---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:149098
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/LaBSE
widget:
- source_sentence: چگونه می توانید واقعاً بدانید که کسی یک جامعه شناسی/روانی است؟
    (علاوه بر این که آنها اسکن مغزی دارند)
  sentences:
  - تفاوت بین وکیل و وکیل چیست؟
  - چگونه می توانم برای آزمون ادبیات انگلیسی خالص UGC آماده شوم؟
  - از کجا می دانید کسی روانپزشکی است یا یک جامعه شناسی؟
- source_sentence: ایده شما از ازدواج چیست؟
  sentences:
  - کدام برنامه برای C و C ++ مهمترین است؟
  - How will the ban on Rs. 1000 and Rs. 500 notes impact Indian economy?
  - ایده ازدواج چیست؟
- source_sentence: کدام یک بهترین لپ تاپ برای خرید زیر 30k است؟
  sentences:
  - چگونه قیمت املاک و مستغلات تحت تأثیر تصمیم دولت هند برای از بین بردن 500 و 1000
    یادداشت قرار می گیرد؟
  - کدام بهترین لپ تاپ برای خرید بالاتر از 25000 پوند و زیر/تا 30000 پوند است؟
  - چگونه استرس در ذهن را کاهش می دهیم؟
- source_sentence: چگونه می توانم به طور جامع برای ادبیات انگلیسی خالص UGC آماده شوم؟
  sentences:
  - چگونه می توانم یک حساب پس انداز تعقیب را بصورت آنلاین ببندم؟
  - چگونه می توانم برای NET JRF در ادبیات انگلیسی آماده شوم؟
  - تفاوت بین گربه و علاقه مندان به GMAT چیست؟
- source_sentence: آیا با دختری که باکره نیست ازدواج خواهید کرد؟
  sentences:
  - زنی با شلوار جین کنار اسبی با زین ایستاده است
  - آیا تا به حال چیزی ماوراء الطبیعه یا فوق طبیعی را تجربه کرده اید؟
  - آیا با کسی که باکره نیست ازدواج می کنید؟
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on sentence-transformers/LaBSE

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision b7f947194ceae0ddf90bafe213722569e274ad28 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 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}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): 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("codersan/FaLaBSE-v6")
# Run inference
sentences = [
    'آیا با دختری که باکره نیست ازدواج خواهید کرد؟',
    'آیا با کسی که باکره نیست ازدواج می کنید؟',
    'زنی با شلوار جین کنار اسبی با زین ایستاده است',
]
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)

<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: 149,098 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: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.54 tokens</li><li>max: 57 tokens</li></ul> |
* Samples:
  | anchor                                                               | positive                                                           |
  |:---------------------------------------------------------------------|:-------------------------------------------------------------------|
  | <code>اگر هند تقسیم نشده بود ، هند امروز چگونه به نظر می رسد؟</code> | <code>اگر پارتیشن اتفاق نیفتاد ، هند امروز چگونه خواهد بود؟</code> |
  | <code>چگونه می توانم وارد امنیت اینترنت شوم؟</code>                  | <code>چگونه می توانم شروع به یادگیری امنیت اطلاعات کنم؟</code>     |
  | <code>برخی از بهترین مؤسسات مربیگری GMAT در دهلی/NCR چیست؟</code>    | <code>بهترین مؤسسات مربیگری برای GMAT در NCR چیست؟</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`: 32
