This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2 on the our own created datasets. 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/all-mpnet-base-v2
Maximum Sequence Length: 384 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, '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:

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("MasterControlAIML/finetuned-ecfr-embeddings")
# Run inference
sentences = [
    'The coordinator serves as the main point of contact between DEA registered locations and the CSOS Certification Authority for issues pertaining to issuance, revocation, and changes to digital certificates.',
    'The role of a CSOS coordinator involves issuing and distributing new digital certificates to DEA registered locations.',
    'The rules only apply to paper orders and prescriptions, not electronic ones.',
]
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]

Training Details

Training Dataset

https://huggingface.co/datasets/MasterControlAIML/triplet_finetuning_embeddings.csv

https://huggingface.co/datasets/MasterControlAIML/pair_class_finetuning_embeddings.csv

https://huggingface.co/datasets/MasterControlAIML/question_answer_finetuning_embeddings.csv

Dataset: MasterControlAIML/pair_class_finetuning_embeddings.csv
Size: 300,000 training samples
Columns: sentence_a, sentence_b, and label
Approximate statistics based on the first 1000 samples:
sentence_a sentence_b label
type string string int
details
min: 9 tokens
mean: 29.84 tokens
max: 164 tokens
min: 4 tokens
mean: 25.59 tokens
max: 73 tokens
0: ~33.30%
1: ~33.30%
2: ~33.40%

Samples:

sentence_a	sentence_b	label
`The purpose is to ensure consistency in the interpretation of terms used across different parts of the Federal Food, Drug, and Cosmetic Act and its regulations.`	`This provision aims to maintain uniformity in how terms are understood throughout the legislation and related rules.`	`0`
`The purpose is to ensure consistency in the interpretation of terms used across different parts of the Federal Food, Drug, and Cosmetic Act and its regulations.`	`The goal is to make sure that different interpretations of terminology vary widely across the Federal Food, Drug, and Cosmetic Act and its regulations.`	`2`
`The purpose is to ensure consistency in the interpretation of terms used across different parts of the Federal Food, Drug, and Cosmetic Act and its regulations.`	`The intention behind this is to guarantee that similar terms are interpreted consistently within the statute and its implementing regulations.`	`1`

Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 3e-05
weight_decay: 0.01
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
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: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
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: 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: 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
use_liger_kernel: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	triplet loss	natural-questions loss	pair-class loss
0.0026	10	1.1707	-	-	-
0.0051	20	1.4398	-	-	-
0.0077	30	1.1177	-	-	-
0.0102	40	1.337	-	-	-
0.0128	50	1.3094	-	-	-
0.0153	60	1.3527	-	-	-
0.0179	70	1.0938	-	-	-
0.0205	80	1.1795	-	-	-
0.0230	90	1.1525	-	-	-
0.0256	100	1.1584	-	-	-
0.0281	110	1.0561	-	-	-
0.0307	120	1.0497	-	-	-
0.0332	130	1.0765	-	-	-
0.0358	140	1.2655	-	-	-
0.0384	150	1.0642	-	-	-
0.0409	160	1.2373	-	-	-
0.0435	170	1.1048	-	-	-
0.0460	180	1.0555	-	-	-
0.0486	190	1.0665	-	-	-
0.0511	200	1.0416	-	-	-
0.0537	210	1.1101	-	-	-
0.0563	220	0.9804	-	-	-
0.0588	230	1.0349	-	-	-
0.0614	240	1.0572	-	-	-
0.0639	250	0.9677	-	-	-
0.0665	260	1.3367	-	-	-
0.0690	270	1.1872	-	-	-
0.0716	280	1.3214	-	-	-
0.0741	290	1.1236	-	-	-
0.0767	300	0.9422	-	-	-
0.0793	310	1.2298	-	-	-
0.0818	320	1.0567	-	-	-
0.0844	330	1.1267	-	-	-
0.0869	340	1.1045	-	-	-
0.0895	350	1.1986	-	-	-
0.0920	360	1.2155	-	-	-
0.0946	370	1.1971	-	-	-
0.0972	380	1.2324	-	-	-
0.0997	390	1.1244	-	-	-
0.1023	400	1.1597	-	-	-
0.1048	410	1.0963	-	-	-
0.1074	420	1.0303	-	-	-
0.1099	430	1.0667	-	-	-
0.1125	440	1.0421	-	-	-
0.1151	450	1.2969	-	-	-
0.1176	460	1.0174	-	-	-
0.1202	470	1.1774	-	-	-
0.1227	480	1.1024	-	-	-
0.1253	490	1.2135	-	-	-
0.1278	500	1.0365	-	-	-
0.1304	510	1.2741	-	-	-
0.1330	520	1.0621	-	-	-
0.1355	530	1.1155	-	-	-
0.1381	540	0.9648	-	-	-
0.1406	550	1.2931	-	-	-
0.1432	560	1.0685	-	-	-
0.1457	570	1.0907	-	-	-
0.1483	580	1.4771	-	-	-
0.1509	590	1.2467	-	-	-
0.1534	600	1.3106	-	-	-
0.1560	610	1.0369	-	-	-
0.1585	620	1.257	-	-	-
0.1611	630	1.5544	-	-	-
0.1636	640	1.2573	-	-	-
0.1662	650	1.2558	-	-	-
0.1688	660	1.4195	-	-	-
0.1713	670	1.2734	-	-	-
0.1739	680	1.3665	-	-	-
0.1764	690	1.2314	-	-	-
0.1790	700	1.6973	-	-	-
0.1815	710	1.5824	-	-	-
0.1841	720	1.3263	-	-	-
0.1867	730	1.18	-	-	-
0.1892	740	1.2197	-	-	-
0.1918	750	2.0825	-	-	-
0.1943	760	1.146	-	-	-
0.1969	770	1.0494	-	-	-
0.1994	780	1.6212	-	-	-
0.2020	790	1.2458	-	-	-
0.2046	800	0.9586	-	-	-
0.2071	810	1.3012	-	-	-
0.2097	820	1.3227	-	-	-
0.2122	830	1.2995	-	-	-
0.2148	840	1.4382	-	-	-
