SentenceTransformer based on sentence-transformers/LaBSE

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: 256 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': 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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Генри Джастис Форд',
    'Форд, Генри Джастис',
    'Я вышел из ванны свеж и бодр, как будто собирался на бал.',
]
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

Unnamed Dataset

Size: 1,000,000 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 3 tokens
mean: 21.82 tokens
max: 127 tokens

min: 4 tokens
mean: 21.16 tokens
max: 136 tokens

min: 1.0
mean: 1.0
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 3 tokens mean: 21.82 tokens max: 127 tokens	min: 4 tokens mean: 21.16 tokens max: 136 tokens	min: 1.0 mean: 1.0 max: 1.0

Samples:

sentence_0	sentence_1	label
`Темех мар.`	`Дело десятое.`	`1.0`
`Уругвайӑн тĕн ĕҫченĕсем`	`Религиозные деятели Уругвая`	`1.0`
`Эп аванах ас тӑватӑп, пилӗк ҫул каялла пахчана эпир лайӑх тасатнӑччӗ.`	`А пять лет тому назад я знал, что сад был чищен.`	`1.0`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 12
per_device_eval_batch_size: 12
num_train_epochs: 1
fp16: True
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: 12
per_device_eval_batch_size: 12
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: 1
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: 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}
tp_size: 0
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
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
0.0012	100	-
0.0024	200	-
0.0036	300	-
0.0048	400	-
0.0060	500	0.5331
0.0072	600	-
0.0084	700	-
0.0096	800	-
0.0108	900	-
0.0120	1000	0.3694
0.0132	1100	-
0.0144	1200	-
0.0156	1300	-
0.0168	1400	-
0.0180	1500	0.3141
0.0192	1600	-
0.0204	1700	-
0.0216	1800	-
0.0228	1900	-
0.0240	2000	0.2836
0.0252	2100	-
0.0264	2200	-
0.0276	2300	-
0.0288	2400	-
0.0300	2500	0.2823
0.0312	2600	-
0.0324	2700	-
0.0336	2800	-
0.0348	2900	-
0.0360	3000	0.265
0.0372	3100	-
0.0384	3200	-
0.0396	3300	-
0.0408	3400	-
0.0420	3500	0.2599
0.0432	3600	-
0.0444	3700	-
0.0456	3800	-
0.0468	3900	-
0.0480	4000	0.234
0.0492	4100	-
0.0504	4200	-
0.0516	4300	-
0.0528	4400	-
0.0540	4500	0.1966
0.0552	4600	-
0.0564	4700	-
0.0576	4800	-
0.0588	4900	-
0.0600	5000	0.2204
0.0612	5100	-
0.0624	5200	-
0.0636	5300	-
0.0648	5400	-
0.0660	5500	0.2272
0.0672	5600	-
0.0684	5700	-
0.0696	5800	-
0.0708	5900	-
0.0720	6000	0.2256
0.0732	6100	-
0.0744	6200	-
0.0756	6300	-
0.0768	6400	-
0.0780	6500	0.2071
0.0792	6600	-
0.0804	6700	-
0.0816	6800	-
0.0828	6900	-
0.0840	7000	0.2113
0.0852	7100	-
0.0864	7200	-
0.0876	7300	-
0.0888	7400	-
0.0900	7500	0.2222
0.0912	7600	-
0.0924	7700	-
0.0936	7800	-
0.0948	7900	-
0.0960	8000	0.2186