- `learning_rate`: 3e-05
- `weight_decay`: 0.15
- `num_train_epochs`: 10
- `warmup_ratio`: 0.15
- `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`: 32
- `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`: 3e-05
- `weight_decay`: 0.15
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.15
- `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`: None
- `hub_always_push`: False
- `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
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

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

| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0429 | 100   | 0.1219        |
| 0.0858 | 200   | 0.0626        |
| 0.1288 | 300   | 0.0489        |
| 0.1717 | 400   | 0.0414        |
| 0.2146 | 500   | 0.0432        |
| 0.2575 | 600   | 0.0419        |
| 0.3004 | 700   | 0.0313        |
| 0.3433 | 800   | 0.0339        |
| 0.3863 | 900   | 0.0317        |
| 0.4292 | 1000  | 0.035         |
| 0.4721 | 1100  | 0.0378        |
| 0.5150 | 1200  | 0.0308        |
| 0.5579 | 1300  | 0.0305        |
| 0.6009 | 1400  | 0.0312        |
| 0.6438 | 1500  | 0.0304        |
| 0.6867 | 1600  | 0.0295        |
| 0.7296 | 1700  | 0.0301        |
| 0.7725 | 1800  | 0.033         |
| 0.8155 | 1900  | 0.0263        |
| 0.8584 | 2000  | 0.0276        |
| 0.9013 | 2100  | 0.0236        |
| 0.9442 | 2200  | 0.0276        |
| 0.9871 | 2300  | 0.0278        |
| 1.0300 | 2400  | 0.0309        |
| 1.0730 | 2500  | 0.0269        |
| 1.1159 | 2600  | 0.0299        |
| 1.1588 | 2700  | 0.0272        |
| 1.2017 | 2800  | 0.029         |
| 1.2446 | 2900  | 0.0309        |
| 1.2876 | 3000  | 0.0247        |
| 1.3305 | 3100  | 0.0244        |
| 1.3734 | 3200  | 0.0261        |
| 1.4163 | 3300  | 0.0254        |
| 1.4592 | 3400  | 0.0273        |
| 1.5021 | 3500  | 0.0298        |
| 1.5451 | 3600  | 0.0225        |
| 1.5880 | 3700  | 0.0278        |
| 1.6309 | 3800  | 0.027         |
| 1.6738 | 3900  | 0.0218        |
| 1.7167 | 4000  | 0.0247        |
| 1.7597 | 4100  | 0.023         |
| 1.8026 | 4200  | 0.0225        |
| 1.8455 | 4300  | 0.0191        |
| 1.8884 | 4400  | 0.0174        |
| 1.9313 | 4500  | 0.0214        |
| 1.9742 | 4600  | 0.018         |
| 2.0172 | 4700  | 0.0227        |
| 2.0601 | 4800  | 0.0222        |
| 2.1030 | 4900  | 0.0211        |
| 2.1459 | 5000  | 0.0204        |
| 2.1888 | 5100  | 0.0215        |
| 2.2318 | 5200  | 0.0206        |
| 2.2747 | 5300  | 0.0213        |
| 2.3176 | 5400  | 0.0168        |
| 2.3605 | 5500  | 0.0189        |
| 2.4034 | 5600  | 0.0206        |
| 2.4464 | 5700  | 0.0194        |
| 2.4893 | 5800  | 0.0182        |
| 2.5322 | 5900  | 0.017         |
| 2.5751 | 6000  | 0.0186        |
| 2.6180 | 6100  | 0.017         |
| 2.6609 | 6200  | 0.0152        |
| 2.7039 | 6300  | 0.0164        |
| 2.7468 | 6400  | 0.0142        |
| 2.7897 | 6500  | 0.0162        |
| 2.8326 | 6600  | 0.0123        |