0.2173	850	0.9914	-	-	-
0.2199	860	1.3789	-	-	-
0.2224	870	0.78	-	-	-
0.2250	880	2.0291	-	-	-
0.2276	890	1.2744	-	-	-
0.2301	900	1.5524	-	-	-
0.2327	910	1.1003	-	-	-
0.2352	920	1.2761	-	-	-
0.2378	930	1.0052	-	-	-
0.2403	940	1.4054	-	-	-
0.2429	950	1.3834	-	-	-
0.2455	960	1.1076	-	-	-
0.2480	970	1.3723	-	-	-
0.2506	980	1.1703	-	-	-
0.2531	990	1.2798	-	-	-
0.2557	1000	1.0384	1.9672	1.5738	0.7390
0.2582	1010	1.0659	-	-	-
0.2608	1020	0.786	-	-	-
0.2634	1030	1.1735	-	-	-
0.2659	1040	1.6015	-	-	-
0.2685	1050	1.1714	-	-	-
0.2710	1060	0.9593	-	-	-
0.2736	1070	0.9219	-	-	-
0.2761	1080	1.2385	-	-	-
0.2787	1090	1.4025	-	-	-
0.2813	1100	1.0528	-	-	-
0.2838	1110	1.0912	-	-	-
0.2864	1120	1.0755	-	-	-
0.2889	1130	0.8722	-	-	-
0.2915	1140	0.8613	-	-	-
0.2940	1150	0.714	-	-	-
0.2966	1160	0.9849	-	-	-
0.2992	1170	1.5536	-	-	-
0.3017	1180	0.8425	-	-	-
0.3043	1190	1.0021	-	-	-
0.3068	1200	0.7547	-	-	-
0.3094	1210	1.1071	-	-	-
0.3119	1220	0.8453	-	-	-
0.3145	1230	0.8416	-	-	-
0.3171	1240	1.0041	-	-	-
0.3196	1250	1.1317	-	-	-
0.3222	1260	0.9105	-	-	-
0.3247	1270	0.8718	-	-	-
0.3273	1280	1.0268	-	-	-
0.3298	1290	1.0069	-	-	-
0.3324	1300	1.0736	-	-	-
0.3350	1310	0.8386	-	-	-
0.3375	1320	0.7839	-	-	-
0.3401	1330	0.8844	-	-	-
0.3426	1340	1.2852	-	-	-
0.3452	1350	0.8269	-	-	-
0.3477	1360	0.8158	-	-	-
0.3503	1370	0.8811	-	-	-
0.3529	1380	0.8127	-	-	-
0.3554	1390	0.9863	-	-	-
0.3580	1400	0.9983	-	-	-
0.3605	1410	1.0913	-	-	-
0.3631	1420	0.8338	-	-	-
0.3656	1430	0.7213	-	-	-
0.3682	1440	1.0293	-	-	-
0.3707	1450	0.8337	-	-	-
0.3733	1460	0.9256	-	-	-
0.3759	1470	1.0214	-	-	-
0.3784	1480	0.8967	-	-	-
0.3810	1490	0.8052	-	-	-
0.3835	1500	0.9588	-	-	-
0.3861	1510	0.9167	-	-	-
0.3886	1520	1.098	-	-	-
0.3912	1530	0.8574	-	-	-
0.3938	1540	0.7859	-	-	-
0.3963	1550	0.7916	-	-	-
0.3989	1560	0.7334	-	-	-
0.4014	1570	0.7245	-	-	-
0.4040	1580	0.6274	-	-	-
0.4065	1590	0.98	-	-	-
0.4091	1600	0.8322	-	-	-
0.4117	1610	0.7733	-	-	-
0.4142	1620	0.9476	-	-	-
0.4168	1630	0.9175	-	-	-
0.4193	1640	0.9388	-	-	-
0.4219	1650	0.8695	-	-	-
0.4244	1660	0.7446	-	-	-
0.4270	1670	0.7037	-	-	-
0.4296	1680	0.857	-	-	-
0.4321	1690	0.8691	-	-	-
0.4347	1700	1.2859	-	-	-
0.4372	1710	0.8383	-	-	-
0.4398	1720	1.0063	-	-	-
0.4423	1730	0.9503	-	-	-
0.4449	1740	0.9286	-	-	-
0.4475	1750	0.7624	-	-	-
0.4500	1760	1.0289	-	-	-
0.4526	1770	0.8778	-	-	-
0.4551	1780	1.0507	-	-	-
0.4577	1790	0.8792	-	-	-
0.4602	1800	0.564	-	-	-
0.4628	1810	0.8385	-	-	-
0.4654	1820	0.7852	-	-	-
0.4679	1830	1.096	-	-	-
0.4705	1840	1.059	-	-	-
0.4730	1850	0.7789	-	-	-
0.4756	1860	0.7415	-	-	-
