README.md

---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:67190
- loss:AdaptiveLayerLoss
- loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-small
datasets:
- stanfordnlp/snli
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: A worker peers out from atop a building under construction.
  sentences:
  - The man pleads for mercy.
  - People and a baby crossing the street.
  - A person is atop of a building.
- source_sentence: An aisle at Best Buy with an employee standing at the computer
    and a Geek Squad sign in the background.
  sentences:
  - the man is watching the stars
  - The employee is wearing a blue shirt.
  - A person balancing.
- source_sentence: A man with a long white beard is examining a camera and another
    man with a black shirt is in the background.
  sentences:
  - a man is with another man
  - Children in uniforms climb a tower.
  - There are five children.
- source_sentence: A black dog with a blue collar is jumping into the water.
  sentences:
  - The dog is playing tug of war with a stick.
  - There is a woman painting.
  - A black dog wearing a blue collar is chasing something into the water.
- source_sentence: A wet child stands in chest deep ocean water.
  sentences:
  - A woman paints a portrait of her best friend.
  - A person in red is cutting the grass on a riding mower
  - The child s playing on the beach.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-small
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.6583157259281618
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.6766541004180908
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.7049362860324137
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.6017583012580872
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.6115046147241897
      name: Cosine Precision
    - type: cosine_recall
      value: 0.8320677570093458
      name: Cosine Recall
    - type: cosine_ap
      value: 0.6995030811464378
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.6272260790824027
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 163.25054931640625
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.6976381461675579
      name: Dot F1
    - type: dot_f1_threshold
      value: 119.20779418945312
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5639409221902018
      name: Dot Precision
    - type: dot_recall
      value: 0.914427570093458
      name: Dot Recall
    - type: dot_ap
      value: 0.643747511442345
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.6571083610021129
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 243.75453186035156
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.7055783910745744
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 295.95947265625
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.5900608917697898
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.8773364485981309
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7072033306346501
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.6590703290069424
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 12.141830444335938
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.7036813518406759
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 14.197540283203125
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.5996708496194199
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.8513434579439252
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.7035256676322055
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.6590703290069424
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 243.75453186035156
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.7055783910745744
      name: Max F1
    - type: max_f1_threshold
      value: 295.95947265625
      name: Max F1 Threshold
    - type: max_precision
      value: 0.6115046147241897
      name: Max Precision
    - type: max_recall
      value: 0.914427570093458
      name: Max Recall
    - type: max_ap
      value: 0.7072033306346501
      name: Max Ap
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.732169941341086
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7344587206087978
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7537099624360986
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7550555196955944
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7468210439584286
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.74849026008206
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6142835401925993
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6100201108417316
      name: Spearman Dot
    - type: pearson_max
      value: 0.7537099624360986
      name: Pearson Max
    - type: spearman_max
      value: 0.7550555196955944
      name: Spearman Max
---

# SentenceTransformer based on microsoft/deberta-v3-small

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) 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:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
- **Language:** en
<!-- - **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': False}) with Transformer model: DebertaV2Model 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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("bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2")
# Run inference
sentences = [
    'A wet child stands in chest deep ocean water.',
    'The child s playing on the beach.',
    'A woman paints a portrait of her best friend.',
]
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]
```

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

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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>

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Binary Classification

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

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.6583     |
| cosine_accuracy_threshold    | 0.6767     |
| cosine_f1                    | 0.7049     |
| cosine_f1_threshold          | 0.6018     |
| cosine_precision             | 0.6115     |
| cosine_recall                | 0.8321     |
| cosine_ap                    | 0.6995     |
| dot_accuracy                 | 0.6272     |
| dot_accuracy_threshold       | 163.2505   |
| dot_f1                       | 0.6976     |
| dot_f1_threshold             | 119.2078   |
| dot_precision                | 0.5639     |
| dot_recall                   | 0.9144     |
| dot_ap                       | 0.6437     |
| manhattan_accuracy           | 0.6571     |
| manhattan_accuracy_threshold | 243.7545   |
| manhattan_f1                 | 0.7056     |
| manhattan_f1_threshold       | 295.9595   |
| manhattan_precision          | 0.5901     |
| manhattan_recall             | 0.8773     |
| manhattan_ap                 | 0.7072     |
| euclidean_accuracy           | 0.6591     |
| euclidean_accuracy_threshold | 12.1418    |
| euclidean_f1                 | 0.7037     |
| euclidean_f1_threshold       | 14.1975    |
| euclidean_precision          | 0.5997     |
| euclidean_recall             | 0.8513     |
| euclidean_ap                 | 0.7035     |
| max_accuracy                 | 0.6591     |
| max_accuracy_threshold       | 243.7545   |
| max_f1                       | 0.7056     |
| max_f1_threshold             | 295.9595   |
| max_precision                | 0.6115     |
| max_recall                   | 0.9144     |
| **max_ap**                   | **0.7072** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7322     |
| **spearman_cosine** | **0.7345** |
| pearson_manhattan   | 0.7537     |
| spearman_manhattan  | 0.7551     |
| pearson_euclidean   | 0.7468     |
| spearman_euclidean  | 0.7485     |
| pearson_dot         | 0.6143     |
| spearman_dot        | 0.61       |
| pearson_max         | 0.7537     |
| spearman_max        | 0.7551     |

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## Bias, Risks and Limitations

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## Training Details

### Training Dataset

#### stanfordnlp/snli

* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 67,190 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                          | sentence2                                                                         | label                        |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
  | type    | string                                                                             | string                                                                            | int                          |
  | details | <ul><li>min: 4 tokens</li><li>mean: 21.19 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.77 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
* Samples:
  | sentence1                                                                                                                              | sentence2                                                                                        | label          |
  |:---------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
  | <code>Without a placebo group, we still won't know if any of the treatments are better than nothing and therefore worth giving.</code> | <code>It is necessary to use a controlled method to ensure the treatments are worthwhile.</code> | <code>0</code> |
  | <code>It was conducted in silence.</code>                                                                                              | <code>It was done silently.</code>                                                               | <code>0</code> |
  | <code>oh Lewisville  any decent food in your cafeteria up there</code>                                                                 | <code>Is there any decent food in your cafeteria up there in Lewisville?</code>                  | <code>0</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "n_layers_per_step": 1,
      "last_layer_weight": 1,
      "prior_layers_weight": 1,
      "kl_div_weight": 1,
      "kl_temperature": 1
  }
  ```