| 2.8755 | 6700  | 0.0162        |
| 2.9185 | 6800  | 0.0138        |
| 2.9614 | 6900  | 0.0163        |
| 3.0043 | 7000  | 0.0138        |
| 3.0472 | 7100  | 0.0164        |
| 3.0901 | 7200  | 0.016         |
| 3.1330 | 7300  | 0.0175        |
| 3.1760 | 7400  | 0.0143        |
| 3.2189 | 7500  | 0.0142        |
| 3.2618 | 7600  | 0.0176        |
| 3.3047 | 7700  | 0.0147        |
| 3.3476 | 7800  | 0.0164        |
| 3.3906 | 7900  | 0.0133        |
| 3.4335 | 8000  | 0.0168        |
| 3.4764 | 8100  | 0.0166        |
| 3.5193 | 8200  | 0.0138        |
| 3.5622 | 8300  | 0.0126        |
| 3.6052 | 8400  | 0.0145        |
| 3.6481 | 8500  | 0.0114        |
| 3.6910 | 8600  | 0.0137        |
| 3.7339 | 8700  | 0.014         |
| 3.7768 | 8800  | 0.0134        |
| 3.8197 | 8900  | 0.0108        |
| 3.8627 | 9000  | 0.012         |
| 3.9056 | 9100  | 0.0102        |
| 3.9485 | 9200  | 0.0119        |
| 3.9914 | 9300  | 0.0122        |
| 4.0343 | 9400  | 0.0116        |
| 4.0773 | 9500  | 0.0136        |
| 4.1202 | 9600  | 0.0135        |
| 4.1631 | 9700  | 0.0108        |
| 4.2060 | 9800  | 0.0119        |
| 4.2489 | 9900  | 0.0142        |
| 4.2918 | 10000 | 0.0111        |
| 4.3348 | 10100 | 0.0131        |
| 4.3777 | 10200 | 0.0103        |
| 4.4206 | 10300 | 0.0124        |
| 4.4635 | 10400 | 0.0163        |
| 4.5064 | 10500 | 0.0123        |
| 4.5494 | 10600 | 0.0112        |
| 4.5923 | 10700 | 0.01          |
| 4.6352 | 10800 | 0.0096        |
| 4.6781 | 10900 | 0.0103        |
| 4.7210 | 11000 | 0.0102        |
| 4.7639 | 11100 | 0.0092        |
| 4.8069 | 11200 | 0.0107        |
| 4.8498 | 11300 | 0.0114        |
| 4.8927 | 11400 | 0.0091        |
| 4.9356 | 11500 | 0.0108        |
| 4.9785 | 11600 | 0.0092        |
| 5.0215 | 11700 | 0.0086        |
| 5.0644 | 11800 | 0.0104        |
| 5.1073 | 11900 | 0.0123        |
| 5.1502 | 12000 | 0.009         |
| 5.1931 | 12100 | 0.0106        |
| 5.2361 | 12200 | 0.0114        |
| 5.2790 | 12300 | 0.0098        |
| 5.3219 | 12400 | 0.0093        |
| 5.3648 | 12500 | 0.0092        |
| 5.4077 | 12600 | 0.011         |
| 5.4506 | 12700 | 0.0113        |
| 5.4936 | 12800 | 0.0091        |
| 5.5365 | 12900 | 0.0079        |
| 5.5794 | 13000 | 0.01          |
| 5.6223 | 13100 | 0.0067        |
| 5.6652 | 13200 | 0.0081        |
| 5.7082 | 13300 | 0.0097        |
| 5.7511 | 13400 | 0.0081        |
| 5.7940 | 13500 | 0.0094        |
| 5.8369 | 13600 | 0.0074        |
| 5.8798 | 13700 | 0.0071        |
| 5.9227 | 13800 | 0.0074        |
| 5.9657 | 13900 | 0.0076        |
| 6.0086 | 14000 | 0.0063        |
| 6.0515 | 14100 | 0.0083        |
| 6.0944 | 14200 | 0.0101        |
| 6.1373 | 14300 | 0.0084        |
| 6.1803 | 14400 | 0.0074        |
| 6.2232 | 14500 | 0.007         |
| 6.2661 | 14600 | 0.0078        |
| 6.3090 | 14700 | 0.0074        |
| 6.3519 | 14800 | 0.0086        |
| 6.3948 | 14900 | 0.0069        |
| 6.4378 | 15000 | 0.0083        |
| 6.4807 | 15100 | 0.0082        |
| 6.5236 | 15200 | 0.0066        |
