---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:11600
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Show me contest time
  sentences:
  - How did Shelton and he compare in momentum during set 2?
  - What is the key factors for Djokovic?
  - What is the how many winners for Djokovic?
- source_sentence: What about Djokovic's result?
  sentences:
  - what venue
  - What's Anisimova’s total return unforced errors?
  - In what year did he go pro?
- source_sentence: prediction for the player?
  sentences:
  - unforced for Djokovic?
  - Show me where is he from
  - form shift?
- source_sentence: long for Sinner?
  sentences:
  - titles for Sinner?
  - result for Djokovic?
  - long for Djokovic?
- source_sentence: What is the can he win for Djokovic?
  sentences:
  - What is the set time for the player?
  - Show me match score
  - form shift?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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("GozdeA/tennis-multi-return-catboost-v2")
# Run inference
sentences = [
    'What is the can he win for Djokovic?',
    'form shift?',
    'What is the set time for the player?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6634, 0.0789],
#         [0.6634, 1.0000, 0.1159],
#         [0.0789, 0.1159, 1.0000]])
```

<!--
### 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.*
-->

<!--
## 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: 11,600 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                         | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 10.77 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.81 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.67 tokens</li><li>max: 28 tokens</li></ul> |
* Samples:
  | anchor                                                              | positive                                          | negative                                                    |
  |:--------------------------------------------------------------------|:--------------------------------------------------|:------------------------------------------------------------|
  | <code>What is the overall return for Djokovic?</code>               | <code>overall for Djokovic?</code>                | <code>What is the return winners for Djokovic?</code>       |
  | <code>What is the return winner count for Alcaraz and Fritz?</code> | <code>how many winners?</code>                    | <code>What is the how good is his return for Sinner?</code> |
  | <code>backhand for he?</code>                                       | <code>What is the backhand quality for he?</code> | <code>What is the backhand today for he?</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"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 2,900 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                           | positive                                                                         | negative                                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 10.9 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.62 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.38 tokens</li><li>max: 25 tokens</li></ul> |
* Samples:
  | anchor                                                                            | positive                                             | negative                                                                  |
  |:----------------------------------------------------------------------------------|:-----------------------------------------------------|:--------------------------------------------------------------------------|
  | <code>How does Shelton's game match up against Lorenzo Sonego's strengths?</code> | <code>key factors?</code>                            | <code>What is the date of birth for Djokovic?</code>                      |
  | <code>What is the what are the key for Sinner?</code>                             | <code>What's the what are the key for Sinner?</code> | <code>What are the what is a for Sinner?</code>                           |
  | <code>professional career stats?</code>                                           | <code>professional career titles?</code>             | <code>How does Shelton's forehand compare to their career average?</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
