This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

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': 128, '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:

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("saikasyap/samskritam-labse-en-sa")
# Run inference
sentences = [
    "In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts.",
    'तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥',
    'क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।',
]
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]

Evaluation

Metrics

Translation

Dataset: eval-en-sa
Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.944
trg2src_accuracy	0.947
mean_accuracy	0.9455

Training Details

Training Dataset

Unnamed Dataset

Size: 257,886 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 6 tokens
mean: 31.6 tokens
max: 128 tokens
min: 7 tokens
mean: 40.18 tokens
max: 128 tokens

Samples:

sentence_0	sentence_1
`It normally connects to port 80 on a computer.[object Object]`	`इदं सामान्यतः एकस्मिन् सङ्गणके पोर्ट् ८० इत्यनेन सम्पर्कं साधयति।[object Object]`
`He who gives to a Brahmana a good bed perfumed with fragrant scents, covered with an excellent sheet, and pillows, gets without any effort on his part a beautiful wife, belonging to a respectable family and of agreeable manners.`	`सुगन्धचित्रास्तरणोपधानं दद्यान्नरो यः शयनं द्विजाय। रूपान्वितां पक्षवती मनोज्ञां भार्यामयत्नोपगतां लभेत् सः।`
`By mid-1665, with the fortress at Purandar besieged and near capture, Shivaji was forced to come to terms with Jai Singh.[object Object]`	`१६६५ तमवर्षस्य मध्यभागे यावत् पुरन्दरस्थस्य दुर्गस्य परिवेष्टनं कृत्वा, ग्रहणस्य समीपे, शिवाजी जयसिङ्घेन सह सन्धानं कर्तुं बाध्यः अभवत्।[object Object]`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
num_train_epochs: 15
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 15
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss	eval-en-sa_mean_accuracy
0.0310	500	0.4289	-
0.0620	1000	0.182	-
0.0931	1500	0.1405	-
0.1241	2000	0.1097	-
0.1551	2500	0.0911	-
0.1861	3000	0.0791	-
0.2171	3500	0.0725	-
0.2482	4000	0.067	-
0.2792	4500	0.0594	-
0.3102	5000	0.0629	-
0.3412	5500	0.0535	-
0.3723	6000	0.0512	-
0.4033	6500	0.0456	-
0.4343	7000	0.0462	-
0.4653	7500	0.043	-
0.4963	8000	0.0425	-
0.5274	8500	0.0412	-
0.5584	9000	0.0418	-
0.5894	9500	0.0415	-
0.6204	10000	0.0409	-
0.6514	10500	0.04	-
0.6825	11000	0.032	-
0.7135	11500	0.0323	-
0.7445	12000	0.0325	-
0.7755	12500	0.0355	-
0.8066	13000	0.0285	-
0.8376	13500	0.0281	-
0.8686	14000	0.0289	-
0.8996	14500	0.033	-
0.9306	15000	0.0336	-
0.9617	15500	0.0335	-
0.9927	16000	0.0278	-
1.0	16118	-	0.913
1.0237	16500	0.0312	-
1.0547	17000	0.0294	-
1.0857	17500	0.0288	-
1.1168	18000	0.0287	-
1.1478	18500	0.0245	-
1.1788	19000	0.0243	-
1.2098	19500	0.022	-
1.2408	20000	0.0266	-
1.2719	20500	0.0224	-
1.3029	21000	0.0283	-
1.3339	21500	0.02	-
1.3649	22000	0.0212	-
1.3960	22500	0.0197	-
1.4270	23000	0.0174	-
1.4580	23500	0.0179	-
1.4890	24000	0.0187	-
1.5200	24500	0.0191	-
