metadata

base_model: BAAI/bge-small-en-v1.5
datasets: []
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:11863
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      In the fiscal year 2022, the emissions were categorized into different
      scopes, with each scope representing a specific source of emissions
    sentences:
      - >-
        Question: What is NetLink proactive in identifying to be more efficient
        in? 
      - >-
        What standard is the Environment, Health, and Safety Management System
        (EHSMS) audited to by a third-party accredited certification body at the
        operational assets level of CLI?
      - >-
        What do the different scopes represent in terms of emissions in the
        fiscal year 2022?
  - source_sentence: >-
      NetLink is committed to protecting the security of all information and
      information systems, including both end-user data and corporate data. To
      this end, management ensures that the appropriate IT policies, personal
      data protection policy, risk mitigation strategies, cyber security
      programmes, systems, processes, and controls are in place to protect our
      IT systems and confidential data
    sentences:
      - '"What recognition did NetLink receive in FY22?"'
      - >-
        What measures does NetLink have in place to protect the security of all
        information and information systems, including end-user data and
        corporate data?
      - >-
        Question: What does Disclosure 102-10 discuss regarding the organization
        and its supply chain?
  - source_sentence: >-
      In the domain of economic performance, the focus is on the financial
      health and growth of the organization, ensuring sustainable profitability
      and value creation for stakeholders
    sentences:
      - >-
        What does NetLink prioritize by investing in its network to ensure
        reliability and quality of infrastructure?
      - >-
        What percentage of the total energy was accounted for by heat, steam,
        and chilled water in 2021 according to the given information?
      - >-
        What is the focus in the domain of economic performance, ensuring
        sustainable profitability and value creation for stakeholders?
  - source_sentence: >-
      Disclosure 102-41 discusses collective bargaining agreements and is found
      on page 98
    sentences:
      - What topic is discussed in Disclosure 102-41 on page 98 of the document?
      - >-
        What was the number of cases in 2021, following a decrease from 42 cases
        in 2020?
      - >-
        What type of data does GRI 101 provide in relation to connecting the
        nation?
  - source_sentence: >-
      Employee health and well-being has never been more topical than it was in
      the past year. We understand that people around the world, including our
      employees, have been increasingly exposed to factors affecting their
      physical and mental wellbeing. We are committed to creating an environment
      that supports our employees and ensures they feel valued and have a sense
      of belonging. We utilised
    sentences:
      - >-
        What aspect of the standard covers the evaluation of the management
        approach?
      - >-
        Question: What is the company's commitment towards its employees' health
        and well-being based on the provided context information?
      - >-
        What types of skills does NetLink focus on developing through their
        training and development opportunities for employees?
model-index:
  - name: BAAI BGE small en v1.5 ESG
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 384
          type: dim_384
        metrics:
          - type: cosine_accuracy@1
            value: 0.786984742476608
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9269156199949422
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.944617718958105
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9597066509314676
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.786984742476608
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3089718733316474
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.18892354379162102
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09597066509314678
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.021860687291016895
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.025747656110970626
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.026239381082169593
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.026658518081429664
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.19459455903970813
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8588156921146056
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.023886995279989515
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.7815055213689623
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9236280873303548
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9421731433870016
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9596223552221191
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7815055213689623
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.30787602911011824
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.18843462867740032
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09596223552221193
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.021708486704693403
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.025656335759176533
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.026171476205194496
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.02665617653394776
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.19396598426779785
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8550811914864019
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.023784308256522512
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.7713057405378067
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9141869678833348
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9346708252549946
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9532158813116413
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7713057405378067
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3047289892944449
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.18693416505099894
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09532158813116413
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.021425159459383523
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.025394082441203752
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.025963078479305412
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.026478218925323375
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.192049680708846
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8456702445512195
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.023531692780408037
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.7428137907780494
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.892438674871449
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9184860490601029
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9411615948748209
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7428137907780494
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.297479558290483
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1836972098120206
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09411615948748209
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.02063371641050138
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.024789963190873596
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.02551350136278064
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.026143377635411698
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.18745029665008597
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8220114494981732
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.022884160441989647
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 32
          type: dim_32
        metrics:
          - type: cosine_accuracy@1
            value: 0.6668633566551463
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8242434460085981
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8640310208210402
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8987608530725786
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6668633566551463
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27474781533619935
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17280620416420805
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08987608530725787
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.018523982129309623
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.022895651278016623
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.024000861689473345
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.02496557925201608
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.17367624271978654
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7532998425142056
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.02100792923667254
            name: Cosine Map@100

BAAI BGE small en v1.5 ESG

This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. 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: BAAI/bge-small-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity
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': 384, '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("elsayovita/bge-small-en-v1.5-esg-v2")
# Run inference
sentences = [
    'Employee health and well-being has never been more topical than it was in the past year. We understand that people around the world, including our employees, have been increasingly exposed to factors affecting their physical and mental wellbeing. We are committed to creating an environment that supports our employees and ensures they feel valued and have a sense of belonging. We utilised',
    "Question: What is the company's commitment towards its employees' health and well-being based on the provided context information?",
    'What types of skills does NetLink focus on developing through their training and development opportunities for employees?',
]
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]

