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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:234000
- loss:MSELoss
base_model: FacebookAI/xlm-roberta-base
widget:
- source_sentence: the night before the night before christmas movie
sentences:
- hu
- ' តើការប្រកួតបាល់បោះលីកធំជាងគេដែលវែងជាងគេបំផុតក្នុងប្រវត្តិសាស្ត្រមានអ្វីខ្លះ'
- Másnapos Karácsony
- source_sentence: when did star wars a new hope come out
sentences:
- Koska alexandrian kirjasto tuhoutui tulipalossa
- bilakah star wars a new hope keluar
- ms
- source_sentence: what is the relative location of new york city
sentences:
- ما هو الموقع النسبي لمدينة نيويورك
- ar
- dov'è stato girato il film i cannoni di Navarone
- source_sentence: how many miles from albuquerque new mexico to amarillo texas
sentences:
- сколько миль от альбукерке нью мексико до амарилло техас
- qui a chanté we all live in a yellow submarine
- ru
- source_sentence: where does food wars anime end in the manga
sentences:
- food wars 动漫是在漫画哪里结束的
- 《食戟之靈》漫畫幾時完
- zh_hk
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- negative_mse
model-index:
- name: SentenceTransformer based on FacebookAI/xlm-roberta-base
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: -19.935108721256256
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: -16.227059066295624
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: -17.03149825334549
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: -14.746585488319397
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: -16.7389914393425
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: -17.699478566646576
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: -16.85505211353302
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: -19.114328920841217
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: -17.86249130964279
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: -16.931141912937164
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: -18.774642050266266
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: -19.68335211277008
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: -19.339339435100555
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: -16.49850606918335
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: -15.982428193092346
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: -16.261471807956696
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: -17.510776221752167
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: -16.528253257274628
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: -17.358270287513733
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: -16.31281077861786
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: -17.586874961853027
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: -17.390474677085876
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: -17.174969613552094
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: -18.12549978494644
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: -18.189936876296997
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: -18.67867261171341
name: Negative Mse
---
# SentenceTransformer based on FacebookAI/xlm-roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base). 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:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
- **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: XLMRobertaModel
(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/xlm-roberta-base-multilingual-mkqa")
# Run inference
sentences = [
'where does food wars anime end in the manga',
'《食戟之靈》漫畫幾時完',
'zh_hk',
]
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** | **-19.9351** | **-16.2271** | **-17.0315** | **-14.7466** | **-16.739** | **-17.6995** | **-16.8551** | **-19.1143** | **-17.8625** | **-16.9311** | **-18.7746** | **-19.6834** | **-19.3393** | **-16.4985** | **-15.9824** | **-16.2615** | **-17.5108** | **-16.5283** | **-17.3583** | **-16.3128** | **-17.5869** | **-17.3905** | **-17.175** | **-18.1255** | **-18.1899** | **-18.6787** |
<!--
## 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: 13.21 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.87 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.38 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
| english | non-english | target | label |
|:--------------------------------------------------|:-------------------------------------------------------|:----------------|:----------------------------------------------------------------------------------------------------------------------------|
| <code>what are all the wizard of oz movies</code> | <code>the wizard of oz ما هي كل افلام</code> | <code>ar</code> | <code>[0.5303382277488708, -0.31762194633483887, -0.2945275902748108, -0.6602655649185181, -1.4617066383361816, ...]</code> |
| <code>what are all the wizard of oz movies</code> | <code>hvad er alle troldmanden fra oz filmene</code> | <code>da</code> | <code>[0.5303382277488708, -0.31762194633483887, -0.2945275902748108, -0.6602655649185181, -1.4617066383361816, ...]</code> |
| <code>what are all the wizard of oz movies</code> | <code>Wie heißen alle Der Zauberer von Oz Filme</code> | <code>de</code> | <code>[0.5303382277488708, -0.31762194633483887, -0.2945275902748108, -0.6602655649185181, -1.4617066383361816, ...]</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: 13.05 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.79 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.38 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
| english | non-english | target | label |
|:--------------------------------------------------------------|:----------------------------------------------------------------------|:----------------|:--------------------------------------------------------------------------------------------------------------------------|
