SentenceTransformer based on FacebookAI/xlm-roberta-large
This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-large on the parsinlu_qqp_pair2class, parsinlu_entail_pair3class, pquad_pair, alpaca_persian_pair, ghaemiyeh_pair, wiki_triplet, wiki_DSimilar_pair2class, miracle_triplet, Estef_pair, all_resaleh_pair and persianQA_pair datasets. It maps sentences & paragraphs to a 1024-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: FacebookAI/xlm-roberta-large
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
- Training Datasets:
- Languages: fa, en, ar, bn, es, fi, fr, hi, id, ja, ko, ru, sw, te, th, zh
Model Sources
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("sentence_transformers_model_id")
sentences = [
'چه اتفاقی در مسجد الحرام برای عبدالمطلب و پسرش رخ داد؟',
'در مسجد الحرام، عبدالمطلب و پسرش توسط ده مرد پابرهنه و شمشیر به دست، مورد حمله قرار گرفتند و از کشتن فرزند عبدالمطلب جلوگیری کردند. این حادثه باعث شد که مردم در مسجد الحرام غرق در هیاهو شوند و صداها درهم آمیخته و صدای زنان محو شود.',
'قائم آل محمد (ص) به اراده الهی قیام کرده و زمانی که او قیام کند، دیگر از شرک و شرک\u200cگرایی اثری نخواهد ماند و دین حق همه دلها را نورباران می\u200cسازد. این مطلب از آیاتی که پیرامون وجود گرانمایه او تأویل شده است بسنده می\u200cشود و این امر در قرآن و روایات به طور جامع بیان شده است.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Training Details
Training Datasets
parsinlu_qqp_pair2class
parsinlu_entail_pair3class
- Dataset: parsinlu_entail_pair3class at c49b2d8
- Size: 2,697 training samples
- Columns:
sentence1, sentence2, and label
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
label |
| type |
string |
string |
int |
| details |
- min: 3 tokens
- mean: 34.16 tokens
- max: 203 tokens
|
- min: 3 tokens
- mean: 17.89 tokens
- max: 73 tokens
|
- 0: ~39.30%
- 1: ~31.60%
- 2: ~29.10%
|
- Samples:
| sentence1 |
sentence2 |
label |
زنان به قدری بخش بزرگی از نیروی کار را تشکیل می دهند که به سختی می توان باور داشت که اگر این امر در مورد زنان صادق نباشد ، این امر می تواند صادق باشد. |
مردان بخش عظیمی از نیروی کار هستند بنابراین تنها افراد مهم هستند. |
2 |
سالها است که کنگره در تلاش است تا اثربخشی مدیریت اطلاعات و فناوری را در دولت فدرال افزایش دهد. |
کنگره بودجه ویژه ای برای مدیریت اطلاعات و فناوری در دولت فدرال دارد. |
1 |
سرامیکهای زیست خنثی پس از قرارگیری در بدن میزبان خواص فیزیکی و مکانیکی خود را حفظ میکند. |
خواص فیزیکی سرامیکها قابل اندازه گیری است. |
