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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:234000
- loss:MSELoss
base_model: google-bert/bert-base-multilingual-cased
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
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 to ar
type: MSE-val-en-to-ar
metrics:
- type: negative_mse
value: -18.93259286880493
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: -15.68576693534851
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: -16.125640273094177
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: -13.388358056545258
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: -15.648126602172852
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: -17.174141108989716
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: -15.814268589019775
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: -18.483880162239075
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: -17.58536398410797
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: -15.706634521484375
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: -17.800691723823547
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: -19.26662176847458
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: -28.38749885559082
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: -15.783128142356873
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: -15.027229487895966
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: -15.598368644714355
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: -16.64138436317444
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: -15.76906442642212
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: -16.91163182258606
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: -15.555775165557861
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: -18.37025284767151
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: -16.945864260196686
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: -16.482776403427124
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: -16.996394097805023
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: -16.82070791721344
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: -17.381685972213745
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-base-multilingual-cased-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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## 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** | **-18.9326** | **-15.6858** | **-16.1256** | **-13.3884** | **-15.6481** | **-17.1741** | **-15.8143** | **-18.4839** | **-17.5854** | **-15.7066** | **-17.8007** | **-19.2666** | **-28.3875** | **-15.7831** | **-15.0272** | **-15.5984** | **-16.6414** | **-15.7691** | **-16.9116** | **-15.5558** | **-18.3703** | **-16.9459** | **-16.4828** | **-16.9964** | **-16.8207** | **-17.3817** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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: 12.34 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.41 tokens</li><li>max: 49 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: 12.44 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.48 tokens</li><li>max: 49 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`: 4
- `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`: 4
- `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 | 0.3783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2734 | 1000 | 0.3256 | 0.3071 | -30.0050 | -29.7152 | -29.7584 | -29.5204 | -29.6875 | -29.9032 | -29.6918 | -29.9795 | -29.9430 | -29.7142 | -29.8220 | -30.0745 | -32.1218 | -29.8042 | -29.7132 | -29.7625 | -29.7677 | -29.6658 | -29.8250 | -29.8242 | -30.1233 | -29.8640 | -29.7497 | -29.6833 | -29.7296 | -29.7063 |
| 0.4102 | 1500 | 0.3007 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5469 | 2000 | 0.2795 | 0.2663 | -25.0193 | -23.8364 | -23.9924 | -22.8145 | -23.7158 | -24.4490 | -23.7719 | -24.6885 | -24.5973 | -23.7662 | -24.4998 | -25.3625 | -30.9153 | -24.0474 | -23.5674 | -23.7934 | -24.1332 | -23.6279 | -24.1308 | -23.8860 | -25.4166 | -24.4840 | -24.1931 | -24.0816 | -24.0634 | -24.2529 |
| 0.6836 | 2500 | 0.2659 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8203 | 3000 | 0.2562 | 0.2487 | -22.9862 | -21.2544 | -21.4573 | -19.8714 | -21.1251 | -22.1884 | -21.1984 | -22.6963 | -22.3069 | -21.1959 | -22.3180 | -23.4410 | -30.2373 | -21.4324 | -20.8799 | -21.1834 | -21.7427 | -21.1291 | -21.7291 | -21.3003 | -23.2994 | -22.1537 | -21.7480 | -21.7521 | -21.6844 | -21.9702 |