| <code>a change to the constitution must be approved by</code> | <code>يجب الموافقة على تغيير الدستور</code> | <code>ar</code> | <code>[1.0918692350387573, 0.8024187684059143, 0.23035858571529388, 0.16300565004348755, -0.6033854484558105, ...]</code> |
| <code>a change to the constitution must be approved by</code> | <code>en ændring af forfatningen skal godkendes af</code> | <code>da</code> | <code>[1.0918692350387573, 0.8024187684059143, 0.23035858571529388, 0.16300565004348755, -0.6033854484558105, ...]</code> |
| <code>a change to the constitution must be approved by</code> | <code>Eine Änderung der Verfassung muss gebilligt werden durch</code> | <code>de</code> | <code>[1.0918692350387573, 0.8024187684059143, 0.23035858571529388, 0.16300565004348755, -0.6033854484558105, ...]</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`: 2e-05
- `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`: 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`: 3
- `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
<details><summary>Click to expand</summary>
| 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 |
|:------:|:-----:|:-------------:|:---------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:--------------------------------:|:--------------------------------:|:--------------------------------:|
| 0.0273 | 100 | 0.7471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0547 | 200 | 0.5344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0820 | 300 | 0.4011 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1094 | 400 | 0.3686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1367 | 500 | 0.3558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1641 | 600 | 0.3527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1914 | 700 | 0.3479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2188 | 800 | 0.3373 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2461 | 900 | 0.3315 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2734 | 1000 | 0.3243 | 0.3143 | -31.0036 | -30.4995 | -30.5974 | -30.3236 | -30.5190 | -30.6680 | -30.5902 | -30.8805 | -30.7873 | -30.6191 | -30.7149 | -30.7932 | -30.8955 | -30.5254 | -30.5554 | -30.5243 | -30.6522 | -30.5353 | -30.5800 | -30.5240 | -30.7348 | -30.7127 | -30.6429 | -30.5608 | -30.5626 | -30.5837 |
| 0.3008 | 1100 | 0.3175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3281 | 1200 | 0.3126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3555 | 1300 | 0.3082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3828 | 1400 | 0.3049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4102 | 1500 | 0.3019 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4375 | 1600 | 0.2988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4649 | 1700 | 0.2979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4922 | 1800 | 0.2926 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5196 | 1900 | 0.2885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5469 | 2000 | 0.2879 | 0.2787 | -26.4435 | -25.3475 | -25.5656 | -24.8280 | -25.4096 | -25.8103 | -25.4399 | -26.1209 | -25.8292 | -25.5216 | -26.0866 | -26.4725 | -26.2586 | -25.5986 | -25.3495 | -25.2907 | -25.6509 | -25.3489 | -25.4795 | -25.3660 | -25.7628 | -25.7572 | -25.6763 | -25.7273 | -25.7893 | -25.8524 |
| 0.5742 | 2100 | 0.2843 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6016 | 2200 | 0.2821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6289 | 2300 | 0.2795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6563 | 2400 | 0.2808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6836 | 2500 | 0.2771 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7110 | 2600 | 0.2745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7383 | 2700 | 0.272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7657 | 2800 | 0.2711 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7930 | 2900 | 0.2685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8203 | 3000 | 0.267 | 0.2638 | -24.2447 | -22.3985 | -22.7542 | -21.5879 | -22.5929 | -23.2891 | -22.6798 | -23.7047 | -23.1739 | -22.7708 | -23.5962 | -24.2250 | -23.9269 | -22.8039 | -22.2681 | -22.3432 | -22.9390 | -22.5717 | -22.8201 | -22.4143 | -23.1236 | -23.1100 | -22.9658 | -23.0786 | -23.2390 | -23.3243 |
| 0.8477 | 3100 | 0.2718 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8750 | 3200 | 0.2674 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9024 | 3300 | 0.2662 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9297 | 3400 | 0.2631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9571 | 3500 | 0.26 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9844 | 3600 | 0.2586 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0118 | 3700 | 0.2575 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0391 | 3800 | 0.2549 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0664 | 3900 | 0.2529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0938 | 4000 | 0.2511 | 0.2469 | -22.9347 | -20.4196 | -20.9011 | -19.3762 | -20.7242 | -21.5322 | -20.7711 | -22.3208 | -21.5176 | -20.9047 | -22.1008 | -22.8701 | -22.4827 | -20.7383 | -20.2571 | -20.3842 | -21.1960 | -20.6791 | -21.0474 | -20.4460 | -21.3999 | -21.3937 | -21.1382 | -21.5265 | -21.6918 | -21.8791 |
| 1.1211 | 4100 | 0.2502 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1485 | 4200 | 0.2491 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1758 | 4300 | 0.248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2032 | 4400 | 0.2463 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2305 | 4500 | 0.2445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2579 | 4600 | 0.2432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2852 | 4700 | 0.2419 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3126 | 4800 | 0.2405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3399 | 4900 | 0.2404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3672 | 5000 | 0.2394 | 0.2354 | -21.7963 | -18.8622 | -19.4636 | -17.6703 | -19.2473 | -20.1437 | -19.3378 | -21.1200 | -20.1560 | -19.4587 | -20.9473 | -21.6343 | -21.2979 | -19.1964 | -18.6653 | -18.8517 | -19.8565 | -19.1500 | -19.6760 | -18.9243 | -19.9718 | -19.9191 | -19.6695 | -20.2707 | -20.4090 | -20.6846 |