1.5511	25000	0.0151	-
1.5821	25500	0.0161	-
1.6131	26000	0.0182	-
1.6441	26500	0.0155	-
1.6751	27000	0.013	-
1.7062	27500	0.0119	-
1.7372	28000	0.0119	-
1.7682	28500	0.0133	-
1.7992	29000	0.0113	-
1.8303	29500	0.011	-
1.8613	30000	0.0133	-
1.8923	30500	0.0114	-
1.9233	31000	0.0139	-
1.9543	31500	0.0131	-
1.9854	32000	0.0115	-
2.0	32236	-	0.9345
2.0164	32500	0.01	-
2.0474	33000	0.01	-
2.0784	33500	0.0091	-
2.1094	34000	0.0131	-
2.1405	34500	0.0096	-
2.1715	35000	0.0095	-
2.2025	35500	0.0103	-
2.2335	36000	0.0101	-
2.2645	36500	0.0102	-
2.2956	37000	0.0102	-
2.3266	37500	0.0085	-
2.3576	38000	0.0087	-
2.3886	38500	0.0103	-
2.4197	39000	0.0058	-
2.4507	39500	0.0086	-
2.4817	40000	0.0088	-
2.5127	40500	0.0088	-
2.5437	41000	0.007	-
2.5748	41500	0.0082	-
2.6058	42000	0.0069	-
2.6368	42500	0.0071	-
2.6678	43000	0.0058	-
2.6988	43500	0.0075	-
2.7299	44000	0.0064	-
2.7609	44500	0.0053	-
2.7919	45000	0.0055	-
2.8229	45500	0.0061	-
2.8540	46000	0.0059	-
2.8850	46500	0.0062	-
2.9160	47000	0.0046	-
2.9470	47500	0.0064	-
2.9780	48000	0.0053	-
3.0	48354	-	0.941
3.0091	48500	0.0048	-
3.0401	49000	0.0059	-
3.0711	49500	0.005	-
3.1021	50000	0.005	0.9415
3.1331	50500	0.0046	-
3.1642	51000	0.005	-
3.1952	51500	0.0051	-
3.2262	52000	0.0041	-
3.2572	52500	0.0052	-
3.2882	53000	0.0052	-
3.3193	53500	0.0053	-
3.3503	54000	0.0041	-
3.3813	54500	0.0042	-
3.4123	55000	0.0026	-
3.4434	55500	0.0045	-
3.4744	56000	0.0045	-
3.5054	56500	0.0054	-
3.5364	57000	0.0055	-
3.5674	57500	0.0046	-
3.5985	58000	0.0045	-
3.6295	58500	0.0041	-
3.6605	59000	0.0037	-
3.6915	59500	0.003	-
3.7225	60000	0.0039	-
3.7536	60500	0.0027	-
3.7846	61000	0.0041	-
3.8156	61500	0.003	-
3.8466	62000	0.0027	-
3.8777	62500	0.0039	-
3.9087	63000	0.0038	-
3.9397	63500	0.0029	-
3.9707	64000	0.0037	-
4.0	64472	-	0.9365
4.0017	64500	0.0023	-
4.0328	65000	0.0034	-
4.0638	65500	0.0033	-
4.0948	66000	0.0033	-
4.1258	66500	0.004	-
4.1568	67000	0.0026	-
4.1879	67500	0.0026	-
4.2189	68000	0.0025	-
4.2499	68500	0.0037	-
4.2809	69000	0.0041	-
4.3119	69500	0.0031	-
4.3430	70000	0.0025	-
4.3740	70500	0.0025	-
4.4050	71000	0.0022	-
4.4360	71500	0.0016	-
4.4671	72000	0.003	-
4.4981	72500	0.0029	-
4.5291	73000	0.003	-
4.5601	73500	0.0025	-
4.5911	74000	0.0027	-
4.6222	74500	0.0028	-
4.6532	75000	0.003	-
4.6842	75500	0.002	-
4.7152	76000	0.0028	-
4.7462	76500	0.0016	-
4.7773	77000	0.0022	-
4.8083	77500	0.0019	-
4.8393	78000	0.0019	-
4.8703	78500	0.0026	-
4.9014	79000	0.0023	-
4.9324	79500	0.0016	-
4.9634	80000	0.0019	-
4.9944	80500	0.0018	-
5.0	80590	-	0.937
5.0254	81000	0.0028	-
5.0565	81500	0.0019	-
5.0875	82000	0.0024	-
5.1185	82500	0.0016	-
5.1495	83000	0.0015	-
5.1805	83500	0.0017	-
5.2116	84000	0.0016	-
5.2426	84500	0.0026	-
5.2736	85000	0.0029	-
5.3046	85500	0.0027	-
5.3356	86000	0.002	-
5.3667	86500	0.002	-
5.3977	87000	0.0021	-
5.4287	87500	0.0011	-
5.4597	88000	0.0016	-
5.4908	88500	0.0019	-
5.5218	89000	0.0027	-
5.5528	89500	0.0012	-
5.5838	90000	0.0012	-
5.6148	90500	0.0016	-
5.6459	91000	0.0019	-
5.6769	91500	0.0016	-
