---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: USS Conyngham (DD-58)
  sentences:
  - '"w jakich patrolach uczestniczył USS ""Conyngham"" (DD-58)?"'
  - Jest ona najstarszą skoczkinią w kadrze norweskiej.
  - kto uczył malarstwa olimpijczyka Bronisława Czecha?
- source_sentence: Danae (obraz Tycjana)
  sentences:
  - jakie różnice występują pomiędzy kolejnymi wersjami obrazu Tycjana Danae?
  - z czego wykonana jest rzeźba Robotnik i kołchoźnica?
  - z jakiego powodu zwołano synod w Whitby?
- source_sentence: dlaczego zapominamy?
  sentences:
  - Zamek w Haapsalu
  - kto był tłumaczem języka angielskiego u Mao Zedonga?
  - Najstarszy z trzech synów Hong Xiuquana; jego matką była Lai Lianying.
- source_sentence: kim był Steve Yzerman?
  sentences:
  - która hala ma najmniejszą widownię w NHL?
  - za co krytykowany był papieski wykład ratyzboński?
  - ' W 1867 oddano do użytku Kolej Warszawsko-Terespolską (całą linię).'
- source_sentence: Herkules na rozstajach
  sentences:
  - jak zinterpretować wymowę obrazu Herkules na rozstajach?
  - Dowódcą grupy był Wiaczesław Razumowicz ps. „Chmara”.
  - z jakiego powodu zwołano synod w Whitby?
model-index:
- name: bge-base-en-v1.5-klej-dyk
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.17307692307692307
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.46153846153846156
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6225961538461539
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7355769230769231
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.17307692307692307
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15384615384615385
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12451923076923076
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0735576923076923
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17307692307692307
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.46153846153846156
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6225961538461539
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7355769230769231
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4433646681639308
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.35053323412698395
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3573926265146405
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.16826923076923078
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4519230769230769
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6009615384615384
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7091346153846154
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.16826923076923078
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15064102564102563
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1201923076923077
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07091346153846154
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.16826923076923078
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4519230769230769
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6009615384615384
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7091346153846154
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.42955891948336516
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3405992445054941
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3484580834493777
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.19230769230769232
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4543269230769231
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5913461538461539
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6899038461538461
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.19230769230769232
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15144230769230768
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11826923076923078
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0689903846153846
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.19230769230769232
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4543269230769231
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5913461538461539
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6899038461538461
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4311008111471328
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3488247863247859
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3560982492053804
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.16346153846153846
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.41586538461538464
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5168269230769231
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5985576923076923
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.16346153846153846
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.13862179487179488
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10336538461538461
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.059855769230769226
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.16346153846153846
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.41586538461538464
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5168269230769231
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5985576923076923
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.37641559536404157
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3052140567765567
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3151839890893904
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.1658653846153846
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.35096153846153844
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.43990384615384615
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5288461538461539
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1658653846153846
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.11698717948717949
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08798076923076924
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.052884615384615384
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1658653846153846
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.35096153846153844
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.43990384615384615
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5288461538461539
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.33823482580826353
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.27800194597069605
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2876731521968676
      name: Cosine Map@100
---

# bge-base-en-v1.5-klej-dyk

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **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': 512, 'do_lower_case': True}) 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): 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Herkules na rozstajach',
    'jak zinterpretować wymowę obrazu Herkules na rozstajach?',
    'Dowódcą grupy był Wiaczesław Razumowicz ps. „Chmara”.',
]
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

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1731     |
| cosine_accuracy@3   | 0.4615     |
| cosine_accuracy@5   | 0.6226     |
| cosine_accuracy@10  | 0.7356     |
| cosine_precision@1  | 0.1731     |
| cosine_precision@3  | 0.1538     |
| cosine_precision@5  | 0.1245     |
| cosine_precision@10 | 0.0736     |
| cosine_recall@1     | 0.1731     |
| cosine_recall@3     | 0.4615     |
| cosine_recall@5     | 0.6226     |
| cosine_recall@10    | 0.7356     |
| cosine_ndcg@10      | 0.4434     |
| cosine_mrr@10       | 0.3505     |
| **cosine_map@100**  | **0.3574** |