0.0972	8100	-
0.0984	8200	-
0.0996	8300	-
0.1008	8400	-
0.1020	8500	0.2137
0.1032	8600	-
0.1044	8700	-
0.1056	8800	-
0.1068	8900	-
0.1080	9000	0.1928
0.1092	9100	-
0.1104	9200	-
0.1116	9300	-
0.1128	9400	-
0.1140	9500	0.2117
0.1152	9600	-
0.1164	9700	-
0.1176	9800	-
0.1188	9900	-
0.1200	10000	0.1987
0.1212	10100	-
0.1224	10200	-
0.1236	10300	-
0.1248	10400	-
0.1260	10500	0.2011
0.1272	10600	-
0.1284	10700	-
0.1296	10800	-
0.1308	10900	-
0.1320	11000	0.1775
0.1332	11100	-
0.1344	11200	-
0.1356	11300	-
0.1368	11400	-
0.1380	11500	0.2048
0.1392	11600	-
0.1404	11700	-
0.1416	11800	-
0.1428	11900	-
0.1440	12000	0.2064
0.1452	12100	-
0.1464	12200	-
0.1476	12300	-
0.1488	12400	-
0.1500	12500	0.1883
0.1512	12600	-
0.1524	12700	-
0.1536	12800	-
0.1548	12900	-
0.1560	13000	0.2084
0.1572	13100	-
0.1584	13200	-
0.1596	13300	-
0.1608	13400	-
0.1620	13500	0.2077
0.1632	13600	-
0.1644	13700	-
0.1656	13800	-
0.1668	13900	-
0.1680	14000	0.1866
0.1692	14100	-
0.1704	14200	-
0.1716	14300	-
0.1728	14400	-
0.1740	14500	0.1859
0.1752	14600	-
0.1764	14700	-
0.1776	14800	-
0.1788	14900	-
0.1800	15000	0.1735
0.1812	15100	-
0.1824	15200	-
0.1836	15300	-
0.1848	15400	-
0.1860	15500	0.171
0.1872	15600	-
0.1884	15700	-
0.1896	15800	-
0.1908	15900	-
0.1920	16000	0.1465
0.1932	16100	-
0.1944	16200	-
0.1956	16300	-
0.1968	16400	-
0.1980	16500	0.1921
0.1992	16600	-
0.2004	16700	-
0.2016	16800	-
0.2028	16900	-
0.2040	17000	0.1669
0.2052	17100	-
0.2064	17200	-
0.2076	17300	-
0.2088	17400	-
0.2100	17500	0.1656
0.2112	17600	-
0.2124	17700	-
0.2136	17800	-
0.2148	17900	-
0.2160	18000	0.1952
0.2172	18100	-
0.2184	18200	-
0.2196	18300	-
0.2208	18400	-
0.2220	18500	0.1658
0.2232	18600	-
0.2244	18700	-
0.2256	18800	-
0.2268	18900	-
0.2280	19000	0.1774
0.2292	19100	-
0.2304	19200	-
0.2316	19300	-
0.2328	19400	-
0.2340	19500	0.1802
0.2352	19600	-
0.2364	19700	-
0.2376	19800	-
0.2388	19900	-
0.2400	20000	0.1724
0.2412	20100	-
0.2424	20200	-
0.2436	20300	-
0.2448	20400	-
0.2460	20500	0.1653
0.2472	20600	-
0.2484	20700	-
0.2496	20800	-
0.2508	20900	-
0.2520	21000	0.1484
0.2532	21100	-
0.2544	21200	-
0.2556	21300	-
0.2568	21400	-
0.2580	21500	0.1544
0.2592	21600	-
0.2604	21700	-
0.2616	21800	-
0.2628	21900	-
0.2640	22000	0.174
0.2652	22100	-
0.2664	22200	-
0.2676	22300	-
0.2688	22400	-
0.2700	22500	0.1488
0.2712	22600	-
0.2724	22700	-
0.2736	22800	-
0.2748	22900	-
0.2760	23000	0.1696
0.2772	23100	-
0.2784	23200	-
0.2796	23300	-
0.2808	23400	-
0.2820	23500	0.1468
0.2832	23600	-
0.2844	23700	-
0.2856	23800	-
0.2868	23900	-
0.2880	24000	0.1738
0.2892	24100	-
0.2904	24200	-
0.2916	24300	-
0.2928	24400	-
0.2940	24500	0.1667
0.2952	24600	-
0.2964	24700	-
0.2976	24800	-
0.2988	24900	-
0.3000	25000	0.1562
0.3012	25100	-
0.3024	25200	-
0.3036	25300	-
0.3048	25400	-
0.3060	25500	0.1628
