--- 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: 0.9885216725241056 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7183246612548828 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9824706124974221 name: Cosine F1 - type: cosine_f1_threshold value: 0.7085607051849365 name: Cosine F1 Threshold - type: cosine_precision value: 0.9782229269572558 name: Cosine Precision - type: cosine_recall value: 0.9867553479166427 name: Cosine Recall - type: cosine_ap value: 0.9971022799526896 name: Cosine Ap - type: cosine_mcc value: 0.9739458779668466 name: Cosine Mcc --- # sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2-name-matcher-original This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/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](https://huggingface.co/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](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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("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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:--------------------------|:-----------| | cosine_accuracy | 0.9885 | | cosine_accuracy_threshold | 0.7183 | | cosine_f1 | 0.9825 | | cosine_f1_threshold | 0.7086 | | cosine_precision | 0.9782 | | cosine_recall | 0.9868 | | **cosine_ap** | **0.9971** | | cosine_mcc | 0.9739 | ## 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 | | | | * 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "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 | | | | * 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "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 | | 2.1643 | 1500 | 0.0084 | - | - | | 2.8850 | 2000 | 0.0065 | - | - | | 2.9989 | 2079 | - | 0.0015 | 0.9968 | | 3.6068 | 2500 | 0.0056 | - | - | | 3.9989 | 2772 | - | 0.0014 | 0.9971 | ### 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 ```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", } ``` #### ContrastiveLoss ```bibtex @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} } ```