#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1683     |
| cosine_accuracy@3   | 0.4519     |
| cosine_accuracy@5   | 0.601      |
| cosine_accuracy@10  | 0.7091     |
| cosine_precision@1  | 0.1683     |
| cosine_precision@3  | 0.1506     |
| cosine_precision@5  | 0.1202     |
| cosine_precision@10 | 0.0709     |
| cosine_recall@1     | 0.1683     |
| cosine_recall@3     | 0.4519     |
| cosine_recall@5     | 0.601      |
| cosine_recall@10    | 0.7091     |
| cosine_ndcg@10      | 0.4296     |
| cosine_mrr@10       | 0.3406     |
| **cosine_map@100**  | **0.3485** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1923     |
| cosine_accuracy@3   | 0.4543     |
| cosine_accuracy@5   | 0.5913     |
| cosine_accuracy@10  | 0.6899     |
| cosine_precision@1  | 0.1923     |
| cosine_precision@3  | 0.1514     |
| cosine_precision@5  | 0.1183     |
| cosine_precision@10 | 0.069      |
| cosine_recall@1     | 0.1923     |
| cosine_recall@3     | 0.4543     |
| cosine_recall@5     | 0.5913     |
| cosine_recall@10    | 0.6899     |
| cosine_ndcg@10      | 0.4311     |
| cosine_mrr@10       | 0.3488     |
| **cosine_map@100**  | **0.3561** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1635     |
| cosine_accuracy@3   | 0.4159     |
| cosine_accuracy@5   | 0.5168     |
| cosine_accuracy@10  | 0.5986     |
| cosine_precision@1  | 0.1635     |
| cosine_precision@3  | 0.1386     |
| cosine_precision@5  | 0.1034     |
| cosine_precision@10 | 0.0599     |
| cosine_recall@1     | 0.1635     |
| cosine_recall@3     | 0.4159     |
| cosine_recall@5     | 0.5168     |
| cosine_recall@10    | 0.5986     |
| cosine_ndcg@10      | 0.3764     |
| cosine_mrr@10       | 0.3052     |
| **cosine_map@100**  | **0.3152** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1659     |
| cosine_accuracy@3   | 0.351      |
| cosine_accuracy@5   | 0.4399     |
| cosine_accuracy@10  | 0.5288     |
| cosine_precision@1  | 0.1659     |
| cosine_precision@3  | 0.117      |
| cosine_precision@5  | 0.088      |
| cosine_precision@10 | 0.0529     |
| cosine_recall@1     | 0.1659     |
| cosine_recall@3     | 0.351      |
| cosine_recall@5     | 0.4399     |
| cosine_recall@10    | 0.5288     |
| cosine_ndcg@10      | 0.3382     |
| cosine_mrr@10       | 0.278      |
| **cosine_map@100**  | **0.2877** |

<!--
## 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: 3,738 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                           | anchor                                                                             |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 90.01 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.82 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                        | anchor                                                                                  |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
  | <code>Londyńska premiera w Ambassadors Theatre na londyńskim West Endzie miała miejsce 25 listopada 1952 roku, a przedstawione grane jest do dziś (od 1974 r.) w sąsiednim St Martin's Theatre. W Polsce była wystawiana m.in. w Teatrze Nowym w Zabrzu.</code> | <code>w którym londyńskim muzeum wystawiana była instalacja My Bed?</code>              |
  | <code>Theridion grallator osiąga długość 5 mm. U niektórych postaci na żółtym odwłoku występuje wzór przypominający uśmiechniętą lub śmiejącą się twarz klowna.</code>                                                                                          | <code>które pająki noszą na grzbiecie wzór przypominający uśmiechniętego klauna?</code> |
  | <code>W 1998 w wyniku sporów o wytyczenie granicy między dwoma państwami wybuchła wojna erytrejsko-etiopska. Zakończyła się porozumieniem zawartym w Algierze 12 grudnia 2000. Od tego czasu strefa graniczna jest patrolowana przez siły pokojowe ONZ.</code>  | <code>jakie były skutki wojny erytrejsko-etiopskiej?</code>                             |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_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`: 10
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

