metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: >-
      The net cash provided by operating activities during fiscal 2023 was
      related to net income of $208 million, adjusted for non-cash items
      including $3.8 billion of depreciation and amortization and $3.3 billion
      related to stock-based compensation expense.
    sentences:
      - >-
        What are the three key aspects encompassed in a company's internal
        control over financial reporting?
      - What was the net cash provided by operating activities for fiscal 2023?
      - What are the two operating segments of NVIDIA as mentioned in the text?
  - source_sentence: >-
      Intellectual Property  To establish and protect our proprietary rights, we
      rely on a combination of patents, trademarks, copyrights, trade secrets,
      including know-how, license agreements, confidentiality procedures,
      non-disclosure agreements with third parties, employee disclosure and
      invention assignment agreements, and other contractual rights.
    sentences:
      - >-
        What condition does Synthroid treat and what type of drug is it
        formulated as?
      - >-
        What legal tools does the company use to protect its intellectual
        property?
      - >-
        In which item and part of a financial document would you find
        information on legal proceedings?
  - source_sentence: >-
      Cost of revenues is comprised of TAC and other costs of revenues. TAC
      includes amounts paid to our distribution partners and Google Network
      partners primarily for ads displayed on their properties. Other cost of
      revenues includes compensation expense related to our data centers and
      operations, content acquisition costs, depreciation expense related to
      technical infrastructure, and inventory and other costs related to devices
      we sell.
    sentences:
      - What is included in the cost of revenues for Google?
      - What was the total net uncertain tax positions as of December 31, 2023?
      - >-
        What portion of the restructuring charges incurred in fiscal 2023 are
        expected to be settled with cash?
  - source_sentence: Comprehensive income (loss) | $ | (362) | | $ | 1,868 | $ | 4,775
    sentences:
      - What measures does the company take to ensure product quality?
      - >-
        How many pages does Item 8, which includes Financial Statements and
        Supplementary Data, span?
      - What was the total comprehensive income for Airbnb, Inc. in 2023?
  - source_sentence: >-
      We make our branded beverage products available to consumers throughout
      the world through our network of independent bottling partners,
      distributors, wholesalers and retailers as well as our consolidated
      bottling and distribution operations.
    sentences:
      - >-
        How does The Coca-Cola Company distribute its beverage products
        globally?
      - >-
        What accounting method is predominantly used to determine inventory
        costs in the Company's supermarket divisions before LIFO adjustments?
      - How are the company's inventories valued?
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
model-index:
  - name: BGE base Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.7142857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8485714285714285
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8814285714285715
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9171428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7142857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28285714285714286
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17628571428571424
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09171428571428569
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7142857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8485714285714285
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8814285714285715
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9171428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8195547708074192
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7879784580498865
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.791495828863575
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.7157142857142857
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8457142857142858
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8814285714285715
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.92
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7157142857142857
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2819047619047619
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17628571428571424
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09199999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7157142857142857
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8457142857142858
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8814285714285715
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.92
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8200080507124731
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7878299319727888
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7911645774121049
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6914285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8471428571428572
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.88
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.91
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6914285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28238095238095234
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.176
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09099999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6914285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8471428571428572
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.88
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.91
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8087696033003087
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7755997732426303
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7799208675704249
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6914285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.83
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.87
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9071428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6914285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27666666666666667
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.174
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0907142857142857
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6914285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.83
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.87
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9071428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8024684596621504
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7686116780045347
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7729258054107728
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6585714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8028571428571428
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8357142857142857
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8828571428571429
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6585714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2676190476190476
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1671428571428571
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08828571428571429
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6585714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8028571428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8357142857142857
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8828571428571429
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7735846622621076
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.738378684807256
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7433829659777168
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the json 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: en
License: apache-2.0

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': 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:

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("girijesh/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'We make our branded beverage products available to consumers throughout the world through our network of independent bottling partners, distributors, wholesalers and retailers as well as our consolidated bottling and distribution operations.',
    'How does The Coca-Cola Company distribute its beverage products globally?',
    "What accounting method is predominantly used to determine inventory costs in the Company's supermarket divisions before LIFO adjustments?",
]
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]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7143
cosine_accuracy@3	0.8486
cosine_accuracy@5	0.8814
cosine_accuracy@10	0.9171
cosine_precision@1	0.7143
cosine_precision@3	0.2829
cosine_precision@5	0.1763
cosine_precision@10	0.0917
cosine_recall@1	0.7143
cosine_recall@3	0.8486
cosine_recall@5	0.8814
cosine_recall@10	0.9171
cosine_ndcg@10	0.8196
cosine_mrr@10	0.788
cosine_map@100	0.7915

