sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:234000 loss:MSELoss arxiv:1908.10084 arxiv:2004.09813 base_model:google-bert/bert-base-multilingual-uncased base_model:finetune:google-bert/bert-base-multilingual-uncased model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: google-bert/bert-base-multilingual-uncased
library_name: sentence-transformers
metrics:
- negative_mse
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:234000
- loss:MSELoss
widget:
- source_sentence: who sings in spite of ourselves with john prine
  sentences:
  - es
  - når ble michael jordan draftet til nba
  - quien canta en spite of ourselves con john prine
- source_sentence: who wrote when you look me in the eyes
  sentences:
  - متى بدأت الفتاة الكشفية في بيع ملفات تعريف الارتباط
  - A écrit when you look me in the eyes
  - fr
- source_sentence: when was fathers day made a national holiday
  sentences:
  - wann wurde der Vatertag zum nationalen Feiertag
  - de
  - ' អ្នកណាច្រៀង i want to sing you a love song'
- source_sentence: what is the density of the continental crust
  sentences:
  - cuál es la densidad de la corteza continental
  - wie zingt i want to sing you a love song
  - es
- source_sentence: who wrote the song i shot the sheriff
  sentences:
  - Quel est l'âge légal pour consommer du vin au Canada?
  - i shot the sheriff şarkısını kim besteledi
  - tr
model-index:
- name: SentenceTransformer based on google-bert/bert-base-multilingual-uncased
  results:
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to ar
      type: MSE-val-en-to-ar
    metrics:
    - type: negative_mse
      value: -20.37721574306488
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to da
      type: MSE-val-en-to-da
    metrics:
    - type: negative_mse
      value: -17.167489230632782
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to de
      type: MSE-val-en-to-de
    metrics:
    - type: negative_mse
      value: -17.10948944091797
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to en
      type: MSE-val-en-to-en
    metrics:
    - type: negative_mse
      value: -15.333698689937592
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to es
      type: MSE-val-en-to-es
    metrics:
    - type: negative_mse
      value: -16.898061335086823
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to fi
      type: MSE-val-en-to-fi
    metrics:
    - type: negative_mse
      value: -18.428558111190796
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to fr
      type: MSE-val-en-to-fr
    metrics:
    - type: negative_mse
      value: -17.04207956790924
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to he
      type: MSE-val-en-to-he
    metrics:
    - type: negative_mse
      value: -19.942057132720947
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to hu
      type: MSE-val-en-to-hu
    metrics:
    - type: negative_mse
      value: -18.757066130638123
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to it
      type: MSE-val-en-to-it
    metrics:
    - type: negative_mse
      value: -17.18708872795105
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to ja
      type: MSE-val-en-to-ja
    metrics:
    - type: negative_mse
      value: -19.915536046028137
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to ko
      type: MSE-val-en-to-ko
    metrics:
    - type: negative_mse
      value: -21.39919400215149
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to km
      type: MSE-val-en-to-km
    metrics:
    - type: negative_mse
      value: -28.658682107925415
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to ms
      type: MSE-val-en-to-ms
    metrics:
    - type: negative_mse
      value: -17.25209951400757
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to nl
      type: MSE-val-en-to-nl
    metrics:
    - type: negative_mse
      value: -16.605134308338165
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to no
      type: MSE-val-en-to-no
    metrics:
    - type: negative_mse
      value: -17.149969935417175
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to pl
      type: MSE-val-en-to-pl
    metrics:
    - type: negative_mse
      value: -17.846450209617615
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to pt
      type: MSE-val-en-to-pt
    metrics:
    - type: negative_mse
      value: -17.19353199005127
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to ru
      type: MSE-val-en-to-ru
    metrics:
    - type: negative_mse
      value: -18.13419610261917
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to sv
      type: MSE-val-en-to-sv
    metrics:
    - type: negative_mse
      value: -17.13200956583023
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to th
      type: MSE-val-en-to-th
    metrics:
    - type: negative_mse
      value: -26.43084228038788
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to tr
      type: MSE-val-en-to-tr
    metrics:
    - type: negative_mse
      value: -18.183308839797974
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to vi
      type: MSE-val-en-to-vi
    metrics:
    - type: negative_mse
      value: -18.749597668647766
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to zh cn
      type: MSE-val-en-to-zh_cn
    metrics:
    - type: negative_mse
      value: -18.811793625354767
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to zh hk
      type: MSE-val-en-to-zh_hk
    metrics:
    - type: negative_mse
      value: -18.54081153869629
      name: Negative Mse
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: MSE val en to zh tw
      type: MSE-val-en-to-zh_tw
    metrics:
    - type: negative_mse
      value: -19.14038509130478
      name: Negative Mse
---

