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LaBSE-en-ru-distilled-each-third-layer

Sentence Similarity
sentence-transformers
Safetensors
Russian
English
bert
feature-extraction
Generated from Trainer
dataset_size:10975066
loss:MSELoss
Eval Results
text-embeddings-inference
Inference Endpoints
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---
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.*
-->
## 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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