NetherQuartz/LaBSE-tokipona

SentenceTransformer based on sentence-transformers/LaBSE

This is a sentence-transformers model finetuned from sentence-transformers/LaBSE on Toki Pona 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, bitext mining 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, 'architecture': '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("NetherQuartz/LaBSE-tokipona")
# 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, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8126, 0.2629],
#         [0.8126, 1.0000, 0.4022],
#         [0.2629, 0.4022, 1.0000]])

Evaluation

Metrics

Knowledge Distillation

• Dataset: eval_data
• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-0.0492

Translation

• Dataset: eval_data
• Evaluated with TranslationEvaluator

Metric	Value
src2trg_accuracy	0.8381
trg2src_accuracy	0.7736
mean_accuracy	0.8058

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: 3 tokens mean: 10.66 tokens max: 43 tokens	min: 4 tokens mean: 13.96 tokens max: 47 tokens	size: 768 elements

• Samples:

  natural	tok	label
  Я держу руку.	mi sewi e luka mi.	[-0.02380160242319107, -0.05106028914451599, -0.054335981607437134, -0.050830986350774765, -0.05793563649058342, ...]
  Я змарыўся ад працы.	tan pali mi la mi pilin lape.	[-0.03956804797053337, 0.008637639693915844, -0.045203790068626404, -0.06055501475930214, -0.06817363947629929, ...]
  Mi bolso necesita ser reparado.	poki mi li pakala.	[-0.06031221151351929, -0.006717301905155182, -0.03342252969741821, -0.03583073988556862, -0.0651949867606163, ...]

• 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: 10.59 tokens max: 43 tokens	min: 4 tokens mean: 13.7 tokens max: 59 tokens	size: 768 elements

• Samples:

  natural	tok	label
  Da quanto tempo sei/state in Germania?	tenpo pi suli seme la sina lon ma Tosi?	[-0.025023534893989563, -0.0016661343397572637, -0.02266993746161461, -0.061682481318712234, -0.035705942660570145, ...]
  Habesne difficultatem hac re?	ni li ike tawa sina anu seme?	[0.033313240855932236, -0.04223407432436943, -0.012467658147215843, -0.06204398348927498, -0.06461521983146667, ...]
  אני לא הולך להפסיד.	mi kama ala anpa.	[-0.05783797428011894, -0.04036393761634827, -0.0631723552942276, -0.03369426354765892, -0.05813731253147125, ...]

