sentence-transformers safetensors sentence-similarity feature-extraction generated_from_trainer dataset_size:29545 loss:MultipleNegativesRankingLoss custom_code arxiv:1908.10084 arxiv:1705.00652 base_model:jxm/cde-small-v1 base_model:finetune:jxm/cde-small-v1 autotrain_compatible endpoints_compatible region:us
---
base_model: jxm/cde-small-v1
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:29545
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: In terms of audited accounts submission for an Applicant, could
    you clarify the scenarios in which the Regulator might agree that a reviewed pro
    forma statement of financial position is not needed, and what factors would be
    considered in making that determination?
  sentences:
  - "DocumentID: 1 | PassageID: 4.2.1.(3) | Passage: Where the regulator in another\
    \ jurisdiction does not permit the implementation of policies, procedures, systems\
    \ and controls consistent with these Rules, the Relevant Person must:\n(a)\tinform\
    \ the Regulator in writing immediately; and\n(b)\tapply appropriate additional\
    \ measures to manage the money laundering risks posed by the relevant branch or\
    \ subsidiary."
  - "DocumentID: 11 | PassageID: 2.3.15.(4) | Passage: The Applicant must submit to\
    \ the Regulator the following records, as applicable:\n(a)\tAudited accounts,\
    \ for the purposes of this Rule and Rule 2.3.2(1), for the last three full financial\
    \ years, noting that:\n(i)\tif the Applicant applies for admission less than ninety\
    \ days after the end of its last financial year, unless the Applicant has audited\
    \ accounts for its latest full financial year, the accounts may be for the three\
    \ years to the end of the previous financial year, but must also include audited\
    \ or reviewed accounts for its most recent semi-annual financial reporting period;\
    \ and\n(ii)\tif the Applicant applies for admission more than six months and seventy-five\
    \ days after the end of its last financial year, audited or reviewed accounts\
    \ for its most recent semi-annual financial reporting period (or longer period\
    \ if available).\n(b)\tUnless the Regulator agrees it is not needed, a reviewed\
    \ pro forma statement of financial position. The review must be conducted by an\
    \ accredited professional auditor of the company or an independent accountant."
  - 'DocumentID: 36 | PassageID: D.1.3. | Passage: Principle 1 – Oversight and responsibility
    of climate-related financial risk exposures.Certain functions related to the management
    of climate-related financial risks may be delegated, but, as with other risks,
    the board is ultimately responsible and accountable for monitoring, managing and
    overseeing climate-related risks for the financial firm.

    '
- source_sentence: A financial institution is interested in multiple designations,
    including the ADGM Green Fund and ADGM Green Bond. For each application, what
    fee will the institution incur?
  sentences:
  - 'DocumentID: 31 | PassageID: 63) | Passage: INITIAL DISCLOSURE OF MATERIAL ESTIMATES.

    Disclosure of material estimates of Contingent Resources

    Section 2.3 of the PRMS Guidelines states that Contingent Resources may be assigned
    for Petroleum Projects that are dependent on ‘technology under development’, and
    further recommended that a number of guidelines are followed in order to distinguish
    these estimates from those that should be classified as Unrecoverable Petroleum.  By
    way of Rule 12.10.1(3), the FSRA fully supports and requires compliance with what
    is set out in the PRMS Guidelines.

    '
  - 'DocumentID: 19 | PassageID: 40) | Passage: REGULATORY REQUIREMENTS FOR AUTHORISED
    PERSONS ENGAGED IN REGULATED ACTIVITIES IN RELATION TO VIRTUAL ASSETS

    Anti-Money Laundering and Countering Financing of Terrorism

    On 21 June 2019, FATF released a revised Guidance for a Risk-Based Approach (RBA)
    for VAs and VASPs, as well as an Interpretative Note for Recommendation 15. This
    built upon previous FATF statements by clarifying a RBA for Anti-Money Laundering
    and Countering the Financing of Terrorism (“AML/CFT”) purposes.   The basic principle
    underlying the FATF Guidelines is that VASPs are expected to “identify, assess,
    and take effective action to mitigate their ML/TF risks” with respect to VAs.

