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: This is a mine in Zimbabwe right now.
  sentences:
  - Esta es una mina de Zimbabwe en este momento.
  - Transformar eso en una respuesta con forma matemática.
  - Centrarse en el liderazgo, la diplomacia y el diseño institucional ayuda también
    a explicar los intentos de paz que fracasan, o que no perduran.
- source_sentence: '"You want me to deliver human rights throughout my global supply
    chain.'
  sentences:
  - '"Quieres que respete los Derechos Humanos en la cadena mundial de suministro.'
  - ¿Qué queremos decir cuando decimos que hacemos matemática... ...o que enseñamos
    matemática?
  - Así que criamos moscas cuyos cerebros fueron salpicados más o menos al azar con
    células direccionables por la luz.
- source_sentence: Figure out some of the other options that are much better.
  sentences:
  - En Kirguistán, en las últimas semanas, ocurrieron niveles de violencia sin precedentes
    entre los kirguíes étnicos y los uzbecos étnicos.
  - Piensen en otras de las opciones que son mucho mejores.
  - La película sale -- la película es una versión en película de la presentación
    de las diapositivas que di hace dos noches, excepto que es mucho más entretenida.
- source_sentence: I've become very close with them, and they've welcomed me like
    family.
  sentences:
  - he logrado una relación estrecha con ellos; soy como de la familia.
  - O que los oídos se oigan a sí mismos... simplemente es imposible;
  - Es un producto farmacéutico.
- source_sentence: All the grayed-out species disappear.
  sentences:
  - 'Los diamantes: quizá todos hemos oído hablar de la película "Diamante de sangre".'
  - Hay un vacío total de capital creativo en Bertie.
  - Van a desaparecer todas las especies en gris.
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: -33.77506732940674
      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: -34.092217683792114
      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: -32.07869827747345
      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/bert-en-es-pt-matryoshka_v3")
# Run inference
sentences = [
    'All the grayed-out species disappear.',
    'Van a desaparecer todas las especies en gris.',
    'Los diamantes: quizá todos hemos oído hablar de la película "Diamante de sangre".',
]
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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## 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** | **-33.7751**  | **-34.0922**  | **-32.0787**     |

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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>[-0.015244179405272007, 0.04601434990763664, -0.052873335778713226, 0.03535117208957672, -0.039562877267599106, ...]</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>[0.0012022971641272306, -0.009590390138328075, -0.032977133989334106, 0.017047710716724396, -0.0028919472824782133, ...]</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>[-0.01942082867026329, 0.1043599545955658, 0.009455358609557152, -0.02814248949289322, -0.017036128789186478, ...]</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>[-0.0616779662668705, -0.044504180550575256, -0.032505787909030914, -0.06641441583633423, 0.003981734160333872, ...]</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>[0.011398598551750183, -0.02500401996076107, -0.009884790517389774, 0.009336900897324085, 0.003082842566072941, ...]</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>[-0.03842132166028023, 0.03635749593377113, -0.02491452544927597, -0.0032229204662144184, 0.0003549510147422552, ...]</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  | 0.0283        | 0.0251          | -22.4432                   | -22.0406                   | -25.1401                      |
| 0.1123 | 2000  | 0.0241        | 0.0227          | -24.1255                   | -23.9880                   | -24.7731                      |
| 0.1685 | 3000  | 0.0224        | 0.0214          | -25.3630                   | -25.2889                   | -25.4316                      |
| 0.2247 | 4000  | 0.0214        | 0.0205          | -27.9225                   | -28.0038                   | -27.3050                      |
| 0.2808 | 5000  | 0.0206        | 0.0199          | -29.4189                   | -29.5093                   | -28.8545                      |
| 0.3370 | 6000  | 0.0202        | 0.0194          | -30.3190                   | -30.4212                   | -29.4919                      |
| 0.3932 | 7000  | 0.0198        | 0.0191          | -31.3278                   | -31.4753                   | -30.3090                      |
| 0.4493 | 8000  | 0.0195        | 0.0188          | -31.4089                   | -31.6387                   | -30.3325                      |
| 0.5055 | 9000  | 0.0193        | 0.0186          | -32.0598                   | -32.2536                   | -30.9067                      |
| 0.5617 | 10000 | 0.0191        | 0.0184          | -32.0989                   | -32.2766                   | -31.0155                      |
| 0.6178 | 11000 | 0.0189        | 0.0183          | -32.2449                   | -32.4302                   | -30.9863                      |
| 0.6740 | 12000 | 0.0187        | 0.0181          | -32.5800                   | -32.8070                   | -31.2254                      |
| 0.7302 | 13000 | 0.0186        | 0.0180          | -32.9225                   | -33.1228                   | -31.5803                      |
| 0.7863 | 14000 | 0.0185        | 0.0179          | -32.9227                   | -33.1304                   | -31.5169                      |
| 0.8425 | 15000 | 0.0184        | 0.0178          | -33.0181                   | -33.2681                   | -31.5791                      |
| 0.8987 | 16000 | 0.0183        | 0.0177          | -33.1309                   | -33.3638                   | -31.6113                      |
| 0.9548 | 17000 | 0.0182        | 0.0176          | -33.1635                   | -33.4414                   | -31.6507                      |
| 1.0110 | 18000 | 0.0181        | 0.0175          | -33.3615                   | -33.6376                   | -31.8086                      |
| 1.0672 | 19000 | 0.018         | 0.0175          | -33.5781                   | -33.8775                   | -32.0611                      |
| 1.1233 | 20000 | 0.0179        | 0.0174          | -33.5645                   | -33.8531                   | -32.0438                      |
| 1.1795 | 21000 | 0.0179        | 0.0173          | -33.6646                   | -33.9817                   | -32.0500                      |
| 1.2357 | 22000 | 0.0179        | 0.0173          | -33.7056                   | -34.0088                   | -32.1065                      |
| 1.2918 | 23000 | 0.0178        | 0.0173          | -33.7397                   | -34.0153                   | -32.1810                      |
| 1.3480 | 24000 | 0.0178        | 0.0172          | -33.7863                   | -34.0887                   | -32.1103                      |
| 1.4042 | 25000 | 0.0177        | 0.0172          | -33.7981                   | -34.0863                   | -32.1683                      |
| 1.4603 | 26000 | 0.0177        | 0.0171          | -33.7458                   | -34.0451                   | -32.0611                      |
| 1.5165 | 27000 | 0.0177        | 0.0171          | -33.7650                   | -34.0652                   | -32.1565                      |
| 1.5727 | 28000 | 0.0176        | 0.0171          | -33.7347                   | -34.0446                   | -32.0698                      |
| 1.6288 | 29000 | 0.0176        | 0.0171          | -33.8011                   | -34.1169                   | -32.0683                      |
| 1.6850 | 30000 | 0.0176        | 0.0170          | -33.7949                   | -34.1010                   | -32.1128                      |
| 1.7412 | 31000 | 0.0176        | 0.0170          | -33.7713                   | -34.0857                   | -32.1020                      |
| 1.7973 | 32000 | 0.0176        | 0.0170          | -33.8393                   | -34.1676                   | -32.1371                      |
| 1.8535 | 33000 | 0.0175        | 0.0170          | -33.7687                   | -34.0887                   | -32.0748                      |
| 1.9097 | 34000 | 0.0175        | 0.0170          | -33.7614                   | -34.0854                   | -32.0550                      |
| 1.9659 | 35000 | 0.0175        | 0.0170          | -33.7751                   | -34.0922                   | -32.0787                      |


### 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",
}
```

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