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

| Epoch      | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.0684     | 1      | 7.2706        | -                      | -                      | -                      | -                     | -                      |
| 0.1368     | 2      | 8.2776        | -                      | -                      | -                      | -                     | -                      |
| 0.2051     | 3      | 7.1399        | -                      | -                      | -                      | -                     | -                      |
| 0.2735     | 4      | 6.6905        | -                      | -                      | -                      | -                     | -                      |
| 0.3419     | 5      | 6.735         | -                      | -                      | -                      | -                     | -                      |
| 0.4103     | 6      | 7.0537        | -                      | -                      | -                      | -                     | -                      |
| 0.4786     | 7      | 6.871         | -                      | -                      | -                      | -                     | -                      |
| 0.5470     | 8      | 6.7277        | -                      | -                      | -                      | -                     | -                      |
| 0.6154     | 9      | 5.9853        | -                      | -                      | -                      | -                     | -                      |
| 0.6838     | 10     | 6.0518        | -                      | -                      | -                      | -                     | -                      |
| 0.7521     | 11     | 5.8291        | -                      | -                      | -                      | -                     | -                      |
| 0.8205     | 12     | 5.0064        | -                      | -                      | -                      | -                     | -                      |
| 0.8889     | 13     | 4.8572        | -                      | -                      | -                      | -                     | -                      |
| 0.9573     | 14     | 5.1899        | 0.2812                 | 0.3335                 | 0.3486                 | 0.2115                | 0.3639                 |
| 1.0256     | 15     | 4.2996        | -                      | -                      | -                      | -                     | -                      |
| 1.0940     | 16     | 4.1475        | -                      | -                      | -                      | -                     | -                      |
| 1.1624     | 17     | 4.6174        | -                      | -                      | -                      | -                     | -                      |
| 1.2308     | 18     | 4.394         | -                      | -                      | -                      | -                     | -                      |
| 1.2991     | 19     | 4.0255        | -                      | -                      | -                      | -                     | -                      |
| 1.3675     | 20     | 3.9722        | -                      | -                      | -                      | -                     | -                      |
| 1.4359     | 21     | 3.9509        | -                      | -                      | -                      | -                     | -                      |
| 1.5043     | 22     | 3.7674        | -                      | -                      | -                      | -                     | -                      |
| 1.5726     | 23     | 3.7572        | -                      | -                      | -                      | -                     | -                      |
| 1.6410     | 24     | 3.9463        | -                      | -                      | -                      | -                     | -                      |
| 1.7094     | 25     | 3.7151        | -                      | -                      | -                      | -                     | -                      |
| 1.7778     | 26     | 3.7771        | -                      | -                      | -                      | -                     | -                      |
| 1.8462     | 27     | 3.5228        | -                      | -                      | -                      | -                     | -                      |
| 1.9145     | 28     | 2.7906        | -                      | -                      | -                      | -                     | -                      |
| 1.9829     | 29     | 3.4555        | 0.3164                 | 0.3529                 | 0.3641                 | 0.2636                | 0.3681                 |
| 2.0513     | 30     | 2.737         | -                      | -                      | -                      | -                     | -                      |
| 2.1197     | 31     | 3.1976        | -                      | -                      | -                      | -                     | -                      |
| 2.1880     | 32     | 3.1363        | -                      | -                      | -                      | -                     | -                      |
| 2.2564     | 33     | 2.9706        | -                      | -                      | -                      | -                     | -                      |
| 2.3248     | 34     | 2.9629        | -                      | -                      | -                      | -                     | -                      |
| 2.3932     | 35     | 2.7226        | -                      | -                      | -                      | -                     | -                      |
| 2.4615     | 36     | 2.4378        | -                      | -                      | -                      | -                     | -                      |
| 2.5299     | 37     | 2.7201        | -                      | -                      | -                      | -                     | -                      |
| 2.5983     | 38     | 2.6802        | -                      | -                      | -                      | -                     | -                      |
| 2.6667     | 39     | 3.1613        | -                      | -                      | -                      | -                     | -                      |
| 2.7350     | 40     | 2.9344        | -                      | -                      | -                      | -                     | -                      |
| 2.8034     | 41     | 2.5254        | -                      | -                      | -                      | -                     | -                      |
| 2.8718     | 42     | 2.5617        | -                      | -                      | -                      | -                     | -                      |
| 2.9402     | 43     | 2.459         | 0.3197                 | 0.3571                 | 0.3640                 | 0.2739                | 0.3733                 |
| 3.0085     | 44     | 2.3785        | -                      | -                      | -                      | -                     | -                      |
| 3.0769     | 45     | 1.9408        | -                      | -                      | -                      | -                     | -                      |
| 3.1453     | 46     | 2.7095        | -                      | -                      | -                      | -                     | -                      |
| 3.2137     | 47     | 2.4774        | -                      | -                      | -                      | -                     | -                      |