### Evaluation Dataset

#### stanfordnlp/snli

* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 14.77 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.74 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                             | score             |
  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "n_layers_per_step": 1,
      "last_layer_weight": 1,
      "prior_layers_weight": 1,
      "kl_div_weight": 1,
      "kl_temperature": 1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 42
- `per_device_eval_batch_size`: 22
- `learning_rate`: 3e-06
- `weight_decay`: 1e-08
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.5
- `save_safetensors`: False
- `fp16`: True
- `hub_model_id`: bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2-tmp
- `hub_strategy`: checkpoint
- `hub_private_repo`: True
- `batch_sampler`: no_duplicates

#### 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`: 42
- `per_device_eval_batch_size`: 22
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 3e-06
- `weight_decay`: 1e-08
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.5
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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`: True
- `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`: bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2-tmp
- `hub_strategy`: checkpoint
- `hub_private_repo`: True
- `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

</details>

### Training Logs
| Epoch | Step | Training Loss | loss   | max_ap | spearman_cosine |
|:-----:|:----:|:-------------:|:------:|:------:|:---------------:|
| 0.1   | 160  | 4.6003        | 4.8299 | 0.6017 | -               |
| 0.2   | 320  | 4.0659        | 4.3436 | 0.6168 | -               |
| 0.3   | 480  | 3.4886        | 4.0840 | 0.6339 | -               |
| 0.4   | 640  | 3.0592        | 3.6422 | 0.6611 | -               |
| 0.5   | 800  | 2.5728        | 3.1927 | 0.6773 | -               |
| 0.6   | 960  | 2.184         | 2.8322 | 0.6893 | -               |
| 0.7   | 1120 | 1.8744        | 2.4892 | 0.6954 | -               |
| 0.8   | 1280 | 1.757         | 2.4453 | 0.7002 | -               |
| 0.9   | 1440 | 1.5872        | 2.2565 | 0.7010 | -               |
| 1.0   | 1600 | 1.446         | 2.1391 | 0.7046 | -               |
| 1.1   | 1760 | 1.3892        | 2.1236 | 0.7058 | -               |
| 1.2   | 1920 | 1.2567        | 1.9738 | 0.7053 | -               |
| 1.3   | 2080 | 1.2233        | 1.8925 | 0.7063 | -               |
| 1.4   | 2240 | 1.1954        | 1.8392 | 0.7075 | -               |
| 1.5   | 2400 | 1.1395        | 1.9081 | 0.7065 | -               |
| 1.6   | 2560 | 1.1211        | 1.8080 | 0.7074 | -               |
| 1.7   | 2720 | 1.0825        | 1.8408 | 0.7073 | -               |
| 1.8   | 2880 | 1.1358        | 1.7363 | 0.7073 | -               |
| 1.9   | 3040 | 1.0628        | 1.8936 | 0.7072 | -               |
| 2.0   | 3200 | 1.1412        | 1.7846 | 0.7072 | -               |
| None  | 0    | -             | 3.0121 | 0.7072 | 0.7345          |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.30.1
- Datasets: 2.19.2
- 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",
}
```

#### AdaptiveLayerLoss
```bibtex
@misc{li20242d,
    title={2D Matryoshka Sentence Embeddings}, 
    author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
    year={2024},
    eprint={2402.14776},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

#### 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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