Evaluation

Metrics

Information Retrieval

Dataset: dim_384
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.787
cosine_accuracy@3	0.9269
cosine_accuracy@5	0.9446
cosine_accuracy@10	0.9597
cosine_precision@1	0.787
cosine_precision@3	0.309
cosine_precision@5	0.1889
cosine_precision@10	0.096
cosine_recall@1	0.0219
cosine_recall@3	0.0257
cosine_recall@5	0.0262
cosine_recall@10	0.0267
cosine_ndcg@10	0.1946
cosine_mrr@10	0.8588
cosine_map@100	0.0239

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7815
cosine_accuracy@3	0.9236
cosine_accuracy@5	0.9422
cosine_accuracy@10	0.9596
cosine_precision@1	0.7815
cosine_precision@3	0.3079
cosine_precision@5	0.1884
cosine_precision@10	0.096
cosine_recall@1	0.0217
cosine_recall@3	0.0257
cosine_recall@5	0.0262
cosine_recall@10	0.0267
cosine_ndcg@10	0.194
cosine_mrr@10	0.8551
cosine_map@100	0.0238

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7713
cosine_accuracy@3	0.9142
cosine_accuracy@5	0.9347
cosine_accuracy@10	0.9532
cosine_precision@1	0.7713
cosine_precision@3	0.3047
cosine_precision@5	0.1869
cosine_precision@10	0.0953
cosine_recall@1	0.0214
cosine_recall@3	0.0254
cosine_recall@5	0.026
cosine_recall@10	0.0265
cosine_ndcg@10	0.192
cosine_mrr@10	0.8457
cosine_map@100	0.0235

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7428
cosine_accuracy@3	0.8924
cosine_accuracy@5	0.9185
cosine_accuracy@10	0.9412
cosine_precision@1	0.7428
cosine_precision@3	0.2975
cosine_precision@5	0.1837
cosine_precision@10	0.0941
cosine_recall@1	0.0206
cosine_recall@3	0.0248
cosine_recall@5	0.0255
cosine_recall@10	0.0261
cosine_ndcg@10	0.1875
cosine_mrr@10	0.822
cosine_map@100	0.0229

Information Retrieval

Dataset: dim_32
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6669
cosine_accuracy@3	0.8242
cosine_accuracy@5	0.864
cosine_accuracy@10	0.8988
cosine_precision@1	0.6669
cosine_precision@3	0.2747
cosine_precision@5	0.1728
cosine_precision@10	0.0899
cosine_recall@1	0.0185
cosine_recall@3	0.0229
cosine_recall@5	0.024
cosine_recall@10	0.025
cosine_ndcg@10	0.1737
cosine_mrr@10	0.7533
cosine_map@100	0.021

Training Details

Training Dataset

Unnamed Dataset

Size: 11,863 training samples
Columns: context and question
Approximate statistics based on the first 1000 samples:
context question
type string string
details
min: 13 tokens
mean: 40.74 tokens
max: 277 tokens

min: 11 tokens
mean: 24.4 tokens
max: 62 tokens

	context	question
type	string	string
details	min: 13 tokens mean: 40.74 tokens max: 277 tokens	min: 11 tokens mean: 24.4 tokens max: 62 tokens

Samples:

context	question
`The engagement with key stakeholders involves various topics and methods throughout the year`	`Question: What does the engagement with key stakeholders involve throughout the year?`
`For unitholders and analysts, the focus is on business and operations, the release of financial results, and the overall performance and announcements`	`Question: What is the focus for unitholders and analysts in terms of business and operations, financial results, performance, and announcements?`
`These are communicated through press releases and other required disclosures via SGXNet and NetLink's website`	`What platform is used to communicate press releases and required disclosures for NetLink?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        384,
        256,
        128,
        64,
        32
    ],
    "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
eval_on_start: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_32_cosine_map@100	dim_384_cosine_map@100	dim_64_cosine_map@100
0.4313	10	4.3426	-	-	-	-	-
0.8625	20	2.7083	-	-	-	-	-
1.0350	24	-	0.0229	0.0233	0.0195	0.0234	0.0220
1.2264	30	2.6835	-	-	-	-	-
1.6577	40	2.1702	-	-	-	-	-
1.9164	46	-	0.0230	0.0234	0.0197	0.0235	0.0221
0.4313	10	2.2406	-	-	-	-	-
0.8625	20	1.8606	-	-	-	-	-
1.0350	24	-	0.0233	0.0236	0.0204	0.0237	0.0225
1.2264	30	2.0645	-	-	-	-	-
1.6577	40	1.6752	-	-	-	-	-
2.0458	49	-	0.0235	0.0237	0.0208	0.0238	0.0228
2.0216	50	1.7855	-	-	-	-	-
2.4528	60	1.7333	-	-	-	-	-
2.8841	70	1.5116	-	-	-	-	-
3.0566	74	-	0.0235	0.0238	0.0210	0.0239	0.0229
3.2480	80	1.7812	-	-	-	-	-
3.6792	90	1.4886	-	-	-	-	-
3.7655	92	-	0.0235	0.0238	0.021	0.0239	0.0229

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.4.0+cu121
Accelerate: 0.32.1
Datasets: 2.21.0
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}
}