---
base_model: liddlefish/privacy_embedding_rag_10k_base_checkpoint_2
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: Żywot św. Stanisława
  sentences:
  - czym różni się Żywot św. Stanisława od Legendy św. Stanisława?
  - czemu gra The Saboteur wywołała wiele kontrowersji?
  - Muzykę do obrazu skomponowali Marco Frisina i Ennio Morricone.
- source_sentence: Jaakow Jicchak Szapira
  sentences:
  - cadykiem którego miasta był Jaakow Jicchak Dan Landau?
  - gdzie zginął przedwojenny minister Antoni Olszewski?
  - ' W 1867 oddano do użytku Kolej Warszawsko-Terespolską (całą linię).'
- source_sentence: Chłopiec z Nariokotome
  sentences:
  - ile wynosiła objętość mózgu chłopca z Nariokotome?
  - czemu gra The Saboteur wywołała wiele kontrowersji?
  - Akcja powieści rozgrywa się w XV-wiecznej Polsce.
- source_sentence: Stany Zjednoczone Polski
  sentences:
  - kiedy miały szansę powstać Stany Zjednoczone Polski?
  - z jakiego powodu Chloé wywołała skandal w Melbourne?
  - komu przysługiwał tytuł autokratora?
- source_sentence: Sen o zastrzyku Irmy
  sentences:
  - gdzie Freud spotkał Irmę we śnie o zastrzyku Irmy?
  - dlaczego Ōkunoshima została wymazana z map Japonii?
  - który samochód wyglądem nawiązuje do Mercedesa-Benza SLS AMG?
model-index:
- name: privacy_embedding_rag_10k_base_checkpoint_2-klej-dyk-v0.1
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.1875
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4543269230769231
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6225961538461539
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7307692307692307
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1875
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15144230769230768
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12451923076923076
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07307692307692307
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1875
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4543269230769231
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6225961538461539
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7307692307692307
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4453345212200682
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.35500896672771654
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.36239083059244687
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.18269230769230768
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.44471153846153844
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6033653846153846
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7115384615384616
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18269230769230768
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.14823717948717946
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12067307692307691
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07115384615384614
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18269230769230768
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.44471153846153844
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6033653846153846
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7115384615384616
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.43488982498130374
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.347151633089133
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3548109777991144
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.1875
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4230769230769231
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5576923076923077
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6682692307692307
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1875
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.14102564102564102
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11153846153846153
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06682692307692308
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1875
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4230769230769231
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5576923076923077
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6682692307692307
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.41398239515933494
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3336862789987789
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3427233175204077
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.17067307692307693
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.36778846153846156
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5120192307692307
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6009615384615384
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.17067307692307693
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.12259615384615384
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10240384615384614
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06009615384615385
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17067307692307693
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.36778846153846156
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5120192307692307
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6009615384615384
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.371201964014572
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2987818605006104
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3066873839005868
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.15865384615384615
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.31009615384615385
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3870192307692308
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49278846153846156
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.15865384615384615
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.10336538461538461
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07740384615384616
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04927884615384615
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.15865384615384615
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.31009615384615385
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3870192307692308
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49278846153846156
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3130531482964966
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2569225045787546
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2651139739879426
      name: Cosine Map@100
---

# privacy_embedding_rag_10k_base_checkpoint_2-klej-dyk-v0.1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [liddlefish/privacy_embedding_rag_10k_base_checkpoint_2](https://huggingface.co./liddlefish/privacy_embedding_rag_10k_base_checkpoint_2). 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:** [liddlefish/privacy_embedding_rag_10k_base_checkpoint_2](https://huggingface.co./liddlefish/privacy_embedding_rag_10k_base_checkpoint_2) <!-- at revision 2ef6f7a59388ab4473ddb885ecc27a40c09f5802 -->
- **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 = [
    'Sen o zastrzyku Irmy',
    'gdzie Freud spotkał Irmę we śnie o zastrzyku Irmy?',
    'dlaczego Ōkunoshima została wymazana z map Japonii?',
]
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   | 0.1875     |
| cosine_accuracy@3   | 0.4543     |
| cosine_accuracy@5   | 0.6226     |
| cosine_accuracy@10  | 0.7308     |
| cosine_precision@1  | 0.1875     |
| cosine_precision@3  | 0.1514     |
| cosine_precision@5  | 0.1245     |
| cosine_precision@10 | 0.0731     |
| cosine_recall@1     | 0.1875     |
| cosine_recall@3     | 0.4543     |
| cosine_recall@5     | 0.6226     |
| cosine_recall@10    | 0.7308     |
| cosine_ndcg@10      | 0.4453     |
| cosine_mrr@10       | 0.355      |
| **cosine_map@100**  | **0.3624** |

