This is a sentence-transformers model finetuned from gerulata/slovakbert. 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: gerulata/slovakbert
Maximum Sequence Length: 512 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': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
  (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})
)

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("mrshu/sturovec-base-sk-v0")
# Run inference
sentences = [
    'Veľká časť malých detí na Ukrajine trpí rôznymi zdravotnými problémami, ktoré boli doteraz pravdepodobne nediagnostikované, no odhaľujú sa v súvislosti s černobyľskou katastrofou. To by mohlo poukazovať na slabý systém zdravotnej starostlivosti a drsné životné podmienky.',
    'Černobyľská katastrofa mala dôsledky mimo bývalého ZSSR.',
    'Blair patrí k anglikánskej cirkvi.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.5042, -0.1152],
#         [ 0.5042,  1.0000, -0.1069],
#         [-0.1152, -0.1069,  1.0000]])

Evaluation

Metrics

Semantic Similarity

Dataset: validation_sts
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8347
spearman_cosine	0.8298

Binary Classification

Datasets: validation_nli and validation_rte
Evaluated with BinaryClassificationEvaluator

Metric	validation_nli	validation_rte
cosine_accuracy	0.6663	0.5235
cosine_accuracy_threshold	0.9708	0.9567
cosine_f1	0.4995	0.6388
cosine_f1_threshold	-0.0148	0.2217
cosine_precision	0.3331	0.471
cosine_recall	0.9988	0.9924
cosine_ap	0.2731	0.3712
cosine_mcc	-0.0283	-0.0635

Multi Task Dev

Evaluated with slovak_embeddings_v1.train.MultiTaskDevEvaluator

Metric	Value
validation_sts_pearson_cosine	0.8347
validation_sts_spearman_cosine	0.8298
validation_nli_cosine_accuracy	0.6663
validation_nli_cosine_accuracy_threshold	0.9708
validation_nli_cosine_f1	0.4995
validation_nli_cosine_f1_threshold	-0.0148
validation_nli_cosine_precision	0.3331
validation_nli_cosine_recall	0.9988
validation_nli_cosine_ap	0.2731
validation_nli_cosine_mcc	-0.0283
validation_rte_cosine_accuracy	0.5235
validation_rte_cosine_accuracy_threshold	0.9567
validation_rte_cosine_f1	0.6388
validation_rte_cosine_f1_threshold	0.2217
validation_rte_cosine_precision	0.471
validation_rte_cosine_recall	0.9924
validation_rte_cosine_ap	0.3712
validation_rte_cosine_mcc	-0.0635
validation_dev_overall	0.4914

Training Details

Training Datasets

Unnamed Dataset

Size: 5,604 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: 5 tokens
mean: 15.61 tokens
max: 63 tokens
min: 5 tokens
mean: 15.51 tokens
max: 51 tokens
min: 0.0
mean: 0.52
max: 1.0

Samples:

sentence_0	sentence_1	label
`Počet obetí útoku na políciu v Nigérii stúpol na 30`	`Počet obetí útoku na autobus v Keni stúpol na šesť`	`0.27999999523162844`
`Očakáva sa, že 39-ročná Terri Schiavo zomrie niekedy v priebehu nasledujúcich dvoch týždňov v hospici v oblasti Tampa, kde strávila niekoľko posledných rokov.`	`39-ročná Terri Schiavo podstúpila zákrok v hospici v oblasti Tampa Bay, kde žije už niekoľko rokov, uviedol jej otec Bob Schindler.`	`0.3599999904632568`
`Žena drží psa, zatiaľ čo iný pes stojí neďaleko na poli.`	`Pani drží jedného psa, zatiaľ čo druhý pes sa hrá na dvore.`	`0.6800000190734863`

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

Unnamed Dataset

Size: 130,900 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 int
details
min: 4 tokens
mean: 28.14 tokens
max: 512 tokens
min: 5 tokens
mean: 14.94 tokens
max: 45 tokens
1: 100.00%

