sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:404981 loss:MSELoss en multilingual ar bg ca cs da de el es et fa fi fr gl gu he hi hr hu hy id it ja ka ko ku lt lv mk mn mr ms my nb nl pl pt ro ru sk sl sq sr sv th tr uk ur vi zh dataset:sentence-transformers/parallel-sentences-talks arxiv:1908.10084 arxiv:2004.09813 base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: FacebookAI/xlm-roberta-base
datasets:
- sentence-transformers/parallel-sentences-talks
language:
- en
- multilingual
- ar
- bg
- ca
- cs
- da
- de
- el
- es
- et
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- hu
- hy
- id
- it
- ja
- ka
- ko
- ku
- lt
- lv
- mk
- mn
- mr
- ms
- my
- nb
- nl
- pl
- pt
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- th
- tr
- uk
- ur
- vi
- zh
library_name: sentence-transformers
metrics:
- negative_mse
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:404981
- loss:MSELoss
widget:
- source_sentence: It's not negative; it's positive.
  sentences:
  - Las partes en conflicto también deben estar preparadas para volver a la mesa de
    negociación si se estanca la implementación del acuerdo.
  - A veces refieren a él como al Campo de Prisioneros de Guerra Número 334, lugar
    donde viven ahora los lakota.
  - No es negativo, es positivo.
- source_sentence: So the first of the three is design for education.
  sentences:
  - El primer enfoque es diseñar para la educación.
  - Las enfermedades cardiacas y  cardiovasculares siguen matando a más gente, no
    sólo en  este país sino también en todo el mundo, que cualquier  otra combinación
    de lo demás, sin embargo casi todos  podemos prevenirlo por completo.
  - Siempre que discutimos uno de estos problemas que tenemos que abordar... el trabajo
    infantil en las granjas de algodón de India, este año vamos a monitorear 50.000
    granjas de algodón en India.
- source_sentence: So take a look around this auditorium today.
  sentences:
  - Lo dispuesto en el acuerdo puede ser complejo, pero también lo es el conflicto
    subyacente.
  - Y puedo ver que algo más murió allí en el fango sangriento y fue enterrado en
    la tormenta de nieve.
  - Miremos alrededor, en este auditorio.
- source_sentence: Every time he has visitors, it's the first place that he takes
    them.
  sentences:
  - Siempre que tiene visitas es el primer lugar al que los lleva.
  - El desempleo en la reserva aborigen de Pine Ridge fluctúa entre el 85% y el 90%.
  - Si la conexión es débil, los motores se quedarán apagados y la mosca seguirá derecho
    en su curso.
- source_sentence: We need a different machine.
  sentences:
  - Vayan al sitio web. Vean los resultados de las auditorías.
  - Necesitamos una máquina diferente.
  - Entonces, ¿dónde nos deja esto?
model-index:
- name: SentenceTransformer based on FacebookAI/xlm-roberta-base
  results:
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: en es
      type: en-es
    metrics:
    - type: negative_mse
      value: -10.183618545532227
      name: Negative Mse
  - task:
      type: translation
      name: Translation
    dataset:
      name: en es
      type: en-es
    metrics:
    - type: src2trg_accuracy
      value: 0.9878787878787879
      name: Src2Trg Accuracy
    - type: trg2src_accuracy
      value: 0.990909090909091
      name: Trg2Src Accuracy
    - type: mean_accuracy
      value: 0.9893939393939395
      name: Mean Accuracy
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts17 es en test
      type: sts17-es-en-test
    metrics:
    - type: pearson_cosine
      value: 0.7671256411244319
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.790302203590485
      name: Spearman Cosine
---

# 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) on the [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) dataset. 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:**
    - [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks)
- **Languages:** en, multilingual, ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, gl, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh
<!-- - **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("vallabh001/xlm-roberta-base-multilingual-en-es")
# Run inference
sentences = [
    'We need a different machine.',
    'Necesitamos una máquina diferente.',
    'Entonces, ¿dónde nos deja esto?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### 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>
-->

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Knowledge Distillation

* Dataset: `en-es`
* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)

| Metric           | Value        |
|:-----------------|:-------------|
| **negative_mse** | **-10.1836** |

#### Translation

* Dataset: `en-es`
* Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)

| Metric            | Value      |
|:------------------|:-----------|
| src2trg_accuracy  | 0.9879     |
| trg2src_accuracy  | 0.9909     |
| **mean_accuracy** | **0.9894** |

#### Semantic Similarity

* Dataset: `sts17-es-en-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7671     |
| **spearman_cosine** | **0.7903** |

