sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:10975066 loss:MSELoss ru en arxiv:1908.10084 arxiv:2004.09813 base_model:cointegrated/LaBSE-en-ru base_model:finetune:cointegrated/LaBSE-en-ru model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: cointegrated/LaBSE-en-ru
language:
- ru
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- negative_mse
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10975066
- loss:MSELoss
widget:
- source_sentence: Такие лодки строились, чтобы получить быстрый доступ к приходящим
    судам.
  sentences:
  - been nice talking to you
  - Нельзя ставить под сомнение притязания клиента, если не были предприняты шаги.
  - Dharangaon Railway Station serves Dharangaon in Jalgaon district in the Indian
    state of Maharashtra.
- source_sentence: Если прилагательные смягчают этнические термины, существительные
    могут сделать их жестче.
  sentences:
  - Вслед за этим последовало секретное письмо А.Б.Чубайса об изъятии у МЦР, переданного
    ему С.Н.Рерихом наследия.
  - Coaches should not give young athletes a hard time.
  - Эшкрофт хотел прослушивать сводки новостей снова и снова
- source_sentence: Земля была мягкой.
  sentences:
  - По мере того, как самообладание покидало его, сердце его все больше наполнялось
    тревогой.
  - Our borders and immigration system, including law enforcement, ought to send a
    message of welcome, tolerance, and justice to members of immigrant communities
    in the United States and in their countries of origin.
  - Начнут действовать льготные условия аренды земель, которые предназначены для реализации
    инвестиционных проектов.
- source_sentence: 'Что же касается рава Кука: мой рав лично знал его и много раз
    с теплотой рассказывал мне о нем как о великом каббалисте.'
  sentences:
  - Вдова Эдгара Эванса, его дети и мать получили 1500 фунтов стерлингов (
  - Please do not make any changes to your address.
  - Мы уже закончили все запланированные дела!
- source_sentence: See Name section.
  sentences:
  - Ms. Packard is the voice of the female blood elf in the video game World of Warcraft.
  - Основным функциональным элементом, реализующим функции управления соединением,
    является абонентский терминал.
  - Yeah, people who might not be hungry.
model-index:
- name: SentenceTransformer based on cointegrated/LaBSE-en-ru
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.5305176535187099
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6347069834349862
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.5553415140113596
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6389336208598283
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.5499910306125031
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6347073809507647
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5305176585564861
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6347078463557637
      name: Spearman Dot
    - type: pearson_max
      value: 0.5553415140113596
      name: Pearson Max
    - type: spearman_max
      value: 0.6389336208598283
      name: Spearman Max
  - task:
      type: knowledge-distillation
      name: Knowledge Distillation
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: negative_mse
      value: -0.006337030936265364
      name: Negative Mse
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.5042796836494269
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.5986471772428711
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.522744495080616
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.5983901280447074
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.522721961447153
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.5986471095414022
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.504279685613151
      name: Pearson Dot
    - type: spearman_dot
      value: 0.598648155615724
      name: Spearman Dot
    - type: pearson_max
      value: 0.522744495080616
      name: Pearson Max
    - type: spearman_max
      value: 0.598648155615724
      name: Spearman Max
---

# SentenceTransformer based on cointegrated/LaBSE-en-ru

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru). 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:** [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru) <!-- at revision cf0714e606d4af551e14ad69a7929cd6b0da7f7e -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): Normalize()
)
```

## 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("whitemouse84/LaBSE-en-ru-distilled-each-third-layer")
# Run inference
sentences = [
    'See Name section.',
    'Ms. Packard is the voice of the female blood elf in the video game World of Warcraft.',
    'Yeah, people who might not be hungry.',
]
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.*
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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.5305     |
| **spearman_cosine** | **0.6347** |
| pearson_manhattan   | 0.5553     |
| spearman_manhattan  | 0.6389     |
| pearson_euclidean   | 0.55       |
| spearman_euclidean  | 0.6347     |
| pearson_dot         | 0.5305     |
| spearman_dot        | 0.6347     |
| pearson_max         | 0.5553     |
| spearman_max        | 0.6389     |

#### Knowledge Distillation

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

| Metric           | Value       |
|:-----------------|:------------|
| **negative_mse** | **-0.0063** |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.5043     |
| **spearman_cosine** | **0.5986** |
| pearson_manhattan   | 0.5227     |
| spearman_manhattan  | 0.5984     |
| pearson_euclidean   | 0.5227     |
| spearman_euclidean  | 0.5986     |
| pearson_dot         | 0.5043     |
| spearman_dot        | 0.5986     |
| pearson_max         | 0.5227     |
| spearman_max        | 0.5986     |

<!--
## 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: 10,975,066 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                           | label                                |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                             | list                                 |
  | details | <ul><li>min: 6 tokens</li><li>mean: 26.93 tokens</li><li>max: 139 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