Samples:

sentence_0	sentence_1	label
`Špecializovaný obchod s oblečením a um som bol manažér na čiastočný úväzok v obchode s kartami a darčekmi, rovnako ako vyučovanie v modelingovej agentúre a modelovanie tak`	`Užila som si čas , keď som učila v modelingovej agentúre .`	`1`
`Parcellsovi hráči a asistenti ho nasledujú verne z mesta do mesta.`	`Parcells urobil veľa práce, aby si získal ich vernosť.`	`1`
`Nie je príliš náročné na mozog`	`Som celkom šikovný, vieš.`	`1`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Unnamed Dataset

Size: 1,241 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 int
details
min: 9 tokens
mean: 59.58 tokens
max: 268 tokens
min: 6 tokens
mean: 15.31 tokens
max: 59 tokens
1: 100.00%

Samples:

sentence_0	sentence_1	label
`Prieskumná kozmická loď NASA Saturn, Cassini, objavila atmosféru okolo mesiaca Enceladus. Toto je prvý takýto objav sondy Cassini, okrem Titanu, o prítomnosti atmosféry okolo mesiaca Saturna.`	`Titan je pätnástym zo známych satelitov Saturnu.`	`1`
Polícia bola privolaná do domu Highgrove House, ktorý patrí princovi Charlesovi a jeho manželke Camille, v utorok večer po tom, čo polícia dostala telefonát, že na pozemku sa nachádza narušiteľ. "Bol zadržaný v areáli a nemal prístup do žiadnych budov," uviedol hovorca polície Tony Rymer. Hovorca usadlosti odmietol médiám komentovať, že je to „vec na políciu“ a že akékoľvek pripomienky k narušeniu by sa mali podávať prostredníctvom nich. Podľa hovorkyne gloucesterskej polície bol muž z anglického Bristolu zatknutý po tom, čo sa okolo pozemku oháňal vidlami. Meno 55-ročného muža nezverejnili, polícia ho však obvinila z prečinu porušovania domovej slobody. Po zložení kaucie ho neskôr prepustili. Camilla a Charles, ktorí boli v tom čase prítomní a spali na sídlisku, neboli zranení.	`Princ Charles vlastní majetok v Bristole.`	`1`
ISLAMABAD, Pakistan - Nepokojné prímerie medzi pakistanskou vládou a militantmi Talibanu v údolí Svát sa v pondelok javilo čoraz krehkejšie, keď vládne sily už druhý deň zaútočili na militantov v susednom okrese, čo spôsobilo, že hlavný vyjednávač Talibanu prerušil rozhovory. Maulana Sufi Muhammad, protalibanský duchovný, ktorý vyjednával mierové rozhovory medzi vládou a Talibanom v Svát, v pondelok zastavil rozhovory na protest proti vojenskej operácii v okrese Lower Dir západne od Svát, uviedol jeho hovorca.	`Maulana Sufi Muhammad je šéfom Tehrik Nifaz Shariat-e-Muhammadi.`	`1`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 20
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: 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: 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: 20
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
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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: no
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: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	validation_sts_spearman_cosine	validation_nli_cosine_ap	validation_rte_cosine_ap	validation_dev_overall
