - - - -
0.1684 73 0.038 - - - - - -
0.1707 74 0.0971 - - - - - -
0.1730 75 0.0384 - - - - - -
0.1753 76 0.0887 - - - - - -
0.1776 77 0.0495 - - - - - -
0.1799 78 0.0203 - - - - - -
0.1822 79 0.0669 - - - - - -
0.1845 80 0.0319 - - - - - -
0.1869 81 0.0177 - - - - - -
0.1892 82 0.0303 - - - - - -
0.1915 83 0.037 - - - - - -
0.1938 84 0.0122 - - - - - -
0.1961 85 0.0377 - - - - - -
0.1984 86 0.0578 - - - - - -
0.2007 87 0.0347 - - - - - -
0.2030 88 0.1288 - - - - - -
0.2053 89 0.0964 - - - - - -
0.2076 90 0.0172 - - - - - -
0.2099 91 0.0726 - - - - - -
0.2122 92 0.0225 - - - - - -
0.2145 93 0.1011 - - - - - -
0.2168 94 0.0248 - - - - - -
0.2191 95 0.0431 - - - - - -
0.2215 96 0.0243 - - - - - -
0.2238 97 0.0221 - - - - - -
0.2261 98 0.0529 - - - - - -
0.2284 99 0.0459 - - - - - -
0.2307 100 0.0869 0.9026 0.8967 0.8950 0.9003 0.8915 0.9009
0.2330 101 0.0685 - - - - - -
0.2353 102 0.0801 - - - - - -
0.2376 103 0.025 - - - - - -
0.2399 104 0.0556 - - - - - -
0.2422 105 0.0146 - - - - - -
0.2445 106 0.0335 - - - - - -
0.2468 107 0.0441 - - - - - -
0.2491 108 0.0187 - - - - - -
0.2514 109 0.1027 - - - - - -
0.2537 110 0.0189 - - - - - -
0.2561 111 0.1262 - - - - - -
0.2584 112 0.1193 - - - - - -
0.2607 113 0.0285 - - - - - -
0.2630 114 0.0226 - - - - - -
0.2653 115 0.1209 - - - - - -
0.2676 116 0.0765 - - - - - -
0.2699 117 0.1405 - - - - - -
0.2722 118 0.0629 - - - - - -
0.2745 119 0.0413 - - - - - -
0.2768 120 0.0572 - - - - - -
0.2791 121 0.0192 - - - - - -
0.2814 122 0.0949 - - - - - -
0.2837 123 0.0398 - - - - - -
0.2860 124 0.0596 - - - - - -
0.2884 125 0.0243 - - - - - -
0.2907 126 0.0636 - - - - - -
0.2930 127 0.0367 - - - - - -
0.2953 128 0.0542 - - - - - -
0.2976 129 0.0149 - - - - - -
0.2999 130 0.097 - - - - - -
0.3022 131 0.0213 - - - - - -
0.3045 132 0.027 - - - - - -
0.3068 133 0.0577 - - - - - -
0.3091 134 0.0143 - - - - - -
0.3114 135 0.0285 - - - - - -
0.3137 136 0.033 - - - - - -
0.3160 137 0.0412 - - - - - -
0.3183 138 0.0125 - - - - - -
0.3206 139 0.0512 - - - - - -
0.3230 140 0.0189 - - - - - -
0.3253 141 0.124 - - - - - -
0.3276 142 0.0118 - - - - - -
0.3299 143 0.017 - - - - - -
0.3322 144 0.025 - - - - - -
0.3345 145 0.0187 - - - - - -
0.3368 146 0.0141 - - - - - -
0.3391 147 0.0325 - - - - - -
0.3414 148 0.0582 - - - - - -
0.3437 149 0.0611 - - - - - -
0.3460 150 0.0261 0.9047 0.8995 0.9003 0.9022 0.8998 0.9032
0.3483 151 0.014 - - - - - -
0.3506 152 0.0077 - - - - - -
0.3529 153 0.022 - - - - - -
0.3552 154 0.0328 - - - - - -
0.3576 155 0.0124 - - - - - -
0.3599 156 0.0103 - - - - - -
0.3622 157 0.0607 - - - - - -
0.3645 158 0.0121 - - - - - -
0.3668 159 0.0761 - - - - - -
0.3691 160 0.0981 - - - - - -
0.3714 161 0.1071 - - - - - -
0.3737 162 0.1307 - - - - - -
0.3760 163 0.0524 - - - - - -
0.3783 164 0.0726 - - - - - -