0.4781	1870	0.726	-	-	-
0.4807	1880	0.7858	-	-	-
0.4833	1890	0.6238	-	-	-
0.4858	1900	1.0395	-	-	-
0.4884	1910	0.7295	-	-	-
0.4909	1920	0.9215	-	-	-
0.4935	1930	0.7803	-	-	-
0.4960	1940	0.6647	-	-	-
0.4986	1950	0.8575	-	-	-
0.5012	1960	0.7408	-	-	-
0.5037	1970	0.772	-	-	-
0.5063	1980	0.6131	-	-	-
0.5088	1990	0.8394	-	-	-
0.5114	2000	0.7548	1.2322	1.2839	0.5386
0.5139	2010	1.0089	-	-	-
0.5165	2020	0.8726	-	-	-
0.5190	2030	1.0068	-	-	-
0.5216	2040	0.6911	-	-	-
0.5242	2050	0.6863	-	-	-
0.5267	2060	0.684	-	-	-
0.5293	2070	0.6317	-	-	-
0.5318	2080	0.8134	-	-	-
0.5344	2090	0.7953	-	-	-
0.5369	2100	0.7435	-	-	-
0.5395	2110	0.7763	-	-	-
0.5421	2120	0.7817	-	-	-
0.5446	2130	0.7257	-	-	-
0.5472	2140	0.7699	-	-	-
0.5497	2150	0.7071	-	-	-
0.5523	2160	0.6143	-	-	-
0.5548	2170	0.8661	-	-	-
0.5574	2180	0.7011	-	-	-
0.5600	2190	0.6527	-	-	-
0.5625	2200	0.7136	-	-	-
0.5651	2210	0.7573	-	-	-
0.5676	2220	0.7446	-	-	-
0.5702	2230	0.7281	-	-	-
0.5727	2240	0.6477	-	-	-
0.5753	2250	0.6089	-	-	-
0.5779	2260	0.9791	-	-	-
0.5804	2270	0.6604	-	-	-
0.5830	2280	0.616	-	-	-
0.5855	2290	0.7537	-	-	-
0.5881	2300	0.8455	-	-	-
0.5906	2310	0.7813	-	-	-
0.5932	2320	0.7018	-	-	-
0.5958	2330	0.8648	-	-	-
0.5983	2340	0.6803	-	-	-
0.6009	2350	0.6801	-	-	-
0.6034	2360	0.8162	-	-	-
0.6060	2370	0.8988	-	-	-
0.6085	2380	0.7448	-	-	-
0.6111	2390	0.6624	-	-	-
0.6137	2400	0.7872	-	-	-
0.6162	2410	0.5589	-	-	-
0.6188	2420	0.8243	-	-	-
0.6213	2430	0.6335	-	-	-
0.6239	2440	0.7356	-	-	-
0.6264	2450	0.5682	-	-	-
0.6290	2460	0.662	-	-	-
0.6316	2470	0.6618	-	-	-
0.6341	2480	0.6632	-	-	-
0.6367	2490	0.6072	-	-	-
0.6392	2500	0.6689	-	-	-
0.6418	2510	0.49	-	-	-
0.6443	2520	0.5977	-	-	-
0.6469	2530	0.831	-	-	-
0.6495	2540	0.8773	-	-	-
0.6520	2550	0.6686	-	-	-
0.6546	2560	0.5799	-	-	-
0.6571	2570	0.6255	-	-	-
0.6597	2580	0.6272	-	-	-
0.6622	2590	0.672	-	-	-
0.6648	2600	0.7736	-	-	-
0.6673	2610	0.561	-	-	-
0.6699	2620	0.4959	-	-	-
0.6725	2630	0.6237	-	-	-
0.6750	2640	0.6684	-	-	-
0.6776	2650	0.6777	-	-	-
0.6801	2660	0.5853	-	-	-
0.6827	2670	0.6338	-	-	-
0.6852	2680	0.4402	-	-	-
0.6878	2690	0.7279	-	-	-
0.6904	2700	0.6405	-	-	-
0.6929	2710	0.6669	-	-	-
0.6955	2720	0.6239	-	-	-
0.6980	2730	0.6889	-	-	-
0.7006	2740	0.6653	-	-	-
0.7031	2750	0.5996	-	-	-
0.7057	2760	0.6477	-	-	-
0.7083	2770	0.6088	-	-	-
0.7108	2780	0.5685	-	-	-
0.7134	2790	0.7533	-	-	-
0.7159	2800	0.7903	-	-	-
0.7185	2810	0.4748	-	-	-
0.7210	2820	0.5296	-	-	-
0.7236	2830	0.6541	-	-	-
0.7262	2840	0.5332	-	-	-
0.7287	2850	0.4696	-	-	-
0.7313	2860	0.5556	-	-	-
0.7338	2870	0.5828	-	-	-
