---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:11641
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: 2026 for Djokovic?
  sentences:
  - What is the serve speed for he?
  - momentum for Djokovic?
  - 2026 for Sinner?
- source_sentence: What are the match time for Djokovic?
  sentences:
  - Show me how many winners
  - What is at stake for Aryna Sabalenka in the next round?
  - What is the key factors for Djokovic?
- source_sentence: What's the points won for Djokovic?
  sentences:
  - What about he's aggressive?
  - What about the player's won?
  - Show me how many winners
- source_sentence: What's the title count for Djokovic?
  sentences:
  - What's the winner count for Djokovic?
  - How does Ben the player's 2025 form compare to their career average?
  - What's the title count for Sinner?
- source_sentence: What is the break point conversion for Sinner?
  sentences:
  - Show me how many winners
  - Show me how many winners
  - service for Sinner?
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-v3")
# Run inference
sentences = [
    'What is the break point conversion for Sinner?',
    'Show me how many winners',
    'service for Sinner?',
]
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, 0.5908, 0.2891],
#         [0.5908, 1.0000, 0.4187],
#         [0.2891, 0.4187, 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: 11,641 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: 26 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: 10.51 tokens</li><li>max: 26 tokens</li></ul> |
* Samples:
  | anchor                                                                | positive                                                | negative                                             |
  |:----------------------------------------------------------------------|:--------------------------------------------------------|:-----------------------------------------------------|
  | <code>What about he's odds?</code>                                    | <code>momentum shift?</code>                            | <code>What happened to he?</code>                    |
  | <code>How far has Nardi advanced at Wimbledon in his best run?</code> | <code>how many titles?</code>                           | <code>What is the what court for he?</code>          |
  | <code>How effective is Swiatek's return in the match?</code>          | <code>How effective is he's return in the match?</code> | <code>How effective is his return in the game</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: 2,911 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: 11.06 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.7 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.53 tokens</li><li>max: 28 tokens</li></ul> |
* Samples:
  | anchor                                    | positive                            | negative                                     |
  |:------------------------------------------|:------------------------------------|:---------------------------------------------|
  | <code>what venue</code>                   | <code>Show me what venue</code>     | <code>venue time?</code>                     |
  | <code>2025 for he?</code>                 | <code>how many titles?</code>       | <code>Show me which court</code>             |
  | <code>What about Djokovic's debut?</code> | <code>What about he's debut?</code> | <code>What about Djokovic's momentum?</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 |
|:-------:|:-----:|:-------------:|
| 0.0687  | 50    | 5.002         |
| 0.1374  | 100   | 4.122         |
| 0.2060  | 150   | 3.3282        |
| 0.2747  | 200   | 2.5309        |
| 0.3434  | 250   | 1.9021        |
| 0.4121  | 300   | 1.7012        |
| 0.4808  | 350   | 1.4657        |
| 0.5495  | 400   | 1.433         |