- `num_train_epochs`: 15
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 15
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `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
- `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}
- `parallelism_config`: 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
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch   | Step  | Training Loss |
|:-------:|:-----:|:-------------:|
| 0.0690  | 50    | 4.8633        |
| 0.1379  | 100   | 4.2929        |
| 0.2069  | 150   | 3.2473        |
| 0.2759  | 200   | 2.4133        |
| 0.3448  | 250   | 2.0601        |
| 0.4138  | 300   | 1.7225        |
| 0.4828  | 350   | 1.631         |
| 0.5517  | 400   | 1.5036        |
| 0.6207  | 450   | 1.3556        |
| 0.6897  | 500   | 1.2699        |
| 0.7586  | 550   | 1.3131        |
| 0.8276  | 600   | 1.1743        |
| 0.8966  | 650   | 1.0491        |
| 0.9655  | 700   | 1.2265        |
| 1.0345  | 750   | 1.0786        |
| 1.1034  | 800   | 1.0451        |
| 1.1724  | 850   | 1.0379        |
| 1.2414  | 900   | 0.9378        |
| 1.3103  | 950   | 0.8659        |
| 1.3793  | 1000  | 0.8908        |
| 1.4483  | 1050  | 0.8333        |
| 1.5172  | 1100  | 0.7814        |
| 1.5862  | 1150  | 0.7764        |
| 1.6552  | 1200  | 0.8071        |
| 1.7241  | 1250  | 0.7394        |
| 1.7931  | 1300  | 0.7137        |
| 1.8621  | 1350  | 0.7669        |
| 1.9310  | 1400  | 0.6652        |
| 2.0     | 1450  | 0.7612        |
| 2.0690  | 1500  | 0.6847        |
| 2.1379  | 1550  | 0.6511        |
| 2.2069  | 1600  | 0.7297        |
| 2.2759  | 1650  | 0.6836        |
| 2.3448  | 1700  | 0.6733        |
| 2.4138  | 1750  | 0.6125        |
| 2.4828  | 1800  | 0.664         |
| 2.5517  | 1850  | 0.6212        |
| 2.6207  | 1900  | 0.6613        |
| 2.6897  | 1950  | 0.645         |
| 2.7586  | 2000  | 0.6311        |
| 2.8276  | 2050  | 0.6823        |
| 2.8966  | 2100  | 0.6608        |
| 2.9655  | 2150  | 0.6408        |
| 3.0345  | 2200  | 0.6364        |
| 3.1034  | 2250  | 0.5752        |
| 3.1724  | 2300  | 0.6431        |
| 3.2414  | 2350  | 0.585         |
| 3.3103  | 2400  | 0.6852        |
| 3.3793  | 2450  | 0.6743        |
| 3.4483  | 2500  | 0.5907        |
| 3.5172  | 2550  | 0.5632        |
| 3.5862  | 2600  | 0.5853        |
| 3.6552  | 2650  | 0.5906        |
| 3.7241  | 2700  | 0.6471        |
| 3.7931  | 2750  | 0.5809        |
| 3.8621  | 2800  | 0.5832        |
| 3.9310  | 2850  | 0.6011        |
| 4.0     | 2900  | 0.5926        |
| 4.0690  | 2950  | 0.5962        |
| 4.1379  | 3000  | 0.6648        |
| 4.2069  | 3050  | 0.5759        |
| 4.2759  | 3100  | 0.5162        |
| 4.3448  | 3150  | 0.5945        |
| 4.4138  | 3200  | 0.5859        |
| 4.4828  | 3250  | 0.6066        |
| 4.5517  | 3300  | 0.5536        |
| 4.6207  | 3350  | 0.5112        |
| 4.6897  | 3400  | 0.5094        |
| 4.7586  | 3450  | 0.5056        |
| 4.8276  | 3500  | 0.573         |
| 4.8966  | 3550  | 0.5425        |
| 4.9655  | 3600  | 0.5641        |
| 5.0345  | 3650  | 0.5409        |
| 5.1034  | 3700  | 0.58          |
| 5.1724  | 3750  | 0.5669        |
| 5.2414  | 3800  | 0.6087        |
| 5.3103  | 3850  | 0.557         |
| 5.3793  | 3900  | 0.5191        |
| 5.4483  | 3950  | 0.5321        |
| 5.5172  | 4000  | 0.5965        |
| 5.5862  | 4050  | 0.5612        |
| 5.6552  | 4100  | 0.6181        |
| 5.7241  | 4150  | 0.5144        |
| 5.7931  | 4200  | 0.5187        |
| 5.8621  | 4250  | 0.5362        |
| 5.9310  | 4300  | 0.5215        |
| 6.0     | 4350  | 0.5578        |
| 6.0690  | 4400  | 0.5291        |
| 6.1379  | 4450  | 0.512         |
| 6.2069  | 4500  | 0.5702        |
| 6.2759  | 4550  | 0.5935        |
| 6.3448  | 4600  | 0.5376        |
| 6.4138  | 4650  | 0.5012        |
| 6.4828  | 4700  | 0.6246        |
| 6.5517  | 4750  | 0.5038        |
| 6.6207  | 4800  | 0.5739        |
| 6.6897  | 4850  | 0.5765        |
| 6.7586  | 4900  | 0.58          |
| 6.8276  | 4950  | 0.5462        |
| 6.8966  | 5000  | 0.5087        |
| 6.9655  | 5050  | 0.5357        |
| 7.0345  | 5100  | 0.5352        |
| 7.1034  | 5150  | 0.5002        |
| 7.1724  | 5200  | 0.5196        |
| 7.2414  | 5250  | 0.5668        |
| 7.3103  | 5300  | 0.5104        |