<!--
## 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

#### en-es

* Dataset: [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [0c70bc6](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/0c70bc6714efb1df12f8a16b9056e4653563d128)
* Size: 404,981 training samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | english                                                                            | non_english                                                                        | label                                |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                             | string                                                                             | list                                 |
  | details | <ul><li>min: 4 tokens</li><li>mean: 25.77 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.42 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
  | english                                                                                                                                                | non_english                                                                                                                              | label                                                                                                                        |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|
  | <code>And then there are certain conceptual things that can also benefit from hand calculating, but I think they're relatively small in number.</code> | <code>Y luego hay ciertas aspectos conceptuales que pueden beneficiarse del cálculo a mano pero creo que son relativamente pocos.</code> | <code>[-0.59398353099823, 0.9714106321334839, 0.6800687313079834, -0.21585586667060852, -0.7509507536888123, ...]</code>     |
  | <code>One thing I often ask about is ancient Greek and how this relates.</code>                                                                        | <code>Algo que pregunto a menudo es sobre el griego antiguo y cómo se relaciona.</code>                                                  | <code>[-0.09777131676673889, 0.07093200832605362, -0.42989036440849304, -0.1457505226135254, 1.4382765293121338, ...]</code> |
  | <code>See, the thing we're doing right now is we're forcing people to learn mathematics.</code>                                                        | <code>Vean, lo que estamos haciendo ahora es forzar a la gente a aprender matemáticas.</code>                                            | <code>[0.39432215690612793, 0.1891053169965744, -0.3788300156593323, 0.438666433095932, 0.2727019190788269, ...]</code>      |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