| 0.6181  | 450   | 1.5156        |
| 0.6868  | 500   | 1.3941        |
| 0.7555  | 550   | 1.2544        |
| 0.8242  | 600   | 1.1585        |
| 0.8929  | 650   | 1.0916        |
| 0.9615  | 700   | 0.9743        |
| 1.0302  | 750   | 1.0443        |
| 1.0989  | 800   | 0.9942        |
| 1.1676  | 850   | 1.0508        |
| 1.2363  | 900   | 0.9211        |
| 1.3049  | 950   | 0.9522        |
| 1.3736  | 1000  | 0.804         |
| 1.4423  | 1050  | 0.8645        |
| 1.5110  | 1100  | 0.8335        |
| 1.5797  | 1150  | 0.7337        |
| 1.6484  | 1200  | 0.7857        |
| 1.7170  | 1250  | 0.8482        |
| 1.7857  | 1300  | 0.7211        |
| 1.8544  | 1350  | 0.7442        |
| 1.9231  | 1400  | 0.7557        |
| 1.9918  | 1450  | 0.7323        |
| 2.0604  | 1500  | 0.677         |
| 2.1291  | 1550  | 0.6635        |
| 2.1978  | 1600  | 0.71          |
| 2.2665  | 1650  | 0.6193        |
| 2.3352  | 1700  | 0.6792        |
| 2.4038  | 1750  | 0.7151        |
| 2.4725  | 1800  | 0.6825        |
| 2.5412  | 1850  | 0.6452        |
| 2.6099  | 1900  | 0.666         |
| 2.6786  | 1950  | 0.5733        |
| 2.7473  | 2000  | 0.5546        |
| 2.8159  | 2050  | 0.6443        |
| 2.8846  | 2100  | 0.6835        |
| 2.9533  | 2150  | 0.6499        |
| 3.0220  | 2200  | 0.6229        |
| 3.0907  | 2250  | 0.6151        |
| 3.1593  | 2300  | 0.539         |
| 3.2280  | 2350  | 0.5997        |
| 3.2967  | 2400  | 0.571         |
| 3.3654  | 2450  | 0.6257        |
| 3.4341  | 2500  | 0.6222        |
| 3.5027  | 2550  | 0.6102        |
| 3.5714  | 2600  | 0.6575        |
| 3.6401  | 2650  | 0.5844        |
| 3.7088  | 2700  | 0.5439        |
| 3.7775  | 2750  | 0.5528        |
| 3.8462  | 2800  | 0.5894        |
| 3.9148  | 2850  | 0.6576        |
| 3.9835  | 2900  | 0.6063        |
| 4.0522  | 2950  | 0.5556        |
| 4.1209  | 3000  | 0.5872        |
| 4.1896  | 3050  | 0.544         |
| 4.2582  | 3100  | 0.5114        |
| 4.3269  | 3150  | 0.587         |
| 4.3956  | 3200  | 0.5392        |
| 4.4643  | 3250  | 0.5846        |
| 4.5330  | 3300  | 0.6077        |
| 4.6016  | 3350  | 0.6597        |
| 4.6703  | 3400  | 0.5425        |
| 4.7390  | 3450  | 0.5493        |
| 4.8077  | 3500  | 0.5291        |
| 4.8764  | 3550  | 0.5145        |
| 4.9451  | 3600  | 0.5534        |
| 5.0137  | 3650  | 0.5018        |
| 5.0824  | 3700  | 0.4948        |
| 5.1511  | 3750  | 0.553         |
| 5.2198  | 3800  | 0.5772        |
| 5.2885  | 3850  | 0.5264        |
| 5.3571  | 3900  | 0.5516        |
| 5.4258  | 3950  | 0.5303        |
| 5.4945  | 4000  | 0.5213        |
| 5.5632  | 4050  | 0.5558        |
| 5.6319  | 4100  | 0.4956        |
| 5.7005  | 4150  | 0.6035        |
| 5.7692  | 4200  | 0.5706        |
| 5.8379  | 4250  | 0.4922        |
| 5.9066  | 4300  | 0.5965        |
| 5.9753  | 4350  | 0.5143        |
| 6.0440  | 4400  | 0.5798        |
| 6.1126  | 4450  | 0.5219        |
| 6.1813  | 4500  | 0.5803        |
| 6.25    | 4550  | 0.5035        |
| 6.3187  | 4600  | 0.5534        |
| 6.3874  | 4650  | 0.546         |
| 6.4560  | 4700  | 0.525         |
| 6.5247  | 4750  | 0.4751        |
| 6.5934  | 4800  | 0.5085        |
| 6.6621  | 4850  | 0.5282        |
| 6.7308  | 4900  | 0.5845        |
| 6.7995  | 4950  | 0.5153        |
| 6.8681  | 5000  | 0.5399        |
| 6.9368  | 5050  | 0.5532        |
| 7.0055  | 5100  | 0.5005        |
| 7.0742  | 5150  | 0.5273        |
| 7.1429  | 5200  | 0.5212        |
| 7.2115  | 5250  | 0.5245        |
| 7.2802  | 5300  | 0.5075        |
| 7.3489  | 5350  | 0.5687        |
| 7.4176  | 5400  | 0.4674        |
| 7.4863  | 5450  | 0.5115        |
| 7.5549  | 5500  | 0.4938        |
| 7.6236  | 5550  | 0.5059        |
| 7.6923  | 5600  | 0.5065        |
| 7.7610  | 5650  | 0.5252        |
| 7.8297  | 5700  | 0.4852        |
| 7.8984  | 5750  | 0.48          |