  | sentence                                                                                                                                                                                             | label                                                                                                                               |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|
  | <code>It is based on the Java Persistence API (JPA), but it does not strictly follow the JSR 338 Specification, as it implements different design patterns and technologies.</code>                  | <code>[-0.012331949546933174, -0.04570527374744415, -0.024963658303022385, -0.03620213270187378, 0.022556383162736893, ...]</code>  |
  | <code>Покупаем вторичное сырье в Каунасе (Переработка вторичного сырья) - Алфенас АНД КО, ЗАО на Bizorg.</code>                                                                                      | <code>[-0.07498518377542496, -0.01913534104824066, -0.01797042042016983, 0.048263177275657654, -0.00016611881437711418, ...]</code> |
  | <code>At the Equal Justice Conference ( EJC ) held in March 2001 in San Diego , LSC and the Project for the Future of Equal Justice held the second Case Management Software pre-conference .</code> | <code>[0.03870972990989685, -0.0638347640633583, -0.01696585863828659, -0.043612319976091385, -0.048241738229990005, ...]</code>    |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

### Evaluation Dataset

#### Unnamed Dataset


* Size: 10,000 evaluation samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                           | label                                |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                             | list                                 |
  | details | <ul><li>min: 5 tokens</li><li>mean: 24.18 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
* Samples:
  | sentence                                                                                                                                                                                                           | label                                                                                                                              |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
  | <code>The Canadian Canoe Museum is a museum dedicated to canoes located in Peterborough, Ontario, Canada.</code>                                                                                                   | <code>[-0.05444105342030525, -0.03650881350040436, -0.041163671761751175, -0.010616903193295002, -0.04094529151916504, ...]</code> |
  | <code>И мне нравилось, что я одновременно зарабатываю и смотрю бои».</code>                                                                                                                                        | <code>[-0.03404555842280388, 0.028203096240758896, -0.056121889501810074, -0.0591997392475605, -0.05523117259144783, ...]</code>   |
  | <code>Ну, а на следующий день, разумеется, Президент Кеннеди объявил блокаду Кубы, и наши корабли остановили у кубинских берегов направлявшийся на Кубу российский корабль, и у него на борту нашли ракеты.</code> | <code>[-0.008193841204047203, 0.00694894278421998, -0.03027420863509178, -0.03290146216750145, 0.01425305474549532, ...]</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`: 0.0001
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: 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`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `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
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch      | Step     | Training Loss | loss       | negative_mse | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:----------:|:--------:|:-------------:|:----------:|:------------:|:-----------------------:|:------------------------:|
| 0          | 0        | -             | -          | -0.2381      | 0.4206                  | -                        |
| 0.0058     | 1000     | 0.0014        | -          | -            | -                       | -                        |
| 0.0117     | 2000     | 0.0009        | -          | -            | -                       | -                        |
| 0.0175     | 3000     | 0.0007        | -          | -            | -                       | -                        |
| 0.0233     | 4000     | 0.0006        | -          | -            | -                       | -                        |
| **0.0292** | **5000** | **0.0005**    | **0.0004** | **-0.0363**  | **0.6393**              | **-**                    |
| 0.0350     | 6000     | 0.0004        | -          | -            | -                       | -                        |
| 0.0408     | 7000     | 0.0004        | -          | -            | -                       | -                        |
| 0.0467     | 8000     | 0.0003        | -          | -            | -                       | -                        |
| 0.0525     | 9000     | 0.0003        | -          | -            | -                       | -                        |
| 0.0583     | 10000    | 0.0003        | 0.0002     | -0.0207      | 0.6350                  | -                        |
| 0.0641     | 11000    | 0.0003        | -          | -            | -                       | -                        |
| 0.0700     | 12000    | 0.0003        | -          | -            | -                       | -                        |
| 0.0758     | 13000    | 0.0002        | -          | -            | -                       | -                        |
| 0.0816     | 14000    | 0.0002        | -          | -            | -                       | -                        |
| 0.0875     | 15000    | 0.0002        | 0.0002     | -0.0157      | 0.6328                  | -                        |
| 0.0933     | 16000    | 0.0002        | -          | -            | -                       | -                        |
| 0.0991     | 17000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1050     | 18000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1108     | 19000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1166     | 20000    | 0.0002        | 0.0001     | -0.0132      | 0.6317                  | -                        |
| 0.1225     | 21000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1283     | 22000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1341     | 23000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1400     | 24000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1458     | 25000    | 0.0002        | 0.0001     | -0.0118      | 0.6251                  | -                        |