### Evaluation Dataset

#### en-es

* Dataset: [en-es](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks) at [0c70bc6](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks/tree/0c70bc6714efb1df12f8a16b9056e4653563d128)
* Size: 990 evaluation samples
* Columns: <code>english</code>, <code>non_english</code>, and <code>label</code>
* Approximate statistics based on the first 990 samples:
  |         | english                                                                            | non_english                                                                        | label                                |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                             | string                                                                             | list                                 |
  | details | <ul><li>min: 4 tokens</li><li>mean: 26.42 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.47 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
  | english                                                                                                                                                    | non_english                                                                                                                                                       | label                                                                                                                       |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
  | <code>Thank you so much, Chris.</code>                                                                                                                     | <code>Muchas gracias Chris.</code>                                                                                                                                | <code>[-0.43312570452690125, 1.0602686405181885, -0.07791059464216232, -0.41704198718070984, 1.676845908164978, ...]</code> |
  | <code>And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.</code>                                     | <code>Y es en verdad un gran honor tener la oportunidad de venir a este escenario por segunda vez. Estoy extremadamente agradecido.</code>                        | <code>[0.27005693316459656, 0.5391747951507568, -0.2580487132072449, -0.6613675951957703, 0.6738824248313904, ...]</code>   |
  | <code>I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.</code> | <code>He quedado conmovido por esta conferencia, y deseo agradecer a todos ustedes sus amables comentarios acerca de lo que tenía que decir la otra noche.</code> | <code>[-0.2532017230987549, 0.04791336879134178, -0.1317490190267563, -0.7357572913169861, 0.23663584887981415, ...]</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
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `bf16`: 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`: 5
- `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`: True
- `fp16`: False
- `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 | en-es loss | en-es_negative_mse | en-es_mean_accuracy | sts17-es-en-test_spearman_cosine |
|:------:|:-----:|:-------------:|:----------:|:------------------:|:-------------------:|:--------------------------------:|
| 0.0158 | 100   | 0.6528        | -          | -                  | -                   | -                                |
| 0.0316 | 200   | 0.5634        | -          | -                  | -                   | -                                |
| 0.0474 | 300   | 0.4418        | -          | -                  | -                   | -                                |
| 0.0632 | 400   | 0.3009        | -          | -                  | -                   | -                                |
| 0.0790 | 500   | 0.2744        | -          | -                  | -                   | -                                |
| 0.0948 | 600   | 0.2677        | -          | -                  | -                   | -                                |
| 0.1106 | 700   | 0.2661        | -          | -                  | -                   | -                                |
| 0.1264 | 800   | 0.2614        | -          | -                  | -                   | -                                |
| 0.1422 | 900   | 0.2583        | -          | -                  | -                   | -                                |
| 0.1580 | 1000  | 0.2582        | -          | -                  | -                   | -                                |
| 0.1738 | 1100  | 0.2579        | -          | -                  | -                   | -                                |
| 0.1896 | 1200  | 0.256         | -          | -                  | -                   | -                                |
| 0.2054 | 1300  | 0.2511        | -          | -                  | -                   | -                                |
| 0.2212 | 1400  | 0.2467        | -          | -                  | -                   | -                                |
| 0.2370 | 1500  | 0.2423        | -          | -                  | -                   | -                                |
| 0.2528 | 1600  | 0.2364        | -          | -                  | -                   | -                                |
| 0.2686 | 1700  | 0.2305        | -          | -                  | -                   | -                                |
| 0.2845 | 1800  | 0.2248        | -          | -                  | -                   | -                                |
| 0.3003 | 1900  | 0.2184        | -          | -                  | -                   | -                                |
| 0.3161 | 2000  | 0.2143        | -          | -                  | -                   | -                                |
| 0.3319 | 2100  | 0.2098        | -          | -                  | -                   | -                                |
| 0.3477 | 2200  | 0.2055        | -          | -                  | -                   | -                                |
| 0.3635 | 2300  | 0.1999        | -          | -                  | -                   | -                                |
| 0.3793 | 2400  | 0.1965        | -          | -                  | -                   | -                                |
| 0.3951 | 2500  | 0.1919        | -          | -                  | -                   | -                                |
| 0.4109 | 2600  | 0.1889        | -          | -                  | -                   | -                                |
| 0.4267 | 2700  | 0.1858        | -          | -                  | -                   | -                                |
| 0.4425 | 2800  | 0.1826        | -          | -                  | -                   | -                                |
| 0.4583 | 2900  | 0.18          | -          | -                  | -                   | -                                |
| 0.4741 | 3000  | 0.1774        | -          | -                  | -                   | -                                |
| 0.4899 | 3100  | 0.1758        | -          | -                  | -                   | -                                |
| 0.5057 | 3200  | 0.1738        | -          | -                  | -                   | -                                |