5.7079	92000	0.0027	-
5.7389	92500	0.0013	-
5.7699	93000	0.0013	-
5.8010	93500	0.0015	-
5.8320	94000	0.0016	-
5.8630	94500	0.002	-
5.8940	95000	0.001	-
5.9251	95500	0.0014	-
5.9561	96000	0.0021	-
5.9871	96500	0.0022	-
6.0	96708	-	0.933
6.0181	97000	0.0016	-
6.0491	97500	0.0015	-
6.0802	98000	0.0011	-
6.1112	98500	0.0016	-
6.1422	99000	0.001	-
6.1732	99500	0.0013	-
6.2042	100000	0.0015	0.9365
6.2353	100500	0.0017	-
6.2663	101000	0.0015	-
6.2973	101500	0.0016	-
6.3283	102000	0.001	-
6.3593	102500	0.0013	-
6.3904	103000	0.0013	-
6.4214	103500	0.0011	-
6.4524	104000	0.0007	-
6.4834	104500	0.0013	-
6.5145	105000	0.0011	-
6.5455	105500	0.0011	-
6.5765	106000	0.0015	-
6.6075	106500	0.002	-
6.6385	107000	0.0011	-
6.6696	107500	0.0013	-
6.7006	108000	0.0017	-
6.7316	108500	0.0008	-
6.7626	109000	0.0011	-
6.7936	109500	0.0008	-
6.8247	110000	0.0009	-
6.8557	110500	0.0014	-
6.8867	111000	0.0014	-
6.9177	111500	0.0014	-
6.9488	112000	0.0014	-
6.9798	112500	0.0013	-
7.0	112826	-	0.9390
7.0108	113000	0.0011	-
7.0418	113500	0.0013	-
7.0728	114000	0.0012	-
7.1039	114500	0.001	-
7.1349	115000	0.0016	-
7.1659	115500	0.0009	-
7.1969	116000	0.0009	-
7.2279	116500	0.0007	-
7.2590	117000	0.0008	-
7.2900	117500	0.0014	-
7.3210	118000	0.0012	-
7.3520	118500	0.0007	-
7.3831	119000	0.001	-
7.4141	119500	0.001	-
7.4451	120000	0.0007	-
7.4761	120500	0.0008	-
7.5071	121000	0.0009	-
7.5382	121500	0.0009	-
7.5692	122000	0.001	-
7.6002	122500	0.0009	-
7.6312	123000	0.0007	-
7.6622	123500	0.0009	-
7.6933	124000	0.0007	-
7.7243	124500	0.0012	-
7.7553	125000	0.001	-
7.7863	125500	0.0005	-
7.8173	126000	0.0005	-
7.8484	126500	0.0008	-
7.8794	127000	0.0014	-
7.9104	127500	0.0014	-
7.9414	128000	0.0009	-
7.9725	128500	0.0008	-
8.0	128944	-	0.94
8.0035	129000	0.0013	-
8.0345	129500	0.0007	-
8.0655	130000	0.0007	-
8.0965	130500	0.0008	-
8.1276	131000	0.0009	-
8.1586	131500	0.0009	-
8.1896	132000	0.0007	-
8.2206	132500	0.0008	-
8.2516	133000	0.0008	-
8.2827	133500	0.0006	-
8.3137	134000	0.0008	-
8.3447	134500	0.001	-
8.3757	135000	0.0006	-
8.4068	135500	0.0007	-
8.4378	136000	0.0007	-
8.4688	136500	0.0009	-
8.4998	137000	0.0008	-
8.5308	137500	0.0006	-
8.5619	138000	0.0008	-
8.5929	138500	0.0007	-
8.6239	139000	0.0008	-
8.6549	139500	0.0006	-
8.6859	140000	0.0005	-
8.7170	140500	0.0006	-
8.7480	141000	0.0006	-
8.7790	141500	0.0006	-
8.8100	142000	0.0005	-
8.8410	142500	0.0006	-
8.8721	143000	0.0005	-
8.9031	143500	0.0006	-
8.9341	144000	0.0009	-
8.9651	144500	0.0007	-
8.9962	145000	0.0007	-
9.0	145062	-	0.938
9.0272	145500	0.0007	-
9.0582	146000	0.0007	-
9.0892	146500	0.0007	-
9.1202	147000	0.0007	-
9.1513	147500	0.0005	-
9.1823	148000	0.0005	-
9.2133	148500	0.0005	-
9.2443	149000	0.0007	-
9.2753	149500	0.0006	-
9.3064	150000	0.0005	0.938
9.3374	150500	0.0005	-
9.3684	151000	0.0004	-
9.3994	151500	0.0007	-
9.4305	152000	0.0006	-
9.4615	152500	0.0006	-
9.4925	153000	0.0012	-
9.5235	153500	0.0015	-
9.5545	154000	0.0006	-
9.5856	154500	0.0004	-
9.6166	155000	0.0004	-
9.6476	155500	0.0007	-
9.6786	156000	0.0005	-