    '
  - "DocumentID: 4 | PassageID: 10.1.1 | Passage: A Person applying to the Regulator\
    \ for any of the following designations:\n(a)\tADGM Green Fund;\n(b)\tADGM Climate\
    \ Transition Fund;\n(c)\tADGM Green Portfolio;\n(d)\tADGM Climate Transition Portfolio;\n\
    (e)\tADGM Green Bond; or\n(f)\tADGM Sustainability Linked Bond\nmust pay to the\
    \ Regulator an application fee of $2,000."
- source_sentence: How does the ADGM expect Authorised Persons to incorporate the
    eligibility of collateral types into their overall risk management framework,
    particularly concerning Islamic finance principles?
  sentences:
  - 'DocumentID: 17 | PassageID: Schedule 1.Part 2.Chapter 5.42.(2) | Passage: In
    determining for the purposes of sub-paragraph ‎(1)‎(b) whether Deposits are accepted
    only on particular occasions, regard is to be had to the frequency of those occasions
    and to any characteristics distinguishing them from each other.'
  - "DocumentID: 9 | PassageID: 6.8.5 | Passage: \n(a)\tA Fund Manager of an Islamic\
    \ REIT may obtain financing either directly or through its Special Purpose Vehicle\
    \ up to 65% of the total gross asset value of the Fund provided that such financing\
    \ is provided in a Shari'a-compliant manner.\n(b)\tUpon becoming aware that the\
    \ borrowing limit set out in 6.8.5(a) has been exceeded, the Fund Manager shall:\n\
    (c)\timmediately inform Unitholders and the Regulator of the details of the breach\
    \ and the proposed remedial action;\n(d)\tuse its best endeavours to reduce the\
    \ excess borrowings;\n(e)\tnot permit the Fund to engage in additional borrowing;\
    \ and\n(f)\tinform Unitholders and the Regulator on a regular basis as to the\
    \ progress of the remedial action."
  - 'DocumentID: 9 | PassageID: 5.1.1.Guidance.(ii) | Passage: The prudential Category
    for Islamic Financial Institutions and other Authorised Persons (acting through
    an Islamic Window) undertaking the Regulated Activity of Managing PSIAs (which
    may be either a Restricted PSIA or an Unrestricted PSIA) is determined in accordance
    with PRU Rule 1.3.  An Authorised Person which Manages PSIAs (whether as an Islamic
    Financial Institution or through an Islamic Window) must comply with the requirements
    in PRU in relation to specific prudential requirements relating to Trading Book
    and Non-Trading Book activities, including Credit Risk, Market Risk, Liquidity
    Risk and Group Risk.'
- source_sentence: Can you please detail the specific Anti-Money Laundering (AML)
    and Countering Financing of Terrorism (CFT) measures and controls that our firm
    must have in place when dealing with Spot Commodities as per the FSRA's requirements?
  sentences:
  - 'DocumentID: 34 | PassageID: 65) | Passage: REGULATORY REQUIREMENTS - SPOT COMMODITY
    ACTIVITIES

    Sanctions

    Pursuant to AML Rule 11.2.1(1), an Authorised Person must have arrangements in
    place to ensure that only Spot Commodities that are not subject to sanctions or
    associated with an entity in the supply chain that is itself subject to a sanction,
    are used as part of its Regulated Activities, or utilised as part of a delivery
    and/or storage facility operated by itself (or by any third parties it uses).  In
    demonstrating compliance with the Rule, an Authorised Person must have powers
    to resolve any breach in a timely fashion, such as taking emergency action itself
    or by compelling the delivery and/or storage facility to take appropriate action.  The
    FSRA expects this to include the Authorised Person having the ability to sanction
    a Member, market participant or the delivery and/or storage facility for acts
    or omissions that compromise compliance with applicable sanctions.