| 0.5215 | 3300  | 0.1706        | -          | -                  | -                   | -                                |
| 0.5373 | 3400  | 0.1678        | -          | -                  | -                   | -                                |
| 0.5531 | 3500  | 0.1664        | -          | -                  | -                   | -                                |
| 0.5689 | 3600  | 0.1647        | -          | -                  | -                   | -                                |
| 0.5847 | 3700  | 0.163         | -          | -                  | -                   | -                                |
| 0.6005 | 3800  | 0.1605        | -          | -                  | -                   | -                                |
| 0.6163 | 3900  | 0.1594        | -          | -                  | -                   | -                                |
| 0.6321 | 4000  | 0.1576        | -          | -                  | -                   | -                                |
| 0.6479 | 4100  | 0.1561        | -          | -                  | -                   | -                                |
| 0.6637 | 4200  | 0.1541        | -          | -                  | -                   | -                                |
| 0.6795 | 4300  | 0.1545        | -          | -                  | -                   | -                                |
| 0.6953 | 4400  | 0.1535        | -          | -                  | -                   | -                                |
| 0.7111 | 4500  | 0.1523        | -          | -                  | -                   | -                                |
| 0.7269 | 4600  | 0.1502        | -          | -                  | -                   | -                                |
| 0.7427 | 4700  | 0.1487        | -          | -                  | -                   | -                                |
| 0.7585 | 4800  | 0.1486        | -          | -                  | -                   | -                                |
| 0.7743 | 4900  | 0.1477        | -          | -                  | -                   | -                                |
| 0.7901 | 5000  | 0.1465        | 0.1390     | -14.681906         | 0.9803              | 0.6371                           |
| 0.8059 | 5100  | 0.1469        | -          | -                  | -                   | -                                |
| 0.8217 | 5200  | 0.1449        | -          | -                  | -                   | -                                |
| 0.8375 | 5300  | 0.1437        | -          | -                  | -                   | -                                |
| 0.8534 | 5400  | 0.142         | -          | -                  | -                   | -                                |
| 0.8692 | 5500  | 0.1423        | -          | -                  | -                   | -                                |
| 0.8850 | 5600  | 0.1424        | -          | -                  | -                   | -                                |
| 0.9008 | 5700  | 0.1415        | -          | -                  | -                   | -                                |
| 0.9166 | 5800  | 0.1407        | -          | -                  | -                   | -                                |
| 0.9324 | 5900  | 0.1396        | -          | -                  | -                   | -                                |
| 0.9482 | 6000  | 0.1388        | -          | -                  | -                   | -                                |
| 0.9640 | 6100  | 0.1391        | -          | -                  | -                   | -                                |
| 0.9798 | 6200  | 0.1368        | -          | -                  | -                   | -                                |
| 0.9956 | 6300  | 0.1366        | -          | -                  | -                   | -                                |
| 1.0114 | 6400  | 0.1367        | -          | -                  | -                   | -                                |
| 1.0272 | 6500  | 0.1343        | -          | -                  | -                   | -                                |
| 1.0430 | 6600  | 0.1341        | -          | -                  | -                   | -                                |
| 1.0588 | 6700  | 0.1349        | -          | -                  | -                   | -                                |
| 1.0746 | 6800  | 0.1327        | -          | -                  | -                   | -                                |
| 1.0904 | 6900  | 0.1334        | -          | -                  | -                   | -                                |
| 1.1062 | 7000  | 0.133         | -          | -                  | -                   | -                                |
| 1.1220 | 7100  | 0.1316        | -          | -                  | -                   | -                                |
| 1.1378 | 7200  | 0.1308        | -          | -                  | -                   | -                                |
| 1.1536 | 7300  | 0.1316        | -          | -                  | -                   | -                                |
| 1.1694 | 7400  | 0.1298        | -          | -                  | -                   | -                                |
| 1.1852 | 7500  | 0.1294        | -          | -                  | -                   | -                                |
| 1.2010 | 7600  | 0.1295        | -          | -                  | -                   | -                                |
| 1.2168 | 7700  | 0.13          | -          | -                  | -                   | -                                |
| 1.2326 | 7800  | 0.1285        | -          | -                  | -                   | -                                |
| 1.2484 | 7900  | 0.1278        | -          | -                  | -                   | -                                |
| 1.2642 | 8000  | 0.1272        | -          | -                  | -                   | -                                |
| 1.2800 | 8100  | 0.1262        | -          | -                  | -                   | -                                |
| 1.2958 | 8200  | 0.1275        | -          | -                  | -                   | -                                |
| 1.3116 | 8300  | 0.1266        | -          | -                  | -                   | -                                |
| 1.3274 | 8400  | 0.1252        | -          | -                  | -                   | -                                |
| 1.3432 | 8500  | 0.1256        | -          | -                  | -                   | -                                |
| 1.3590 | 8600  | 0.1246        | -          | -                  | -                   | -                                |
| 1.3748 | 8700  | 0.1254        | -          | -                  | -                   | -                                |
| 1.3906 | 8800  | 0.1242        | -          | -                  | -                   | -                                |
| 1.4064 | 8900  | 0.1249        | -          | -                  | -                   | -                                |
