sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:3560698 loss:ModifiedMatryoshkaLoss arxiv:1908.10084 base_model:google-bert/bert-base-multilingual-cased base_model:finetune:google-bert/bert-base-multilingual-cased model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: google-bert/bert-base-multilingual-cased
library_name: sentence-transformers
metrics:
- negative_mse
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3560698
- loss:ModifiedMatryoshkaLoss
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.
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><summary>Click to expand</summary>

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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**     |

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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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