--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:234000 - loss:MSELoss base_model: google-bert/bert-base-multilingual-uncased widget: - source_sentence: who sings in spite of ourselves with john prine sentences: - es - når ble michael jordan draftet til nba - quien canta en spite of ourselves con john prine - source_sentence: who wrote when you look me in the eyes sentences: - متى بدأت الفتاة الكشفية في بيع ملفات تعريف الارتباط - A écrit when you look me in the eyes - fr - source_sentence: when was fathers day made a national holiday sentences: - wann wurde der Vatertag zum nationalen Feiertag - de - ' អ្នកណាច្រៀង i want to sing you a love song' - source_sentence: what is the density of the continental crust sentences: - cuál es la densidad de la corteza continental - wie zingt i want to sing you a love song - es - source_sentence: who wrote the song i shot the sheriff sentences: - Quel est l'âge légal pour consommer du vin au Canada? - i shot the sheriff şarkısını kim besteledi - tr pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - negative_mse model-index: - name: SentenceTransformer based on google-bert/bert-base-multilingual-uncased results: - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to ar type: MSE-val-en-to-ar metrics: - type: negative_mse value: -20.37721574306488 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to da type: MSE-val-en-to-da metrics: - type: negative_mse value: -17.167489230632782 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to de type: MSE-val-en-to-de metrics: - type: negative_mse value: -17.10948944091797 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to en type: MSE-val-en-to-en metrics: - type: negative_mse value: -15.333698689937592 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to es type: MSE-val-en-to-es metrics: - type: negative_mse value: -16.898061335086823 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to fi type: MSE-val-en-to-fi metrics: - type: negative_mse value: -18.428558111190796 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to fr type: MSE-val-en-to-fr metrics: - type: negative_mse value: -17.04207956790924 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to he type: MSE-val-en-to-he metrics: - type: negative_mse value: -19.942057132720947 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to hu type: MSE-val-en-to-hu metrics: - type: negative_mse value: -18.757066130638123 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to it type: MSE-val-en-to-it metrics: - type: negative_mse value: -17.18708872795105 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to ja type: MSE-val-en-to-ja metrics: - type: negative_mse value: -19.915536046028137 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to ko type: MSE-val-en-to-ko metrics: - type: negative_mse value: -21.39919400215149 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to km type: MSE-val-en-to-km metrics: - type: negative_mse value: -28.658682107925415 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to ms type: MSE-val-en-to-ms metrics: - type: negative_mse value: -17.25209951400757 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to nl type: MSE-val-en-to-nl metrics: - type: negative_mse value: -16.605134308338165 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to no type: MSE-val-en-to-no metrics: - type: negative_mse value: -17.149969935417175 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to pl type: MSE-val-en-to-pl metrics: - type: negative_mse value: -17.846450209617615 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to pt type: MSE-val-en-to-pt metrics: - type: negative_mse value: -17.19353199005127 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to ru type: MSE-val-en-to-ru metrics: - type: negative_mse value: -18.13419610261917 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to sv type: MSE-val-en-to-sv metrics: - type: negative_mse value: -17.13200956583023 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to th type: MSE-val-en-to-th metrics: - type: negative_mse value: -26.43084228038788 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to tr type: MSE-val-en-to-tr metrics: - type: negative_mse value: -18.183308839797974 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to vi type: MSE-val-en-to-vi metrics: - type: negative_mse value: -18.749597668647766 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to zh cn type: MSE-val-en-to-zh_cn metrics: - type: negative_mse value: -18.811793625354767 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to zh hk type: MSE-val-en-to-zh_hk metrics: - type: negative_mse value: -18.54081153869629 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en to zh tw type: MSE-val-en-to-zh_tw metrics: - type: negative_mse value: -19.14038509130478 name: Negative Mse --- # SentenceTransformer based on google-bert/bert-base-multilingual-uncased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased). It maps sentences & paragraphs to a 768-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:** [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 dimensions - **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': 768, '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("luanafelbarros/bert-base-multilingual-uncased-matryoshka-mkqa") # Run inference sentences = [ 'who wrote the song i shot the sheriff', 'i shot the sheriff şarkısını kim besteledi', 'tr', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Knowledge Distillation * Datasets: `MSE-val-en-to-ar`, `MSE-val-en-to-da`, `MSE-val-en-to-de`, `MSE-val-en-to-en`, `MSE-val-en-to-es`, `MSE-val-en-to-fi`, `MSE-val-en-to-fr`, `MSE-val-en-to-he`, `MSE-val-en-to-hu`, `MSE-val-en-to-it`, `MSE-val-en-to-ja`, `MSE-val-en-to-ko`, `MSE-val-en-to-km`, `MSE-val-en-to-ms`, `MSE-val-en-to-nl`, `MSE-val-en-to-no`, `MSE-val-en-to-pl`, `MSE-val-en-to-pt`, `MSE-val-en-to-ru`, `MSE-val-en-to-sv`, `MSE-val-en-to-th`, `MSE-val-en-to-tr`, `MSE-val-en-to-vi`, `MSE-val-en-to-zh_cn`, `MSE-val-en-to-zh_hk` and `MSE-val-en-to-zh_tw` * Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | MSE-val-en-to-ar | MSE-val-en-to-da | MSE-val-en-to-de | MSE-val-en-to-en | MSE-val-en-to-es | MSE-val-en-to-fi | MSE-val-en-to-fr | MSE-val-en-to-he | MSE-val-en-to-hu | MSE-val-en-to-it | MSE-val-en-to-ja | MSE-val-en-to-ko | MSE-val-en-to-km | MSE-val-en-to-ms | MSE-val-en-to-nl | MSE-val-en-to-no | MSE-val-en-to-pl | MSE-val-en-to-pt | MSE-val-en-to-ru | MSE-val-en-to-sv | MSE-val-en-to-th | MSE-val-en-to-tr | MSE-val-en-to-vi | MSE-val-en-to-zh_cn | MSE-val-en-to-zh_hk | MSE-val-en-to-zh_tw | |:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:--------------------|:--------------------|:--------------------| | **negative_mse** | **-20.3772** | **-17.1675** | **-17.1095** | **-15.3337** | **-16.8981** | **-18.4286** | **-17.0421** | **-19.9421** | **-18.7571** | **-17.1871** | **-19.9155** | **-21.3992** | **-28.6587** | **-17.2521** | **-16.6051** | **-17.15** | **-17.8465** | **-17.1935** | **-18.1342** | **-17.132** | **-26.4308** | **-18.1833** | **-18.7496** | **-18.8118** | **-18.5408** | **-19.1404** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 234,000 training samples * Columns: english, non-english, target, and label * Approximate statistics based on the first 1000 samples: | | english | non-english | target | label | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------| | type | string | string | string | list | | details | | | | | * Samples: | english | non-english | target | label | |:-------------------------------------------------|:--------------------------------------------------------|:----------------|:------------------------------------------------------------------------------------------------------------------------| | who