| 6.5665 | 15300 | 0.0086        |
| 6.6094 | 15400 | 0.0059        |
| 6.6524 | 15500 | 0.0052        |
| 6.6953 | 15600 | 0.0081        |
| 6.7382 | 15700 | 0.0054        |
| 6.7811 | 15800 | 0.0063        |
| 6.8240 | 15900 | 0.0065        |
| 6.8670 | 16000 | 0.0068        |
| 6.9099 | 16100 | 0.0047        |
| 6.9528 | 16200 | 0.0065        |
| 6.9957 | 16300 | 0.0064        |
| 7.0386 | 16400 | 0.0051        |
| 7.0815 | 16500 | 0.0066        |
| 7.1245 | 16600 | 0.0069        |
| 7.1674 | 16700 | 0.0074        |
| 7.2103 | 16800 | 0.0062        |
| 7.2532 | 16900 | 0.0071        |
| 7.2961 | 17000 | 0.005         |
| 7.3391 | 17100 | 0.008         |
| 7.3820 | 17200 | 0.0047        |
| 7.4249 | 17300 | 0.0073        |
| 7.4678 | 17400 | 0.0078        |
| 7.5107 | 17500 | 0.0058        |
| 7.5536 | 17600 | 0.0055        |
| 7.5966 | 17700 | 0.0049        |
| 7.6395 | 17800 | 0.0046        |
| 7.6824 | 17900 | 0.0051        |
| 7.7253 | 18000 | 0.005         |
| 7.7682 | 18100 | 0.0059        |
| 7.8112 | 18200 | 0.0056        |
| 7.8541 | 18300 | 0.0049        |
| 7.8970 | 18400 | 0.0038        |
| 7.9399 | 18500 | 0.005         |
| 7.9828 | 18600 | 0.005         |
| 8.0258 | 18700 | 0.0036        |
| 8.0687 | 18800 | 0.0049        |
| 8.1116 | 18900 | 0.0067        |
| 8.1545 | 19000 | 0.0056        |
| 8.1974 | 19100 | 0.0061        |
| 8.2403 | 19200 | 0.0054        |
| 8.2833 | 19300 | 0.0046        |
| 8.3262 | 19400 | 0.0048        |
| 8.3691 | 19500 | 0.0052        |
| 8.4120 | 19600 | 0.0059        |
| 8.4549 | 19700 | 0.0053        |
| 8.4979 | 19800 | 0.0049        |
| 8.5408 | 19900 | 0.0036        |
| 8.5837 | 20000 | 0.0049        |
| 8.6266 | 20100 | 0.0033        |
| 8.6695 | 20200 | 0.0049        |
| 8.7124 | 20300 | 0.0043        |
| 8.7554 | 20400 | 0.0039        |
| 8.7983 | 20500 | 0.0038        |
| 8.8412 | 20600 | 0.0035        |
| 8.8841 | 20700 | 0.0041        |
| 8.9270 | 20800 | 0.0042        |
| 8.9700 | 20900 | 0.0056        |
| 9.0129 | 21000 | 0.0031        |
| 9.0558 | 21100 | 0.004         |
| 9.0987 | 21200 | 0.0043        |
| 9.1416 | 21300 | 0.0047        |
| 9.1845 | 21400 | 0.0051        |
| 9.2275 | 21500 | 0.0032        |
| 9.2704 | 21600 | 0.0045        |
| 9.3133 | 21700 | 0.0038        |
| 9.3562 | 21800 | 0.0045        |
| 9.3991 | 21900 | 0.0047        |
| 9.4421 | 22000 | 0.0048        |
| 9.4850 | 22100 | 0.0042        |
| 9.5279 | 22200 | 0.0039        |
| 9.5708 | 22300 | 0.0042        |
| 9.6137 | 22400 | 0.003         |
| 9.6567 | 22500 | 0.0031        |
| 9.6996 | 22600 | 0.0042        |
| 9.7425 | 22700 | 0.0028        |
| 9.7854 | 22800 | 0.0037        |
| 9.8283 | 22900 | 0.0035        |
| 9.8712 | 23000 | 0.0033        |
| 9.9142 | 23100 | 0.0029        |
| 9.9571 | 23200 | 0.0048        |
| 10.0   | 23300 | 0.0039        |

</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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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