This is a sentence-transformers model finetuned from sergeyzh/BERTA on the duplicates-checker-finetuning-preview 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: sergeyzh/BERTA
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- duplicates-checker-finetuning-preview

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': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
  (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("sentence_transformers_model_id")
# Run inference
queries = [
    "USSD-\u043a\u043e\u043c\u0430\u043d\u0434\u0430 \u0434\u043b\u044f \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438 \u0431\u0430\u043b\u0430\u043d\u0441\u0430 \u0421\u0431\u0435\u0440\u041c\u043e\u0431\u0430\u0439\u043b - *100#.",
]
documents = [
    'Чтобы узнать баланс СберМобайл, наберите *100#.',
    'statement_statement',
    'СберМобайл',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.9729, 0.2598, 0.0023]])

Evaluation

Metrics

Binary Classification

Datasets: binary-sts-validation and binary-sts-test
Evaluated with BinaryClassificationEvaluator

Metric	binary-sts-validation	binary-sts-test
cosine_accuracy	0.9156	0.9065
cosine_accuracy_threshold	0.6416	0.684
cosine_f1	0.9197	0.9095
cosine_f1_threshold	0.6109	0.6464
cosine_precision	0.8819	0.881
cosine_recall	0.9609	0.94
cosine_ap	0.9155	0.9208
cosine_mcc	0.8345	0.8148

Training Details

Training Dataset

duplicates-checker-finetuning-preview

Dataset: duplicates-checker-finetuning-preview
Size: 6,921 training samples
Columns: sentence1, sentence2, label, task_type, product, and stratify_col

Approximate statistics based on the first 1000 samples:

	sentence1	sentence2	label	task_type	product	stratify_col
type	string	string	int	string	string	string
details	min: 5 tokens mean: 19.33 tokens max: 45 tokens	min: 6 tokens mean: 20.3 tokens max: 44 tokens	0: ~49.60% 1: ~50.40%	min: 5 tokens mean: 5.0 tokens max: 5 tokens	min: 5 tokens mean: 10.06 tokens max: 17 tokens	min: 11 tokens mean: 16.06 tokens max: 23 tokens

Samples:

sentence1	sentence2	label	task_type	product	stratify_col
`Облигации Федерального Займа выпускает Министерство финансов РФ, а не Центральный Банк.`	`Облигации Федерального Займа выпускает Министерство финансов РФ, а не СберБанк.`	`0`	`correction_correction`	`Облигации`	`0_correction_correction_Облигации`
`Льгота на долгосрочное владение паями ОПИФ действует при владении более 3 лет, а не 1 года.`	`Лимит дохода для ЛДВ по ОПИФ составляет 3 млн рублей за каждый год владения, а не 1 млн.`	`0`	`correction_correction`	`Открытый паевой инвестиционный фонд`	`0_correction_correction_Открытый паевой инвестиционный фонд`
`Продажа паев ЗПИФ на бирже не требует поиска покупателя, в отличие от продажи по договору купли-продажи.`	`Потенциальный доход от фонда Современный 8 включает рентный доход и доход от роста стоимости, а не только рентный.`	`0`	`correction_correction`	`Закрытый паевой инвестиционный фонд`	`0_correction_correction_Закрытый паевой инвестиционный фонд`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Evaluation Dataset

duplicates-checker-finetuning-preview

Dataset: duplicates-checker-finetuning-preview
Size: 865 evaluation samples
Columns: sentence1, sentence2, label, task_type, product, and stratify_col

Approximate statistics based on the first 865 samples:

	sentence1	sentence2	label	task_type	product	stratify_col
type	string	string	int	string	string	string
details	min: 7 tokens mean: 19.34 tokens max: 37 tokens	min: 8 tokens mean: 20.46 tokens max: 39 tokens	0: ~49.71% 1: ~50.29%	min: 5 tokens mean: 5.0 tokens max: 5 tokens	min: 5 tokens mean: 9.94 tokens max: 17 tokens	min: 11 tokens mean: 15.94 tokens max: 23 tokens

