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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2560698
- loss:ModifiedMatryoshkaLoss
base_model: google-bert/bert-base-multilingual-cased
widget:
- source_sentence: We got off the exit, we found a Shoney's restaurant.
sentences:
- Nos alejamos de la salida, comenzamos a buscar un -- encontramos un restaurante
Shoney's.
- Reduzcan sus emisiones de dióxido de carbono con todo el rango de opciones que
tienen y luego compren o adquieran compensaciones para el resto que no han reducido
completamente.
- En el momento que nos invitaron a ir allí teníamos sede en San Francisco. Así
que fuimos de un lado a otro durante el resto de 2009, pasando la mitad del tiempo
en el condado de Bertie.
- 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:
- 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.
- Tiene que ayudarles a alcanzar un compromiso equitativo, y a asegurar que una
amplia coalición de partidarios locales regionales e internacionales les ayuden
a implementar el acuerdo.
- Y es un renegado y visionario absoluto, y esa es la razón por la que ahora vivo
y trabajo allí.
- source_sentence: Figure out some of the other options that are much better.
sentences:
- Así que no sólo estamos reclutando a las multinacionales, les estamos dando las
herramientas para entregar este bien público, el respeto por los Derechos Humanos,
y lo estamos verificando.
- Piensen en otras de las opciones que son mucho mejores.
- Termina la propiedad comunal de las tierras de reserva.
- source_sentence: He is 16 years old, loves hunting and fishing and being outside
and doing anything with his hands, and so for him, Studio H means that he can
stay interested in his education through that hands-on engagement.
sentences:
- Tiene 16 años, le encanta cazar, pescar y estar al aire libre y hacer tareas manuales.
Para él Studio H representa el nexo educativo mediante esa motivación práctica.
- Carbón capturado y secuestrado -- eso es lo que CCS significa -- es probable que
se convierta en la aplicación determinante que nos posibilitará continuar utilizando
combustibles fósiles en un modo que sea seguro.
- El condado de Bertie no es la excepción.
- source_sentence: There are thousands of these blue dots all over the county.
sentences:
- Me gusta crisis climática en vez de colapso climático, pero de nuevo, aquellos
de ustedes que son buenos en diseño de marcas, necesito su ayuda en esto.
- Si miran con cuidado, se ve que su cráneo ha sido sustituido por una cúpula transparente
de plexiglás así que el funcionamiento de su cerebro se puede observar y controlar
con luz.
- Hay miles de estos puntos azules en todo el condado.
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: -31.070706248283386
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: -31.284737586975098
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: -29.748335480690002
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_v1")
# Run inference
sentences = [
'There are thousands of these blue dots all over the county.',
'Hay miles de estos puntos azules en todo el condado.',
'Me gusta crisis climática en vez de colapso climático, pero de nuevo, aquellos de ustedes que son buenos en diseño de marcas, necesito su ayuda en esto.',
]
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** | **-31.0707** | **-31.2847** | **-29.7483** |
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*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: 2,560,698 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>[-0.0616779625415802, -0.04450426995754242, -0.03250579163432121, -0.06641441583633423, 0.003981655463576317, ...]</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`: 1
- `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`: 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`: ['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 |
|:------:|:-----:|:-------------:|:---------------:|:--------------------------:|:--------------------------:|:-----------------------------:|
| 0.0781 | 1000 | 0.0252 | 0.0231 | -24.4152 | -24.3443 | -25.3002 |
| 0.1562 | 2000 | 0.0222 | 0.0212 | -25.3038 | -25.3995 | -24.8563 |
| 0.2343 | 3000 | 0.021 | 0.0204 | -27.0894 | -27.2195 | -26.2906 |
| 0.3124 | 4000 | 0.0204 | 0.0198 | -28.7895 | -28.9815 | -28.0121 |
| 0.3905 | 5000 | 0.02 | 0.0194 | -29.1917 | -29.3694 | -28.0828 |
| 0.4686 | 6000 | 0.0196 | 0.0191 | -30.0902 | -30.2569 | -28.9723 |
| 0.5467 | 7000 | 0.0194 | 0.0189 | -30.3385 | -30.5334 | -29.1280 |
| 0.6248 | 8000 | 0.0192 | 0.0188 | -30.6629 | -30.8491 | -29.4291 |
| 0.7029 | 9000 | 0.0191 | 0.0186 | -30.6934 | -30.8920 | -29.4820 |
| 0.7810 | 10000 | 0.019 | 0.0185 | -31.0134 | -31.2205 | -29.6545 |
| 0.8591 | 11000 | 0.0189 | 0.0185 | -31.0993 | -31.2950 | -29.8062 |
| 0.9372 | 12000 | 0.0188 | 0.0184 | -31.0707 | -31.2847 | -29.7483 |
### 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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