1 |
- Loss:
SoftmaxLoss
pquad_pair
alpaca_persian_pair
ghaemiyeh_pair
wiki_triplet
wiki_DSimilar_pair2class
miracle_triplet
Estef_pair
all_resaleh_pair
persianQA_pair
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
gradient_accumulation_steps: 2
torch_empty_cache_steps: 400
weight_decay: 0.01
lr_scheduler_type: cosine
warmup_ratio: 0.1
seed: 2024
data_seed: 2024
fp16: True
group_by_length: True
batch_sampler: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 24
per_device_eval_batch_size: 24
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
torch_empty_cache_steps: 400
learning_rate: 5e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 3
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: 2024
data_seed: 2024
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: True
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: False
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
eval_on_start: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
Training Logs
Click to expand
| Epoch |
Step |
Training Loss |
| 0.0101 |
100 |
1.2173 |
| 0.0203 |
200 |
0.8005 |
| 0.0304 |
300 |
0.6933 |
| 0.0406 |
400 |
0.5501 |
| 0.0507 |
500 |
0.5745 |
| 0.0609 |
600 |
0.5772 |
| 0.0710 |
700 |
0.5297 |
| 0.0812 |
800 |
0.6225 |
| 0.0913 |
900 |
0.5587 |
| 0.1015 |
1000 |
0.5391 |
| 0.1116 |
1100 |
0.5087 |
| 0.1218 |
1200 |
0.5091 |
| 0.1319 |
1300 |
0.5353 |
| 0.1421 |
1400 |
0.4989 |
| 0.1522 |
1500 |
0.5317 |
| 0.1624 |
1600 |
0.519 |
| 0.1725 |
1700 |
0.5118 |
| 0.1827 |
1800 |
0.4773 |
| 0.1928 |
1900 |
0.4411 |
| 0.2030 |
2000 |
0.4618 |
| 0.2131 |
2100 |
0.3866 |
| 0.2233 |
2200 |
0.4524 |
| 0.2334 |
2300 |
0.5271 |
| 0.2436 |
2400 |
0.4519 |
| 0.2537 |
2500 |
0.4865 |
| 0.2639 |
2600 |
0.52 |
| 0.2740 |
2700 |
0.54 |
| 0.2841 |
2800 |
0.4525 |
| 0.2943 |
2900 |
0.5002 |
| 0.3044 |
3000 |
0.532 |
| 0.3146 |
3100 |
0.4829 |
| 0.3247 |
3200 |
0.4658 |
| 0.3349 |
3300 |
0.5031 |
| 0.3450 |
3400 |
0.4907 |
| 0.3552 |
3500 |
0.5019 |
| 0.3653 |
3600 |
0.4788 |
| 0.3755 |
3700 |
0.4884 |
| 0.3856 |
3800 |
0.4998 |
| 0.3958 |
3900 |
0.4321 |
| 0.4059 |
4000 |
0.4428 |
| 0.4161 |
4100 |
0.4564 |
| 0.4262 |
4200 |
0.4349 |
| 0.4364 |
4300 |
0.4219 |
| 0.4465 |
4400 |
0.4411 |
| 0.4567 |
4500 |
0.4448 |
| 0.4668 |
4600 |
0.4334 |
| 0.4770 |
4700 |
0.4255 |
| 0.4871 |
4800 |
0.4147 |
| 0.4973 |
4900 |
0.4263 |
| 0.5074 |
5000 |
0.4483 |
| 0.5176 |