0.7364	2880	0.5641	-	-	-
0.7389	2890	0.5416	-	-	-
0.7415	2900	0.4942	-	-	-
0.7441	2910	0.7297	-	-	-
0.7466	2920	0.5595	-	-	-
0.7492	2930	0.6382	-	-	-
0.7517	2940	0.6296	-	-	-
0.7543	2950	0.5557	-	-	-
0.7568	2960	0.5127	-	-	-
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1.7873	6990	0.3371	-	-	-
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1.7924	7010	0.4205	-	-	-
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2.8381	11100	0.29	-	-	-
2.8407	11110	0.445	-	-	-
2.8433	11120	0.4506	-	-	-
2.8458	11130	0.3571	-	-	-
2.8484	11140	0.413	-	-	-
2.8509	11150	0.3169	-	-	-
2.8535	11160	0.3587	-	-	-
2.8560	11170	0.4717	-	-	-
2.8586	11180	0.3758	-	-	-
2.8612	11190	0.384	-	-	-
2.8637	11200	0.2987	-	-	-
2.8663	11210	0.4913	-	-	-
2.8688	11220	0.474	-	-	-
2.8714	11230	0.4519	-	-	-
2.8739	11240	0.3698	-	-	-
2.8765	11250	0.389	-	-	-
2.8791	11260	0.3621	-	-	-
2.8816	11270	0.5046	-	-	-
2.8842	11280	0.3724	-	-	-
2.8867	11290	0.4003	-	-	-
2.8893	11300	0.3711	-	-	-
2.8918	11310	0.4393	-	-	-
2.8944	11320	0.6715	-	-	-
2.8970	11330	0.3232	-	-	-
2.8995	11340	0.3302	-	-	-
2.9021	11350	0.3334	-	-	-
2.9046	11360	0.4568	-	-	-
2.9072	11370	0.3423	-	-	-
2.9097	11380	0.3241	-	-	-
2.9123	11390	0.3032	-	-	-
2.9149	11400	0.3338	-	-	-
2.9174	11410	0.3642	-	-	-
2.9200	11420	0.5081	-	-	-
2.9225	11430	0.4581	-	-	-
2.9251	11440	0.4019	-	-	-
2.9276	11450	0.4075	-	-	-
2.9302	11460	0.4445	-	-	-
2.9328	11470	0.3452	-	-	-
2.9353	11480	0.4069	-	-	-
2.9379	11490	0.419	-	-	-
2.9404	11500	0.37	-	-	-
2.9430	11510	0.4321	-	-	-
2.9455	11520	0.3993	-	-	-
2.9481	11530	0.4222	-	-	-
2.9507	11540	0.4761	-	-	-
2.9532	11550	0.4414	-	-	-
2.9558	11560	0.5994	-	-	-
2.9583	11570	0.4572	-	-	-
2.9609	11580	0.3808	-	-	-
2.9634	11590	0.4309	-	-	-
2.9660	11600	0.4366	-	-	-
2.9686	11610	0.4015	-	-	-
2.9711	11620	0.5918	-	-	-
2.9737	11630	0.6513	-	-	-
2.9762	11640	0.6191	-	-	-
2.9788	11650	0.3044	-	-	-
2.9813	11660	0.4612	-	-	-
2.9839	11670	0.4446	-	-	-
2.9864	11680	0.4184	-	-	-
2.9890	11690	0.383	-	-	-
2.9916	11700	0.4387	-	-	-
2.9941	11710	0.529	-	-	-
2.9967	11720	0.5059	-	-	-
2.9992	11730	0.4155	-	-	-

Framework Versions

Python: 3.8.10
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.4.1+cu121
Accelerate: 1.0.1
Datasets: 3.1.0
Tokenizers: 0.20.3

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

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}
}

	sentence_a	sentence_b	label
type	string	string	int
details	min: 9 tokens mean: 29.84 tokens max: 164 tokens	min: 4 tokens mean: 25.59 tokens max: 73 tokens	0: ~33.30% 1: ~33.30% 2: ~33.40%