README.md

---
base_model: BAAI/bge-small-en-v1.5
datasets: []
language: []
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
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:723
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: how do different regions contribute to my returns
  sentences:
  - '[{"get_portfolio(None)": "portfolio"}, {"filter(''portfolio'',''ticker'',''=='',''<TICKER1>'')":
    "portfolio"}, {"get_attribute(''portfolio'',[''losses''],''<DATES>'')": "portfolio"}]'
  - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''region'',None,''returns'')":
    "portfolio"}]'
  - '[{"get_portfolio(None)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],''<DATES>'')":
    "portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}, {"get_attribute([''<TICKER1>''],[''returns''],''<DATES>'')":
    "<TICKER1>_performance_data"}]'
- source_sentence: how have I done in US equity this year?
  sentences:
  - '[{"get_portfolio([''weight''])": "portfolio"}, {"get_attribute(''portfolio'',[''dividend
    yield''],''<DATES>'')": "portfolio"}, {"calculate(''portfolio'',[''dividend yield'',''weight''],''multiply'',''weighted_yield'')":
    "portfolio"}, {"aggregate(''portfolio'',''ticker'',''weighted_yield'',''sum'',None)":
    "portfolio_yield"}]'
  - '[{"get_portfolio(None)": "portfolio"}, {"get_attribute(''portfolio'',[''dividend
    yield''],''<DATES>'')": "portfolio"}, {"calculate(''portfolio'',[''dividend yield'',
    ''marketValue''],''multiply'',''div_income'')": "portfolio"}, {"sort(''portfolio'',''div_income'',''desc'')":
    "portfolio"}]'
  - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''asset_class'',''us
    equity'',''returns'')": "portfolio"}]'
- source_sentence: What is the total value of my cash?
  sentences:
  - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''sector'',''sector
    utilities'',''portfolio'')": "portfolio"}]'
  - '[{"get_portfolio([''type'', ''marketValue''])": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''CASH'')":
    "portfolio"}, {"aggregate(''portfolio'',''ticker'',''marketValue'',''sum'',None)":
    "buying_power"}]'
  - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''sector'',''sector
    information technology'',''returns'')": "portfolio"}]'
- source_sentence: What is the exposure of my account to Chinese market?
  sentences:
  - '[{"get_portfolio([''marketValue''])": "portfolio"}, {"sort(''portfolio'',''marketValue'',''asc'')":
    "portfolio"}]'
  - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''region'',''china'',''portfolio'')":
    "portfolio"}]'
  - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''volatility'',None,''portfolio'')":
    "portfolio"}]'
- source_sentence: Which of my investments are projected to generate the most return?
  sentences:
  - '[{"get_portfolio([''marketValue''])": "portfolio"}, {"get_attribute(''portfolio'',[''<TICKER1>''],''<DATES>'')":
    "portfolio"}, {"calculate(''portfolio'',[''marketValue'', ''<TICKER1>''],''multiply'',''expo_<TICKER1>'')":
    "portfolio"}, {"sort(''portfolio'',''expo_<TICKER1>'',''desc'')": "portfolio"},
    {"aggregate(''portfolio'',''ticker'',''expo_<TICKER1>'',''sum'',None)": "port_expo_<TICKER1>"}]'
  - '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''asset_class'',''us
    equity'',''returns'')": "portfolio"}]'
  - '[{"get_portfolio(None)": "portfolio"}, {"get_expected_attribute(''portfolio'',[''returns''])":
    "portfolio"}, {"sort(''portfolio'',''returns'',''desc'')": "portfolio"}]'
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.6643835616438356
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8287671232876712
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.863013698630137
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9178082191780822
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6643835616438356
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27625570776255703
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17260273972602735
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0917808219178082
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.018455098934550992
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.023021308980213092
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.02397260273972603
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.02549467275494673
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1736543171752474
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7480294629267232
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.020863027954722068
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.6643835616438356
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.8287671232876712
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.863013698630137
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.9178082191780822
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.6643835616438356
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.27625570776255703
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.17260273972602735
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.0917808219178082
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.018455098934550992
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.023021308980213092
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.02397260273972603
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.02549467275494673
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.1736543171752474
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.7480294629267232
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.020863027954722068
      name: Dot Map@100
---

# SentenceTransformer based on BAAI/bge-small-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/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](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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({'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:

```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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Which of my investments are projected to generate the most return?',