5100 |
0.4437 |
| 0.5277 |
5200 |
0.4062 |
| 0.5379 |
5300 |
0.3974 |
| 0.5480 |
5400 |
0.3455 |
| 0.5581 |
5500 |
0.3383 |
| 0.5683 |
5600 |
0.4156 |
| 0.5784 |
5700 |
0.4296 |
| 0.5886 |
5800 |
0.4115 |
| 0.5987 |
5900 |
0.3977 |
| 0.6089 |
6000 |
0.3736 |
| 0.6190 |
6100 |
0.4001 |
| 0.6292 |
6200 |
0.3721 |
| 0.6393 |
6300 |
0.4244 |
| 0.6495 |
6400 |
0.3653 |
| 0.6596 |
6500 |
0.394 |
| 0.6698 |
6600 |
0.3749 |
| 0.6799 |
6700 |
0.3964 |
| 0.6901 |
6800 |
0.3958 |
| 0.7002 |
6900 |
0.3585 |
| 0.7104 |
7000 |
0.3609 |
| 0.7205 |
7100 |
0.3645 |
| 0.7307 |
7200 |
0.4257 |
| 0.7408 |
7300 |
0.3894 |
| 0.7510 |
7400 |
0.3714 |
| 0.7611 |
7500 |
0.4011 |
| 0.7713 |
7600 |
0.4147 |
| 0.7814 |
7700 |
0.3923 |
| 0.7916 |
7800 |
0.345 |
| 0.8017 |
7900 |
0.387 |
| 0.8119 |
8000 |
0.3609 |
| 0.8220 |
8100 |
0.4609 |
| 0.8321 |
8200 |
0.4027 |
| 0.8423 |
8300 |
0.368 |
| 0.8524 |
8400 |
0.3547 |
| 0.8626 |
8500 |
0.3978 |
| 0.8727 |
8600 |
0.3667 |
| 0.8829 |
8700 |
0.3599 |
| 0.8930 |
8800 |
0.3476 |
| 0.9032 |
8900 |
0.3617 |
| 0.9133 |
9000 |
0.4207 |
| 0.9235 |
9100 |
0.4382 |
| 0.9336 |
9200 |
0.377 |
| 0.9438 |
9300 |
0.3602 |
| 0.9539 |
9400 |
0.3025 |
| 0.9641 |
9500 |
0.3186 |
| 0.9742 |
9600 |
0.3121 |
| 0.9844 |
9700 |
0.2976 |
| 0.9945 |
9800 |
0.3133 |
| 1.0047 |
9900 |
0.4134 |
| 1.0148 |
10000 |
0.4225 |
| 1.0250 |
10100 |
0.3739 |
| 1.0351 |
10200 |
0.3789 |
| 1.0453 |
10300 |
0.3096 |
| 1.0554 |
10400 |
0.3306 |
| 1.0656 |
10500 |
0.2934 |
| 1.0757 |
10600 |
0.3379 |
| 1.0859 |
10700 |
0.3441 |
| 1.0960 |
10800 |
0.3407 |
| 1.1061 |
10900 |
0.2935 |
| 1.1163 |
11000 |
0.3357 |
| 1.1264 |
11100 |
0.2743 |
| 1.1366 |
11200 |
0.3177 |
| 1.1467 |
11300 |
0.2951 |
| 1.1569 |
11400 |
0.3293 |
| 1.1670 |
11500 |
0.2638 |
| 1.1772 |
11600 |
0.2723 |
| 1.1873 |
11700 |
0.2616 |
| 1.1975 |
11800 |
0.251 |
| 1.2076 |
11900 |
0.1992 |
| 1.2178 |
12000 |
0.213 |
| 1.2279 |
12100 |
0.2288 |
| 1.2381 |
12200 |
0.2777 |
| 1.2482 |
12300 |
0.1971 |
| 1.2584 |
12400 |
0.2549 |
| 1.2685 |
12500 |
0.2604 |
| 1.2787 |
12600 |
0.2657 |
| 1.2888 |
12700 |
0.2064 |
| 1.2990 |
12800 |
0.238 |
| 1.3091 |
12900 |
0.2239 |
| 1.3193 |
13000 |
0.2004 |
| 1.3294 |
13100 |
0.2283 |
| 1.3396 |
13200 |
0.1925 |
| 1.3497 |
13300 |
0.2301 |
| 1.3599 |
13400 |
0.2076 |
| 1.3700 |
13500 |
0.2103 |
| 1.3802 |
13600 |
0.1967 |
| 1.3903 |
13700 |
0.2302 |
| 1.4004 |
13800 |
0.1867 |
| 1.4106 |