0.3072	25600	-
0.3084	25700	-
0.3096	25800	-
0.3108	25900	-
0.3120	26000	0.1392
0.3132	26100	-
0.3144	26200	-
0.3156	26300	-
0.3168	26400	-
0.3180	26500	0.1507
0.3192	26600	-
0.3204	26700	-
0.3216	26800	-
0.3228	26900	-
0.3240	27000	0.1646
0.3252	27100	-
0.3264	27200	-
0.3276	27300	-
0.3288	27400	-
0.3300	27500	0.1433
0.3312	27600	-
0.3324	27700	-
0.3336	27800	-
0.3348	27900	-
0.3360	28000	0.1689
0.3372	28100	-
0.3384	28200	-
0.3396	28300	-
0.3408	28400	-
0.3420	28500	0.1432
0.3432	28600	-
0.3444	28700	-
0.3456	28800	-
0.3468	28900	-
0.3480	29000	0.1534
0.3492	29100	-
0.3504	29200	-
0.3516	29300	-
0.3528	29400	-
0.3540	29500	0.1487
0.3552	29600	-
0.3564	29700	-
0.3576	29800	-
0.3588	29900	-
0.3600	30000	0.1439
0.3612	30100	-
0.3624	30200	-
0.3636	30300	-
0.3648	30400	-
0.3660	30500	0.1397
0.3672	30600	-
0.3684	30700	-
0.3696	30800	-
0.3708	30900	-
0.3720	31000	0.1542
0.3732	31100	-
0.3744	31200	-
0.3756	31300	-
0.3768	31400	-
0.3780	31500	0.1448
0.3792	31600	-
0.3804	31700	-
0.3816	31800	-
0.3828	31900	-
0.3840	32000	0.1608
0.3852	32100	-
0.3864	32200	-
0.3876	32300	-
0.3888	32400	-
0.3900	32500	0.1486
0.3912	32600	-
0.3924	32700	-
0.3936	32800	-
0.3948	32900	-
0.3960	33000	0.1274
0.3972	33100	-
0.3984	33200	-
0.3996	33300	-
0.4008	33400	-
0.4020	33500	0.1451
0.4032	33600	-
0.4044	33700	-
0.4056	33800	-
0.4068	33900	-
0.4080	34000	0.1316
0.4092	34100	-
0.4104	34200	-
0.4116	34300	-
0.4128	34400	-
0.4140	34500	0.1306
0.4152	34600	-
0.4164	34700	-
0.4176	34800	-
0.4188	34900	-
0.4200	35000	0.1382
0.4212	35100	-
0.4224	35200	-
0.4236	35300	-
0.4248	35400	-
0.4260	35500	0.1322
0.4272	35600	-
0.4284	35700	-
0.4296	35800	-
0.4308	35900	-
0.4320	36000	0.1617
0.4332	36100	-
0.4344	36200	-
0.4356	36300	-
0.4368	36400	-
0.4380	36500	0.14
0.4392	36600	-
0.4404	36700	-
0.4416	36800	-
0.4428	36900	-
0.4440	37000	0.1321
0.4452	37100	-
0.4464	37200	-
0.4476	37300	-
0.4488	37400	-
0.4500	37500	0.1464
0.4512	37600	-
0.4524	37700	-
0.4536	37800	-
0.4548	37900	-
0.4560	38000	0.1236
0.4572	38100	-
0.4584	38200	-
0.4596	38300	-
0.4608	38400	-
0.4620	38500	0.147
0.4632	38600	-
0.4644	38700	-
0.4656	38800	-
0.4668	38900	-
0.4680	39000	0.1376
0.4692	39100	-
0.4704	39200	-
0.4716	39300	-
0.4728	39400	-
0.4740	39500	0.1342
0.4752	39600	-
0.4764	39700	-
0.4776	39800	-
0.4788	39900	-
0.4800	40000	0.123
0.4812	40100	-
0.4824	40200	-
0.4836	40300	-
0.4848	40400	-
0.4860	40500	0.1312
0.4872	40600	-
0.4884	40700	-
0.4896	40800	-
0.4908	40900	-
0.4920	41000	0.1325
0.4932	41100	-
0.4944	41200	-
0.4956	41300	-
0.4968	41400	-
0.4980	41500	0.1203
0.4992	41600	-
0.5004	41700	-
0.5016	41800	-
0.5028	41900	-
0.5040	42000	0.1258
0.5052	42100	-
0.5064	42200	-
0.5076	42300	-
0.5088	42400	-
0.5100	42500	0.141
0.5112	42600	-
0.5124	42700	-
0.5136	42800	-
0.5148	42900	-
