SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the csv dataset. 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
- Training Dataset:
- csv
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}) with Transformer model: 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.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("gmunkhtur/paraphrase-mongolian-minilm-mntoken")
# Run inference
sentences = [
'Олон улсын наадмын шалгаруулалт',
'Драмын урлагийн шилдгүүдийг тодруулдаг наадам.',
'Мөн нийт экспортын хэмжээ 10 хувиар, түүн дунд нүүрсний экспорт 50 хувиар\xa0 буурсан юм.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Semantic Similarity
- Datasets:
dev-t
andtest-t
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | dev-t | test-t |
---|---|---|
pearson_cosine | 0.574 | 0.5938 |
spearman_cosine | 0.546 | 0.5613 |
Training Details
Training Dataset
csv
- Dataset: csv
- Size: 23,525 training samples
- Columns:
sentence1
,sentence2
, andscore
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score type string string float details - min: 3 tokens
- mean: 12.82 tokens
- max: 90 tokens
- min: 3 tokens
- mean: 12.44 tokens
- max: 77 tokens
- min: 0.02
- mean: 0.49
- max: 1.0
- Samples:
sentence1 sentence2 score Хүн амын нягтаршил багатай, газар хөдлөлийн идэвхигүй бүс, газрын гадарга нь тэгш, үер усны давтамж бага газарт Цөмийн энергийн станцийг барьж байгуулах шаардлагатай гэнэ
Энэ станцад захын нэг дээд сургууль эзэмшсэн нөхөр очоод ажиллахгүй.
0.2018195390701294
Уг компани тендерт гадаадынхныг урьсан ба өрөгдлийг нь зургадугаар сарын 3 хүртэл хүлээн авсан байна
«Коммерсантъ» сонин 24-ний өдрийн дугаартаа өгүүлсэн байна
0.2372543811798095
Би “Өүлэн эх”-ийг анх бүтээсэн
Би “Хорин нэгэн зул”-ыг анх бүтээсэн.
0.6730476021766663
- Loss:
CosineSimilarityLoss
with these parameters:{ "loss_fct": "torch.nn.modules.loss.MSELoss" }
Evaluation Dataset
csv
- Dataset: csv
- Size: 23,525 evaluation samples
- Columns:
sentence1
,sentence2
, andscore
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score type string string float details - min: 4 tokens
- mean: 12.78 tokens
- max: 123 tokens
- min: 4 tokens
- mean: 12.69 tokens
- max: 59 tokens
- min: -0.04
- mean: 0.48
- max: 0.98
- Samples:
sentence1 sentence2 score Анхны тоглолт маань одоо бодоход үнэхээр гоё болж байсан
Яг ямар чиглэлээр тоглохоо мэдэхгүй жаахан охин байсан ч би маш их зүйл сурсан
0.2749532461166382
"Домогт Ану хатан" нь Монголын түүхэн дэх хатан хааны тухай өгүүлдэг
"Домогт Ану хатан" нь Б.Шүүдэрцэцэгийн бүтээл юм.
0.3653741478919983
Советийн хурлаар "Эрдэнэт" болон "Монголросцветмет нэгдэл"-ийн талаар ярилцах ажээ
Асгатын мөнгөний ордыг түшиглэн Орос-Монголын хамтарсан компани байгуулахаар болсон бөгөөд энэ асуудлыг хуралдаанаар хөндөнө гэдгийг эх сурвалж хэлсэн.
0.599888801574707
- Loss:
CosineSimilarityLoss
with these parameters:{ "loss_fct": "torch.nn.modules.loss.MSELoss" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16num_train_epochs
: 5warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | Validation Loss | dev-t_spearman_cosine | test-t_spearman_cosine |
---|---|---|---|---|---|
0 | 0 | - | - | 0.2295 | - |
0.5663 | 500 | 0.0403 | - | - | - |
1.1325 | 1000 | 0.0332 | 0.0320 | 0.5316 | - |
1.6988 | 1500 | 0.0188 | - | - | - |
2.2650 | 2000 | 0.0135 | 0.0311 | 0.5361 | - |
2.8313 | 2500 | 0.0085 | - | - | - |
3.3975 | 3000 | 0.0074 | 0.0310 | 0.5352 | - |
3.9638 | 3500 | 0.0054 | - | - | - |
4.5300 | 4000 | 0.0045 | 0.0308 | 0.5460 | - |
5.0 | 4415 | - | - | - | 0.5613 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
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",
}
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Evaluation results
- Pearson Cosine on dev tself-reported0.574
- Spearman Cosine on dev tself-reported0.546
- Pearson Cosine on test tself-reported0.594
- Spearman Cosine on test tself-reported0.561