| 1.4223 | 9000  | 0.1233        | -          | -                  | -                   | -                                |
| 1.4381 | 9100  | 0.1238        | -          | -                  | -                   | -                                |
| 1.4539 | 9200  | 0.1231        | -          | -                  | -                   | -                                |
| 1.4697 | 9300  | 0.122         | -          | -                  | -                   | -                                |
| 1.4855 | 9400  | 0.1217        | -          | -                  | -                   | -                                |
| 1.5013 | 9500  | 0.1225        | -          | -                  | -                   | -                                |
| 1.5171 | 9600  | 0.1213        | -          | -                  | -                   | -                                |
| 1.5329 | 9700  | 0.1208        | -          | -                  | -                   | -                                |
| 1.5487 | 9800  | 0.1214        | -          | -                  | -                   | -                                |
| 1.5645 | 9900  | 0.1205        | -          | -                  | -                   | -                                |
| 1.5803 | 10000 | 0.12          | 0.1120     | -12.20076          | 0.9843              | 0.7137                           |
| 1.5961 | 10100 | 0.1205        | -          | -                  | -                   | -                                |
| 1.6119 | 10200 | 0.12          | -          | -                  | -                   | -                                |
| 1.6277 | 10300 | 0.1187        | -          | -                  | -                   | -                                |
| 1.6435 | 10400 | 0.1184        | -          | -                  | -                   | -                                |
| 1.6593 | 10500 | 0.1178        | -          | -                  | -                   | -                                |
| 1.6751 | 10600 | 0.1188        | -          | -                  | -                   | -                                |
| 1.6909 | 10700 | 0.1184        | -          | -                  | -                   | -                                |
| 1.7067 | 10800 | 0.1168        | -          | -                  | -                   | -                                |
| 1.7225 | 10900 | 0.1175        | -          | -                  | -                   | -                                |
| 1.7383 | 11000 | 0.1158        | -          | -                  | -                   | -                                |
| 1.7541 | 11100 | 0.1159        | -          | -                  | -                   | -                                |
| 1.7699 | 11200 | 0.1178        | -          | -                  | -                   | -                                |
| 1.7857 | 11300 | 0.1158        | -          | -                  | -                   | -                                |
| 1.8015 | 11400 | 0.1161        | -          | -                  | -                   | -                                |
| 1.8173 | 11500 | 0.1151        | -          | -                  | -                   | -                                |
| 1.8331 | 11600 | 0.1147        | -          | -                  | -                   | -                                |
| 1.8489 | 11700 | 0.1152        | -          | -                  | -                   | -                                |
| 1.8647 | 11800 | 0.1144        | -          | -                  | -                   | -                                |
| 1.8805 | 11900 | 0.1145        | -          | -                  | -                   | -                                |
| 1.8963 | 12000 | 0.1144        | -          | -                  | -                   | -                                |
| 1.9121 | 12100 | 0.1139        | -          | -                  | -                   | -                                |
| 1.9279 | 12200 | 0.1144        | -          | -                  | -                   | -                                |
| 1.9437 | 12300 | 0.1144        | -          | -                  | -                   | -                                |
| 1.9595 | 12400 | 0.1124        | -          | -                  | -                   | -                                |
| 1.9753 | 12500 | 0.1134        | -          | -                  | -                   | -                                |
| 1.9912 | 12600 | 0.1133        | -          | -                  | -                   | -                                |
| 2.0070 | 12700 | 0.1125        | -          | -                  | -                   | -                                |
| 2.0228 | 12800 | 0.1108        | -          | -                  | -                   | -                                |
| 2.0386 | 12900 | 0.1112        | -          | -                  | -                   | -                                |
| 2.0544 | 13000 | 0.1109        | -          | -                  | -                   | -                                |
| 2.0702 | 13100 | 0.1105        | -          | -                  | -                   | -                                |
| 2.0860 | 13200 | 0.1112        | -          | -                  | -                   | -                                |
| 2.1018 | 13300 | 0.1105        | -          | -                  | -                   | -                                |
| 2.1176 | 13400 | 0.1105        | -          | -                  | -                   | -                                |
| 2.1334 | 13500 | 0.11          | -          | -                  | -                   | -                                |
| 2.1492 | 13600 | 0.1096        | -          | -                  | -                   | -                                |
| 2.1650 | 13700 | 0.1098        | -          | -                  | -                   | -                                |
| 2.1808 | 13800 | 0.1093        | -          | -                  | -                   | -                                |
| 2.1966 | 13900 | 0.1089        | -          | -                  | -                   | -                                |
| 2.2124 | 14000 | 0.1091        | -          | -                  | -                   | -                                |
| 2.2282 | 14100 | 0.1091        | -          | -                  | -                   | -                                |
| 2.2440 | 14200 | 0.1086        | -          | -                  | -                   | -                                |
| 2.2598 | 14300 | 0.1089        | -          | -                  | -                   | -                                |
| 2.2756 | 14400 | 0.1087        | -          | -                  | -                   | -                                |
| 2.2914 | 14500 | 0.1083        | -          | -                  | -                   | -                                |
| 2.3072 | 14600 | 0.1091        | -          | -                  | -                   | -                                |