# SentenceTransformer based on google-bert/bert-base-multilingual-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased). 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-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) <!-- at revision 7cbf9a625e29989f6b9c6c2fa68234c304f7e38f -->
- **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-base-multilingual-uncased-matryoshka-mkqa")
# Run inference
sentences = [
    'who wrote the song i shot the sheriff',
    'i shot the sheriff şarkısını kim besteledi',
    'tr',
]
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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You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Evaluation

### Metrics

#### Knowledge Distillation

* Datasets: `MSE-val-en-to-ar`, `MSE-val-en-to-da`, `MSE-val-en-to-de`, `MSE-val-en-to-en`, `MSE-val-en-to-es`, `MSE-val-en-to-fi`, `MSE-val-en-to-fr`, `MSE-val-en-to-he`, `MSE-val-en-to-hu`, `MSE-val-en-to-it`, `MSE-val-en-to-ja`, `MSE-val-en-to-ko`, `MSE-val-en-to-km`, `MSE-val-en-to-ms`, `MSE-val-en-to-nl`, `MSE-val-en-to-no`, `MSE-val-en-to-pl`, `MSE-val-en-to-pt`, `MSE-val-en-to-ru`, `MSE-val-en-to-sv`, `MSE-val-en-to-th`, `MSE-val-en-to-tr`, `MSE-val-en-to-vi`, `MSE-val-en-to-zh_cn`, `MSE-val-en-to-zh_hk` and `MSE-val-en-to-zh_tw`
* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)

| Metric           | MSE-val-en-to-ar | MSE-val-en-to-da | MSE-val-en-to-de | MSE-val-en-to-en | MSE-val-en-to-es | MSE-val-en-to-fi | MSE-val-en-to-fr | MSE-val-en-to-he | MSE-val-en-to-hu | MSE-val-en-to-it | MSE-val-en-to-ja | MSE-val-en-to-ko | MSE-val-en-to-km | MSE-val-en-to-ms | MSE-val-en-to-nl | MSE-val-en-to-no | MSE-val-en-to-pl | MSE-val-en-to-pt | MSE-val-en-to-ru | MSE-val-en-to-sv | MSE-val-en-to-th | MSE-val-en-to-tr | MSE-val-en-to-vi | MSE-val-en-to-zh_cn | MSE-val-en-to-zh_hk | MSE-val-en-to-zh_tw |
|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:--------------------|:--------------------|:--------------------|
| **negative_mse** | **-20.3772**     | **-17.1675**     | **-17.1095**     | **-15.3337**     | **-16.8981**     | **-18.4286**     | **-17.0421**     | **-19.9421**     | **-18.7571**     | **-17.1871**     | **-19.9155**     | **-21.3992**     | **-28.6587**     | **-17.2521**     | **-16.6051**     | **-17.15**       | **-17.8465**     | **-17.1935**     | **-18.1342**     | **-17.132**      | **-26.4308**     | **-18.1833**     | **-18.7496**     | **-18.8118**        | **-18.5408**        | **-19.1404**        |

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## Bias, Risks and Limitations

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### Recommendations

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 234,000 training samples
* Columns: <code>english</code>, <code>non-english</code>, <code>target</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | english                                                                            | non-english                                                                       | target                                                                          | label                                |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                          | list                                 |
  | details | <ul><li>min: 10 tokens</li><li>mean: 11.48 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.27 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.38 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
  | english                                          | non-english                                             | target          | label                                                                                                                   |
  |:-------------------------------------------------|:--------------------------------------------------------|:----------------|:------------------------------------------------------------------------------------------------------------------------|
  | <code>who plays hope on days of our lives</code> | <code>من الذي يلعب الأمل في أيام حياتنا</code>          | <code>ar</code> | <code>[0.2171212136745453, 0.5138550996780396, 0.5517176389694214, -1.0655105113983154, 1.5853567123413086, ...]</code> |
  | <code>who plays hope on days of our lives</code> | <code>hvem spiller hope i Horton-sagaen</code>          | <code>da</code> | <code>[0.2171212136745453, 0.5138550996780396, 0.5517176389694214, -1.0655105113983154, 1.5853567123413086, ...]</code> |
  | <code>who plays hope on days of our lives</code> | <code>Wer spielt die Hope in Zeit der Sehnsucht?</code> | <code>de</code> | <code>[0.2171212136745453, 0.5138550996780396, 0.5517176389694214, -1.0655105113983154, 1.5853567123413086, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