• 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	0.0009	-	-	-
0.1559	200	0.0007	-	-	-
0.2338	300	0.0006	-	-	-
0.3118	400	0.0006	-	-	-
0.3897	500	0.0005	-	-	-
0.4677	600	0.0005	-	-	-
0.5456	700	0.0005	-	-	-
0.6235	800	0.0004	-	-	-
0.7015	900	0.0004	-	-	-
0.7794	1000	0.0004	-	-	-
0.8574	1100	0.0004	-	-	-
0.9353	1200	0.0004	-	-	-
1.0133	1300	0.0004	-	-	-
1.0912	1400	0.0004	-	-	-
1.1691	1500	0.0004	-	-	-
1.2471	1600	0.0004	-	-	-
1.3250	1700	0.0003	-	-	-
1.4030	1800	0.0003	-	-	-
1.4809	1900	0.0003	-	-	-
1.5588	2000	0.0003	0.0003	-0.0572	0.7559
1.6368	2100	0.0003	-	-	-
1.7147	2200	0.0003	-	-	-
1.7927	2300	0.0003	-	-	-
1.8706	2400	0.0003	-	-	-
1.9486	2500	0.0003	-	-	-
2.0265	2600	0.0003	-	-	-
2.1044	2700	0.0003	-	-	-
2.1824	2800	0.0003	-	-	-
2.2603	2900	0.0003	-	-	-
2.3383	3000	0.0003	-	-	-
2.4162	3100	0.0003	-	-	-
2.4942	3200	0.0003	-	-	-
2.5721	3300	0.0003	-	-	-
2.6500	3400	0.0003	-	-	-
2.7280	3500	0.0003	-	-	-
2.8059	3600	0.0003	-	-	-
2.8839	3700	0.0003	-	-	-
2.9618	3800	0.0003	-	-	-
3.0398	3900	0.0003	-	-	-
3.1177	4000	0.0003	0.0003	-0.0528	0.7850
3.1956	4100	0.0003	-	-	-
3.2736	4200	0.0003	-	-	-
3.3515	4300	0.0003	-	-	-
3.4295	4400	0.0003	-	-	-
3.5074	4500	0.0003	-	-	-
3.5853	4600	0.0003	-	-	-
3.6633	4700	0.0003	-	-	-
3.7412	4800	0.0003	-	-	-
3.8192	4900	0.0003	-	-	-
3.8971	5000	0.0003	-	-	-
3.9751	5100	0.0003	-	-	-
4.0530	5200	0.0003	-	-	-
4.1309	5300	0.0003	-	-	-
4.2089	5400	0.0003	-	-	-
4.2868	5500	0.0003	-	-	-
4.3648	5600	0.0003	-	-	-
4.4427	5700	0.0003	-	-	-
4.5207	5800	0.0003	-	-	-
4.5986	5900	0.0003	-	-	-
4.6765	6000	0.0003	0.0003	-0.0512	0.7936
4.7545	6100	0.0003	-	-	-
4.8324	6200	0.0003	-	-	-
4.9104	6300	0.0003	-	-	-
4.9883	6400	0.0003	-	-	-
5.0663	6500	0.0003	-	-	-
5.1442	6600	0.0003	-	-	-
5.2221	6700	0.0003	-	-	-
5.3001	6800	0.0003	-	-	-
5.3780	6900	0.0003	-	-	-
5.4560	7000	0.0003	-	-	-
5.5339	7100	0.0003	-	-	-
5.6118	7200	0.0003	-	-	-
5.6898	7300	0.0003	-	-	-
5.7677	7400	0.0003	-	-	-
5.8457	7500	0.0003	-	-	-
5.9236	7600	0.0003	-	-	-
6.0016	7700	0.0003	-	-	-
6.0795	7800	0.0003	-	-	-
6.1574	7900	0.0003	-	-	-
6.2354	8000	0.0003	0.0003	-0.0504	0.8022
6.3133	8100	0.0003	-	-	-
6.3913	8200	0.0003	-	-	-
6.4692	8300	0.0003	-	-	-
6.5472	8400	0.0003	-	-	-
6.6251	8500	0.0003	-	-	-
6.7030	8600	0.0003	-	-	-
6.7810	8700	0.0003	-	-	-
6.8589	8800	0.0003	-	-	-
6.9369	8900	0.0003	-	-	-
7.0148	9000	0.0003	-	-	-
7.0928	9100	0.0003	-	-	-
7.1707	9200	0.0003	-	-	-
7.2486	9300	0.0003	-	-	-
7.3266	9400	0.0003	-	-	-
7.4045	9500	0.0003	-	-	-
7.4825	9600	0.0003	-	-	-
7.5604	9700	0.0003	-	-	-
7.6383	9800	0.0003	-	-	-
7.7163	9900	0.0003	-	-	-
7.7942	10000	0.0003	0.0003	-0.0497	0.8034
7.8722	10100	0.0003	-	-	-
7.9501	10200	0.0003	-	-	-
8.0281	10300	0.0003	-	-	-
8.1060	10400	0.0003	-	-	-
8.1839	10500	0.0003	-	-	-
8.2619	10600	0.0003	-	-	-
8.3398	10700	0.0003	-	-	-
8.4178	10800	0.0003	-	-	-
8.4957	10900	0.0003	-	-	-
8.5737	11000	0.0003	-	-	-
8.6516	11100	0.0003	-	-	-
8.7295	11200	0.0003	-	-	-
8.8075	11300	0.0003	-	-	-
8.8854	11400	0.0003	-	-	-
8.9634	11500	0.0003	-	-	-
9.0413	11600	0.0003	-	-	-
9.1193	11700	0.0003	-	-	-
9.1972	11800	0.0003	-	-	-
9.2751	11900	0.0003	-	-	-
9.3531	12000	0.0003	0.0003	-0.0495	0.8049
9.4310	12100	0.0003	-	-	-
9.5090	12200	0.0003	-	-	-
9.5869	12300	0.0003	-	-	-
9.6648	12400	0.0003	-	-	-
9.7428	12500	0.0003	-	-	-
9.8207	12600	0.0003	-	-	-
9.8987	12700	0.0003	-	-	-
9.9766	12800	0.0003	-	-	-
10.0546	12900	0.0003	-	-	-
10.1325	13000	0.0003	-	-	-
10.2104	13100	0.0003	-	-	-
10.2884	13200	0.0003	-	-	-
10.3663	13300	0.0003	-	-	-
10.4443	13400	0.0003	-	-	-
10.5222	13500	0.0003	-	-	-
10.6002	13600	0.0003	-	-	-
10.6781	13700	0.0003	-	-	-
10.7560	13800	0.0003	-	-	-
10.8340	13900	0.0003	-	-	-
10.9119	14000	0.0003	0.0003	-0.0492	0.8058
10.9899	14100	0.0003	-	-	-
11.0678	14200	0.0003	-	-	-
11.1458	14300	0.0003	-	-	-
11.2237	14400	0.0003	-	-	-
11.3016	14500	0.0003	-	-	-
11.3796	14600	0.0003	-	-	-
11.4575	14700	0.0003	-	-	-
11.5355	14800	0.0003	-	-	-
11.6134	14900	0.0003	-	-	-
11.6913	15000	0.0003	-	-	-
11.7693	15100	0.0003	-	-	-
11.8472	15200	0.0003	-	-	-
11.9252	15300	0.0003	-	-	-
12.0	15396	-	0.0003	-0.0492	0.8058

• 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

@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

@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",
}