--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1267 - loss:CoSENTLoss base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 datasets: [] metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: Give me suggestions for a high-quality DSLR camera sentences: - faq query - subscription query - faq query - source_sentence: Aidez-moi à configurer une nouvelle adresse e-mail sentences: - order query - faq query - feedback query - source_sentence: Как я могу изменить адрес доставки? sentences: - support query - product query - product query - source_sentence: ساعدني في حذف الملفات الغير مرغوب فيها من هاتفي sentences: - technical support query - product recommendation - faq query - source_sentence: Envoyez-moi la politique de garantie de ce produit sentences: - faq query - account query - faq query pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: MiniLM dev type: MiniLM-dev metrics: - type: pearson_cosine value: 0.6538226572138826 name: Pearson Cosine - type: spearman_cosine value: 0.6336766646599241 name: Spearman Cosine - type: pearson_manhattan value: 0.5799895241429639 name: Pearson Manhattan - type: spearman_manhattan value: 0.5525776786782183 name: Spearman Manhattan - type: pearson_euclidean value: 0.5732001104236694 name: Pearson Euclidean - type: spearman_euclidean value: 0.5394971970682657 name: Spearman Euclidean - type: pearson_dot value: 0.6359725423136287 name: Pearson Dot - type: spearman_dot value: 0.6237936341101822 name: Spearman Dot - type: pearson_max value: 0.6538226572138826 name: Pearson Max - type: spearman_max value: 0.6336766646599241 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: MiniLM test type: MiniLM-test metrics: - type: pearson_cosine value: 0.6682368113711722 name: Pearson Cosine - type: spearman_cosine value: 0.6222011918428743 name: Spearman Cosine - type: pearson_manhattan value: 0.5714617063306076 name: Pearson Manhattan - type: spearman_manhattan value: 0.5481366191719228 name: Spearman Manhattan - type: pearson_euclidean value: 0.5726946277850402 name: Pearson Euclidean - type: spearman_euclidean value: 0.549312247309557 name: Spearman Euclidean - type: pearson_dot value: 0.6396412507506479 name: Pearson Dot - type: spearman_dot value: 0.6107388175009413 name: Spearman Dot - type: pearson_max value: 0.6682368113711722 name: Pearson Max - type: spearman_max value: 0.6222011918428743 name: Spearman Max --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 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 tokens - **Similarity Function:** Cosine Similarity ### 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("philipp-zettl/MiniLM-similarity-small") # Run inference sentences = [ 'Envoyez-moi la politique de garantie de ce produit', 'faq query', 'account query', ] 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 * Dataset: `MiniLM-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.6538 | | **spearman_cosine** | **0.6337** | | pearson_manhattan | 0.58 | | spearman_manhattan | 0.5526 | | pearson_euclidean | 0.5732 | | spearman_euclidean | 0.5395 | | pearson_dot | 0.636 | | spearman_dot | 0.6238 | | pearson_max | 0.6538 | | spearman_max | 0.6337 | #### Semantic Similarity * Dataset: `MiniLM-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.6682 | | **spearman_cosine** | **0.6222** | | pearson_manhattan | 0.5715 | | spearman_manhattan | 0.5481 | | pearson_euclidean | 0.5727 | | spearman_euclidean | 0.5493 | | pearson_dot | 0.6396 | | spearman_dot | 0.6107 | | pearson_max | 0.6682 | | spearman_max | 0.6222 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,267 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------------------|:---------------------------|:-----------------| | Get information on the next art exhibition | product query | 0.0 | | Show me how to update my profile | product query | 0.0 | | Покажите мне доступные варианты полетов в Турцию | faq query | 0.0 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 159 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:---------------------------------------------------------------|:---------------------------|:-----------------| | Sende mir die Bestellbestätigung per E-Mail | order query | 0.0 | | How do I add a new payment method? | faq query | 1.0 | | No puedo conectar mi impresora, ¿puedes ayudarme? | support query | 1.0 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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`: 2 - `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`: {'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 - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | MiniLM-dev_spearman_cosine | MiniLM-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:--------------------------:|:---------------------------:| | 0.0629 | 10 | 6.2479 | 2.5890 | 0.1448 | - | | 0.1258 | 20 | 4.3549 | 2.2787 | 0.1965 | - | | 0.1887 | 30 | 3.5969 | 2.0104 | 0.2599 | - | | 0.2516 | 40 | 2.4979 | 1.7269 | 0.3357 | - | | 0.3145 | 50 | 2.5551 | 1.5747 | 0.4439 | - | | 0.3774 | 60 | 3.1446 | 1.4892 | 0.4750 | - | | 0.4403 | 70 | 2.1353 | 1.5305 | 0.4662 | - | | 0.5031 | 80 | 2.9341 | 1.3718 | 0.4848 | - | | 0.5660 | 90 | 2.8709 | 1.2469 | 0.5316 | - | | 0.6289 | 100 | 2.1367 | 1.2558 | 0.5436 | - | | 0.6918 | 110 | 2.2735 | 1.2939 | 0.5392 | - | | 0.7547 | 120 | 2.8646 | 1.1206 | 0.5616 | - | | 0.8176 | 130 | 3.3204 | 1.0213 | 0.5662 | - | | 0.8805 | 140 | 0.8989 | 0.9866 | 0.5738 | - | | 0.9434 | 150 | 0.0057 | 0.9961 | 0.5674 | - | | 1.0063 | 160 | 0.0019 | 1.0111 | 0.5674 | - | | 1.0692 | 170 | 0.4617 | 1.0275 | 0.5747 | - | | 1.1321 | 180 | 0.0083 | 1.0746 | 0.5732 | - | | 1.1950 | 190 | 0.5048 | 1.0968 | 0.5753 | - | | 1.2579 | 200 | 0.0002 | 1.0840 | 0.5738 | - | | 1.3208 | 210 | 0.07 | 1.0364 | 0.5753 | - | | 1.3836 | 220 | 0.0 | 0.9952 | 0.5750 | - | | 1.4465 | 230 | 0.0 | 0.9922 | 0.5744 | - | | 1.5094 | 240 | 0.0 | 0.9923 | 0.5726 | - | | 1.0126 | 250 | 0.229 | 0.9930 | 0.5729 | - | | 1.0755 | 260 | 2.2061 | 0.9435 | 0.5880 | - | | 1.1384 | 270 | 2.7711 | 0.8892 | 0.6078 | - | | 1.2013 | 280 | 0.7528 | 0.8886 | 0.6148 | - | | 1.2642 | 290 | 0.386 | 0.8927 | 0.6162 | - | | 1.3270 | 300 | 0.8902 | 0.8710 | 0.6267 | - | | 1.3899 | 310 | 0.9534 | 0.8429 | 0.6337 | - | | 1.4403 | 318 | - | - | - | 0.6222 | ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu121 - Accelerate: 0.33.0 - Datasets: 2.21.0 - Tokenizers: 0.19.1 ## 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", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```