| 2.3230 | 14700 | 0.1083        | -          | -                  | -                   | -                                |
| 2.3388 | 14800 | 0.1088        | -          | -                  | -                   | -                                |
| 2.3546 | 14900 | 0.1071        | -          | -                  | -                   | -                                |
| 2.3704 | 15000 | 0.1085        | 0.1015     | -11.243325         | 0.9843              | 0.7625                           |
| 2.3862 | 15100 | 0.1077        | -          | -                  | -                   | -                                |
| 2.4020 | 15200 | 0.1076        | -          | -                  | -                   | -                                |
| 2.4178 | 15300 | 0.108         | -          | -                  | -                   | -                                |
| 2.4336 | 15400 | 0.1066        | -          | -                  | -                   | -                                |
| 2.4494 | 15500 | 0.1062        | -          | -                  | -                   | -                                |
| 2.4652 | 15600 | 0.1065        | -          | -                  | -                   | -                                |
| 2.4810 | 15700 | 0.1058        | -          | -                  | -                   | -                                |
| 2.4968 | 15800 | 0.1071        | -          | -                  | -                   | -                                |
| 2.5126 | 15900 | 0.1071        | -          | -                  | -                   | -                                |
| 2.5284 | 16000 | 0.1066        | -          | -                  | -                   | -                                |
| 2.5442 | 16100 | 0.1067        | -          | -                  | -                   | -                                |
| 2.5601 | 16200 | 0.1057        | -          | -                  | -                   | -                                |
| 2.5759 | 16300 | 0.106         | -          | -                  | -                   | -                                |
| 2.5917 | 16400 | 0.1061        | -          | -                  | -                   | -                                |
| 2.6075 | 16500 | 0.1047        | -          | -                  | -                   | -                                |
| 2.6233 | 16600 | 0.1057        | -          | -                  | -                   | -                                |
| 2.6391 | 16700 | 0.106         | -          | -                  | -                   | -                                |
| 2.6549 | 16800 | 0.1055        | -          | -                  | -                   | -                                |
| 2.6707 | 16900 | 0.105         | -          | -                  | -                   | -                                |
| 2.6865 | 17000 | 0.1047        | -          | -                  | -                   | -                                |
| 2.7023 | 17100 | 0.1042        | -          | -                  | -                   | -                                |
| 2.7181 | 17200 | 0.1057        | -          | -                  | -                   | -                                |
| 2.7339 | 17300 | 0.1051        | -          | -                  | -                   | -                                |
| 2.7497 | 17400 | 0.1055        | -          | -                  | -                   | -                                |
| 2.7655 | 17500 | 0.1047        | -          | -                  | -                   | -                                |
| 2.7813 | 17600 | 0.1043        | -          | -                  | -                   | -                                |
| 2.7971 | 17700 | 0.1034        | -          | -                  | -                   | -                                |
| 2.8129 | 17800 | 0.1039        | -          | -                  | -                   | -                                |
| 2.8287 | 17900 | 0.1038        | -          | -                  | -                   | -                                |
| 2.8445 | 18000 | 0.1032        | -          | -                  | -                   | -                                |
| 2.8603 | 18100 | 0.103         | -          | -                  | -                   | -                                |
| 2.8761 | 18200 | 0.1035        | -          | -                  | -                   | -                                |
| 2.8919 | 18300 | 0.1024        | -          | -                  | -                   | -                                |
| 2.9077 | 18400 | 0.1032        | -          | -                  | -                   | -                                |
| 2.9235 | 18500 | 0.1031        | -          | -                  | -                   | -                                |
| 2.9393 | 18600 | 0.1034        | -          | -                  | -                   | -                                |
| 2.9551 | 18700 | 0.1033        | -          | -                  | -                   | -                                |
| 2.9709 | 18800 | 0.1036        | -          | -                  | -                   | -                                |
| 2.9867 | 18900 | 0.1029        | -          | -                  | -                   | -                                |
| 3.0025 | 19000 | 0.1024        | -          | -                  | -                   | -                                |
| 3.0183 | 19100 | 0.1017        | -          | -                  | -                   | -                                |
| 3.0341 | 19200 | 0.1012        | -          | -                  | -                   | -                                |
| 3.0499 | 19300 | 0.1016        | -          | -                  | -                   | -                                |
| 3.0657 | 19400 | 0.1012        | -          | -                  | -                   | -                                |
| 3.0815 | 19500 | 0.1009        | -          | -                  | -                   | -                                |
| 3.0973 | 19600 | 0.1015        | -          | -                  | -                   | -                                |
| 3.1131 | 19700 | 0.1014        | -          | -                  | -                   | -                                |
| 3.1290 | 19800 | 0.1004        | -          | -                  | -                   | -                                |
| 3.1448 | 19900 | 0.1011        | -          | -                  | -                   | -                                |
| 3.1606 | 20000 | 0.1006        | 0.0952     | -10.662492         | 0.9879              | 0.7811                           |
| 3.1764 | 20100 | 0.1007        | -          | -                  | -                   | -                                |
| 3.1922 | 20200 | 0.1015        | -          | -                  | -                   | -                                |
| 3.2080 | 20300 | 0.1005        | -          | -                  | -                   | -                                |