### Evaluation Dataset

#### Unnamed Dataset


* Size: 13,000 evaluation samples
* Columns: <code>english</code>, <code>non-english</code>, <code>target</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | english                                                                            | non-english                                                                       | target                                                                          | label                                |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                             | string                                                                            | string                                                                          | list                                 |
  | details | <ul><li>min: 10 tokens</li><li>mean: 11.53 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.37 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.38 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
  | english                                                     | non-english                                                     | target          | label                                                                                                                        |
  |:------------------------------------------------------------|:----------------------------------------------------------------|:----------------|:-----------------------------------------------------------------------------------------------------------------------------|
  | <code>who played prudence on nanny and the professor</code> | <code>من لعب دور "prudence" فى "nanny and the professor"</code> | <code>ar</code> | <code>[-0.2837616801261902, -0.4943353235721588, 0.020107418298721313, 0.7796109318733215, -0.47365888953208923, ...]</code> |
  | <code>who played prudence on nanny and the professor</code> | <code>hvem spiller prudence på nanny and the professor</code>   | <code>da</code> | <code>[-0.2837616801261902, -0.4943353235721588, 0.020107418298721313, 0.7796109318733215, -0.47365888953208923, ...]</code> |
  | <code>who played prudence on nanny and the professor</code> | <code>Wer spielte Prudence in Nanny and the Professor</code>    | <code>de</code> | <code>[-0.2837616801261902, -0.4943353235721588, 0.020107418298721313, 0.7796109318733215, -0.47365888953208923, ...]</code> |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 1e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True