plays hope on days of our lives | من الذي يلعب الأمل في أيام حياتنا | ar | [0.2171212136745453, 0.5138550996780396, 0.5517176389694214, -1.0655105113983154, 1.5853567123413086, ...] | | who plays hope on days of our lives | hvem spiller hope i Horton-sagaen | da | [0.2171212136745453, 0.5138550996780396, 0.5517176389694214, -1.0655105113983154, 1.5853567123413086, ...] | | who plays hope on days of our lives | Wer spielt die Hope in Zeit der Sehnsucht? | de | [0.2171212136745453, 0.5138550996780396, 0.5517176389694214, -1.0655105113983154, 1.5853567123413086, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) ### Evaluation Dataset #### Unnamed Dataset * Size: 13,000 evaluation samples * Columns: english, non-english, target, and label * Approximate statistics based on the first 1000 samples: | | english | non-english | target | label | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------| | type | string | string | string | list | | details | | | | | * Samples: | english | non-english | target | label | |:------------------------------------------------------------|:----------------------------------------------------------------|:----------------|:-----------------------------------------------------------------------------------------------------------------------------| | who played prudence on nanny and the professor | من لعب دور "prudence" فى "nanny and the professor" | ar | [-0.2837616801261902, -0.4943353235721588, 0.020107418298721313, 0.7796109318733215, -0.47365888953208923, ...] | | who played prudence on nanny and the professor | hvem spiller prudence på nanny and the professor | da | [-0.2837616801261902, -0.4943353235721588, 0.020107418298721313, 0.7796109318733215, -0.47365888953208923, ...] | | who played prudence on nanny and the professor | Wer spielte Prudence in Nanny and the Professor | de | [-0.2837616801261902, -0.4943353235721588, 0.020107418298721313, 0.7796109318733215, -0.47365888953208923, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 1e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `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`: 1e-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`: {'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 - `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`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | MSE-val-en-to-ar_negative_mse | MSE-val-en-to-da_negative_mse | MSE-val-en-to-de_negative_mse | MSE-val-en-to-en_negative_mse | MSE-val-en-to-es_negative_mse | MSE-val-en-to-fi_negative_mse | MSE-val-en-to-fr_negative_mse | MSE-val-en-to-he_negative_mse | MSE-val-en-to-hu_negative_mse | MSE-val-en-to-it_negative_mse | MSE-val-en-to-ja_negative_mse | MSE-val-en-to-ko_negative_mse | MSE-val-en-to-km_negative_mse | MSE-val-en-to-ms_negative_mse | MSE-val-en-to-nl_negative_mse | MSE-val-en-to-no_negative_mse | MSE-val-en-to-pl_negative_mse | MSE-val-en-to-pt_negative_mse | MSE-val-en-to-ru_negative_mse | MSE-val-en-to-sv_negative_mse | MSE-val-en-to-th_negative_mse | MSE-val-en-to-tr_negative_mse | MSE-val-en-to-vi_negative_mse | MSE-val-en-to-zh_cn_negative_mse | MSE-val-en-to-zh_hk_negative_mse | MSE-val-en-to-zh_tw_negative_mse | |:------:|:----:|:-------------:|:---------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:--------------------------------:|:--------------------------------:|:--------------------------------:| | 0.1367 | 500 | 0.3588 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2734 | 1000 | 0.3078 | 0.2868 | -27.3597 | -26.5326 | -26.5313 | -26.0601 | -26.4280 | -26.8319 | -26.4885 | -27.1627 | -26.9695 | -26.5628 | -27.2583 | -27.7239 | -31.2177 | -26.6501 | -26.4197 | -26.4809 | -26.6655 | -26.4345 | -26.6570 | -26.5526 | -30.4823 | -26.9554 | -27.1040 | -27.0230 | -26.9012 | -27.0515 | | 0.4102 | 1500 | 0.2846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5469 | 2000 | 0.2707 | 0.2617 | -24.6096 | -22.8821 | -22.8752 | -21.8660 | -22.7026 | -23.6128 | -22.7468 | -24.2281 | -23.6469 | -22.9147 | -24.3616 | -25.2999 | -30.4061 | -23.0865 | -22.5916 | -22.8392 | -23.1451 | -22.7741 | -23.2652 | -22.9440 | -29.2747 | -23.5285 | -23.8786 | -23.6384 | -23.5170 | -23.8081 | | 0.6836 | 2500 | 0.2613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8203 | 3000 | 0.2542 | 0.2491 | -23.2261 | -21.0314 | -20.9970 | -19.7599 | -20.8388 | -21.9791 | -20.8374 | -22.8299 | -22.0605 | -21.0367 | -22.9281 | -24.1290 | -29.9238 | -21.2195 | -20.6506 | -20.9939 | -21.4204 | -20.9651 | -21.5594 | -21.0815 | -28.3947 | -21.8046 | -22.2153 | -21.9866 | -21.8474 | -22.1930 | | 0.9571 | 3500 | 0.248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0938 | 4000 | 0.2438 | 0.2420 | -22.4435 | -19.9880 | -19.9588 | -18.5856 | -19.7880 | -20.9892 | -19.8194 | -21.9951 | -21.1703 | -19.9940 | -22.1052 | -23.3569 | -29.5927 | -20.1685 | -19.5862 | -19.9676 | -20.4346 | -19.9623 | -20.6201 | -20.0273 | -27.9725 | -20.8061 | -21.2406 | -21.0913 | -20.9345 | -21.3353 | | 1.2305 | 4500 | 0.2401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3672 | 5000 | 0.2371 | 0.2373 | -21.9444 | -19.3005 | -19.2441 | -17.7989 | -19.0868 | -20.3950 | -19.1305 | -21.5127 | -20.6068 | -19.3250 | -21.5673 | -22.8791 | -29.3793 | -19.4702 | -18.8669 | -19.2886 | -19.8258 | -19.3057 | -20.0101 | -19.3345 | -27.5779 | -20.1899 | -20.6284 | -20.5167 | -20.3229 | -20.7721 | | 1.5040 | 5500 | 0.2349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6407 | 6000 | 0.2336 | 0.2346 | -21.6615 | -18.9016 | -18.8657 | -17.3452 | -18.6869 | -20.0105 | -18.7528 | -21.1990 | -20.2645 | -18.9266 | -21.2386 | -22.6295 | -29.2204 | -19.0695 | -18.4641 | -18.9026 | -19.4506 | -18.9074 | -19.6659 | -18.9515 | -27.3466 | -19.8162 | -20.2736 | -20.1841 | -19.9848 | -20.4531 | | 1.7774 | 6500 | 0.2319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9141 | 7000 | 0.2309 | 0.2332 | -21.5220 | -18.7091 | -18.6632 | -17.1205 | -18.4809 | -19.8342 | -18.5557 | -21.0604 | -20.0990 | -18.7323 | -21.0808 | -22.4971 | -29.1680 | -18.8630 | -18.2583 | -18.6989 | -19.2859 | -18.7163 | -19.4929 | -18.7442 | -27.2443 | -19.6327 | -20.1037 | -20.0234 | -19.8106 | -20.3017 | | 0.1367 | 500 | 0.2302 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2734 | 1000 | 0.2261 | 0.2290 | -21.1100 | -18.0936 | -18.0277 | -16.4059 | -17.8516 | -19.2687 | -17.9684 | -20.6744 | -19.5689 | -18.1063 | -20.6725 | -22.0790 | -28.9503 | -18.2049 | -17.5842 | -18.0814 | -18.7115 | -18.1111 | -18.9581 | -18.1032 | -26.8510 | -19.0325 | -19.5538 | -19.6006 | -19.3362 | -19.8807 | | 0.4102 | 1500 | 0.222 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5469 | 2000 | 0.2188 | 0.2246 | -20.5835 | -17.4530 | -17.3853 | -15.6663 | -17.1929 | -18.6930 | -17.3208 | -20.1688 | -19.0165 | -17.4784 | -20.1460 | -21.6056 | -28.7345 | -17.5632 | -16.9100 | -17.4263 | -18.0993 | -17.4835 | -18.3902 | -17.4462 | -26.5854 | -18.4647 | -19.0091 | -19.0492 | -18.7904 | -19.3776 | | 0.6836 | 2500 | 0.2166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8203 | 3000 | 0.2148 | 0.2226 | -20.3772 | -17.1675 | -17.1095 | -15.3337 | -16.8981 | -18.4286 | -17.0421 | -19.9421 | -18.7571 | -17.1871 | -19.9155 | -21.3992 | -28.6587 | -17.2521 | -16.6051 | -17.1500 | -17.8465 | -17.1935 | -18.1342 | -17.1320 | -26.4308 | -18.1833 | -18.7496 | -18.8118 | -18.5408 | -19.1404 | | 0.9571 | 3500 | 0.2133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.46.3 - PyTorch: 2.5.1+cu121 - Accelerate: 1.1.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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", } ``` #### MSELoss ```bibtex @inproceedings{reimers-2020-multilingual-sentence-bert, title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2020", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2004.09813", } ```