metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:2130621
  - loss:ContrastiveLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
  - source_sentence: Kim Chol-sam
    sentences:
      - Stankevich Sergey Nikolayevich
      - Kim Chin-So’k
      - Julen Lopetegui Agote
  - source_sentence: دينا بنت عبد الحميد
    sentences:
      - Alexia van Amsberg
      - Anthony Nicholas Colin Maitland Biddulph, 5th Baron Biddulph
      - Dina bint Abdul-Hamíd
  - source_sentence: Մուհամեդ բեն Նաիֆ Ալ Սաուդ
    sentences:
      - Karpov Anatoly Evgenyevich
      - GNPower Mariveles Coal Plant [former]
      - Muhammed bin Nayef bin Abdul Aziz Al Saud
  - source_sentence: Edward Gnehm
    sentences:
      - Шауэрте, Хартмут
      - Ханзада Филипп, Эдинбург герцогі
      - AFX
  - source_sentence: Schori i Lidingö
    sentences:
      - Yordan Canev
      - ကားပေါ့ အန်နာတိုလီ
      - BYSTROV, Mikhail Ivanovich
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - cosine_mcc
model-index:
  - name: >-
      sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2-name-matcher-original
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: sentence transformers paraphrase multilingual MiniLM L12 v2
          type: sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2
        metrics:
          - type: cosine_accuracy
            value: 0.9846242227629088
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.6801187992095947
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.9765449140552956
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.6780189275741577
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.9721848413657824
            name: Cosine Precision
          - type: cosine_recall
            value: 0.9809442711989229
            name: Cosine Recall
          - type: cosine_ap
            value: 0.9955904030209028
            name: Cosine Ap
          - type: cosine_mcc
            value: 0.9651303277408154
            name: Cosine Mcc

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2-name-matcher-original

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
Language: en
License: apache-2.0

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Schori i Lidingö',
    'Yordan Canev',
    'ကားပေါ့ အန်နာတိုလီ',
]
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

Binary Classification

Dataset: sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.9846
cosine_accuracy_threshold	0.6801
cosine_f1	0.9765
cosine_f1_threshold	0.678
cosine_precision	0.9722
cosine_recall	0.9809
cosine_ap	0.9956
cosine_mcc	0.9651

Training Details

Training Dataset

Unnamed Dataset

Size: 2,130,621 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 3 tokens
mean: 9.32 tokens
max: 57 tokens

min: 3 tokens
mean: 9.16 tokens
max: 54 tokens

min: 0.0
mean: 0.34
max: 1.0

	sentence1	sentence2	label
type	string	string	float
details	min: 3 tokens mean: 9.32 tokens max: 57 tokens	min: 3 tokens mean: 9.16 tokens max: 54 tokens	min: 0.0 mean: 0.34 max: 1.0

Samples:

sentence1	sentence2	label
`캐스린 설리번`	`Kathryn D. Sullivanová`	`1.0`
`ଶିବରାଜ ଅଧାଲରାଓ ପାଟିଲ`	`Aleksander Lubocki`	`0.0`
`Пырванов, Георги`	`アナトーリー・セルジュコフ`	`0.0`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Evaluation Dataset

Unnamed Dataset

Size: 2,663,276 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 3 tokens
mean: 9.34 tokens
max: 102 tokens

min: 4 tokens
mean: 9.11 tokens
max: 100 tokens

min: 0.0
mean: 0.33
max: 1.0

	sentence1	sentence2	label
type	string	string	float
details	min: 3 tokens mean: 9.34 tokens max: 102 tokens	min: 4 tokens mean: 9.11 tokens max: 100 tokens	min: 0.0 mean: 0.33 max: 1.0

Samples:

sentence1	sentence2	label
`Ева Херман`	`I Xuan Karlos`	`0.0`
`Кличков Андрій Євгенович`	`Андрэй Яўгенавіч Клычкоў`	`1.0`
`Кинах А.`	`Senator John Hickenlooper`	`0.0`

Loss: ContrastiveLoss with these parameters:

{
    "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
    "margin": 0.5,
    "size_average": true
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 768
per_device_eval_batch_size: 768
gradient_accumulation_steps: 4
learning_rate: 3e-05
weight_decay: 0.01
num_train_epochs: 4
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
optim: adafactor

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 768
per_device_eval_batch_size: 768
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 3e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
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}
tp_size: 0
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: adafactor
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
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	sentence-transformers-paraphrase-multilingual-MiniLM-L12-v2_cosine_ap
-1	-1	-	-	0.7140
0.7207	500	0.038	-	-
0.9989	693	-	0.0028	0.9911
1.4425	1000	0.0128	-	-
1.9989	1386	-	0.0021	0.9956

Framework Versions

Python: 3.12.9
Sentence Transformers: 3.4.1
Transformers: 4.51.3
PyTorch: 2.7.0+cu126
Accelerate: 1.6.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}