| 3.2238 | 20400 | 0.1017        | -          | -                  | -                   | -                                |
| 3.2396 | 20500 | 0.1012        | -          | -                  | -                   | -                                |
| 3.2554 | 20600 | 0.0998        | -          | -                  | -                   | -                                |
| 3.2712 | 20700 | 0.0997        | -          | -                  | -                   | -                                |
| 3.2870 | 20800 | 0.1001        | -          | -                  | -                   | -                                |
| 3.3028 | 20900 | 0.1009        | -          | -                  | -                   | -                                |
| 3.3186 | 21000 | 0.1           | -          | -                  | -                   | -                                |
| 3.3344 | 21100 | 0.1001        | -          | -                  | -                   | -                                |
| 3.3502 | 21200 | 0.1008        | -          | -                  | -                   | -                                |
| 3.3660 | 21300 | 0.0996        | -          | -                  | -                   | -                                |
| 3.3818 | 21400 | 0.0993        | -          | -                  | -                   | -                                |
| 3.3976 | 21500 | 0.1004        | -          | -                  | -                   | -                                |
| 3.4134 | 21600 | 0.0996        | -          | -                  | -                   | -                                |
| 3.4292 | 21700 | 0.0993        | -          | -                  | -                   | -                                |
| 3.4450 | 21800 | 0.0997        | -          | -                  | -                   | -                                |
| 3.4608 | 21900 | 0.0997        | -          | -                  | -                   | -                                |
| 3.4766 | 22000 | 0.0997        | -          | -                  | -                   | -                                |
| 3.4924 | 22100 | 0.0984        | -          | -                  | -                   | -                                |
| 3.5082 | 22200 | 0.0999        | -          | -                  | -                   | -                                |
| 3.5240 | 22300 | 0.099         | -          | -                  | -                   | -                                |
| 3.5398 | 22400 | 0.0992        | -          | -                  | -                   | -                                |
| 3.5556 | 22500 | 0.0988        | -          | -                  | -                   | -                                |
| 3.5714 | 22600 | 0.0989        | -          | -                  | -                   | -                                |
| 3.5872 | 22700 | 0.0989        | -          | -                  | -                   | -                                |
| 3.6030 | 22800 | 0.0978        | -          | -                  | -                   | -                                |
| 3.6188 | 22900 | 0.0987        | -          | -                  | -                   | -                                |
| 3.6346 | 23000 | 0.0997        | -          | -                  | -                   | -                                |
| 3.6504 | 23100 | 0.0994        | -          | -                  | -                   | -                                |
| 3.6662 | 23200 | 0.0984        | -          | -                  | -                   | -                                |
| 3.6820 | 23300 | 0.0985        | -          | -                  | -                   | -                                |
| 3.6979 | 23400 | 0.0983        | -          | -                  | -                   | -                                |
| 3.7137 | 23500 | 0.0992        | -          | -                  | -                   | -                                |
| 3.7295 | 23600 | 0.0983        | -          | -                  | -                   | -                                |
| 3.7453 | 23700 | 0.0987        | -          | -                  | -                   | -                                |
| 3.7611 | 23800 | 0.0983        | -          | -                  | -                   | -                                |
| 3.7769 | 23900 | 0.0969        | -          | -                  | -                   | -                                |
| 3.7927 | 24000 | 0.0984        | -          | -                  | -                   | -                                |
| 3.8085 | 24100 | 0.0976        | -          | -                  | -                   | -                                |
| 3.8243 | 24200 | 0.0984        | -          | -                  | -                   | -                                |
| 3.8401 | 24300 | 0.0974        | -          | -                  | -                   | -                                |
| 3.8559 | 24400 | 0.0982        | -          | -                  | -                   | -                                |
| 3.8717 | 24500 | 0.0983        | -          | -                  | -                   | -                                |
| 3.8875 | 24600 | 0.0986        | -          | -                  | -                   | -                                |
| 3.9033 | 24700 | 0.0977        | -          | -                  | -                   | -                                |
| 3.9191 | 24800 | 0.0974        | -          | -                  | -                   | -                                |
| 3.9349 | 24900 | 0.0979        | -          | -                  | -                   | -                                |
| 3.9507 | 25000 | 0.0974        | 0.0916     | -10.330441         | 0.9904              | 0.7840                           |
| 3.9665 | 25100 | 0.0974        | -          | -                  | -                   | -                                |
| 3.9823 | 25200 | 0.097         | -          | -                  | -                   | -                                |
| 3.9981 | 25300 | 0.0978        | -          | -                  | -                   | -                                |
| 4.0139 | 25400 | 0.0969        | -          | -                  | -                   | -                                |
| 4.0297 | 25500 | 0.0966        | -          | -                  | -                   | -                                |
| 4.0455 | 25600 | 0.0965        | -          | -                  | -                   | -                                |
| 4.0613 | 25700 | 0.0974        | -          | -                  | -                   | -                                |
| 4.0771 | 25800 | 0.0966        | -          | -                  | -                   | -                                |
| 4.0929 | 25900 | 0.0964        | -          | -                  | -                   | -                                |
| 4.1087 | 26000 | 0.0961        | -          | -                  | -                   | -                                |
| 4.1245 | 26100 | 0.0958        | -          | -                  | -                   | -                                |