13900 |
0.1793 |
| 1.4207 |
14000 |
0.1959 |
| 1.4309 |
14100 |
0.1483 |
| 1.4410 |
14200 |
0.1675 |
| 1.4512 |
14300 |
0.1883 |
| 1.4613 |
14400 |
0.1896 |
| 1.4715 |
14500 |
0.1774 |
| 1.4816 |
14600 |
0.1634 |
| 1.4918 |
14700 |
0.1593 |
| 1.5019 |
14800 |
0.1952 |
| 1.5121 |
14900 |
0.1845 |
| 1.5222 |
15000 |
0.1874 |
| 1.5324 |
15100 |
0.1678 |
| 1.5425 |
15200 |
0.1383 |
| 1.5527 |
15300 |
0.1202 |
| 1.5628 |
15400 |
0.1535 |
| 1.5730 |
15500 |
0.1996 |
| 1.5831 |
15600 |
0.1604 |
| 1.5933 |
15700 |
0.1658 |
| 1.6034 |
15800 |
0.1417 |
| 1.6136 |
15900 |
0.1486 |
| 1.6237 |
16000 |
0.1574 |
| 1.6339 |
16100 |
0.1505 |
| 1.6440 |
16200 |
0.1561 |
| 1.6542 |
16300 |
0.1317 |
| 1.6643 |
16400 |
0.1633 |
| 1.6744 |
16500 |
0.1567 |
| 1.6846 |
16600 |
0.1388 |
| 1.6947 |
16700 |
0.1461 |
| 1.7049 |
16800 |
0.142 |
| 1.7150 |
16900 |
0.1229 |
| 1.7252 |
17000 |
0.152 |
| 1.7353 |
17100 |
0.1547 |
| 1.7455 |
17200 |
0.1481 |
| 1.7556 |
17300 |
0.1412 |
| 1.7658 |
17400 |
0.1611 |
| 1.7759 |
17500 |
0.1497 |
| 1.7861 |
17600 |
0.1485 |
| 1.7962 |
17700 |
0.1184 |
| 1.8064 |
17800 |
0.1686 |
| 1.8165 |
17900 |
0.1326 |
| 1.8267 |
18000 |
0.1665 |
| 1.8368 |
18100 |
0.1561 |
| 1.8470 |
18200 |
0.1527 |
| 1.8571 |
18300 |
0.1372 |
| 1.8673 |
18400 |
0.1811 |
| 1.8774 |
18500 |
0.12 |
| 1.8876 |
18600 |
0.1366 |
| 1.8977 |
18700 |
0.1432 |
| 1.9079 |
18800 |
0.17 |
| 1.9180 |
18900 |
0.1779 |
| 1.9282 |
19000 |
0.1565 |
| 1.9383 |
19100 |
0.1471 |
| 1.9484 |
19200 |
0.1266 |
| 1.9586 |
19300 |
0.1204 |
| 1.9687 |
19400 |
0.0959 |
| 1.9789 |
19500 |
0.1228 |
| 1.9890 |
19600 |
0.1347 |
| 1.9992 |
19700 |
0.0911 |
| 2.0093 |
19800 |
0.2626 |
| 2.0195 |
19900 |
0.1626 |
| 2.0296 |
20000 |
0.1461 |
| 2.0398 |
20100 |
0.1219 |
| 2.0499 |
20200 |
0.1223 |
| 2.0601 |
20300 |
0.1203 |
| 2.0702 |
20400 |
0.1312 |
| 2.0804 |
20500 |
0.1246 |
| 2.0905 |
20600 |
0.1374 |
| 2.1007 |
20700 |
0.1185 |
| 2.1108 |
20800 |
0.1175 |
| 2.1210 |
20900 |
0.1013 |
| 2.1311 |
21000 |
0.1205 |
| 2.1413 |
21100 |
0.1206 |
| 2.1514 |
21200 |
0.1085 |
| 2.1616 |
21300 |
0.1112 |
| 2.1717 |
21400 |
0.1046 |
| 2.1819 |
21500 |
0.0908 |
| 2.1920 |
21600 |
0.0807 |
| 2.2022 |
21700 |
0.0754 |
| 2.2123 |
21800 |
0.0773 |
| 2.2224 |
21900 |
0.0815 |
| 2.2326 |
22000 |
0.1078 |
| 2.2427 |
22100 |
0.0679 |
| 2.2529 |
22200 |
0.0824 |
| 2.2630 |
22300 |
0.0962 |
| 2.2732 |
22400 |
0.1108 |
| 2.2833 |
