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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: As of the end of 2023, Hilton's development pipeline included projects
in 118 countries and territories.
sentences:
- What was the total net income attributed to AT&T common stockholders in 2023?
- How many countries and territories did Hilton's development pipeline encompass
as of the end of 2023?
- What caused the increase in Medicare receivables in 2023 compared to 2022?
- source_sentence: Alex G. Balazs was appointed as the Executive Vice President and
Chief Technology Officer effective September 5, 2023.
sentences:
- What page of IBM's Form 10-K contains the Financial Statement Schedule?
- When was Alex G. Balazs appointed as the Executive Vice President and Chief Technology
Officer?
- How much were the valuation allowances provided for deferred tax assets related
to loss carryforwards as of December 31, 2023?
- source_sentence: 'HP''s global wellness program emphasizes five pillars of wellness:
physical, financial, emotional, life balance, and social/community.'
sentences:
- What are the five pillars of wellness emphasized in HP's global wellness program?
- What was the fair value of money market mutual funds measured at as of January
31, 2023 and how was it categorized in the fair value hierarchy?
- What amount was authorized for future share repurchases by the company as of October
31, 2023?
- source_sentence: Item 3, titled 'Legal Proceedings' in a 10-K filing, directs to
Note 16 where specific information is further detailed in Item 8 of Part II.
sentences:
- What was the grant date fair value of options vested for HP in fiscal years 2023,
2022, and 2021?
- What is the balance at the end of the year for Comcast's Total Equity in 2023?
- What is indicated by Item 3, 'Legal Proceedings', in a 10-K filing?
- source_sentence: During 2023, we received approximately $220 of cash collateral,
on a net basis.
sentences:
- How much cash collateral did AT&T receive on a net basis during 2023?
- What percentage of FedEx Corporation's consolidated revenues did jet fuel costs
represent in 2023?
- What measures has Bank of America taken to streamline its organizational structure?
pipeline_tag: sentence-similarity
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
model-index:
- name: BGE base Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7128571428571429
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8428571428571429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8842857142857142
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.92
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7128571428571429
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28095238095238095
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17685714285714288
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09199999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7128571428571429
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8428571428571429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8842857142857142
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.92
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8195233962517928
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7870022675736963
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7905145024165581
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7157142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8457142857142858
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8814285714285715
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9228571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7157142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2819047619047619
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1762857142857143
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09228571428571428
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7157142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8457142857142858
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8814285714285715
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9228571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.821183673183428
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7884829931972789
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7916656681436871
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7114285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8414285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8842857142857142
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9157142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7114285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28047619047619043
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17685714285714285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09157142857142858
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7114285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8414285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8842857142857142
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9157142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8157881706696753
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7834812925170066
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7870779881453726
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6957142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.82
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8685714285714285
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9057142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6957142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1737142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09057142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6957142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.82
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8685714285714285
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9057142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8018105093606251
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7683497732426302
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7722509873826792
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6528571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7942857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8314285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8757142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6528571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26476190476190475
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1662857142857143
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08757142857142856
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6528571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7942857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8314285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8757142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7667522193115596
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7315833333333331
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7359673420065519
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** en
- **License:** apache-2.0
### 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': 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): 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("sud-962081/bge-base-financial-matryoshka")
# Run inference
sentences = [
'During 2023, we received approximately $220 of cash collateral, on a net basis.',
'How much cash collateral did AT&T receive on a net basis during 2023?',
"What percentage of FedEx Corporation's consolidated revenues did jet fuel costs represent in 2023?",
]
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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.7129 | 0.7157 | 0.7114 | 0.6957 | 0.6529 |
| cosine_accuracy@3 | 0.8429 | 0.8457 | 0.8414 | 0.82 | 0.7943 |
| cosine_accuracy@5 | 0.8843 | 0.8814 | 0.8843 | 0.8686 | 0.8314 |
| cosine_accuracy@10 | 0.92 | 0.9229 | 0.9157 | 0.9057 | 0.8757 |
| cosine_precision@1 | 0.7129 | 0.7157 | 0.7114 | 0.6957 | 0.6529 |
| cosine_precision@3 | 0.281 | 0.2819 | 0.2805 | 0.2733 | 0.2648 |
| cosine_precision@5 | 0.1769 | 0.1763 | 0.1769 | 0.1737 | 0.1663 |
| cosine_precision@10 | 0.092 | 0.0923 | 0.0916 | 0.0906 | 0.0876 |
| cosine_recall@1 | 0.7129 | 0.7157 | 0.7114 | 0.6957 | 0.6529 |
| cosine_recall@3 | 0.8429 | 0.8457 | 0.8414 | 0.82 | 0.7943 |
| cosine_recall@5 | 0.8843 | 0.8814 | 0.8843 | 0.8686 | 0.8314 |
| cosine_recall@10 | 0.92 | 0.9229 | 0.9157 | 0.9057 | 0.8757 |
| **cosine_ndcg@10** | **0.8195** | **0.8212** | **0.8158** | **0.8018** | **0.7668** |
| cosine_mrr@10 | 0.787 | 0.7885 | 0.7835 | 0.7683 | 0.7316 |
| cosine_map@100 | 0.7905 | 0.7917 | 0.7871 | 0.7723 | 0.736 |
<!--
## 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
#### json
* Dataset: json
* Size: 6,300 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 44.47 tokens</li><li>max: 260 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.17 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|
| <code>SmartFlex benefits and the 'Best of Both' work model at The Hershey Company allow employees to balance professional and personal demands through flexible work arrangements.</code> | <code>How does The Hershey Company ensure flexibility and work-life balance for its employees?</code> |
| <code>In February 2024, our Board authorized an additional $2.0 billion stock repurchase program, with no expiration from the date of authorization.</code> | <code>What amount was authorized for common stock repurchase by the company's Board in February 2024?</code> |
| <code>Beginning in 2025, the first GM EVs will be constructed using the North American Charging Standard (NACS) hardware.</code> | <code>What significant change is set for General Motors' EVs starting in 2025 regarding charging hardware?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `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
- `use_ipex`: False
- `bf16`: True
- `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`: True
- `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_fused
- `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
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0 | 7 | - | 0.8036 | 0.8049 | 0.7942 | 0.7726 | 0.7375 |
| 1.4848 | 10 | 2.2028 | - | - | - | - | - |
| 2.0 | 14 | - | 0.8169 | 0.8173 | 0.8127 | 0.8000 | 0.7602 |
| 2.9697 | 20 | 0.9836 | - | - | - | - | - |
| 3.0 | 21 | - | 0.8187 | 0.8214 | 0.8142 | 0.8017 | 0.7658 |
| **3.4848** | **24** | **-** | **0.8195** | **0.8212** | **0.8158** | **0.8018** | **0.7668** |
* The bold row denotes the saved checkpoint.
### 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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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