| 0.1516     | 26000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1574     | 27000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1633     | 28000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1691     | 29000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1749     | 30000    | 0.0002        | 0.0001     | -0.0109      | 0.6304                  | -                        |
| 0.1808     | 31000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1866     | 32000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1924     | 33000    | 0.0002        | -          | -            | -                       | -                        |
| 0.1983     | 34000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2041     | 35000    | 0.0001        | 0.0001     | -0.0102      | 0.6280                  | -                        |
| 0.2099     | 36000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2158     | 37000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2216     | 38000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2274     | 39000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2333     | 40000    | 0.0001        | 0.0001     | -0.0098      | 0.6272                  | -                        |
| 0.2391     | 41000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2449     | 42000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2507     | 43000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2566     | 44000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2624     | 45000    | 0.0001        | 0.0001     | -0.0093      | 0.6378                  | -                        |
| 0.2682     | 46000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2741     | 47000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2799     | 48000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2857     | 49000    | 0.0001        | -          | -            | -                       | -                        |
| 0.2916     | 50000    | 0.0001        | 0.0001     | -0.0089      | 0.6325                  | -                        |
| 0.2974     | 51000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3032     | 52000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3091     | 53000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3149     | 54000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3207     | 55000    | 0.0001        | 0.0001     | -0.0087      | 0.6328                  | -                        |
| 0.3266     | 56000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3324     | 57000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3382     | 58000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3441     | 59000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3499     | 60000    | 0.0001        | 0.0001     | -0.0085      | 0.6357                  | -                        |
| 0.3557     | 61000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3615     | 62000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3674     | 63000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3732     | 64000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3790     | 65000    | 0.0001        | 0.0001     | -0.0083      | 0.6366                  | -                        |
| 0.3849     | 66000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3907     | 67000    | 0.0001        | -          | -            | -                       | -                        |
| 0.3965     | 68000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4024     | 69000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4082     | 70000    | 0.0001        | 0.0001     | -0.0080      | 0.6325                  | -                        |
| 0.4140     | 71000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4199     | 72000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4257     | 73000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4315     | 74000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4374     | 75000    | 0.0001        | 0.0001     | -0.0078      | 0.6351                  | -                        |
| 0.4432     | 76000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4490     | 77000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4548     | 78000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4607     | 79000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4665     | 80000    | 0.0001        | 0.0001     | -0.0077      | 0.6323                  | -                        |
| 0.4723     | 81000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4782     | 82000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4840     | 83000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4898     | 84000    | 0.0001        | -          | -            | -                       | -                        |
| 0.4957     | 85000    | 0.0001        | 0.0001     | -0.0076      | 0.6316                  | -                        |
| 0.5015     | 86000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5073     | 87000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5132     | 88000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5190     | 89000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5248     | 90000    | 0.0001        | 0.0001     | -0.0074      | 0.6306                  | -                        |
| 0.5307     | 91000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5365     | 92000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5423     | 93000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5481     | 94000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5540     | 95000    | 0.0001        | 0.0001     | -0.0073      | 0.6305                  | -                        |
| 0.5598     | 96000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5656     | 97000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5715     | 98000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5773     | 99000    | 0.0001        | -          | -            | -                       | -                        |
| 0.5831     | 100000   | 0.0001        | 0.0001     | -0.0072      | 0.6333                  | -                        |