| 7.9670  | 5800  | 0.5503        |
| 8.0357  | 5850  | 0.5164        |
| 8.1044  | 5900  | 0.5756        |
| 8.1731  | 5950  | 0.5175        |
| 8.2418  | 6000  | 0.5033        |
| 8.3104  | 6050  | 0.4992        |
| 8.3791  | 6100  | 0.5299        |
| 8.4478  | 6150  | 0.4862        |
| 8.5165  | 6200  | 0.548         |
| 8.5852  | 6250  | 0.454         |
| 8.6538  | 6300  | 0.4941        |
| 8.7225  | 6350  | 0.5088        |
| 8.7912  | 6400  | 0.5065        |
| 8.8599  | 6450  | 0.4921        |
| 8.9286  | 6500  | 0.4756        |
| 8.9973  | 6550  | 0.5258        |
| 9.0659  | 6600  | 0.4658        |
| 9.1346  | 6650  | 0.4894        |
| 9.2033  | 6700  | 0.5097        |
| 9.2720  | 6750  | 0.493         |
| 9.3407  | 6800  | 0.5311        |
| 9.4093  | 6850  | 0.5157        |
| 9.4780  | 6900  | 0.5142        |
| 9.5467  | 6950  | 0.4664        |
| 9.6154  | 7000  | 0.528         |
| 9.6841  | 7050  | 0.5645        |
| 9.7527  | 7100  | 0.5214        |
| 9.8214  | 7150  | 0.4777        |
| 9.8901  | 7200  | 0.5449        |
| 9.9588  | 7250  | 0.492         |
| 10.0275 | 7300  | 0.4591        |
| 10.0962 | 7350  | 0.4576        |
| 10.1648 | 7400  | 0.4692        |
| 10.2335 | 7450  | 0.5415        |
| 10.3022 | 7500  | 0.4803        |
| 10.3709 | 7550  | 0.5487        |
| 10.4396 | 7600  | 0.5706        |
| 10.5082 | 7650  | 0.4815        |
| 10.5769 | 7700  | 0.4585        |
| 10.6456 | 7750  | 0.4861        |
| 10.7143 | 7800  | 0.4247        |
| 10.7830 | 7850  | 0.4906        |
| 10.8516 | 7900  | 0.5371        |
| 10.9203 | 7950  | 0.5393        |
| 10.9890 | 8000  | 0.4788        |
| 11.0577 | 8050  | 0.5038        |
| 11.1264 | 8100  | 0.4838        |
| 11.1951 | 8150  | 0.515         |
| 11.2637 | 8200  | 0.5299        |
| 11.3324 | 8250  | 0.5044        |
| 11.4011 | 8300  | 0.5045        |
| 11.4698 | 8350  | 0.465         |
| 11.5385 | 8400  | 0.5253        |
| 11.6071 | 8450  | 0.4517        |
| 11.6758 | 8500  | 0.5048        |
| 11.7445 | 8550  | 0.4733        |
| 11.8132 | 8600  | 0.47          |
| 11.8819 | 8650  | 0.4552        |
| 11.9505 | 8700  | 0.4203        |
| 12.0192 | 8750  | 0.395         |
| 12.0879 | 8800  | 0.5411        |
| 12.1566 | 8850  | 0.4911        |
| 12.2253 | 8900  | 0.4641        |
| 12.2940 | 8950  | 0.4608        |
| 12.3626 | 9000  | 0.4839        |
| 12.4313 | 9050  | 0.4491        |
| 12.5    | 9100  | 0.517         |
| 12.5687 | 9150  | 0.5031        |
| 12.6374 | 9200  | 0.4869        |
| 12.7060 | 9250  | 0.4856        |
| 12.7747 | 9300  | 0.4754        |
| 12.8434 | 9350  | 0.5167        |
| 12.9121 | 9400  | 0.5004        |
| 12.9808 | 9450  | 0.5293        |
| 13.0495 | 9500  | 0.4566        |
| 13.1181 | 9550  | 0.477         |
| 13.1868 | 9600  | 0.4501        |
| 13.2555 | 9650  | 0.4791        |
| 13.3242 | 9700  | 0.4746        |
| 13.3929 | 9750  | 0.4702        |
| 13.4615 | 9800  | 0.469         |
| 13.5302 | 9850  | 0.5046        |
| 13.5989 | 9900  | 0.4895        |
| 13.6676 | 9950  | 0.5223        |
| 13.7363 | 10000 | 0.4245        |
| 13.8049 | 10050 | 0.4701        |
| 13.8736 | 10100 | 0.4548        |
| 13.9423 | 10150 | 0.4998        |
| 14.0110 | 10200 | 0.4345        |
| 14.0797 | 10250 | 0.4371        |
| 14.1484 | 10300 | 0.5009        |
| 14.2170 | 10350 | 0.4816        |
| 14.2857 | 10400 | 0.4665        |
| 14.3544 | 10450 | 0.5047        |
| 14.4231 | 10500 | 0.5132        |
| 14.4918 | 10550 | 0.473         |
| 14.5604 | 10600 | 0.4387        |
| 14.6291 | 10650 | 0.4775        |
| 14.6978 | 10700 | 0.4522        |
| 14.7665 | 10750 | 0.4807        |
| 14.8352 | 10800 | 0.482         |
| 14.9038 | 10850 | 0.4625        |
| 14.9725 | 10900 | 0.5052        |

</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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