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7157
cosine_accuracy@3	0.8457
cosine_accuracy@5	0.8814
cosine_accuracy@10	0.92
cosine_precision@1	0.7157
cosine_precision@3	0.2819
cosine_precision@5	0.1763
cosine_precision@10	0.092
cosine_recall@1	0.7157
cosine_recall@3	0.8457
cosine_recall@5	0.8814
cosine_recall@10	0.92
cosine_ndcg@10	0.82
cosine_mrr@10	0.7878
cosine_map@100	0.7912

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6914
cosine_accuracy@3	0.8471
cosine_accuracy@5	0.88
cosine_accuracy@10	0.91
cosine_precision@1	0.6914
cosine_precision@3	0.2824
cosine_precision@5	0.176
cosine_precision@10	0.091
cosine_recall@1	0.6914
cosine_recall@3	0.8471
cosine_recall@5	0.88
cosine_recall@10	0.91
cosine_ndcg@10	0.8088
cosine_mrr@10	0.7756
cosine_map@100	0.7799

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6914
cosine_accuracy@3	0.83
cosine_accuracy@5	0.87
cosine_accuracy@10	0.9071
cosine_precision@1	0.6914
cosine_precision@3	0.2767
cosine_precision@5	0.174
cosine_precision@10	0.0907
cosine_recall@1	0.6914
cosine_recall@3	0.83
cosine_recall@5	0.87
cosine_recall@10	0.9071
cosine_ndcg@10	0.8025
cosine_mrr@10	0.7686
cosine_map@100	0.7729

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6586
cosine_accuracy@3	0.8029
cosine_accuracy@5	0.8357
cosine_accuracy@10	0.8829
cosine_precision@1	0.6586
cosine_precision@3	0.2676
cosine_precision@5	0.1671
cosine_precision@10	0.0883
cosine_recall@1	0.6586
cosine_recall@3	0.8029
cosine_recall@5	0.8357
cosine_recall@10	0.8829
cosine_ndcg@10	0.7736
cosine_mrr@10	0.7384
cosine_map@100	0.7434

Training Details

Training Dataset

json

Dataset: json
Size: 6,300 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 8 tokens
mean: 44.98 tokens
max: 439 tokens

min: 7 tokens
mean: 20.31 tokens
max: 45 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 44.98 tokens max: 439 tokens	min: 7 tokens mean: 20.31 tokens max: 45 tokens

Samples:

positive	anchor
Change in control events potentially triggering benefits under the CIC Plan and Mr. Begor’s agreement would occur, subject to certain exceptions, if (1) any person acquires 20% or more of our voting stock; (2) upon a merger or other business combination, our shareholders receive less than two-thirds of the common stock and combined voting power of the new company; (3) members of the current Board of Directors ceasing to constitute a majority of the Board of Directors, except for new directors that are regularly elected; (4) we sell or otherwise dispose of all or substantially all of our assets; or (5) we liquidate or dissolve.	`What events potentially trigger benefits under Mark W. Begor's change in control agreement and the CIC Plan?`
`The growth in marketplace revenue was primarily due to the impact of the pricing update to increase our seller transaction fee for the Etsy marketplace from 5% to 6.5% beginning on April 11, 2022, and an increase in foreign currency payments, which we earn an additional transaction fee on, in the year ended December 31, 2023.`	`What drove the growth in marketplace revenue for the year ended December 31, 2023?`
`We are focused on ensuring that we efficiently allocate our resources to the areas with the highest potential for profitable growth. ... The uncertain macroeconomic environment in many of these markets is expected to continue and we aim to ensure our investments in these international markets are appropriate relative to the size of the opportunity.`	`What are Hershey's goals for international expansion and how are they being approached?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
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: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
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
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: 4
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: 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: 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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_map@100	dim_512_cosine_map@100	dim_256_cosine_map@100	dim_128_cosine_map@100	dim_64_cosine_map@100
0.9697	6	-	0.7527	0.7516	0.7454	0.7253	0.6808
1.6162	10	2.3351	-	-	-	-	-
1.9394	12	-	0.7740	0.7699	0.7707	0.7474	0.7188
2.9091	18	-	0.7784	0.7790	0.7735	0.7575	0.7275
3.2323	20	1.0519	-	-	-	-	-
3.8788	24	-	0.7818	0.7784	0.7763	0.7581	0.7293
0.9697	6	-	0.7836	0.7826	0.7817	0.7664	0.7353
1.6162	10	0.8132	-	-	-	-	-
1.9394	12	-	0.7887	0.7887	0.7837	0.7714	0.7409
2.9091	18	-	0.7897	0.7902	0.7798	0.7721	0.7410
3.2323	20	0.6098	-	-	-	-	-
3.8788	24	-	0.7915	0.7912	0.7799	0.7729	0.7434

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 1.0.1
Datasets: 2.19.1
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}
}