| 7.3793  | 5350  | 0.5029        |
| 7.4483  | 5400  | 0.481         |
| 7.5172  | 5450  | 0.5567        |
| 7.5862  | 5500  | 0.5425        |
| 7.6552  | 5550  | 0.4884        |
| 7.7241  | 5600  | 0.4854        |
| 7.7931  | 5650  | 0.5459        |
| 7.8621  | 5700  | 0.5201        |
| 7.9310  | 5750  | 0.5288        |
| 8.0     | 5800  | 0.5055        |
| 8.0690  | 5850  | 0.4656        |
| 8.1379  | 5900  | 0.5538        |
| 8.2069  | 5950  | 0.5513        |
| 8.2759  | 6000  | 0.5078        |
| 8.3448  | 6050  | 0.508         |
| 8.4138  | 6100  | 0.5403        |
| 8.4828  | 6150  | 0.4711        |
| 8.5517  | 6200  | 0.5024        |
| 8.6207  | 6250  | 0.4886        |
| 8.6897  | 6300  | 0.5446        |
| 8.7586  | 6350  | 0.4953        |
| 8.8276  | 6400  | 0.5395        |
| 8.8966  | 6450  | 0.571         |
| 8.9655  | 6500  | 0.567         |
| 9.0345  | 6550  | 0.5684        |
| 9.1034  | 6600  | 0.543         |
| 9.1724  | 6650  | 0.5449        |
| 9.2414  | 6700  | 0.4713        |
| 9.3103  | 6750  | 0.5046        |
| 9.3793  | 6800  | 0.5785        |
| 9.4483  | 6850  | 0.4744        |
| 9.5172  | 6900  | 0.5364        |
| 9.5862  | 6950  | 0.5523        |
| 9.6552  | 7000  | 0.5245        |
| 9.7241  | 7050  | 0.5005        |
| 9.7931  | 7100  | 0.5355        |
| 9.8621  | 7150  | 0.5248        |
| 9.9310  | 7200  | 0.4924        |
| 10.0    | 7250  | 0.4885        |
| 10.0690 | 7300  | 0.4708        |
| 10.1379 | 7350  | 0.5075        |
| 10.2069 | 7400  | 0.4943        |
| 10.2759 | 7450  | 0.4926        |
| 10.3448 | 7500  | 0.4757        |
| 10.4138 | 7550  | 0.5305        |
| 10.4828 | 7600  | 0.4626        |
| 10.5517 | 7650  | 0.5161        |
| 10.6207 | 7700  | 0.48          |
| 10.6897 | 7750  | 0.466         |
| 10.7586 | 7800  | 0.5556        |
| 10.8276 | 7850  | 0.51          |
| 10.8966 | 7900  | 0.5185        |
| 10.9655 | 7950  | 0.5485        |
| 11.0345 | 8000  | 0.4591        |
| 11.1034 | 8050  | 0.523         |
| 11.1724 | 8100  | 0.5295        |
| 11.2414 | 8150  | 0.4482        |
| 11.3103 | 8200  | 0.5275        |
| 11.3793 | 8250  | 0.4849        |
| 11.4483 | 8300  | 0.5374        |
| 11.5172 | 8350  | 0.4621        |
| 11.5862 | 8400  | 0.4374        |
| 11.6552 | 8450  | 0.4855        |
| 11.7241 | 8500  | 0.5147        |
| 11.7931 | 8550  | 0.564         |
| 11.8621 | 8600  | 0.4763        |
| 11.9310 | 8650  | 0.4456        |
| 12.0    | 8700  | 0.4906        |
| 12.0690 | 8750  | 0.4912        |
| 12.1379 | 8800  | 0.4556        |
| 12.2069 | 8850  | 0.4936        |
| 12.2759 | 8900  | 0.4864        |
| 12.3448 | 8950  | 0.5262        |
| 12.4138 | 9000  | 0.458         |
| 12.4828 | 9050  | 0.5631        |
| 12.5517 | 9100  | 0.5144        |
| 12.6207 | 9150  | 0.4966        |
| 12.6897 | 9200  | 0.5589        |
| 12.7586 | 9250  | 0.4718        |
| 12.8276 | 9300  | 0.5124        |
| 12.8966 | 9350  | 0.5362        |
| 12.9655 | 9400  | 0.482         |
| 13.0345 | 9450  | 0.4821        |
| 13.1034 | 9500  | 0.4984        |
| 13.1724 | 9550  | 0.4646        |
| 13.2414 | 9600  | 0.4825        |
| 13.3103 | 9650  | 0.4957        |
| 13.3793 | 9700  | 0.4739        |
| 13.4483 | 9750  | 0.523         |
| 13.5172 | 9800  | 0.4892        |
| 13.5862 | 9850  | 0.4803        |
| 13.6552 | 9900  | 0.502         |
| 13.7241 | 9950  | 0.4828        |
| 13.7931 | 10000 | 0.5034        |
| 13.8621 | 10050 | 0.5151        |
| 13.9310 | 10100 | 0.5292        |
| 14.0    | 10150 | 0.5227        |
| 14.0690 | 10200 | 0.4853        |
| 14.1379 | 10250 | 0.4528        |
| 14.2069 | 10300 | 0.4591        |
| 14.2759 | 10350 | 0.4482        |
| 14.3448 | 10400 | 0.4412        |
| 14.4138 | 10450 | 0.4854        |
| 14.4828 | 10500 | 0.4734        |
| 14.5517 | 10550 | 0.4749        |
| 14.6207 | 10600 | 0.5448        |
| 14.6897 | 10650 | 0.5117        |
| 14.7586 | 10700 | 0.4776        |
| 14.8276 | 10750 | 0.4638        |
| 14.8966 | 10800 | 0.5636        |
| 14.9655 | 10850 | 0.547         |

</details>

### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.0.0
- Transformers: 4.57.6
- PyTorch: 2.10.0+cu128
- Accelerate: 1.13.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2

## 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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