0.5160	43000	0.1473
0.5172	43100	-
0.5184	43200	-
0.5196	43300	-
0.5208	43400	-
0.5220	43500	0.1247
0.5232	43600	-
0.5244	43700	-
0.5256	43800	-
0.5268	43900	-
0.5280	44000	0.1259
0.5292	44100	-
0.5304	44200	-
0.5316	44300	-
0.5328	44400	-
0.5340	44500	0.1372
0.5352	44600	-
0.5364	44700	-
0.5376	44800	-
0.5388	44900	-
0.5400	45000	0.1413
0.5412	45100	-
0.5424	45200	-
0.5436	45300	-
0.5448	45400	-
0.5460	45500	0.1157
0.5472	45600	-
0.5484	45700	-
0.5496	45800	-
0.5508	45900	-
0.5520	46000	0.127
0.5532	46100	-
0.5544	46200	-
0.5556	46300	-
0.5568	46400	-
0.5580	46500	0.1202
0.5592	46600	-
0.5604	46700	-
0.5616	46800	-
0.5628	46900	-
0.5640	47000	0.1199
0.5652	47100	-
0.5664	47200	-
0.5676	47300	-
0.5688	47400	-
0.5700	47500	0.1309
0.5712	47600	-
0.5724	47700	-
0.5736	47800	-
0.5748	47900	-
0.5760	48000	0.1276
0.5772	48100	-
0.5784	48200	-
0.5796	48300	-
0.5808	48400	-
0.5820	48500	0.1278
0.5832	48600	-
0.5844	48700	-
0.5856	48800	-
0.5868	48900	-
0.5880	49000	0.1175
0.5892	49100	-
0.5904	49200	-
0.5916	49300	-
0.5928	49400	-
0.5940	49500	0.1327
0.5952	49600	-
0.5964	49700	-
0.5976	49800	-
0.5988	49900	-
0.6000	50000	0.1109
0.6012	50100	-
0.6024	50200	-
0.6036	50300	-
0.6048	50400	-
0.6060	50500	0.1248
0.6072	50600	-
0.6084	50700	-
0.6096	50800	-
0.6108	50900	-
0.6120	51000	0.1296
0.6132	51100	-
0.6144	51200	-
0.6156	51300	-
0.6168	51400	-
0.6180	51500	0.1323
0.6192	51600	-
0.6204	51700	-
0.6216	51800	-
0.6228	51900	-
0.6240	52000	0.1155
0.6252	52100	-
0.6264	52200	-
0.6276	52300	-
0.6288	52400	-
0.6300	52500	0.1245
0.6312	52600	-
0.6324	52700	-
0.6336	52800	-
0.6348	52900	-
0.6360	53000	0.1238
0.6372	53100	-
0.6384	53200	-
0.6396	53300	-
0.6408	53400	-
0.6420	53500	0.12
0.6432	53600	-
0.6444	53700	-
0.6456	53800	-
0.6468	53900	-
0.6480	54000	0.1116
0.6492	54100	-
0.6504	54200	-
0.6516	54300	-
0.6528	54400	-
0.6540	54500	0.1305
0.6552	54600	-
0.6564	54700	-
0.6576	54800	-
0.6588	54900	-
0.6600	55000	0.1355
0.6612	55100	-
0.6624	55200	-
0.6636	55300	-
0.6648	55400	-
0.6660	55500	0.1139
0.6672	55600	-
0.6684	55700	-
0.6696	55800	-
0.6708	55900	-
0.6720	56000	0.1251
0.6732	56100	-
0.6744	56200	-
0.6756	56300	-
0.6768	56400	-
0.6780	56500	0.1211
0.6792	56600	-
0.6804	56700	-
0.6816	56800	-
0.6828	56900	-
0.6840	57000	0.1123
0.6852	57100	-
0.6864	57200	-
0.6876	57300	-
0.6888	57400	-
0.6900	57500	0.1071
0.6912	57600	-
0.6924	57700	-
0.6936	57800	-
0.6948	57900	-
0.6960	58000	0.112
0.6972	58100	-
0.6984	58200	-
0.6996	58300	-
0.7008	58400	-
0.7020	58500	0.1038
0.7032	58600	-
0.7044	58700	-
0.7056	58800	-
0.7068	58900	-
0.7080	59000	0.1238
0.7092	59100	-
0.7104	59200	-
0.7116	59300	-
0.7128	59400	-
0.7140	59500	0.1001
0.7152	59600	-
0.7164	59700	-
0.7176	59800	-
0.7188	59900	-
0.7200	60000	0.0948
0.7212	60100	-
0.7224	60200	-
0.7236	60300	-
