---
language:
- ru
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:904
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: Какой у тебя план на будущее?
  sentences:
  - Работа — это скучно, если не считать, что Уголовный розыск считает меня своим
    работником.
  - Я дам вам парабеллум, если дружба станет слишком серьезной!
  - План? Из Васюков полетят сигналы на Марс, а я буду на Земле собирать деньги на
    билет.
- source_sentence: Какой у тебя любимый фильм?
  sentences:
  - Может быть, тебе дать еще список фильмов, где много денег?
  - Вам нужно путешествовать так, чтобы потом не забыть, где памятник.
  - А доисторические спортсмены в матрацах не тренируются?
- source_sentence: Как ты проводишь свободное время?
  sentences:
  - Напиток? Командовать парадом буду я!
  - Нас топят — мы выплываем, а свободное время — это для плавания!
  - От мертвого осла уши получишь у Пушкина, а от фильмов — только кадры.
- source_sentence: Как ты проводишь свободное время?
  sentences:
  - Картина битвы мне ясна, но я предпочитаю не сражаться с скукой.
  - Спорт — это для тех, кто не знает, что они произошли от коровы!
  - Тайный союз меча и орала! Свободное время — это когда можно ничего не делать и
    не переживать!
- source_sentence: Какой у тебя любимый фильм?
  sentences:
  - А что, разве я похож на человека, который не любит читать между строк?
  - У нас хотя и не Париж, но кино у нас всегда с интригой!
  - Фильм? Знойная женщина, мечта поэта — вот мой любимый сюжет!
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: BGE m3 for Ostap project
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 1024
      type: dim_1024
    metrics:
    - type: cosine_accuracy@1
      value: 0.14933628318584072
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2665929203539823
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.34292035398230086
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4856194690265487
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14933628318584072
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08886430678466074
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06858407079646017
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04856194690265486
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14933628318584072
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2665929203539823
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.34292035398230086
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4856194690265487
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2942645243659726
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.23620329400196635
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2600956714540916
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.14601769911504425
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.26548672566371684
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3473451327433628
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.48672566371681414
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14601769911504425
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08849557522123894
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06946902654867257
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.048672566371681415
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14601769911504425
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.26548672566371684
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3473451327433628
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.48672566371681414
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2931785163867407
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2343512958280655
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2581995173126666
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.14823008849557523
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.26548672566371684
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.34513274336283184
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4944690265486726
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14823008849557523
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08849557522123894
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06902654867256636
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04944690265486726
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14823008849557523
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.26548672566371684
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.34513274336283184
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4944690265486726
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2965536225707287
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.23654261483354377
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2597641504609653
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.14491150442477876
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2688053097345133
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.34845132743362833
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4911504424778761
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14491150442477876
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08960176991150441
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06969026548672566
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.049115044247787606
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14491150442477876
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2688053097345133
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.34845132743362833
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4911504424778761
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2942530832557106
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2342999367888746
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2580055991240585
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.14712389380530974
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2665929203539823
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.34623893805309736
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4944690265486726
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14712389380530974
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08886430678466076
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06924778761061946
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04944690265486726
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14712389380530974
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2665929203539823
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.34623893805309736
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4944690265486726
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2963702071144291
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2362221695462843
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.25976571809408944
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.14601769911504425
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.26991150442477874
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3473451327433628
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.497787610619469
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14601769911504425
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08997050147492625
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06946902654867257
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.049778761061946904
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14601769911504425
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.26991150442477874
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3473451327433628
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.497787610619469
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.29684044099735196
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.23588767734232302
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2592174386566743
      name: Cosine Map@100
---

# BGE m3 for Ostap project

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** ru
- **License:** apache-2.0

### 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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): 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("fitlemon/bge-m3-ru-ostap")
# Run inference
sentences = [
    'Какой у тебя любимый фильм?',
    'У нас хотя и не Париж, но кино у нас всегда с интригой!',
    'Фильм? Знойная женщина, мечта поэта — вот мой любимый сюжет!',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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