    '
  - "DocumentID: 18 | PassageID: 3.2 | Passage: Financial Services Permissions. VC\
    \ Managers operating in ADGM require a Financial Services Permission (“FSP”) to\
    \ undertake any Regulated Activity pertaining to VC Funds and/or co-investments\
    \ by third parties in VC Funds. The Regulated Activities covered by the FSP will\
    \ be dependent on the VC Managers’ investment strategy and business model.\n(a)\t\
    Managing a Collective Investment Fund: this includes carrying out fund management\
    \ activities in respect of a VC Fund.\n(b)\tAdvising on Investments or Credit\
    \ : for VC Managers these activities will be restricted to activities related\
    \ to co-investment alongside a VC Fund which the VC Manager manages, such as recommending\
    \ that a client invest in an investee company alongside the VC Fund and on the\
    \ strategy and structure required to make the investment.\n(c)\tArranging Deals\
    \ in Investments: VC Managers may also wish to make arrangements to facilitate\
    \ co-investments in the investee company.\nAuthorisation fees and supervision\
    \ fees for a VC Manager are capped at USD 10,000 regardless of whether one or\
    \ both of the additional Regulated Activities in b) and c) above in relation to\
    \ co-investments are included in its FSP. The FSP will include restrictions appropriate\
    \ to the business model of a VC Manager."
  - 'DocumentID: 24 | PassageID: 3.9 | Passage: Principle 2 – High Standards for Authorisation.
    This discerning approach is shown by the FSRA’s power to only permit VAs that
    it deems ‘acceptable’, as determined by risk factors such as security and traceability,
    in order to prevent the build-up of risk from illiquid or immature assets. Additionally,
    we do not permit stablecoins based on the algorithmic model of valuation to the
    underlying fiat currency.'
- source_sentence: What are the common scenarios or instances where assets and liabilities
    are not covered by the bases of accounting in Rule 5.3.2, and how should an Insurer
    address these in their reporting?
  sentences:
  - 'DocumentID: 1 | PassageID: 14.4.1.Guidance.1. | Passage: Relevant Persons are
    reminded that in accordance with Federal AML Legislation, Relevant Persons or
    any of their Employees must not tip off any Person, that is, inform any Person
    that he is being scrutinised, or investigated by any other competent authority,
    for possible involvement in suspicious Transactions or activity related to money
    laundering or terrorist financing.'
  - "DocumentID: 12 | PassageID: 5.3.1.Guidance | Passage: \nThe exceptions provided\
    \ in this Chapter relate to the following:\na.\tspecific Rules in respect of certain\
    \ assets and liabilities, intended to achieve a regulatory objective not achieved\
    \ by application of either or both of the bases of accounting set out in Rule\
    \ ‎5.3.2;\nb.\tassets and liabilities that are not dealt with in either or both\
    \ of the bases of accounting set out in Rule ‎5.3.2; and\nc.\tthe overriding power\
    \ of the Regulator, set out in Rule ‎5.1.6, to require an Insurer to adopt a particular\
    \ measurement for a specific asset or liability."
  - 'DocumentID: 1 | PassageID: 6.2.1.Guidance.2. | Passage: The risk assessment under
    Rule ‎6.2.1(c) should identify actions to mitigate risks associated with undertaking
    NFTF business generally, and the use of eKYC specifically. This is because distinct
    risks are often likely to arise where business is conducted entirely in an NFTF
    manner, compared to when the business relationship includes a mix of face-to-face
    and NFTF interactions. The assessment should make reference to risk mitigation
    measures recommended by the Regulator, a competent authority of the U.A.E., FATF,
    and other relevant bodies.


    '
---

# SentenceTransformer based on jxm/cde-small-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jxm/cde-small-v1](https://huggingface.co/jxm/cde-small-v1) on the csv dataset. It maps sentences & paragraphs to a None-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:** [jxm/cde-small-v1](https://huggingface.co/jxm/cde-small-v1) <!-- at revision 9e2ed1d8d569d34458913d2d246935c1b2324d11 -->
- **Maximum Sequence Length:** None tokens
- **Output Dimensionality:** None tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({}) with Transformer model: DatasetTransformer 
)
```

## 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("jebish7/cde-small-v1_MNR_3")
# Run inference
sentences = [
    'What are the common scenarios or instances where assets and liabilities are not covered by the bases of accounting in Rule 5.3.2, and how should an Insurer address these in their reporting?',
    'DocumentID: 12 | PassageID: 5.3.1.Guidance | Passage: \nThe exceptions provided in this Chapter relate to the following:\na.\tspecific Rules in respect of certain assets and liabilities, intended to achieve a regulatory objective not achieved by application of either or both of the bases of accounting set out in Rule \u200e5.3.2;\nb.\tassets and liabilities that are not dealt with in either or both of the bases of accounting set out in Rule \u200e5.3.2; and\nc.\tthe overriding power of the Regulator, set out in Rule \u200e5.1.6, to require an Insurer to adopt a particular measurement for a specific asset or liability.',
    'DocumentID: 1 | PassageID: 14.4.1.Guidance.1. | Passage: Relevant Persons are reminded that in accordance with Federal AML Legislation, Relevant Persons or any of their Employees must not tip off any Person, that is, inform any Person that he is being scrutinised, or investigated by any other competent authority, for possible involvement in suspicious Transactions or activity related to money laundering or terrorist financing.',
]
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]
```

