This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2. It maps sentences & paragraphs to a 384-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/paraphrase-multilingual-MiniLM-L12-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 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, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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 = [
    '我只想暖和一下。',
    'mi wile kama seli taso.',
    'tomo tawa sina li lon ni.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7361, 0.2725],
#         [0.7361, 1.0000, 0.2417],
#         [0.2725, 0.2417, 1.0000]])

Evaluation

Metrics

Knowledge Distillation

Dataset: eval_data
Evaluated with MSEEvaluator

Metric	Value
negative_mse	-1.9607

Translation

Dataset: eval_data
Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.6918
trg2src_accuracy	0.6346
mean_accuracy	0.6632

Training Details

Training Dataset

Unnamed Dataset

Size: 82,069 training samples
Columns: natural, tok, and label
Approximate statistics based on the first 1000 samples:
natural tok label
type string string list
details
min: 4 tokens
mean: 10.99 tokens
max: 45 tokens
min: 4 tokens
mean: 15.64 tokens
max: 55 tokens
size: 384 elements

Samples:

natural	tok	label
`Я держу руку.`	`mi sewi e luka mi.`	`[-0.17412713170051575, 0.2601699233055115, 0.3189601004123688, 0.009355960413813591, -0.030796436592936516, ...]`
`Я змарыўся ад працы.`	`tan pali mi la mi pilin lape.`	`[0.1258312165737152, 0.173202782869339, 0.16050441563129425, 0.2519824206829071, -0.035661786794662476, ...]`
`Mi bolso necesita ser reparado.`	`poki mi li pakala.`	`[-0.22065182030200958, 0.3290186822414398, -0.006242208182811737, 0.18535998463630676, 0.3087056577205658, ...]`

Loss: MSELoss

Evaluation Dataset

Unnamed Dataset

Size: 4,267 evaluation samples
Columns: natural, tok, and label
Approximate statistics based on the first 1000 samples:
natural tok label
type string string list
details
min: 5 tokens
mean: 11.0 tokens
max: 44 tokens
min: 4 tokens
mean: 15.4 tokens
max: 66 tokens
size: 384 elements

Samples:

natural	tok	label
`Da quanto tempo sei/state in Germania?`	`tenpo pi suli seme la sina lon ma Tosi?`	`[0.43582403659820557, 0.4226286709308624, 0.06436676532030106, -0.38238099217414856, -0.13951840996742249, ...]`
`Habesne difficultatem hac re?`	`ni li ike tawa sina anu seme?`	`[0.22038640081882477, 0.03845325857400894, 0.20817194879055023, 0.08335897326469421, -0.10346948355436325, ...]`
`אני לא הולך להפסיד.`	`mi kama ala anpa.`	`[0.3058338761329651, 0.06292764097452164, 0.019105680286884308, -0.04162227734923363, -0.10258055478334427, ...]`

Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 2e-05
num_train_epochs: 12
warmup_ratio: 0.1
fp16: True
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: 64
per_device_eval_batch_size: 64
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: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 12
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: 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_fused
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	eval_data_negative_mse	eval_data_mean_accuracy
0.0779	100	0.0257	-	-	-
0.1559	200	0.0235	-	-	-
0.2338	300	0.0221	-	-	-
0.3118	400	0.0217	-	-	-
0.3897	500	0.0209	-	-	-
0.4677	600	0.0201	-	-	-
0.5456	700	0.0192	-	-	-
0.6235	800	0.0186	-	-	-
0.7015	900	0.0176	-	-	-
0.7794	1000	0.0171	-	-	-
0.8574	1100	0.0166	-	-	-
0.9353	1200	0.0159	-	-	-
1.0133	1300	0.0154	-	-	-
1.0912	1400	0.015	-	-	-
1.1691	1500	0.0145	-	-	-
1.2471	1600	0.0143	-	-	-
1.3250	1700	0.014	-	-	-
1.4030	1800	0.0139	-	-	-
1.4809	1900	0.0136	-	-	-
1.5588	2000	0.0134	0.0122	-2.3609	0.5603
1.6368	2100	0.0133	-	-	-
1.7147	2200	0.0133	-	-	-
1.7927	2300	0.0132	-	-	-
1.8706	2400	0.0131	-	-	-
1.9486	2500	0.0131	-	-	-
2.0265	2600	0.0129	-	-	-
2.1044	2700	0.0127	-	-	-
2.1824	2800	0.0125	-	-	-
2.2603	2900	0.0125	-	-	-
2.3383	3000	0.0124	-	-	-
2.4162	3100	0.0123	-	-	-
2.4942	3200	0.0122	-	-	-
2.5721	3300	0.0121	-	-	-
2.6500	3400	0.0123	-	-	-
2.7280	3500	0.0122	-	-	-
2.8059	3600	0.0122	-	-	-
2.8839	3700	0.0121	-	-	-
2.9618	3800	0.0122	-	-	-
3.0398	3900	0.012	-	-	-
3.1177	4000	0.0119	0.0110	-2.1275	0.6289
3.1956	4100	0.0118	-	-	-
3.2736	4200	0.0118	-	-	-
3.3515	4300	0.0117	-	-	-
3.4295	4400	0.0117	-	-	-
3.5074	4500	0.0116	-	-	-
3.5853	4600	0.0116	-	-	-
3.6633	4700	0.0117	-	-	-
3.7412	4800	0.0117	-	-	-
3.8192	4900	0.0116	-	-	-
3.8971	5000	0.0117	-	-	-
3.9751	5100	0.0115	-	-	-
4.0530	5200	0.0115	-	-	-
4.1309	5300	0.0113	-	-	-
4.2089	5400	0.0113	-	-	-
4.2868	5500	0.0114	-	-	-
4.3648	5600	0.0114	-	-	-
4.4427	5700	0.0113	-	-	-
4.5207	5800	0.0112	-	-	-
4.5986	5900	0.0113	-	-	-
4.6765	6000	0.0113	0.0107	-2.0522	0.6478
4.7545	6100	0.0112	-	-	-
4.8324	6200	0.0112	-	-	-
4.9104	6300	0.0113	-	-	-
4.9883	6400	0.0113	-	-	-
5.0663	6500	0.011	-	-	-
5.1442	6600	0.011	-	-	-
5.2221	6700	0.011	-	-	-
5.3001	6800	0.0109	-	-	-
5.3780	6900	0.0111	-	-	-
5.4560	7000	0.0111	-	-	-
5.5339	7100	0.011	-	-	-
5.6118	7200	0.0109	-	-	-
5.6898	7300	0.011	-	-	-
5.7677	7400	0.011	-	-	-
5.8457	7500	0.0111	-	-	-
5.9236	7600	0.011	-	-	-
6.0016	7700	0.0112	-	-	-
6.0795	7800	0.0108	-	-	-
6.1574	7900	0.0108	-	-	-
6.2354	8000	0.0107	0.0105	-2.0098	0.6524
6.3133	8100	0.0108	-	-	-
6.3913	8200	0.0108	-	-	-
6.4692	8300	0.0108	-	-	-
6.5472	8400	0.0109	-	-	-
6.6251	8500	0.0108	-	-	-
6.7030	8600	0.0108	-	-	-
6.7810	8700	0.0108	-	-	-
6.8589	8800	0.0107	-	-	-
6.9369	8900	0.0109	-	-	-
7.0148	9000	0.0108	-	-	-
7.0928	9100	0.0106	-	-	-
7.1707	9200	0.0107	-	-	-
7.2486	9300	0.0106	-	-	-
7.3266	9400	0.0105	-	-	-
7.4045	9500	0.0105	-	-	-
7.4825	9600	0.0107	-	-	-
7.5604	9700	0.0107	-	-	-
7.6383	9800	0.0108	-	-	-
7.7163	9900	0.0107	-	-	-
7.7942	10000	0.0106	0.0103	-1.9857	0.6582
7.8722	10100	0.0106	-	-	-
7.9501	10200	0.0106	-	-	-
8.0281	10300	0.0106	-	-	-
8.1060	10400	0.0105	-	-	-
8.1839	10500	0.0103	-	-	-
8.2619	10600	0.0105	-	-	-
8.3398	10700	0.0105	-	-	-
8.4178	10800	0.0105	-	-	-
8.4957	10900	0.0105	-	-	-
8.5737	11000	0.0105	-	-	-
8.6516	11100	0.0105	-	-	-
8.7295	11200	0.0106	-	-	-
8.8075	11300	0.0104	-	-	-
8.8854	11400	0.0106	-	-	-
8.9634	11500	0.0106	-	-	-
9.0413	11600	0.0105	-	-	-
9.1193	11700	0.0103	-	-	-
9.1972	11800	0.0102	-	-	-
9.2751	11900	0.0105	-	-	-
9.3531	12000	0.0104	0.0103	-1.9715	0.6624
9.4310	12100	0.0104	-	-	-
9.5090	12200	0.0104	-	-	-
9.5869	12300	0.0105	-	-	-
9.6648	12400	0.0104	-	-	-
9.7428	12500	0.0103	-	-	-
9.8207	12600	0.0105	-	-	-
9.8987	12700	0.0105	-	-	-
9.9766	12800	0.0104	-	-	-
10.0546	12900	0.0103	-	-	-
10.1325	13000	0.0103	-	-	-
10.2104	13100	0.0102	-	-	-
10.2884	13200	0.0103	-	-	-
10.3663	13300	0.0105	-	-	-
10.4443	13400	0.0103	-	-	-
10.5222	13500	0.0104	-	-	-
10.6002	13600	0.0104	-	-	-
10.6781	13700	0.0103	-	-	-
10.7560	13800	0.0103	-	-	-
10.8340	13900	0.0103	-	-	-
10.9119	14000	0.0103	0.0102	-1.9607	0.6632
10.9899	14100	0.0102	-	-	-
11.0678	14200	0.0103	-	-	-
11.1458	14300	0.0103	-	-	-
11.2237	14400	0.0103	-	-	-
11.3016	14500	0.0102	-	-	-
11.3796	14600	0.0104	-	-	-
11.4575	14700	0.0103	-	-	-
11.5355	14800	0.0103	-	-	-
11.6134	14900	0.0103	-	-	-
11.6913	15000	0.0102	-	-	-
11.7693	15100	0.0102	-	-	-
11.8472	15200	0.0103	-	-	-
11.9252	15300	0.0102	-	-	-
12.0	15396	-	0.0102	-1.9607	0.6632

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.13.7
Sentence Transformers: 5.3.0
Transformers: 4.55.2
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.7.0
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",
}

MSELoss

bibtex

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}

	natural	tok	label
type	string	string	list
details	min: 4 tokens mean: 10.99 tokens max: 45 tokens	min: 4 tokens mean: 15.64 tokens max: 55 tokens	size: 384 elements

NetherQuartz/paraphrase-MiniLM-tokipona

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Knowledge Distillation

Translation

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MSELoss

NetherQuartz/
paraphrase-MiniLM-tokipona