0.3806 165 0.0636 - - - - - -
0.3829 166 0.0428 - - - - - -
0.3852 167 0.0111 - - - - - -
0.3875 168 0.0542 - - - - - -
0.3899 169 0.0193 - - - - - -
0.3922 170 0.0095 - - - - - -
0.3945 171 0.0464 - - - - - -
0.3968 172 0.0167 - - - - - -
0.3991 173 0.0209 - - - - - -
0.4014 174 0.0359 - - - - - -
0.4037 175 0.071 - - - - - -
0.4060 176 0.0189 - - - - - -
0.4083 177 0.0448 - - - - - -
0.4106 178 0.0161 - - - - - -
0.4129 179 0.0427 - - - - - -
0.4152 180 0.0229 - - - - - -
0.4175 181 0.0274 - - - - - -
0.4198 182 0.0173 - - - - - -
0.4221 183 0.0123 - - - - - -
0.4245 184 0.0395 - - - - - -
0.4268 185 0.015 - - - - - -
0.4291 186 0.0168 - - - - - -
0.4314 187 0.0165 - - - - - -
0.4337 188 0.0412 - - - - - -
0.4360 189 0.0961 - - - - - -
0.4383 190 0.0551 - - - - - -
0.4406 191 0.0685 - - - - - -
0.4429 192 0.1561 - - - - - -
0.4452 193 0.0333 - - - - - -
0.4475 194 0.0567 - - - - - -
0.4498 195 0.0081 - - - - - -
0.4521 196 0.0297 - - - - - -
0.4544 197 0.0131 - - - - - -
0.4567 198 0.0322 - - - - - -
0.4591 199 0.0224 - - - - - -
0.4614 200 0.0068 0.8989 0.8941 0.8983 0.8985 0.8975 0.9002
0.4637 201 0.0115 - - - - - -
0.4660 202 0.0098 - - - - - -
0.4683 203 0.101 - - - - - -
0.4706 204 0.0282 - - - - - -
0.4729 205 0.0721 - - - - - -
0.4752 206 0.0123 - - - - - -
0.4775 207 0.1014 - - - - - -
0.4798 208 0.0257 - - - - - -
0.4821 209 0.1126 - - - - - -
0.4844 210 0.0586 - - - - - -
0.4867 211 0.0307 - - - - - -
0.4890 212 0.0226 - - - - - -
0.4913 213 0.0471 - - - - - -
0.4937 214 0.025 - - - - - -
0.4960 215 0.0799 - - - - - -
0.4983 216 0.0173 - - - - - -
0.5006 217 0.0208 - - - - - -
0.5029 218 0.0461 - - - - - -
0.5052 219 0.0592 - - - - - -
0.5075 220 0.0076 - - - - - -
0.5098 221 0.0156 - - - - - -
0.5121 222 0.0149 - - - - - -
0.5144 223 0.0138 - - - - - -
0.5167 224 0.0526 - - - - - -
0.5190 225 0.0689 - - - - - -
0.5213 226 0.0191 - - - - - -
0.5236 227 0.0094 - - - - - -
0.5260 228 0.0125 - - - - - -
0.5283 229 0.0632 - - - - - -
0.5306 230 0.0773 - - - - - -
0.5329 231 0.0147 - - - - - -
0.5352 232 0.0145 - - - - - -
0.5375 233 0.0068 - - - - - -
0.5398 234 0.0673 - - - - - -
0.5421 235 0.0131 - - - - - -
0.5444 236 0.0217 - - - - - -
0.5467 237 0.0126 - - - - - -
0.5490 238 0.0172 - - - - - -
0.5513 239 0.0122 - - - - - -
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Framework Versions

  • Python: 3.11.9
  • Sentence Transformers: 3.0.1
  • Transformers: 4.44.2
  • PyTorch: 2.4.0+cu121
  • Accelerate: 0.33.0
  • Datasets: 2.19.2
  • Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers 

@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",
}

MatryoshkaLoss 

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss 

@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}
}
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