| 4.1403 | 26200 | 0.0964        | -          | -                  | -                   | -                                |
| 4.1561 | 26300 | 0.097         | -          | -                  | -                   | -                                |
| 4.1719 | 26400 | 0.0967        | -          | -                  | -                   | -                                |
| 4.1877 | 26500 | 0.0968        | -          | -                  | -                   | -                                |
| 4.2035 | 26600 | 0.0965        | -          | -                  | -                   | -                                |
| 4.2193 | 26700 | 0.0956        | -          | -                  | -                   | -                                |
| 4.2351 | 26800 | 0.0963        | -          | -                  | -                   | -                                |
| 4.2509 | 26900 | 0.0958        | -          | -                  | -                   | -                                |
| 4.2668 | 27000 | 0.0969        | -          | -                  | -                   | -                                |
| 4.2826 | 27100 | 0.0951        | -          | -                  | -                   | -                                |
| 4.2984 | 27200 | 0.0958        | -          | -                  | -                   | -                                |
| 4.3142 | 27300 | 0.0956        | -          | -                  | -                   | -                                |
| 4.3300 | 27400 | 0.0965        | -          | -                  | -                   | -                                |
| 4.3458 | 27500 | 0.0952        | -          | -                  | -                   | -                                |
| 4.3616 | 27600 | 0.0956        | -          | -                  | -                   | -                                |
| 4.3774 | 27700 | 0.0956        | -          | -                  | -                   | -                                |
| 4.3932 | 27800 | 0.0966        | -          | -                  | -                   | -                                |
| 4.4090 | 27900 | 0.0972        | -          | -                  | -                   | -                                |
| 4.4248 | 28000 | 0.0954        | -          | -                  | -                   | -                                |
| 4.4406 | 28100 | 0.0961        | -          | -                  | -                   | -                                |
| 4.4564 | 28200 | 0.0963        | -          | -                  | -                   | -                                |
| 4.4722 | 28300 | 0.0958        | -          | -                  | -                   | -                                |
| 4.4880 | 28400 | 0.0961        | -          | -                  | -                   | -                                |
| 4.5038 | 28500 | 0.0961        | -          | -                  | -                   | -                                |
| 4.5196 | 28600 | 0.0956        | -          | -                  | -                   | -                                |
| 4.5354 | 28700 | 0.0955        | -          | -                  | -                   | -                                |
| 4.5512 | 28800 | 0.0957        | -          | -                  | -                   | -                                |
| 4.5670 | 28900 | 0.0953        | -          | -                  | -                   | -                                |
| 4.5828 | 29000 | 0.0952        | -          | -                  | -                   | -                                |
| 4.5986 | 29100 | 0.0964        | -          | -                  | -                   | -                                |
| 4.6144 | 29200 | 0.0955        | -          | -                  | -                   | -                                |
| 4.6302 | 29300 | 0.0948        | -          | -                  | -                   | -                                |
| 4.6460 | 29400 | 0.0946        | -          | -                  | -                   | -                                |
| 4.6618 | 29500 | 0.0953        | -          | -                  | -                   | -                                |
| 4.6776 | 29600 | 0.0954        | -          | -                  | -                   | -                                |
| 4.6934 | 29700 | 0.0956        | -          | -                  | -                   | -                                |
| 4.7092 | 29800 | 0.0958        | -          | -                  | -                   | -                                |
| 4.7250 | 29900 | 0.0956        | -          | -                  | -                   | -                                |
| 4.7408 | 30000 | 0.0962        | 0.0900     | -10.183619         | 0.9894              | 0.7903                           |
| 4.7566 | 30100 | 0.0953        | -          | -                  | -                   | -                                |
| 4.7724 | 30200 | 0.0959        | -          | -                  | -                   | -                                |
| 4.7882 | 30300 | 0.0949        | -          | -                  | -                   | -                                |
| 4.8040 | 30400 | 0.0958        | -          | -                  | -                   | -                                |
| 4.8198 | 30500 | 0.0952        | -          | -                  | -                   | -                                |
| 4.8357 | 30600 | 0.0952        | -          | -                  | -                   | -                                |
| 4.8515 | 30700 | 0.095         | -          | -                  | -                   | -                                |
| 4.8673 | 30800 | 0.0949        | -          | -                  | -                   | -                                |
| 4.8831 | 30900 | 0.0949        | -          | -                  | -                   | -                                |
| 4.8989 | 31000 | 0.0953        | -          | -                  | -                   | -                                |
| 4.9147 | 31100 | 0.0955        | -          | -                  | -                   | -                                |
| 4.9305 | 31200 | 0.0964        | -          | -                  | -                   | -                                |
| 4.9463 | 31300 | 0.0955        | -          | -                  | -                   | -                                |
| 4.9621 | 31400 | 0.0955        | -          | -                  | -                   | -                                |
| 4.9779 | 31500 | 0.0954        | -          | -                  | -                   | -                                |
| 4.9937 | 31600 | 0.0959        | -          | -                  | -                   | -                                |

</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.46.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- 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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