#### Information Retrieval

* Datasets: `dim_1024`, `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_1024   | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1   | 0.1493     | 0.146      | 0.1482     | 0.1449     | 0.1471     | 0.146      |
| cosine_accuracy@3   | 0.2666     | 0.2655     | 0.2655     | 0.2688     | 0.2666     | 0.2699     |
| cosine_accuracy@5   | 0.3429     | 0.3473     | 0.3451     | 0.3485     | 0.3462     | 0.3473     |
| cosine_accuracy@10  | 0.4856     | 0.4867     | 0.4945     | 0.4912     | 0.4945     | 0.4978     |
| cosine_precision@1  | 0.1493     | 0.146      | 0.1482     | 0.1449     | 0.1471     | 0.146      |
| cosine_precision@3  | 0.0889     | 0.0885     | 0.0885     | 0.0896     | 0.0889     | 0.09       |
| cosine_precision@5  | 0.0686     | 0.0695     | 0.069      | 0.0697     | 0.0692     | 0.0695     |
| cosine_precision@10 | 0.0486     | 0.0487     | 0.0494     | 0.0491     | 0.0494     | 0.0498     |
| cosine_recall@1     | 0.1493     | 0.146      | 0.1482     | 0.1449     | 0.1471     | 0.146      |
| cosine_recall@3     | 0.2666     | 0.2655     | 0.2655     | 0.2688     | 0.2666     | 0.2699     |
| cosine_recall@5     | 0.3429     | 0.3473     | 0.3451     | 0.3485     | 0.3462     | 0.3473     |
| cosine_recall@10    | 0.4856     | 0.4867     | 0.4945     | 0.4912     | 0.4945     | 0.4978     |
| **cosine_ndcg@10**  | **0.2943** | **0.2932** | **0.2966** | **0.2943** | **0.2964** | **0.2968** |
| cosine_mrr@10       | 0.2362     | 0.2344     | 0.2365     | 0.2343     | 0.2362     | 0.2359     |
| cosine_map@100      | 0.2601     | 0.2582     | 0.2598     | 0.258      | 0.2598     | 0.2592     |

<!--
## 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: 904 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 904 samples:
  |         | question                                                                          | answer                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 10.16 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.91 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
  | question                                       | answer                                                                              |
  |:-----------------------------------------------|:------------------------------------------------------------------------------------|
  | <code>Как ты проводишь свободное время?</code> | <code>Любителя бьют, а время — не ждет!</code>                                      |
  | <code>Какой у тебя план на будущее?</code>     | <code>План на будущее? Широкие массы миллиардеров уже составили его за меня.</code> |
  | <code>Какой у тебя любимый цвет?</code>        | <code>Вы мне в конце концов не художник, не дизайнер и не стилист.</code>           |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: False
- `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_fused
- `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`: None
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | dim_1024_cosine_ndcg@10 | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.0885     | 10      | 6.8669        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.1770     | 20      | 4.9384        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.2655     | 30      | 3.1491        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.3540     | 40      | 2.5456        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.4425     | 50      | 3.6943        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.5310     | 60      | 1.8947        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.6195     | 70      | 2.1762        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.7080     | 80      | 1.9446        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.7965     | 90      | 1.5278        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.8850     | 100     | 2.0417        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.9735     | 110     | 3.7804        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.0        | 113     | -             | 0.2751                  | 0.2747                 | 0.2761                 | 0.2786                 | 0.2764                 | 0.2715                |
| 1.0619     | 120     | 1.9706        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.1504     | 130     | 1.7073        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.2389     | 140     | 1.3279        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.3274     | 150     | 1.2724        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.4159     | 160     | 2.4455        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.5044     | 170     | 0.5255        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.5929     | 180     | 2.5764        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.6814     | 190     | 1.56          | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.7699     | 200     | 0.9105        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.8584     | 210     | 1.9859        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.9469     | 220     | 1.6355        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.0088     | 227     | -             | 0.2837                  | 0.2852                 | 0.2880                 | 0.2899                 | 0.2926                 | 0.2902                |
| 2.0265     | 230     | 0.6769        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.1150     | 240     | 0.764         | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.2035     | 250     | 1.0598        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.2920     | 260     | 0.9267        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.3805     | 270     | 0.9687        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.4690     | 280     | 0.7875        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.5575     | 290     | 1.3853        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.6460     | 300     | 0.8114        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.7345     | 310     | 1.6069        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.8230     | 320     | 0.8149        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.9115     | 330     | 0.8858        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.0        | 340     | 0.7858        | 0.2920                  | 0.2917                 | 0.2929                 | 0.2927                 | 0.2967                 | 0.2969                |
| 3.0885     | 350     | 0.5889        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.1770     | 360     | 0.3542        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.2655     | 370     | 0.5868        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.3540     | 380     | 0.4988        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.4425     | 390     | 0.4577        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.5310     | 400     | 0.4735        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.6195     | 410     | 1.2588        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.7080     | 420     | 0.6346        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.7965     | 430     | 0.3013        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.8850     | 440     | 0.6734        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.9735     | 450     | 0.3469        | -                       | -                      | -                      | -                      | -                      | -                     |
| **3.9912** | **452** | **-**         | **0.2943**              | **0.2932**             | **0.2966**             | **0.2943**             | **0.2964**             | **0.2968**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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