| 3.2821     | 48     | 2.2178        | -                      | -                      | -                      | -                     | -                      |
| 3.3504     | 49     | 2.0884        | -                      | -                      | -                      | -                     | -                      |
| 3.4188     | 50     | 2.1044        | -                      | -                      | -                      | -                     | -                      |
| 3.4872     | 51     | 2.1504        | -                      | -                      | -                      | -                     | -                      |
| 3.5556     | 52     | 2.1177        | -                      | -                      | -                      | -                     | -                      |
| 3.6239     | 53     | 2.2283        | -                      | -                      | -                      | -                     | -                      |
| 3.6923     | 54     | 2.3964        | -                      | -                      | -                      | -                     | -                      |
| 3.7607     | 55     | 2.0972        | -                      | -                      | -                      | -                     | -                      |
| 3.8291     | 56     | 2.0961        | -                      | -                      | -                      | -                     | -                      |
| 3.8974     | 57     | 1.783         | -                      | -                      | -                      | -                     | -                      |
| **3.9658** | **58** | **2.1031**    | **0.3246**             | **0.3533**             | **0.3603**             | **0.2829**            | **0.3687**             |
| 4.0342     | 59     | 1.6699        | -                      | -                      | -                      | -                     | -                      |
| 4.1026     | 60     | 1.6675        | -                      | -                      | -                      | -                     | -                      |
| 4.1709     | 61     | 2.1672        | -                      | -                      | -                      | -                     | -                      |
| 4.2393     | 62     | 1.8881        | -                      | -                      | -                      | -                     | -                      |
| 4.3077     | 63     | 1.701         | -                      | -                      | -                      | -                     | -                      |
| 4.3761     | 64     | 1.9154        | -                      | -                      | -                      | -                     | -                      |
| 4.4444     | 65     | 1.4549        | -                      | -                      | -                      | -                     | -                      |
| 4.5128     | 66     | 1.5444        | -                      | -                      | -                      | -                     | -                      |
| 4.5812     | 67     | 1.8352        | -                      | -                      | -                      | -                     | -                      |
| 4.6496     | 68     | 1.7908        | -                      | -                      | -                      | -                     | -                      |
| 4.7179     | 69     | 1.6876        | -                      | -                      | -                      | -                     | -                      |
| 4.7863     | 70     | 1.7366        | -                      | -                      | -                      | -                     | -                      |
| 4.8547     | 71     | 1.8689        | -                      | -                      | -                      | -                     | -                      |
| 4.9231     | 72     | 1.4676        | -                      | -                      | -                      | -                     | -                      |
| 4.9915     | 73     | 1.5045        | 0.3170                 | 0.3538                 | 0.3606                 | 0.2829                | 0.3675                 |
| 5.0598     | 74     | 1.2155        | -                      | -                      | -                      | -                     | -                      |
| 5.1282     | 75     | 1.4365        | -                      | -                      | -                      | -                     | -                      |
| 5.1966     | 76     | 1.7451        | -                      | -                      | -                      | -                     | -                      |
| 5.2650     | 77     | 1.4537        | -                      | -                      | -                      | -                     | -                      |
| 5.3333     | 78     | 1.3813        | -                      | -                      | -                      | -                     | -                      |
| 5.4017     | 79     | 1.4035        | -                      | -                      | -                      | -                     | -                      |
| 5.4701     | 80     | 1.3912        | -                      | -                      | -                      | -                     | -                      |
| 5.5385     | 81     | 1.3286        | -                      | -                      | -                      | -                     | -                      |
| 5.6068     | 82     | 1.5153        | -                      | -                      | -                      | -                     | -                      |
| 5.6752     | 83     | 1.6745        | -                      | -                      | -                      | -                     | -                      |
| 5.7436     | 84     | 1.4323        | -                      | -                      | -                      | -                     | -                      |
| 5.8120     | 85     | 1.5299        | -                      | -                      | -                      | -                     | -                      |
| 5.8803     | 86     | 1.488         | -                      | -                      | -                      | -                     | -                      |
| 5.9487     | 87     | 1.5195        | 0.3206                 | 0.3556                 | 0.3530                 | 0.2878                | 0.3605                 |
| 6.0171     | 88     | 1.2999        | -                      | -                      | -                      | -                     | -                      |
| 6.0855     | 89     | 1.1511        | -                      | -                      | -                      | -                     | -                      |
| 6.1538     | 90     | 1.552         | -                      | -                      | -                      | -                     | -                      |
| 6.2222     | 91     | 1.35          | -                      | -                      | -                      | -                     | -                      |
| 6.2906     | 92     | 1.218         | -                      | -                      | -                      | -                     | -                      |
| 6.3590     | 93     | 1.1712        | -                      | -                      | -                      | -                     | -                      |
| 6.4274     | 94     | 1.3381        | -                      | -                      | -                      | -                     | -                      |
