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---
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) <!-- at revision 3f076fdb1ab68d5b2880cb87a0886f315b8146f8 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Evaluation
### Metrics
#### Knowledge Distillation
* Datasets: `MSE-val-en-es`, `MSE-val-en-pt` and `MSE-val-en-pt-br`
* Evaluated with [<code>MSEEvaluator</code>](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** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 3,560,698 training samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 25.46 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.67 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
| english | non_english | label |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
| <code>And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.</code> | <code>Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos.</code> | <code>[-0.04180986061692238, 0.12620249390602112, -0.14501447975635529, 0.09695684909820557, -0.10850819200277328, ...]</code> |
| <code>One thing I often ask about is ancient Greek and how this relates.</code> | <code>Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona.</code> | <code>[0.0034368489868938923, -0.02741478756070137, -0.09426739811897278, 0.04873204976320267, -0.008266829885542393, ...]</code> |
| <code>See, the thing we're doing right now is we're forcing people to learn mathematics.</code> | <code>Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas.</code> | <code>[-0.05048828944563866, 0.2713043689727783, 0.024581076577305794, -0.07316197454929352, -0.044288791716098785, ...]</code> |
* Loss: <code>__main__.ModifiedMatryoshkaLoss</code> 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: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 25.68 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 27.31 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
| english | non_english | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
| <code>Thank you so much, Chris.</code> | <code>Muchas gracias Chris.</code> | <code>[-0.1432434469461441, -0.10335833579301834, -0.07549277693033218, -0.1542435735464096, 0.009247343055903912, ...]</code> |
| <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code> | <code>Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido.</code> | <code>[0.02740730345249176, -0.0601208470761776, -0.023767368867993355, 0.02245006151497364, 0.007412586361169815, ...]</code> |
| <code>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.</code> | <code>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.</code> | <code>[-0.09117366373538971, 0.08627621084451675, -0.05912208557128906, -0.007647979073226452, 0.0008422975661233068, ...]</code> |
* Loss: <code>__main__.ModifiedMatryoshkaLoss</code> 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
<details><summary>Click to expand</summary>
- `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
</details>
### 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",
}
```
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