Samples:

sentence1	sentence2	label	task_type	product	stratify_col
`Какой тариф Сбера подходит для начинающих инвесторов на ИИС-3?`	`Какой тарифный план Сбера рекомендован для новичков, использующих ИИС-3?`	`1`	`question_question`	`Индивидуальный инвестиционный счёт`	`1_question_question_Индивидуальный инвестиционный счёт`
`Какие типы кредитных карт Сбера вы предлагаете, и какие преимущества у каждой из них?`	`Расскажите о видах Кредитных СберКарт и их плюсах.`	`1`	`question_question`	`Кредитная СберКарта`	`1_question_question_Кредитная СберКарта`
`При отсутствии трудовой книжки стаж подтверждается справками из архива.`	`При отсутствии трудовой книжки стаж подтверждается устными показаниями свидетелей.`	`0`	`statement_statement`	`Перевод пенсии`	`0_statement_statement_Перевод пенсии`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
learning_rate: 1.2006506775681832e-05
weight_decay: 0.04243902303817388
num_train_epochs: 50
warmup_ratio: 0.27192485622024914
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 1.2006506775681832e-05
weight_decay: 0.04243902303817388
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 50
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.27192485622024914
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: 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
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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	binary-sts-validation_cosine_ap	binary-sts-test_cosine_ap
0.2304	50	0.2427	-	-	-
0.4608	100	0.2435	0.2850	0.8052	-
0.6912	150	0.2318	-	-	-
0.9217	200	0.2341	0.2740	0.8085	-
1.1521	250	0.2303	-	-	-
1.3825	300	0.2277	0.2554	0.8147	-
1.6129	350	0.2239	-	-	-
1.8433	400	0.2048	0.2293	0.8187	-
2.0737	450	0.1955	-	-	-
2.3041	500	0.1913	0.2015	0.8221	-
2.5346	550	0.1878	-	-	-
2.7650	600	0.1743	0.1771	0.8230	-
2.9954	650	0.1714	-	-	-
3.2258	700	0.1679	0.1575	0.8183	-
3.4562	750	0.153	-	-	-
3.6866	800	0.1437	0.1449	0.8182	-
3.9171	850	0.1421	-	-	-
4.1475	900	0.1318	0.1371	0.8195	-
4.3779	950	0.1345	-	-	-
4.6083	1000	0.1265	0.1307	0.8263	-
4.8387	1050	0.1282	-	-	-
5.0691	1100	0.1245	0.1258	0.8399	-
5.2995	1150	0.1132	-	-	-
5.5300	1200	0.1142	0.1208	0.8432	-
5.7604	1250	0.1138	-	-	-
5.9908	1300	0.1119	0.1162	0.8512	-
6.2212	1350	0.1034	-	-	-
6.4516	1400	0.1034	0.1122	0.8584	-
6.6820	1450	0.1026	-	-	-
6.9124	1500	0.0985	0.1083	0.8605	-
7.1429	1550	0.0905	-	-	-
7.3733	1600	0.0912	0.1049	0.8690	-
7.6037	1650	0.0869	-	-	-
7.8341	1700	0.0876	0.1018	0.8716	-
8.0645	1750	0.0884	-	-	-
8.2949	1800	0.0833	0.0980	0.8801	-
8.5253	1850	0.0734	-	-	-
8.7558	1900	0.0764	0.0956	0.8809	-
8.9862	1950	0.0786	-	-	-
9.2166	2000	0.0728	0.0928	0.8831	-
9.4470	2050	0.0683	-	-	-
9.6774	2100	0.0674	0.0907	0.8880	-
9.9078	2150	0.0675	-	-	-
10.1382	2200	0.0604	0.0886	0.8902	-
10.3687	2250	0.0611	-	-	-
10.5991	2300	0.0584	0.0864	0.8956	-
10.8295	2350	0.0588	-	-	-
11.0599	2400	0.0624	0.0847	0.9026	-
11.2903	2450	0.0505	-	-	-