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</details>
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<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
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### Out-of-Scope Use

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## Training Details

### Training Dataset

#### csv

* Dataset: csv
* Size: 29,545 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              |
  | details | <ul><li>min: 16 tokens</li><li>mean: 34.95 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 132.0 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                         | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>If a financial institution offers Money Remittance as one of its services, under what circumstances is it deemed to be holding Relevant Money and therefore subject to regulatory compliance (a)?</code> | <code>DocumentID: 13 | PassageID: 3.7.1.Guidance.1. | Passage: An Authorised Person is considered to be holding Relevant Money and subject to (a) where it offers Payment Services alongside currency exchange or Money Remittance.<br></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
  | <code>What are the consequences for a Recognised Body or Authorised Person if they fail to comply with ADGM's requirements regarding severance payments?</code>                                                | <code>DocumentID: 7 | PassageID: APP1.A1.2.Guidance.9. | Passage: Severance payments. Where an Authorised Person or Recognised Body provides discretionary payouts on termination of employment ("severance payments", also called "golden parachutes"), such payment should generally be subject to appropriate limits or shareholder approval. In any case, such payouts should be aligned with the Authorised Person or Recognised Body's overall financial condition and performance over an appropriate time horizon and should not be payable in the case of failure or threatened failure of the Authorised Person or Recognised Body, particularly to an individual whose actions may have contributed to the failure or potential failure of the Authorised Person or Recognised Body.<br></code> |
  | <code>If a Public Fund is structured as an Investment Trust, to whom should the Fund Manager report the review findings regarding delegated Regulated Activities or outsourced functions?</code>               | <code>DocumentID: 6 | PassageID: PART 5.12.12.8.(1) | Passage: A Fund Manager or the Trustee of a Public Fund, which has delegated any Regulated Activities or outsourced any functions, must conduct a review of the carrying out of the relevant activities or functions by the Service Provider and present the findings of the review to either:<br>(a)	the Fund's Governing Body every 6 months at the Fund's board meeting; or<br>(b)	in the case of a Fund structured as an Investment Trust, to the Trustee.</code>                                                                                                                                                                                                                                                                                |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 16
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates

#### 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`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `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
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.1082 | 100  | 1.9962        |
| 0.2165 | 200  | 1.1626        |
| 0.3247 | 300  | 0.9907        |
| 0.4329 | 400  | 0.8196        |
| 0.5411 | 500  | 0.8082        |
| 0.6494 | 600  | 0.6944        |
| 0.7576 | 700  | 0.6559        |
| 0.8658 | 800  | 0.6242        |
| 0.9740 | 900  | 0.6299        |
| 1.0823 | 1000 | 0.6051        |
| 1.1905 | 1100 | 0.567         |
| 1.2987 | 1200 | 0.4679        |
| 1.4069 | 1300 | 0.3443        |
| 1.5152 | 1400 | 0.3356        |
| 1.6234 | 1500 | 0.2958        |
| 1.7316 | 1600 | 0.254         |
| 1.8398 | 1700 | 0.2694        |
| 1.9481 | 1800 | 0.2497        |
| 2.0563 | 1900 | 0.2671        |
| 2.1645 | 2000 | 0.2558        |
| 2.2727 | 2100 | 0.1943        |
| 2.3810 | 2200 | 0.1242        |
| 2.4892 | 2300 | 0.116         |
| 2.5974 | 2400 | 0.1081        |
| 2.7056 | 2500 | 0.1056        |
| 2.8139 | 2600 | 0.107         |
| 2.9221 | 2700 | 0.1154        |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.4.0
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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