| 0.5890     | 101000   | 0.0001        | -          | -            | -                       | -                        |
| 0.5948     | 102000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6006     | 103000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6065     | 104000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6123     | 105000   | 0.0001        | 0.0001     | -0.0071      | 0.6351                  | -                        |
| 0.6181     | 106000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6240     | 107000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6298     | 108000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6356     | 109000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6415     | 110000   | 0.0001        | 0.0001     | -0.0070      | 0.6330                  | -                        |
| 0.6473     | 111000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6531     | 112000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6589     | 113000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6648     | 114000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6706     | 115000   | 0.0001        | 0.0001     | -0.0070      | 0.6336                  | -                        |
| 0.6764     | 116000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6823     | 117000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6881     | 118000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6939     | 119000   | 0.0001        | -          | -            | -                       | -                        |
| 0.6998     | 120000   | 0.0001        | 0.0001     | -0.0069      | 0.6305                  | -                        |
| 0.7056     | 121000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7114     | 122000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7173     | 123000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7231     | 124000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7289     | 125000   | 0.0001        | 0.0001     | -0.0068      | 0.6362                  | -                        |
| 0.7348     | 126000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7406     | 127000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7464     | 128000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7522     | 129000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7581     | 130000   | 0.0001        | 0.0001     | -0.0067      | 0.6340                  | -                        |
| 0.7639     | 131000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7697     | 132000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7756     | 133000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7814     | 134000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7872     | 135000   | 0.0001        | 0.0001     | -0.0067      | 0.6365                  | -                        |
| 0.7931     | 136000   | 0.0001        | -          | -            | -                       | -                        |
| 0.7989     | 137000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8047     | 138000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8106     | 139000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8164     | 140000   | 0.0001        | 0.0001     | -0.0066      | 0.6339                  | -                        |
| 0.8222     | 141000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8281     | 142000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8339     | 143000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8397     | 144000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8456     | 145000   | 0.0001        | 0.0001     | -0.0066      | 0.6352                  | -                        |
| 0.8514     | 146000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8572     | 147000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8630     | 148000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8689     | 149000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8747     | 150000   | 0.0001        | 0.0001     | -0.0065      | 0.6357                  | -                        |
| 0.8805     | 151000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8864     | 152000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8922     | 153000   | 0.0001        | -          | -            | -                       | -                        |
| 0.8980     | 154000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9039     | 155000   | 0.0001        | 0.0001     | -0.0065      | 0.6336                  | -                        |
| 0.9097     | 156000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9155     | 157000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9214     | 158000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9272     | 159000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9330     | 160000   | 0.0001        | 0.0001     | -0.0064      | 0.6334                  | -                        |
| 0.9389     | 161000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9447     | 162000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9505     | 163000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9563     | 164000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9622     | 165000   | 0.0001        | 0.0001     | -0.0064      | 0.6337                  | -                        |
| 0.9680     | 166000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9738     | 167000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9797     | 168000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9855     | 169000   | 0.0001        | -          | -            | -                       | -                        |
| 0.9913     | 170000   | 0.0001        | 0.0001     | -0.0063      | 0.6347                  | -                        |
| 0.9972     | 171000   | 0.0001        | -          | -            | -                       | -                        |
| 1.0        | 171486   | -             | -          | -            | -                       | 0.5986                   |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0
- Accelerate: 0.33.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## 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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