11.5207	2500	0.0513	0.0845	0.8974	-
11.7512	2550	0.0556	-	-	-
11.9816	2600	0.053	0.0813	0.9021	-
12.2120	2650	0.0445	-	-	-
12.4424	2700	0.0471	0.0812	0.9044	-
12.6728	2750	0.0446	-	-	-
12.9032	2800	0.046	0.0804	0.9006	-
13.1336	2850	0.0435	-	-	-
13.3641	2900	0.0367	0.0800	0.9054	-
13.5945	2950	0.0396	-	-	-
13.8249	3000	0.0425	0.0796	0.9036	-
14.0553	3050	0.0398	-	-	-
14.2857	3100	0.0299	0.0772	0.9109	-
14.5161	3150	0.0357	-	-	-
14.7465	3200	0.0376	0.0762	0.9085	-
14.9770	3250	0.0334	-	-	-
15.2074	3300	0.0307	0.0765	0.9099	-
15.4378	3350	0.0283	-	-	-
15.6682	3400	0.0314	0.0765	0.9134	-
15.8986	3450	0.0335	-	-	-
16.1290	3500	0.0265	0.0749	0.9114	-
16.3594	3550	0.0233	-	-	-
16.5899	3600	0.0254	0.0754	0.9174	-
16.8203	3650	0.0288	-	-	-
17.0507	3700	0.0261	0.0743	0.9196	-
17.2811	3750	0.0238	-	-	-
17.5115	3800	0.0222	0.0748	0.9171	-
17.7419	3850	0.025	-	-	-
17.9724	3900	0.0252	0.0743	0.9181	-
18.2028	3950	0.0197	-	-	-
18.4332	4000	0.019	0.0736	0.9195	-
18.6636	4050	0.021	-	-	-
18.8940	4100	0.0222	0.0731	0.9229	-
19.1244	4150	0.0202	-	-	-
19.3548	4200	0.0211	0.0740	0.9191	-
19.5853	4250	0.0169	-	-	-
19.8157	4300	0.0174	0.0745	0.9200	-
20.0461	4350	0.0177	-	-	-
20.2765	4400	0.0168	0.0736	0.9155	-
20.5069	4450	0.0182	-	-	-
20.7373	4500	0.0151	0.0740	0.9154	-
20.9677	4550	0.0163	-	-	-
21.1982	4600	0.0146	0.0740	0.9180	-
21.4286	4650	0.0128	-	-	-
21.6590	4700	0.0154	0.0734	0.9196	-
21.8894	4750	0.0149	-	-	-
22.1198	4800	0.0147	0.0743	0.9175	-
22.3502	4850	0.0132	-	-	-
22.5806	4900	0.0142	0.0745	0.9189	-
22.8111	4950	0.0152	-	-	-
23.0415	5000	0.013	0.0734	0.9261	-
23.2719	5050	0.0118	-	-	-
23.5023	5100	0.0119	0.0739	0.9265	-
23.7327	5150	0.012	-	-	-
23.9631	5200	0.0123	0.0738	0.9246	-
24.1935	5250	0.0131	-	-	-
24.4240	5300	0.0115	0.0725	0.9264	-
24.6544	5350	0.0116	-	-	-
24.8848	5400	0.011	0.0731	0.9258	-
25.1152	5450	0.0108	-	-	-
25.3456	5500	0.0112	0.0728	0.9276	-
25.5760	5550	0.0119	-	-	-
25.8065	5600	0.0084	0.0732	0.9267	-
26.0369	5650	0.0108	-	-	-
26.2673	5700	0.0105	0.0734	0.9296	-
26.4977	5750	0.0083	-	-	-
26.7281	5800	0.0102	0.0733	0.9305	-
26.9585	5850	0.0102	-	-	-
27.1889	5900	0.0074	0.0731	0.9279	-
27.4194	5950	0.0086	-	-	-
27.6498	6000	0.0091	0.0741	0.9253	-
27.8802	6050	0.0105	-	-	-
28.1106	6100	0.0098	0.0738	0.9277	-
28.3410	6150	0.0079	-	-	-
28.5714	6200	0.009	0.0723	0.9319	-
28.8018	6250	0.0082	-	-	-
29.0323	6300	0.009	0.0727	0.9302	-
29.2627	6350	0.0092	-	-	-
29.4931	6400	0.0078	0.0731	0.9348	-
29.7235	6450	0.0079	-	-	-
29.9539	6500	0.0091	0.0734	0.9361	-
30.1843	6550	0.0091	-	-	-
30.4147	6600	0.0069	0.0735	0.9380	-
30.6452	6650	0.0075	-	-	-
30.8756	6700	0.0075	0.0731	0.9384	-
31.1060	6750	0.007	-	-	-
31.3364	6800	0.0064	0.0739	0.9365	-
31.5668	6850	0.0083	-	-	-
31.7972	6900	0.0076	0.0732	0.9373	-
32.0276	6950	0.0073	-	-	-
32.2581	7000	0.0075	0.0740	0.9403	-
32.4885	7050	0.0068	-	-	-
32.7189	7100	0.0083	0.0730	0.9399	-
32.9493	7150	0.0053	-	-	-
33.1797	7200	0.0076	0.0725	0.9387	-