0.7248	60400	-
0.7260	60500	0.1271
0.7272	60600	-
0.7284	60700	-
0.7296	60800	-
0.7308	60900	-
0.7320	61000	0.1117
0.7332	61100	-
0.7344	61200	-
0.7356	61300	-
0.7368	61400	-
0.7380	61500	0.1122
0.7392	61600	-
0.7404	61700	-
0.7416	61800	-
0.7428	61900	-
0.7440	62000	0.0972
0.7452	62100	-
0.7464	62200	-
0.7476	62300	-
0.7488	62400	-
0.7500	62500	0.1135
0.7512	62600	-
0.7524	62700	-
0.7536	62800	-
0.7548	62900	-
0.7560	63000	0.1092
0.7572	63100	-
0.7584	63200	-
0.7596	63300	-
0.7608	63400	-
0.7620	63500	0.1155
0.7632	63600	-
0.7644	63700	-
0.7656	63800	-
0.7668	63900	-
0.7680	64000	0.1065
0.7692	64100	-
0.7704	64200	-
0.7716	64300	-
0.7728	64400	-
0.7740	64500	0.1211
0.7752	64600	-
0.7764	64700	-
0.7776	64800	-
0.7788	64900	-
0.7800	65000	0.116
0.7812	65100	-
0.7824	65200	-
0.7836	65300	-
0.7848	65400	-
0.7860	65500	0.1138
0.7872	65600	-
0.7884	65700	-
0.7896	65800	-
0.7908	65900	-
0.7920	66000	0.1155
0.7932	66100	-
0.7944	66200	-
0.7956	66300	-
0.7968	66400	-
0.7980	66500	0.1059
0.7992	66600	-
0.8004	66700	-
0.8016	66800	-
0.8028	66900	-
0.8040	67000	0.1189
0.8052	67100	-
0.8064	67200	-
0.8076	67300	-
0.8088	67400	-
0.8100	67500	0.1089
0.8112	67600	-
0.8124	67700	-
0.8136	67800	-
0.8148	67900	-
0.8160	68000	0.1016
0.8172	68100	-
0.8184	68200	-
0.8196	68300	-
0.8208	68400	-
0.8220	68500	0.121
0.8232	68600	-
0.8244	68700	-
0.8256	68800	-
0.8268	68900	-
0.8280	69000	0.1185
0.8292	69100	-
0.8304	69200	-
0.8316	69300	-
0.8328	69400	-
0.8340	69500	0.1026
0.8352	69600	-
0.8364	69700	-
0.8376	69800	-
0.8388	69900	-
0.8400	70000	0.1209
0.8412	70100	-
0.8424	70200	-
0.8436	70300	-
0.8448	70400	-
0.8460	70500	0.1103
0.8472	70600	-
0.8484	70700	-
0.8496	70800	-
0.8508	70900	-
0.8520	71000	0.1098
0.8532	71100	-
0.8544	71200	-
0.8556	71300	-
0.8568	71400	-
0.8580	71500	0.1055
0.8592	71600	-
0.8604	71700	-
0.8616	71800	-
0.8628	71900	-
0.8640	72000	0.1045
0.8652	72100	-
0.8664	72200	-
0.8676	72300	-
0.8688	72400	-
0.8700	72500	0.1126
0.8712	72600	-
0.8724	72700	-
0.8736	72800	-
0.8748	72900	-
0.8760	73000	0.1058
0.8772	73100	-
0.8784	73200	-
0.8796	73300	-
0.8808	73400	-
0.8820	73500	0.1138
0.8832	73600	-
0.8844	73700	-
0.8856	73800	-
0.8868	73900	-
0.8880	74000	0.1071
0.8892	74100	-
0.8904	74200	-
0.8916	74300	-
0.8928	74400	-
0.8940	74500	0.1091
0.8952	74600	-
0.8964	74700	-
0.8976	74800	-
0.8988	74900	-
0.9000	75000	0.1143
0.9012	75100	-
0.9024	75200	-

Framework Versions

Python: 3.12.10
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@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

@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}
}

Downloads last month: -

Safetensors

Model size

0.5B params

Tensor type

F32

Model tree for lingtrain/labse-chuvash-2

Base model

sentence-transformers/LaBSE

Finetuned

(72)

this model