| 0.9571 | 3500 | 0.2475 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0938 | 4000 | 0.2411 | 0.2375 | -21.8220 | -19.6064 | -19.9128 | -17.9872 | -19.5372 | -20.7666 | -19.6563 | -21.4985 | -20.9295 | -19.6182 | -20.9963 | -22.2441 | -29.7291 | -19.8001 | -19.2003 | -19.5189 | -20.2697 | -19.5946 | -20.3160 | -19.6652 | -21.9553 | -20.6678 | -20.2305 | -20.3719 | -20.2700 | -20.6528 |
| 1.2305 | 4500 | 0.2351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3672 | 5000 | 0.23 | 0.2296 | -21.0058 | -18.4861 | -18.7926 | -16.6395 | -18.4034 | -19.7517 | -18.5299 | -20.6663 | -19.9769 | -18.4977 | -20.0496 | -21.4171 | -29.3272 | -18.6213 | -17.9746 | -18.3449 | -19.2392 | -18.4960 | -19.3377 | -18.5079 | -20.9805 | -19.5803 | -19.1385 | -19.4256 | -19.2708 | -19.7140 |
| 1.5040 | 5500 | 0.2257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6407 | 6000 | 0.2222 | 0.2245 | -20.4317 | -17.7592 | -18.1037 | -15.7487 | -17.6947 | -19.0287 | -17.8518 | -20.1401 | -19.3864 | -17.7539 | -19.4615 | -20.8562 | -29.1081 | -17.8707 | -17.1892 | -17.6230 | -18.5879 | -17.7857 | -18.7075 | -17.7347 | -20.2941 | -18.8814 | -18.4449 | -18.8036 | -18.6146 | -19.1169 |
| 1.7774 | 6500 | 0.2186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9141 | 7000 | 0.2158 | 0.2199 | -19.9961 | -17.0956 | -17.4488 | -14.9930 | -17.0238 | -18.4442 | -17.1720 | -19.6005 | -18.7765 | -17.1020 | -18.8972 | -20.3720 | -28.8656 | -17.1949 | -16.4824 | -16.9655 | -17.9687 | -17.1229 | -18.0911 | -17.0128 | -19.6600 | -18.2823 | -17.8109 | -18.2341 | -18.0582 | -18.5735 |
| 2.0509 | 7500 | 0.2135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1876 | 8000 | 0.2109 | 0.2167 | -19.6376 | -16.6362 | -17.0307 | -14.4461 | -16.5766 | -18.0419 | -16.7080 | -19.2403 | -18.3971 | -16.6443 | -18.5251 | -20.0263 | -28.7414 | -16.7279 | -15.9992 | -16.5092 | -17.5170 | -16.6766 | -17.7151 | -16.5403 | -19.2861 | -17.8316 | -17.3764 | -17.8453 | -17.6606 | -18.1844 |
| 2.3243 | 8500 | 0.2088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4610 | 9000 | 0.2074 | 0.2149 | -19.4358 | -16.3728 | -16.7740 | -14.1447 | -16.3289 | -17.8191 | -16.4582 | -19.0369 | -18.1738 | -16.3903 | -18.3565 | -19.8207 | -28.6133 | -16.4804 | -15.7354 | -16.2673 | -17.3034 | -16.4190 | -17.4826 | -16.2566 | -18.9971 | -17.5950 | -17.1273 | -17.6066 | -17.4124 | -17.9799 |
| 2.5978 | 9500 | 0.2059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7345 | 10000 | 0.2047 | 0.2134 | -19.2764 | -16.1718 | -16.5449 | -13.8928 | -16.1098 | -17.5866 | -16.2421 | -18.8665 | -17.9798 | -16.1538 | -18.1695 | -19.6218 | -28.5605 | -16.2479 | -15.4962 | -16.0522 | -17.0797 | -16.2106 | -17.3130 | -16.0278 | -18.8206 | -17.3910 | -16.9231 | -17.4203 | -17.2266 | -17.7903 |
| 2.8712 | 10500 | 0.2033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 3.0079 | 11000 | 0.2024 | 0.2120 | -19.1026 | -15.9149 | -16.3497 | -13.6750 | -15.8828 | -17.3842 | -16.0397 | -18.6612 | -17.7796 | -15.9436 | -17.9779 | -19.4370 | -28.4678 | -16.0245 | -15.2818 | -15.8265 | -16.8594 | -15.9988 | -17.1163 | -15.8106 | -18.5870 | -17.1548 | -16.7074 | -17.2082 | -17.0233 | -17.5910 |
| 3.1447 | 11500 | 0.201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 3.2814 | 12000 | 0.2004 | 0.2112 | -19.0406 | -15.8196 | -16.2516 | -13.5420 | -15.7688 | -17.2734 | -15.9280 | -18.5894 | -17.6966 | -15.8265 | -17.8933 | -19.3785 | -28.4539 | -15.9129 | -15.1631 | -15.7175 | -16.7540 | -15.8974 | -17.0251 | -15.6875 | -18.4807 | -17.0615 | -16.6087 | -17.1051 | -16.9423 | -17.4923 |
| 3.4181 | 12500 | 0.1997 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 3.5548 | 13000 | 0.1995 | 0.2108 | -18.9779 | -15.7524 | -16.1996 | -13.4723 | -15.7211 | -17.2272 | -15.8790 | -18.5412 | -17.6416 | -15.7862 | -17.8502 | -19.3124 | -28.4179 | -15.8513 | -15.1030 | -15.6645 | -16.7053 | -15.8355 | -16.9742 | -15.6246 | -18.4384 | -17.0053 | -16.5478 | -17.0674 | -16.8851 | -17.4527 |
| 3.6916 | 13500 | 0.1991 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 3.8283 | 14000 | 0.1987 | 0.2103 | -18.9326 | -15.6858 | -16.1256 | -13.3884 | -15.6481 | -17.1741 | -15.8143 | -18.4839 | -17.5854 | -15.7066 | -17.8007 | -19.2666 | -28.3875 | -15.7831 | -15.0272 | -15.5984 | -16.6414 | -15.7691 | -16.9116 | -15.5558 | -18.3703 | -16.9459 | -16.4828 | -16.9964 | -16.8207 | -17.3817 |
| 3.9650 | 14500 | 0.1989 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
### 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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