33.4101	7250	0.0055	-	-	-
33.6406	7300	0.007	0.0728	0.9396	-
33.8710	7350	0.0071	-	-	-
34.1014	7400	0.0058	0.0736	0.9396	-
34.3318	7450	0.0063	-	-	-
34.5622	7500	0.0066	0.0735	0.9396	-
34.7926	7550	0.0068	-	-	-
35.0230	7600	0.0056	0.0738	0.9388	-
35.2535	7650	0.0074	-	-	-
35.4839	7700	0.0061	0.0738	0.9392	-
35.7143	7750	0.0062	-	-	-
35.9447	7800	0.0054	0.0733	0.9396	-
36.1751	7850	0.0058	-	-	-
36.4055	7900	0.0061	0.0733	0.9397	-
36.6359	7950	0.0052	-	-	-
36.8664	8000	0.0062	0.0731	0.9396	-
37.0968	8050	0.0051	-	-	-
37.3272	8100	0.0066	0.0733	0.9395	-
37.5576	8150	0.0049	-	-	-
37.7880	8200	0.0051	0.0727	0.9391	-
38.0184	8250	0.0046	-	-	-
38.2488	8300	0.0056	0.0732	0.9383	-
38.4793	8350	0.0039	-	-	-
38.7097	8400	0.0047	0.0734	0.9389	-
38.9401	8450	0.0057	-	-	-
39.1705	8500	0.0064	0.0740	0.9402	-
39.4009	8550	0.0049	-	-	-
39.6313	8600	0.0057	0.0742	0.9409	-
39.8618	8650	0.0049	-	-	-
40.0922	8700	0.0057	0.0740	0.9414	-
40.3226	8750	0.0056	-	-	-
40.5530	8800	0.0043	0.0742	0.9408	-
40.7834	8850	0.0046	-	-	-
41.0138	8900	0.0051	0.0740	0.9409	-
41.2442	8950	0.0043	-	-	-
41.4747	9000	0.0046	0.0742	0.9410	-
41.7051	9050	0.0059	-	-	-
41.9355	9100	0.0044	0.0739	0.9409	-
42.1659	9150	0.0049	-	-	-
42.3963	9200	0.0048	0.0738	0.9418	-
42.6267	9250	0.0047	-	-	-
42.8571	9300	0.0036	0.0744	0.9416	-
43.0876	9350	0.0041	-	-	-
43.3180	9400	0.0049	0.0745	0.9416	-
43.5484	9450	0.0047	-	-	-
43.7788	9500	0.0048	0.0742	0.9415	-
44.0092	9550	0.0038	-	-	-
44.2396	9600	0.005	0.0741	0.9416	-
44.4700	9650	0.0045	-	-	-
44.7005	9700	0.004	0.0743	0.9416	-
44.9309	9750	0.0038	-	-	-
45.1613	9800	0.0042	0.0739	0.9416	-
45.3917	9850	0.005	-	-	-
45.6221	9900	0.0051	0.0742	0.9418	-
45.8525	9950	0.004	-	-	-
46.0829	10000	0.004	0.0744	0.9418	-
46.3134	10050	0.0035	-	-	-
46.5438	10100	0.0041	0.0743	0.9420	-
46.7742	10150	0.0041	-	-	-
47.0046	10200	0.0063	0.0744	0.9421	-
47.2350	10250	0.0039	-	-	-
47.4654	10300	0.0044	0.0744	0.9421	-
47.6959	10350	0.0043	-	-	-
47.9263	10400	0.0034	0.0744	0.9423	-
48.1567	10450	0.0048	-	-	-
48.3871	10500	0.0033	0.0744	0.9424	-
48.6175	10550	0.0048	-	-	-
48.8479	10600	0.0041	0.0745	0.9423	-
49.0783	10650	0.0035	-	-	-
49.3088	10700	0.0036	0.0744	0.9423	-
49.5392	10750	0.0037	-	-	-
49.7696	10800	0.0051	0.0744	0.9423	-
50.0	10850	0.0036	-	-	-
-1	-1	-	-	0.9155	0.9208

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.7
Sentence Transformers: 5.0.0
Transformers: 4.54.1
PyTorch: 2.5.1
Accelerate: 1.9.0
Datasets: 4.0.0
Tokenizers: 0.21.4

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",
}

galkinv42/sergeyzh_BERTA-final-finetuned-duplicates

SentenceTransformer based on sergeyzh/BERTA

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Binary Classification

Training Details

Training Dataset

duplicates-checker-finetuning-preview

Evaluation Dataset

duplicates-checker-finetuning-preview

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

galkinv42/
sergeyzh_BERTA-final-finetuned-duplicates