    '[{"get_portfolio(None)": "portfolio"}, {"get_expected_attribute(\'portfolio\',[\'returns\'])": "portfolio"}, {"sort(\'portfolio\',\'returns\',\'desc\')": "portfolio"}]',
    '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'asset_class\',\'us equity\',\'returns\')": "portfolio"}]',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval

* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.6644     |
| cosine_accuracy@3   | 0.8288     |
| cosine_accuracy@5   | 0.863      |
| cosine_accuracy@10  | 0.9178     |
| cosine_precision@1  | 0.6644     |
| cosine_precision@3  | 0.2763     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.0918     |
| cosine_recall@1     | 0.0185     |
| cosine_recall@3     | 0.023      |
| cosine_recall@5     | 0.024      |
| cosine_recall@10    | 0.0255     |
| cosine_ndcg@10      | 0.1737     |
| cosine_mrr@10       | 0.748      |
| **cosine_map@100**  | **0.0209** |
| dot_accuracy@1      | 0.6644     |
| dot_accuracy@3      | 0.8288     |
| dot_accuracy@5      | 0.863      |
| dot_accuracy@10     | 0.9178     |
| dot_precision@1     | 0.6644     |
| dot_precision@3     | 0.2763     |
| dot_precision@5     | 0.1726     |
| dot_precision@10    | 0.0918     |
| dot_recall@1        | 0.0185     |
| dot_recall@3        | 0.023      |
| dot_recall@5        | 0.024      |
| dot_recall@10       | 0.0255     |
| dot_ndcg@10         | 0.1737     |
| dot_mrr@10          | 0.748      |
| dot_map@100         | 0.0209     |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 723 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                       | sentence_1                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              |
  | details | <ul><li>min: 5 tokens</li><li>mean: 11.8 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 84.41 tokens</li><li>max: 194 tokens</li></ul> |
* Samples:
  | sentence_0                                              | sentence_1                                                                                                                                                              |
  |:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what is my portfolio 3 year cagr?</code>          | <code>[{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'<DATES>')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}]</code> |
  | <code>what is my 1 year rate of return</code>           | <code>[{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'<DATES>')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}]</code> |
  | <code>show backtest of my performance this year?</code> | <code>[{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'<DATES>')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}]</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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 6
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `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`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 6
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | cosine_map@100 |
|:------:|:----:|:--------------:|
| 0.0274 | 2    | 0.0136         |
| 0.0548 | 4    | 0.0137         |
| 0.0822 | 6    | 0.0139         |
| 0.1096 | 8    | 0.0142         |
| 0.1370 | 10   | 0.0145         |
| 0.1644 | 12   | 0.0144         |
| 0.1918 | 14   | 0.0147         |
| 0.2192 | 16   | 0.0151         |
| 0.2466 | 18   | 0.0153         |
| 0.2740 | 20   | 0.0158         |
| 0.3014 | 22   | 0.0165         |
| 0.3288 | 24   | 0.0163         |
| 0.3562 | 26   | 0.0167         |
| 0.3836 | 28   | 0.0171         |
| 0.4110 | 30   | 0.0175         |
| 0.4384 | 32   | 0.0177         |
| 0.4658 | 34   | 0.0180         |
| 0.4932 | 36   | 0.0183         |
| 0.5205 | 38   | 0.0185         |
| 0.5479 | 40   | 0.0186         |
| 0.5753 | 42   | 0.0186         |
| 0.6027 | 44   | 0.0186         |
| 0.6301 | 46   | 0.0186         |
| 0.6575 | 48   | 0.0187         |
| 0.6849 | 50   | 0.0189         |
| 0.7123 | 52   | 0.0190         |
| 0.7397 | 54   | 0.0189         |
| 0.7671 | 56   | 0.0188         |
| 0.7945 | 58   | 0.0189         |
| 0.8219 | 60   | 0.0192         |
| 0.8493 | 62   | 0.0193         |
| 0.8767 | 64   | 0.0194         |
| 0.9041 | 66   | 0.0194         |
| 0.9315 | 68   | 0.0197         |
| 0.9589 | 70   | 0.0200         |
| 0.9863 | 72   | 0.0201         |
| 1.0    | 73   | 0.0202         |
| 1.0137 | 74   | 0.0203         |
| 1.0411 | 76   | 0.0202         |
| 1.0685 | 78   | 0.0203         |
| 1.0959 | 80   | 0.0205         |
| 1.1233 | 82   | 0.0207         |
| 1.1507 | 84   | 0.0207         |
| 1.1781 | 86   | 0.0206         |
| 1.2055 | 88   | 0.0205         |
| 1.2329 | 90   | 0.0205         |
| 1.2603 | 92   | 0.0205         |
| 1.2877 | 94   | 0.0204         |
| 1.3151 | 96   | 0.0204         |
| 1.3425 | 98   | 0.0205         |
| 1.3699 | 100  | 0.0205         |
| 1.3973 | 102  | 0.0205         |
| 1.4247 | 104  | 0.0205         |
| 1.4521 | 106  | 0.0204         |
| 1.4795 | 108  | 0.0205         |
| 1.5068 | 110  | 0.0208         |
| 1.5342 | 112  | 0.0206         |
| 1.5616 | 114  | 0.0205         |
| 1.5890 | 116  | 0.0206         |
| 1.6164 | 118  | 0.0205         |
| 1.6438 | 120  | 0.0205         |
| 1.6712 | 122  | 0.0205         |
| 1.6986 | 124  | 0.0207         |
| 1.7260 | 126  | 0.0207         |
| 1.7534 | 128  | 0.0207         |
| 1.7808 | 130  | 0.0205         |
| 1.8082 | 132  | 0.0206         |
| 1.8356 | 134  | 0.0208         |
| 1.8630 | 136  | 0.0206         |
| 1.8904 | 138  | 0.0206         |
| 1.9178 | 140  | 0.0206         |
| 1.9452 | 142  | 0.0205         |
| 1.9726 | 144  | 0.0206         |
| 2.0    | 146  | 0.0207         |
| 2.0274 | 148  | 0.0209         |


### Framework Versions
- Python: 3.10.9
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

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

#### 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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