| 6.4957     | 95     | 1.1716        | -                      | -                      | -                      | -                     | -                      |
| 6.5641     | 96     | 1.2117        | -                      | -                      | -                      | -                     | -                      |
| 6.6325     | 97     | 1.5349        | -                      | -                      | -                      | -                     | -                      |
| 6.7009     | 98     | 1.4564        | -                      | -                      | -                      | -                     | -                      |
| 6.7692     | 99     | 1.3541        | -                      | -                      | -                      | -                     | -                      |
| 6.8376     | 100    | 1.2468        | -                      | -                      | -                      | -                     | -                      |
| 6.9060     | 101    | 1.1519        | -                      | -                      | -                      | -                     | -                      |
| 6.9744     | 102    | 1.2421        | 0.3150                 | 0.3555                 | 0.3501                 | 0.2858                | 0.3575                 |
| 7.0427     | 103    | 1.0096        | -                      | -                      | -                      | -                     | -                      |
| 7.1111     | 104    | 1.1405        | -                      | -                      | -                      | -                     | -                      |
| 7.1795     | 105    | 1.2958        | -                      | -                      | -                      | -                     | -                      |
| 7.2479     | 106    | 1.35          | -                      | -                      | -                      | -                     | -                      |
| 7.3162     | 107    | 1.1291        | -                      | -                      | -                      | -                     | -                      |
| 7.3846     | 108    | 0.9968        | -                      | -                      | -                      | -                     | -                      |
| 7.4530     | 109    | 1.0454        | -                      | -                      | -                      | -                     | -                      |
| 7.5214     | 110    | 1.102         | -                      | -                      | -                      | -                     | -                      |
| 7.5897     | 111    | 1.1328        | -                      | -                      | -                      | -                     | -                      |
| 7.6581     | 112    | 1.5988        | -                      | -                      | -                      | -                     | -                      |
| 7.7265     | 113    | 1.2992        | -                      | -                      | -                      | -                     | -                      |
| 7.7949     | 114    | 1.2572        | -                      | -                      | -                      | -                     | -                      |
| 7.8632     | 115    | 1.1414        | -                      | -                      | -                      | -                     | -                      |
| 7.9316     | 116    | 1.1432        | -                      | -                      | -                      | -                     | -                      |
| 8.0        | 117    | 1.1181        | 0.3154                 | 0.3545                 | 0.3509                 | 0.2884                | 0.3578                 |
| 8.0684     | 118    | 0.9365        | -                      | -                      | -                      | -                     | -                      |
| 8.1368     | 119    | 1.3286        | -                      | -                      | -                      | -                     | -                      |
| 8.2051     | 120    | 1.3711        | -                      | -                      | -                      | -                     | -                      |
| 8.2735     | 121    | 1.2001        | -                      | -                      | -                      | -                     | -                      |
| 8.3419     | 122    | 1.165         | -                      | -                      | -                      | -                     | -                      |
| 8.4103     | 123    | 1.0575        | -                      | -                      | -                      | -                     | -                      |
| 8.4786     | 124    | 1.105         | -                      | -                      | -                      | -                     | -                      |
| 8.5470     | 125    | 1.077         | -                      | -                      | -                      | -                     | -                      |
| 8.6154     | 126    | 1.2217        | -                      | -                      | -                      | -                     | -                      |
| 8.6838     | 127    | 1.3254        | -                      | -                      | -                      | -                     | -                      |
| 8.7521     | 128    | 1.2165        | -                      | -                      | -                      | -                     | -                      |
| 8.8205     | 129    | 1.3021        | -                      | -                      | -                      | -                     | -                      |
| 8.8889     | 130    | 1.0927        | -                      | -                      | -                      | -                     | -                      |
| 8.9573     | 131    | 1.3961        | 0.3150                 | 0.3540                 | 0.3490                 | 0.2882                | 0.3588                 |
| 9.0256     | 132    | 1.0779        | -                      | -                      | -                      | -                     | -                      |
| 9.0940     | 133    | 0.901         | -                      | -                      | -                      | -                     | -                      |
| 9.1624     | 134    | 1.313         | -                      | -                      | -                      | -                     | -                      |
| 9.2308     | 135    | 1.1409        | -                      | -                      | -                      | -                     | -                      |
| 9.2991     | 136    | 1.1635        | -                      | -                      | -                      | -                     | -                      |
| 9.3675     | 137    | 1.0244        | -                      | -                      | -                      | -                     | -                      |
| 9.4359     | 138    | 1.0576        | -                      | -                      | -                      | -                     | -                      |
| 9.5043     | 139    | 1.0101        | -                      | -                      | -                      | -                     | -                      |
| 9.5726     | 140    | 1.1516        | 0.3152                 | 0.3561                 | 0.3485                 | 0.2877                | 0.3574                 |

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

### Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.1
- Accelerate: 0.27.2
- Datasets: 2.19.1
- 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",
}
```

#### 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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