--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3560698 - loss:ModifiedMatryoshkaLoss base_model: google-bert/bert-base-multilingual-cased widget: - source_sentence: And then finally, turn it back to the real world. sentences: - Y luego, finalmente, devolver eso al mundo real. - Parece que el único rasgo que sobrevive a la decapitación es la vanidad. - y yo digo que no estoy seguro. Voy a pensarlo a groso modo. - source_sentence: Figure out some of the other options that are much better. sentences: - Piensen en otras de las opciones que son mucho mejores. - Éste solía ser un tema bipartidista, y sé que en este grupo realmente lo es. - El acuerdo general de paz para Sudán firmado en 2005 resultó ser menos amplio que lo previsto, y sus disposiciones aún podrían engendrar un retorno a gran escala de la guerra entre el norte y el sur. - source_sentence: 'The call to action I offer today -- my TED wish -- is this: Honor the treaties.' sentences: - Esta es la intersección más directa, obvia, de las dos cosas. - 'El llamado a la acción que propongo hoy, mi TED Wish, es el siguiente: Honrar los tratados.' - Los restaurantes del condado se pueden contar con los dedos de una mano... Barbacoa Bunn es mi favorito. - source_sentence: So for us, this was a graphic public campaign called Connect Bertie. sentences: - Para nosotros esto era una campaña gráfica llamada Conecta a Bertie. - En cambio, los líderes locales se comprometieron a revisarlos más adelante. - Con el tiempo, la gente hace lo que se le paga por hacer. - source_sentence: And in the audio world that's when the microphone gets too close to its sound source, and then it gets in this self-destructive loop that creates a very unpleasant sound. sentences: - Esta es una mina de Zimbabwe en este momento. - Estábamos en la I-40. - Y, en el mundo del audio, es cuando el micrófono se acerca demasiado a su fuente de sonido, y entra en este bucle autodestructivo que crea un sonido muy desagradable. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - negative_mse model-index: - name: SentenceTransformer based on google-bert/bert-base-multilingual-cased results: - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en es type: MSE-val-en-es metrics: - type: negative_mse value: -29.5114666223526 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en pt type: MSE-val-en-pt metrics: - type: negative_mse value: -29.913604259490967 name: Negative Mse - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: MSE val en pt br type: MSE-val-en-pt-br metrics: - type: negative_mse value: -27.732226252555847 name: Negative Mse --- # SentenceTransformer based on google-bert/bert-base-multilingual-cased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased). 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-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) - **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/TriLingual-BERT-Distil") # Run inference sentences = [ "And in the audio world that's when the microphone gets too close to its sound source, and then it gets in this self-destructive loop that creates a very unpleasant sound.", 'Y, en el mundo del audio, es cuando el micrófono se acerca demasiado a su fuente de sonido, y entra en este bucle autodestructivo que crea un sonido muy desagradable.', 'Esta es una mina de Zimbabwe en este momento.', ] 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-es`, `MSE-val-en-pt` and `MSE-val-en-pt-br` * Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | MSE-val-en-es | MSE-val-en-pt | MSE-val-en-pt-br | |:-----------------|:--------------|:--------------|:-----------------| | **negative_mse** | **-29.5115** | **-29.9136** | **-27.7322** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 3,560,698 training samples * Columns: english, non_english, and label * Approximate statistics based on the first 1000 samples: | | english | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | string | list | | details | | | | * Samples: | english | non_english | label | |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------| | And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number. | Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos. | [-0.04180986061692238, 0.12620249390602112, -0.14501447975635529, 0.09695684909820557, -0.10850819200277328, ...] | | One thing I often ask about is ancient Greek and how this relates. | Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona. | [0.0034368489868938923, -0.02741478756070137, -0.09426739811897278, 0.04873204976320267, -0.008266829885542393, ...] | | See, the thing we're doing right now is we're forcing people to learn mathematics. | Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas. | [-0.05048828944563866, 0.2713043689727783, 0.024581076577305794, -0.07316197454929352, -0.044288791716098785, ...] | * Loss: __main__.ModifiedMatryoshkaLoss with these parameters: ```json { "loss": "MSELoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 6,974 evaluation samples * Columns: english, non_english, and label * Approximate statistics based on the first 1000 samples: | | english | non_english | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------| | type | string | string | list | | details | | | | * Samples: | english | non_english | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------| | Thank you so much, Chris. | Muchas gracias Chris. | [-0.1432434469461441, -0.10335833579301834, -0.07549277693033218, -0.1542435735464096, 0.009247343055903912, ...] | | And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful. | Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido. | [0.02740730345249176, -0.0601208470761776, -0.023767368867993355, 0.02245006151497364, 0.007412586361169815, ...] | | I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night. | He quedado conmovido por esta conferencia, y deseo agradecer a todos ustedes sus amables comentarios acerca de lo que tenía que decir la otra noche. | [-0.09117366373538971, 0.08627621084451675, -0.05912208557128906, -0.007647979073226452, 0.0008422975661233068, ...] | * Loss: __main__.ModifiedMatryoshkaLoss with these parameters: ```json { "loss": "MSELoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 200 - `per_device_eval_batch_size`: 200 - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `warmup_ratio`: 0.1 - `fp16`: True - `label_names`: ['label'] #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 200 - `per_device_eval_batch_size`: 200 - `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`: 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`: ['label'] - `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-es_negative_mse | MSE-val-en-pt_negative_mse | MSE-val-en-pt-br_negative_mse | |:------:|:-----:|:-------------:|:---------------:|:--------------------------:|:--------------------------:|:-----------------------------:| | 0.0562 | 1000 | 0.0626 | 0.0513 | -21.2968 | -20.7534 | -24.2460 | | 0.1123 | 2000 | 0.0478 | 0.0432 | -22.1192 | -21.8663 | -23.2775 | | 0.1685 | 3000 | 0.0423 | 0.0391 | -21.6697 | -21.5869 | -21.6856 | | 0.0562 | 1000 | 0.0396 | 0.0376 | -21.7666 | -21.7181 | -21.6779 | | 0.1123 | 2000 | 0.0381 | 0.0358 | -23.4969 | -23.5022 | -22.9817 | | 0.1685 | 3000 | 0.0362 | 0.0339 | -24.7639 | -24.8878 | -23.8888 | | 0.2247 | 4000 | 0.0347 | 0.0323 | -26.5721 | -26.7422 | -25.4072 | | 0.2808 | 5000 | 0.0332 | 0.0310 | -27.6024 | -27.8268 | -26.4132 | | 0.3370 | 6000 | 0.0321 | 0.0299 | -27.7974 | -28.0294 | -26.6213 | | 0.3932 | 7000 | 0.0312 | 0.0292 | -28.2719 | -28.4834 | -27.0468 | | 0.4493 | 8000 | 0.0305 | 0.0285 | -28.2561 | -28.5574 | -26.8752 | | 0.5055 | 9000 | 0.0299 | 0.0280 | -28.6342 | -28.9112 | -27.2933 | | 0.5617 | 10000 | 0.0294 | 0.0275 | -28.5512 | -28.8469 | -27.1072 | | 0.6178 | 11000 | 0.029 | 0.0271 | -28.6788 | -28.9608 | -27.2056 | | 0.6740 | 12000 | 0.0286 | 0.0267 | -29.0159 | -29.3281 | -27.4770 | | 0.7302 | 13000 | 0.0283 | 0.0264 | -28.9224 | -29.2461 | -27.3500 | | 0.7863 | 14000 | 0.028 | 0.0261 | -29.1044 | -29.4303 | -27.4377 | | 0.8425 | 15000 | 0.0277 | 0.0259 | -29.2340 | -29.5758 | -27.6223 | | 0.8987 | 16000 | 0.0275 | 0.0257 | -29.1356 | -29.4699 | -27.4667 | | 0.9548 | 17000 | 0.0273 | 0.0255 | -29.3281 | -29.6671 | -27.7174 | | 1.0110 | 18000 | 0.0271 | 0.0253 | -29.2991 | -29.6635 | -27.6675 | | 1.0672 | 19000 | 0.0268 | 0.0251 | -29.3581 | -29.7326 | -27.6587 | | 1.1233 | 20000 | 0.0266 | 0.0250 | -29.4233 | -29.7941 | -27.7913 | | 1.1795 | 21000 | 0.0265 | 0.0248 | -29.3941 | -29.7583 | -27.6951 | | 1.2357 | 22000 | 0.0264 | 0.0247 | -29.5963 | -29.9737 | -27.9191 | | 1.2918 | 23000 | 0.0262 | 0.0245 | -29.4587 | -29.8472 | -27.7702 | | 1.3480 | 24000 | 0.0262 | 0.0244 | -29.4977 | -29.8868 | -27.8142 | | 1.4042 | 25000 | 0.026 | 0.0244 | -29.5356 | -29.9184 | -27.8426 | | 1.4603 | 26000 | 0.0259 | 0.0243 | -29.5614 | -29.9388 | -27.8360 | | 1.5165 | 27000 | 0.0259 | 0.0242 | -29.5362 | -29.9353 | -27.8223 | | 1.5727 | 28000 | 0.0258 | 0.0241 | -29.5088 | -29.9043 | -27.7884 | | 1.6288 | 29000 | 0.0258 | 0.0241 | -29.4550 | -29.8543 | -27.6788 | | 1.6850 | 30000 | 0.0257 | 0.0240 | -29.5373 | -29.9282 | -27.7855 | | 1.7412 | 31000 | 0.0256 | 0.0239 | -29.5195 | -29.9096 | -27.7866 | | 1.7973 | 32000 | 0.0256 | 0.0239 | -29.5292 | -29.9266 | -27.7579 | | 1.8535 | 33000 | 0.0256 | 0.0239 | -29.5202 | -29.9196 | -27.7408 | | 1.9097 | 34000 | 0.0255 | 0.0239 | -29.5090 | -29.9126 | -27.7311 | | 1.9659 | 35000 | 0.0255 | 0.0238 | -29.5115 | -29.9136 | -27.7322 | ### 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: 3.2.0 - 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", } ```