This is a sentence-transformers model finetuned from ai-forever/ruRoberta-large. 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: ai-forever/ruRoberta-large
Maximum Sequence Length: 514 tokens
Output Dimensionality: 1024 tokens
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': 514, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'НЕТ ДО 20.04!!!!!!!! 12.01.16  Аллергокомпонент f77 - бета-лактоглобулин nBos d 5, IgE (ImmunoCAP)',
    'Панель аллергенов животных № 70 IgE (эпителий морской свинки, эпителий кролика, хомяк, крыса, мышь),',
    'Ультразвуковое исследование плода',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 19,383 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: 5 tokens
mean: 30.0 tokens
max: 121 tokens
min: 5 tokens
mean: 30.73 tokens
max: 105 tokens

Samples:

sentence_0	sentence_1
`Ингибитор VIII фактора`	`Исследование уровня антигена фактора Виллебранда`
`13.01.02 Антитела к экстрагируемому нуклеарному АГ (ЭНА/ENA-скрин), сыворотка крови`	`Антитела к экстрагируемому ядерному антигену, кач.`
`Нет 12.4.092 Аллерген f203 - фисташковые орехи, IgE`	`Панель аллергенов деревьев № 2 IgE (клен ясенелистный, тополь, вяз, дуб, пекан),`

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 4
per_device_eval_batch_size: 4
num_train_epochs: 11
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
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
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: 11
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
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss
0.1032	500	0.7937
0.2064	1000	0.5179
0.3095	1500	0.5271
0.4127	2000	0.5696
0.5159	2500	0.5232
0.6191	3000	0.6401
0.7222	3500	0.6337
0.8254	4000	0.9436
0.9286	4500	1.3872
1.0318	5000	1.3834
1.1350	5500	0.9831
1.2381	6000	1.0122
1.3413	6500	1.3708
1.4445	7000	1.3794
1.5477	7500	1.3784
1.6508	8000	1.3856
1.7540	8500	1.3809
1.8572	9000	1.3776
1.9604	9500	1.0041
2.0636	10000	0.8559
2.1667	10500	0.8531
2.2699	11000	0.8446
2.3731	11500	0.8487
2.4763	12000	1.0807
2.5794	12500	1.3792
2.6826	13000	1.3923
2.7858	13500	1.3787
2.8890	14000	1.3803
2.9922	14500	1.3641
3.0953	15000	1.3725
3.1985	15500	1.3624
3.3017	16000	1.3659
3.4049	16500	1.3609
3.5080	17000	1.3496
3.6112	17500	1.3639
3.7144	18000	1.3487
3.8176	18500	1.3463
3.9208	19000	1.336
4.0239	19500	1.3451
4.1271	20000	1.3363
4.2303	20500	1.3411
4.3335	21000	1.3376
4.4366	21500	1.3294
4.5398	22000	1.3281
4.6430	22500	1.3323
4.7462	23000	1.3411
4.8494	23500	1.3162
4.9525	24000	1.3204
5.0557	24500	1.324
5.1589	25000	1.3253
5.2621	25500	1.3283
5.3652	26000	1.3298
5.4684	26500	1.3144
5.5716	27000	1.3162
5.6748	27500	1.3148
5.7780	28000	1.3254
5.8811	28500	1.319
5.9843	29000	1.3134
6.0875	29500	1.3184
6.1907	30000	1.3049
6.2939	30500	1.3167
6.3970	31000	1.3192
6.5002	31500	1.2926
6.6034	32000	1.3035
6.7066	32500	1.3117
6.8097	33000	1.3093
6.9129	33500	1.278
7.0161	34000	1.3143
7.1193	34500	1.3144
7.2225	35000	1.304
7.3256	35500	1.3066
7.4288	36000	1.2916
7.5320	36500	1.2943
7.6352	37000	1.2883
7.7383	37500	1.3014
7.8415	38000	1.3005
7.9447	38500	1.2699
8.0479	39000	1.3042
8.1511	39500	1.289
8.2542	40000	1.3012
8.3574	40500	1.3017
8.4606	41000	1.272
8.5638	41500	1.2939
8.6669	42000	1.2764
8.7701	42500	1.2908
8.8733	43000	1.2619
8.9765	43500	1.2791
9.0797	44000	1.2722
9.1828	44500	1.278
9.2860	45000	1.2911
9.3892	45500	1.2791
9.4924	46000	1.2791
9.5955	46500	1.2782
9.6987	47000	1.2789
9.8019	47500	1.2858
9.9051	48000	1.2601
10.0083	48500	1.29
10.1114	49000	1.276
10.2146	49500	1.2801
10.3178	50000	1.2853
10.4210	50500	1.2655
10.5241	51000	1.271
10.6273	51500	1.2633
10.7305	52000	1.2565
10.8337	52500	1.2755
10.9369	53000	1.2567

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.31.0
Datasets: 2.20.0
Tokenizers: 0.19.1

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