0.3333	39	-	0.7072	0.2996	0.4315	0.4794
0.6667	78	-	0.7111	0.2988	0.4297	0.4799
1.0	117	-	0.7182	0.2968	0.4279	0.4810
1.3333	156	-	0.7272	0.2940	0.4256	0.4823
1.6667	195	-	0.7371	0.2908	0.4195	0.4825
2.0	234	-	0.7460	0.2862	0.4106	0.4809
2.3333	273	-	0.7457	0.2824	0.4026	0.4769
2.6667	312	-	0.7377	0.2822	0.3962	0.4721
3.0	351	-	0.7415	0.2815	0.3943	0.4724
3.3333	390	-	0.7471	0.2809	0.3916	0.4732
3.6667	429	-	0.7522	0.2800	0.3903	0.4742
4.0	468	-	0.7542	0.2796	0.3888	0.4742
4.2735	500	1.2693	-	-	-	-
4.3333	507	-	0.7587	0.2788	0.3887	0.4754
4.6667	546	-	0.7613	0.2780	0.3879	0.4757
5.0	585	-	0.7642	0.2777	0.3867	0.4762
5.3333	624	-	0.7673	0.2769	0.3865	0.4769
5.6667	663	-	0.7674	0.2781	0.3861	0.4772
6.0	702	-	0.7731	0.2769	0.3845	0.4782
6.3333	741	-	0.7776	0.2764	0.3853	0.4797
6.6667	780	-	0.7799	0.2758	0.3843	0.4800
7.0	819	-	0.7825	0.2762	0.3842	0.4810
7.3333	858	-	0.7856	0.2756	0.3830	0.4814
7.6667	897	-	0.7866	0.2754	0.3824	0.4814
8.0	936	-	0.7913	0.2748	0.3803	0.4821
8.3333	975	-	0.7915	0.2746	0.3803	0.4821
8.5470	1000	0.4279	-	-	-	-
8.6667	1014	-	0.7925	0.2746	0.3789	0.4820
9.0	1053	-	0.7959	0.2739	0.3803	0.4834
9.3333	1092	-	0.7974	0.2739	0.3762	0.4825
9.6667	1131	-	0.7980	0.2740	0.3796	0.4839
10.0	1170	-	0.8002	0.2738	0.3800	0.4847
10.3333	1209	-	0.7971	0.2743	0.3770	0.4828
10.6667	1248	-	0.8002	0.2741	0.3760	0.4835
11.0	1287	-	0.8026	0.2737	0.3763	0.4842
11.3333	1326	-	0.8017	0.2740	0.3744	0.4834
11.6667	1365	-	0.8037	0.2741	0.3730	0.4836
12.0	1404	-	0.8074	0.2737	0.3729	0.4847
12.3333	1443	-	0.8062	0.2736	0.3747	0.4848
12.6667	1482	-	0.8094	0.2735	0.3732	0.4854
12.8205	1500	0.2922	-	-	-	-
13.0	1521	-	0.8102	0.2739	0.3706	0.4849
13.3333	1560	-	0.8148	0.2723	0.3739	0.4870
13.6667	1599	-	0.8136	0.2726	0.3729	0.4864
14.0	1638	-	0.8140	0.2740	0.3688	0.4856
14.3333	1677	-	0.8120	0.2738	0.3699	0.4852
14.6667	1716	-	0.8153	0.2733	0.3693	0.4859
15.0	1755	-	0.8211	0.2726	0.3692	0.4876
15.3333	1794	-	0.8212	0.2726	0.3711	0.4883
15.6667	1833	-	0.8189	0.2740	0.3711	0.4880
16.0	1872	-	0.8224	0.2736	0.3696	0.4885
16.3333	1911	-	0.8234	0.2726	0.3692	0.4884
16.6667	1950	-	0.8248	0.2733	0.3677	0.4886
17.0	1989	-	0.8276	0.2728	0.3662	0.4889
17.0940	2000	0.2114	-	-	-	-
17.3333	2028	-	0.8264	0.2710	0.3714	0.4896
17.6667	2067	-	0.8283	0.2713	0.3721	0.4906
18.0	2106	-	0.8269	0.2724	0.3699	0.4897
18.3333	2145	-	0.8291	0.2723	0.3718	0.4911
18.6667	2184	-	0.8302	0.2720	0.3719	0.4914
19.0	2223	-	0.8298	0.2731	0.3712	0.4914

Framework Versions

Python: 3.13.0
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.1+cu128
Accelerate: 1.12.0
Datasets: 4.4.1
Tokenizers: 0.22.1

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

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	label
type	string	string	float
details	min: 5 tokens mean: 15.61 tokens max: 63 tokens	min: 5 tokens mean: 15.51 tokens max: 51 tokens	min: 0.0 mean: 0.52 max: 1.0