#### 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 1e-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`: None
- `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-to-ar_negative_mse | MSE-val-en-to-da_negative_mse | MSE-val-en-to-de_negative_mse | MSE-val-en-to-en_negative_mse | MSE-val-en-to-es_negative_mse | MSE-val-en-to-fi_negative_mse | MSE-val-en-to-fr_negative_mse | MSE-val-en-to-he_negative_mse | MSE-val-en-to-hu_negative_mse | MSE-val-en-to-it_negative_mse | MSE-val-en-to-ja_negative_mse | MSE-val-en-to-ko_negative_mse | MSE-val-en-to-km_negative_mse | MSE-val-en-to-ms_negative_mse | MSE-val-en-to-nl_negative_mse | MSE-val-en-to-no_negative_mse | MSE-val-en-to-pl_negative_mse | MSE-val-en-to-pt_negative_mse | MSE-val-en-to-ru_negative_mse | MSE-val-en-to-sv_negative_mse | MSE-val-en-to-th_negative_mse | MSE-val-en-to-tr_negative_mse | MSE-val-en-to-vi_negative_mse | MSE-val-en-to-zh_cn_negative_mse | MSE-val-en-to-zh_hk_negative_mse | MSE-val-en-to-zh_tw_negative_mse |
|:------:|:----:|:-------------:|:---------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:--------------------------------:|:--------------------------------:|:--------------------------------:|
| 0.1367 | 500  | 0.3588        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 0.2734 | 1000 | 0.3078        | 0.2868          | -27.3597                      | -26.5326                      | -26.5313                      | -26.0601                      | -26.4280                      | -26.8319                      | -26.4885                      | -27.1627                      | -26.9695                      | -26.5628                      | -27.2583                      | -27.7239                      | -31.2177                      | -26.6501                      | -26.4197                      | -26.4809                      | -26.6655                      | -26.4345                      | -26.6570                      | -26.5526                      | -30.4823                      | -26.9554                      | -27.1040                      | -27.0230                         | -26.9012                         | -27.0515                         |
| 0.4102 | 1500 | 0.2846        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 0.5469 | 2000 | 0.2707        | 0.2617          | -24.6096                      | -22.8821                      | -22.8752                      | -21.8660                      | -22.7026                      | -23.6128                      | -22.7468                      | -24.2281                      | -23.6469                      | -22.9147                      | -24.3616                      | -25.2999                      | -30.4061                      | -23.0865                      | -22.5916                      | -22.8392                      | -23.1451                      | -22.7741                      | -23.2652                      | -22.9440                      | -29.2747                      | -23.5285                      | -23.8786                      | -23.6384                         | -23.5170                         | -23.8081                         |
| 0.6836 | 2500 | 0.2613        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 0.8203 | 3000 | 0.2542        | 0.2491          | -23.2261                      | -21.0314                      | -20.9970                      | -19.7599                      | -20.8388                      | -21.9791                      | -20.8374                      | -22.8299                      | -22.0605                      | -21.0367                      | -22.9281                      | -24.1290                      | -29.9238                      | -21.2195                      | -20.6506                      | -20.9939                      | -21.4204                      | -20.9651                      | -21.5594                      | -21.0815                      | -28.3947                      | -21.8046                      | -22.2153                      | -21.9866                         | -21.8474                         | -22.1930                         |
| 0.9571 | 3500 | 0.248         | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 1.0938 | 4000 | 0.2438        | 0.2420          | -22.4435                      | -19.9880                      | -19.9588                      | -18.5856                      | -19.7880                      | -20.9892                      | -19.8194                      | -21.9951                      | -21.1703                      | -19.9940                      | -22.1052                      | -23.3569                      | -29.5927                      | -20.1685                      | -19.5862                      | -19.9676                      | -20.4346                      | -19.9623                      | -20.6201                      | -20.0273                      | -27.9725                      | -20.8061                      | -21.2406                      | -21.0913                         | -20.9345                         | -21.3353                         |
| 1.2305 | 4500 | 0.2401        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 1.3672 | 5000 | 0.2371        | 0.2373          | -21.9444                      | -19.3005                      | -19.2441                      | -17.7989                      | -19.0868                      | -20.3950                      | -19.1305                      | -21.5127                      | -20.6068                      | -19.3250                      | -21.5673                      | -22.8791                      | -29.3793                      | -19.4702                      | -18.8669                      | -19.2886                      | -19.8258                      | -19.3057                      | -20.0101                      | -19.3345                      | -27.5779                      | -20.1899                      | -20.6284                      | -20.5167                         | -20.3229                         | -20.7721                         |
| 1.5040 | 5500 | 0.2349        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 1.6407 | 6000 | 0.2336        | 0.2346          | -21.6615                      | -18.9016                      | -18.8657                      | -17.3452                      | -18.6869                      | -20.0105                      | -18.7528                      | -21.1990                      | -20.2645                      | -18.9266                      | -21.2386                      | -22.6295                      | -29.2204                      | -19.0695                      | -18.4641                      | -18.9026                      | -19.4506                      | -18.9074                      | -19.6659                      | -18.9515                      | -27.3466                      | -19.8162                      | -20.2736                      | -20.1841                         | -19.9848                         | -20.4531                         |
| 1.7774 | 6500 | 0.2319        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 1.9141 | 7000 | 0.2309        | 0.2332          | -21.5220                      | -18.7091                      | -18.6632                      | -17.1205                      | -18.4809                      | -19.8342                      | -18.5557                      | -21.0604                      | -20.0990                      | -18.7323                      | -21.0808                      | -22.4971                      | -29.1680                      | -18.8630                      | -18.2583                      | -18.6989                      | -19.2859                      | -18.7163                      | -19.4929                      | -18.7442                      | -27.2443                      | -19.6327                      | -20.1037                      | -20.0234                         | -19.8106                         | -20.3017                         |
| 0.1367 | 500  | 0.2302        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 0.2734 | 1000 | 0.2261        | 0.2290          | -21.1100                      | -18.0936                      | -18.0277                      | -16.4059                      | -17.8516                      | -19.2687                      | -17.9684                      | -20.6744                      | -19.5689                      | -18.1063                      | -20.6725                      | -22.0790                      | -28.9503                      | -18.2049                      | -17.5842                      | -18.0814                      | -18.7115                      | -18.1111                      | -18.9581                      | -18.1032                      | -26.8510                      | -19.0325                      | -19.5538                      | -19.6006                         | -19.3362                         | -19.8807                         |
| 0.4102 | 1500 | 0.222         | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 0.5469 | 2000 | 0.2188        | 0.2246          | -20.5835                      | -17.4530                      | -17.3853                      | -15.6663                      | -17.1929                      | -18.6930                      | -17.3208                      | -20.1688                      | -19.0165                      | -17.4784                      | -20.1460                      | -21.6056                      | -28.7345                      | -17.5632                      | -16.9100                      | -17.4263                      | -18.0993                      | -17.4835                      | -18.3902                      | -17.4462                      | -26.5854                      | -18.4647                      | -19.0091                      | -19.0492                         | -18.7904                         | -19.3776                         |
| 0.6836 | 2500 | 0.2166        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |
| 0.8203 | 3000 | 0.2148        | 0.2226          | -20.3772                      | -17.1675                      | -17.1095                      | -15.3337                      | -16.8981                      | -18.4286                      | -17.0421                      | -19.9421                      | -18.7571                      | -17.1871                      | -19.9155                      | -21.3992                      | -28.6587                      | -17.2521                      | -16.6051                      | -17.1500                      | -17.8465                      | -17.1935                      | -18.1342                      | -17.1320                      | -26.4308                      | -18.1833                      | -18.7496                      | -18.8118                         | -18.5408                         | -19.1404                         |
| 0.9571 | 3500 | 0.2133        | -               | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                             | -                                | -                                | -                                |


### 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",
}
```

#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}
```

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