| 1.3946 | 5100 | 0.2375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4219 | 5200 | 0.2374 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4493 | 5300 | 0.236 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4766 | 5400 | 0.2335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5040 | 5500 | 0.2346 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5313 | 5600 | 0.2335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5587 | 5700 | 0.232 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5860 | 5800 | 0.2314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6133 | 5900 | 0.2304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6407 | 6000 | 0.2303 | 0.2289 | -21.1967 | -17.9192 | -18.5833 | -16.6276 | -18.3510 | -19.2977 | -18.4551 | -20.3960 | -19.3202 | -18.5573 | -20.1420 | -20.9358 | -20.6084 | -18.2396 | -17.7261 | -17.9322 | -19.0167 | -18.2305 | -18.8471 | -17.9794 | -19.1440 | -19.0105 | -18.7845 | -19.4778 | -19.6095 | -19.9643 |
| 1.6680 | 6100 | 0.2294 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6954 | 6200 | 0.229 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7227 | 6300 | 0.2275 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7501 | 6400 | 0.2285 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7774 | 6500 | 0.2279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8048 | 6600 | 0.2275 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8321 | 6700 | 0.2256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8594 | 6800 | 0.2259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8868 | 6900 | 0.2237 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9141 | 7000 | 0.2232 | 0.2242 | -20.6888 | -17.2295 | -17.9547 | -15.8517 | -17.7267 | -18.6854 | -17.8191 | -19.8853 | -18.7432 | -17.9054 | -19.5866 | -20.4321 | -20.1381 | -17.5215 | -16.9982 | -17.2683 | -18.4340 | -17.5295 | -18.2454 | -17.3006 | -18.5072 | -18.3554 | -18.1438 | -18.9634 | -19.0843 | -19.4826 |
| 1.9415 | 7100 | 0.2231 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9688 | 7200 | 0.2225 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9962 | 7300 | 0.2235 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0235 | 7400 | 0.2224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0509 | 7500 | 0.2206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0782 | 7600 | 0.2205 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1056 | 7700 | 0.2196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1329 | 7800 | 0.22 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1602 | 7900 | 0.2188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1876 | 8000 | 0.2184 | 0.2209 | -20.3380 | -16.7285 | -17.5078 | -15.3142 | -17.2366 | -18.1903 | -17.3419 | -19.5057 | -18.2970 | -17.4283 | -19.1880 | -20.0709 | -19.7478 | -17.0291 | -16.5125 | -16.7629 | -17.9586 | -17.0487 | -17.7907 | -16.8237 | -18.0585 | -17.8714 | -17.6527 | -18.5499 | -18.6504 | -19.0688 |
| 2.2149 | 8100 | 0.2189 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2423 | 8200 | 0.2178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2696 | 8300 | 0.2185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2970 | 8400 | 0.2175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3243 | 8500 | 0.2183 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3517 | 8600 | 0.2176 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3790 | 8700 | 0.2169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4063 | 8800 | 0.2172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4337 | 8900 | 0.2153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4610 | 9000 | 0.2162 | 0.2187 | -20.1028 | -16.4147 | -17.2107 | -14.9595 | -16.9406 | -17.9101 | -17.0441 | -19.2680 | -18.0594 | -17.1276 | -18.9403 | -19.8407 | -19.5169 | -16.6976 | -16.1859 | -16.4554 | -17.6828 | -16.7360 | -17.5378 | -16.5167 | -17.7710 | -17.5853 | -17.3717 | -18.3032 | -18.3627 | -18.8466 |
| 2.4884 | 9100 | 0.2159 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5157 | 9200 | 0.2161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5431 | 9300 | 0.2148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5704 | 9400 | 0.2148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5978 | 9500 | 0.2154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6251 | 9600 | 0.2142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6524 | 9700 | 0.2144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6798 | 9800 | 0.215 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7071 | 9900 | 0.2142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7345 | 10000 | 0.2139 | 0.2174 | -19.9351 | -16.2271 | -17.0315 | -14.7466 | -16.7390 | -17.6995 | -16.8551 | -19.1143 | -17.8625 | -16.9311 | -18.7746 | -19.6834 | -19.3393 | -16.4985 | -15.9824 | -16.2615 | -17.5108 | -16.5283 | -17.3583 | -16.3128 | -17.5869 | -17.3905 | -17.1750 | -18.1255 | -18.1899 | -18.6787 |
| 2.7618 | 10100 | 0.2134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7892 | 10200 | 0.2141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8165 | 10300 | 0.2147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8439 | 10400 | 0.2138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8712 | 10500 | 0.2133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8986 | 10600 | 0.2129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9259 | 10700 | 0.2129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9532 | 10800 | 0.2129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9806 | 10900 | 0.214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.46.2
- 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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