22500 |
0.0619 |
| 2.2935 |
22600 |
0.0829 |
| 2.3036 |
22700 |
0.0792 |
| 2.3138 |
22800 |
0.0782 |
| 2.3239 |
22900 |
0.0743 |
| 2.3341 |
23000 |
0.0788 |
| 2.3442 |
23100 |
0.0638 |
| 2.3544 |
23200 |
0.0927 |
| 2.3645 |
23300 |
0.0763 |
| 2.3747 |
23400 |
0.0782 |
| 2.3848 |
23500 |
0.0813 |
| 2.3950 |
23600 |
0.0736 |
| 2.4051 |
23700 |
0.0612 |
| 2.4153 |
23800 |
0.0593 |
| 2.4254 |
23900 |
0.0543 |
| 2.4356 |
24000 |
0.046 |
| 2.4457 |
24100 |
0.0472 |
| 2.4559 |
24200 |
0.0648 |
| 2.4660 |
24300 |
0.058 |
| 2.4762 |
24400 |
0.0603 |
| 2.4863 |
24500 |
0.0486 |
| 2.4964 |
24600 |
0.0605 |
| 2.5066 |
24700 |
0.0745 |
| 2.5167 |
24800 |
0.0621 |
| 2.5269 |
24900 |
0.0576 |
| 2.5370 |
25000 |
0.0567 |
| 2.5472 |
25100 |
0.0418 |
| 2.5573 |
25200 |
0.0405 |
| 2.5675 |
25300 |
0.0684 |
| 2.5776 |
25400 |
0.0597 |
| 2.5878 |
25500 |
0.0564 |
| 2.5979 |
25600 |
0.0576 |
| 2.6081 |
25700 |
0.0383 |
| 2.6182 |
25800 |
0.0592 |
| 2.6284 |
25900 |
0.0487 |
| 2.6385 |
26000 |
0.0569 |
| 2.6487 |
26100 |
0.0533 |
| 2.6588 |
26200 |
0.0497 |
| 2.6690 |
26300 |
0.0629 |
| 2.6791 |
26400 |
0.0563 |
| 2.6893 |
26500 |
0.0568 |
| 2.6994 |
26600 |
0.045 |
| 2.7096 |
26700 |
0.0562 |
| 2.7197 |
26800 |
0.0571 |
| 2.7299 |
26900 |
0.0599 |
| 2.7400 |
27000 |
0.0596 |
| 2.7502 |
27100 |
0.0552 |
| 2.7603 |
27200 |
0.0545 |
| 2.7704 |
27300 |
0.064 |
| 2.7806 |
27400 |
0.0493 |
| 2.7907 |
27500 |
0.0518 |
| 2.8009 |
27600 |
0.0517 |
| 2.8110 |
27700 |
0.0609 |
| 2.8212 |
27800 |
0.0603 |
| 2.8313 |
27900 |
0.0665 |
| 2.8415 |
28000 |
0.062 |
| 2.8516 |
28100 |
0.0581 |
| 2.8618 |
28200 |
0.0822 |
| 2.8719 |
28300 |
0.0666 |
| 2.8821 |
28400 |
0.0545 |
| 2.8922 |
28500 |
0.0671 |
| 2.9024 |
28600 |
0.0444 |
| 2.9125 |
28700 |
0.1006 |
| 2.9227 |
28800 |
0.0825 |
| 2.9328 |
28900 |
0.0649 |
| 2.9430 |
29000 |
0.0684 |
| 2.9531 |
29100 |
0.0444 |
| 2.9633 |
29200 |
0.0492 |
| 2.9734 |
29300 |
0.051 |
| 2.9836 |
29400 |
0.0511 |
| 2.9937 |
29500 |
0.0561 |
Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.0.1
- Transformers: 4.43.1
- PyTorch: 2.3.0
- Accelerate: 0.31.0
- Datasets: 2.19.2
- Tokenizers: 0.19.1
Citation
BibTeX
Sentence Transformers and SoftmaxLoss
@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",
}
ContrastiveLoss
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
MultipleNegativesRankingLoss
@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}
}