9.7096	156500	0.0006	-
9.7407	157000	0.0004	-
9.7717	157500	0.0004	-
9.8027	158000	0.0006	-
9.8337	158500	0.0004	-
9.8647	159000	0.0005	-
9.8958	159500	0.0005	-
9.9268	160000	0.0004	-
9.9578	160500	0.0007	-
9.9888	161000	0.0008	-
10.0	161180	-	0.9405
10.0199	161500	0.0009	-
10.0509	162000	0.0007	-
10.0819	162500	0.0007	-
10.1129	163000	0.0007	-
10.1439	163500	0.0005	-
10.1750	164000	0.0005	-
10.2060	164500	0.0004	-
10.2370	165000	0.0006	-
10.2680	165500	0.0006	-
10.2990	166000	0.0005	-
10.3301	166500	0.0005	-
10.3611	167000	0.0006	-
10.3921	167500	0.0006	-
10.4231	168000	0.0003	-
10.4542	168500	0.0005	-
10.4852	169000	0.001	-
10.5162	169500	0.0007	-
10.5472	170000	0.0003	-
10.5782	170500	0.0005	-
10.6093	171000	0.0003	-
10.6403	171500	0.0004	-
10.6713	172000	0.0006	-
10.7023	172500	0.0006	-
10.7333	173000	0.0005	-
10.7644	173500	0.0004	-
10.7954	174000	0.0003	-
10.8264	174500	0.0007	-
10.8574	175000	0.0005	-
10.8884	175500	0.0003	-
10.9195	176000	0.0006	-
10.9505	176500	0.001	-
10.9815	177000	0.0007	-
11.0	177298	-	0.9345
11.0125	177500	0.0003	-
11.0436	178000	0.0003	-
11.0746	178500	0.0005	-
11.1056	179000	0.0005	-
11.1366	179500	0.0007	-
11.1676	180000	0.0008	-
11.1987	180500	0.0004	-
11.2297	181000	0.0006	-
11.2607	181500	0.0006	-
11.2917	182000	0.0009	-
11.3227	182500	0.0005	-
11.3538	183000	0.0004	-
11.3848	183500	0.0004	-
11.4158	184000	0.0005	-
11.4468	184500	0.0003	-
11.4779	185000	0.0002	-
11.5089	185500	0.0003	-
11.5399	186000	0.0007	-
11.5709	186500	0.0003	-
11.6019	187000	0.0003	-
11.6330	187500	0.0004	-
11.6640	188000	0.0007	-
11.6950	188500	0.0003	-
11.7260	189000	0.0003	-
11.7570	189500	0.0004	-
11.7881	190000	0.0004	-
11.8191	190500	0.0003	-
11.8501	191000	0.0003	-
11.8811	191500	0.0003	-
11.9121	192000	0.0002	-
11.9432	192500	0.0008	-
11.9742	193000	0.0004	-
12.0	193416	-	0.944
12.0052	193500	0.0005	-
12.0362	194000	0.0002	-
12.0673	194500	0.0003	-
12.0983	195000	0.0004	-
12.1293	195500	0.0005	-
12.1603	196000	0.0004	-
12.1913	196500	0.0002	-
12.2224	197000	0.0002	-
12.2534	197500	0.0003	-
12.2844	198000	0.0003	-
12.3154	198500	0.0005	-
12.3464	199000	0.0004	-
12.3775	199500	0.0004	-
12.4085	200000	0.0003	0.9435
12.4395	200500	0.0003	-
12.4705	201000	0.0004	-
12.5016	201500	0.0009	-
12.5326	202000	0.0005	-
12.5636	202500	0.0003	-
12.5946	203000	0.0003	-
12.6256	203500	0.0002	-
12.6567	204000	0.0003	-
12.6877	204500	0.0002	-
12.7187	205000	0.0005	-
12.7497	205500	0.0003	-
12.7807	206000	0.0004	-
12.8118	206500	0.0003	-
12.8428	207000	0.0003	-
12.8738	207500	0.0003	-
12.9048	208000	0.0003	-
12.9358	208500	0.0006	-
12.9669	209000	0.0004	-
12.9979	209500	0.0004	-
13.0	209534	-	0.9455

Framework Versions

Python: 3.10.17
Sentence Transformers: 4.1.0
Transformers: 4.46.3
PyTorch: 2.2.0+cu121
Accelerate: 1.1.1
Datasets: 2.18.0
Tokenizers: 0.20.3

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}
}

	sentence_0	sentence_1
type	string	string
details	min: 6 tokens mean: 31.6 tokens max: 128 tokens	min: 7 tokens mean: 40.18 tokens max: 128 tokens