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gmunkhtur/paraphrase-multilingual-minilm-l12-v3-mn

sourceHugging Faceupdated 2y agoView on Hugging Face
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Model Card

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 <!-- at revision 8d6b950845285729817bf8e1af1861502c2fed0c -->
  • —Maximum Sequence Length: 128 tokens
  • —Output Dimensionality: 384 dimensions
  • —Similarity Function: Cosine Similarity
  • —Training Dataset:
  • —csv <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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:

bash
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("gmunkhtur/paraphrase-multilingual-minilm-l12-v3-mn")
# Run inference
sentences = [
    'Тэд “Зэрлэг цэцэгсийн хүлэмж” кино хийсэн',
    'Тэдний хоёр дахь бүтээл “Зүрхээр наадагч” кино юм.',
    'Францын агуу дуучин эмэгтэй Эдит Пиаф.',
]
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]

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Evaluation

Metrics

Semantic Similarity
MetricValue
pearson_cosine0.5175
spearman_cosine0.4878

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Training Details

Training Dataset

csv
  • —Dataset: csv
  • —Size: 11,113 training samples
  • —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
  • —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 4 tokens</li><li>mean: 14.51 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.07 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: -0.09</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
  • —Samples: | sentence1 | sentence2 | score | |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|:--------------------------------| | <code>Тэднээс 6 нь л ажиллагаатай юм</code> | <code>,Sothеbey’s-ийн зарсан төхөөрөмж ажилладаг гэж Sothеbey’s-ийн сайтан дээрх барааны танилцуулгад бичсэн байна.</code> | <code>0.0607873052358627</code> | | <code>Түүний хит дуу нь "Миллион алых розe"</code> | <code>Тэрбээр "Миллион алых розe" дууг олон хэлээр дуулсан.</code> | <code>0.7139256596565247</code> | | <code>“Нисванис” хамтлаг олон арга хэмжээ зохион байгуулдаг</code> | <code>Хамтлагийн тоглолт нь рок цугларалт, шоу юм</code> | <code>0.454399824142456</code> |
  • —Loss: <code>CosineSimilarityLoss</code> with these parameters:
json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }

Evaluation Dataset

csv
  • —Dataset: csv
  • —Size: 11,113 evaluation samples
  • —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
  • —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 4 tokens</li><li>mean: 14.52 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.92 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: -0.09</li><li>mean: 0.48</li><li>max: 0.98</li></ul> |
  • —Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:--------------------------------| | <code>Америк хөгжмийн зохиолуудыг бэлтгэл хийж байж тоглодог</code> | <code>Монгол, Америк хоёр улсын хөгжмийн нотны тэмдэглэгээ өөр.</code> | <code>0.5598467588424683</code> | | <code>Энэхүү хэсэг нь дөрвөн ангитай байна.</code> | <code>Зохиолч Б.Мөнхболд энэхүү хөгжмийг бичихдээ Галдан бошготийн түүхийг таван жил судласан.</code> | <code>0.2354833334684372</code> | | <code>Д.Шинэцог удахгүй тоглолт хийхээр төлөвлөж байна</code> | <code>Д.Шинэцог Монголын шилдэг хөгжмийн зохиолчдын бүтээлийг тоглоно</code> | <code>0.3461045920848846</code> |
  • —Loss: <code>CosineSimilarityLoss</code> with these parameters:
json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }

Training Hyperparameters

Non-Default Hyperparameters
  • —eval_strategy: steps
  • —per_device_train_batch_size: 16
  • —per_device_eval_batch_size: 16
  • —num_train_epochs: 1
  • —warmup_ratio: 0.1
  • —fp16: True
  • —batch_sampler: no_duplicates
All Hyperparameters

<details><summary>Click to expand</summary>

  • —overwrite_output_dir: False
  • —do_predict: False
  • —eval_strategy: steps
  • —prediction_loss_only: True
  • —per_device_train_batch_size: 16
  • —per_device_eval_batch_size: 16
  • —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: 1.0
  • —num_train_epochs: 1
  • —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: False
  • —ignore_data_skip: False
  • —fsdp: []
  • —fsdp_min_num_params: 0
  • —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
  • —fsdp_transformer_layer_cls_to_wrap: None
  • —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: None
  • —hub_always_push: False
  • —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
  • —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
  • —eval_on_start: False
  • —use_liger_kernel: False
  • —eval_use_gather_object: False
  • —average_tokens_across_devices: False
  • —prompts: None
  • —batch_sampler: no_duplicates
  • —multi_dataset_batch_sampler: proportional

</details>

Training Logs

EpochStepTraining LossValidation Losssts-dev_spearman_cosine
00--0.2082
0.17991000.0433--
0.35972000.0367--
0.53963000.033--
0.71944000.0351--
0.89935000.03120.03290.4878

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
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",
}

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