#### 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   | 0.1827     |
| cosine_accuracy@3   | 0.4447     |
| cosine_accuracy@5   | 0.6034     |
| cosine_accuracy@10  | 0.7115     |
| cosine_precision@1  | 0.1827     |
| cosine_precision@3  | 0.1482     |
| cosine_precision@5  | 0.1207     |
| cosine_precision@10 | 0.0712     |
| cosine_recall@1     | 0.1827     |
| cosine_recall@3     | 0.4447     |
| cosine_recall@5     | 0.6034     |
| cosine_recall@10    | 0.7115     |
| cosine_ndcg@10      | 0.4349     |
| cosine_mrr@10       | 0.3472     |
| **cosine_map@100**  | **0.3548** |

#### 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   | 0.1875     |
| cosine_accuracy@3   | 0.4231     |
| cosine_accuracy@5   | 0.5577     |
| cosine_accuracy@10  | 0.6683     |
| cosine_precision@1  | 0.1875     |
| cosine_precision@3  | 0.141      |
| cosine_precision@5  | 0.1115     |
| cosine_precision@10 | 0.0668     |
| cosine_recall@1     | 0.1875     |
| cosine_recall@3     | 0.4231     |
| cosine_recall@5     | 0.5577     |
| cosine_recall@10    | 0.6683     |
| cosine_ndcg@10      | 0.414      |
| cosine_mrr@10       | 0.3337     |
| **cosine_map@100**  | **0.3427** |

#### 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   | 0.1707     |
| cosine_accuracy@3   | 0.3678     |
| cosine_accuracy@5   | 0.512      |
| cosine_accuracy@10  | 0.601      |
| cosine_precision@1  | 0.1707     |
| cosine_precision@3  | 0.1226     |
| cosine_precision@5  | 0.1024     |
| cosine_precision@10 | 0.0601     |
| cosine_recall@1     | 0.1707     |
| cosine_recall@3     | 0.3678     |
| cosine_recall@5     | 0.512      |
| cosine_recall@10    | 0.601      |
| cosine_ndcg@10      | 0.3712     |
| cosine_mrr@10       | 0.2988     |
| **cosine_map@100**  | **0.3067** |

#### 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   | 0.1587     |
| cosine_accuracy@3   | 0.3101     |
| cosine_accuracy@5   | 0.387      |
| cosine_accuracy@10  | 0.4928     |
| cosine_precision@1  | 0.1587     |
| cosine_precision@3  | 0.1034     |
| cosine_precision@5  | 0.0774     |
| cosine_precision@10 | 0.0493     |
| cosine_recall@1     | 0.1587     |
| cosine_recall@3     | 0.3101     |
| cosine_recall@5     | 0.387      |
| cosine_recall@10    | 0.4928     |
| cosine_ndcg@10      | 0.3131     |
| cosine_mrr@10       | 0.2569     |
| **cosine_map@100**  | **0.2651** |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## 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: 7 tokens</li><li>mean: 89.43 tokens</li><li>max: 507 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 30.98 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | anchor                                                                                                                      |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
  | <code>Zespół Blaua (zespół Jabsa, ang. Blau syndrome, BS) – rzadka choroba genetyczna o dziedziczeniu autosomalnym dominującym, charakteryzująca się ziarniniakowym zapaleniem stawów o wczesnym początku, zapaleniem jagodówki (uveitis) i wysypką skórną, a także kamptodaktylią.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>jakie choroby genetyczne dziedziczą się autosomalnie dominująco?</code>                                               |
  | <code>Gorgippia Gorgippia – starożytne miasto bosporańskie nad Morzem Czarnym, którego pozostałości znajdują się obecnie pod współczesną zabudową centralnej części miasta Anapa w Kraju Krasnodarskim w Rosji.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>gdzie obecnie znajduje się starożytne miasto Gorgippia?</code>                                                        |
  | <code>Ulubionym dystansem Rücker było 400 metrów i to na nim notowała największe indywidualne sukcesy : srebrny medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) 6. miejsce w Pucharze Świata w Lekkoatletyce (Hawana 1992) 5. miejsce na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) srebro podczas Mistrzostw Świata w Lekkoatletyce (Sewilla 1999) złota medalistka mistrzostw Niemiec Duże sukcesy odnosiła także w sztafecie 4 x 400 metrów : złoto Mistrzostw Europy juniorów w lekkoatletyce (Varaždin 1989) złoty medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) brąz na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) brązowy medal podczas Igrzysk Olimpijskich (Atlanta 1996) brąz na Halowych Mistrzostwach Świata w Lekkoatletyce (Paryż 1997) złoto Mistrzostw Świata w Lekkoatletyce (Ateny 1997) brązowy medal Mistrzostw Świata w Lekkoatletyce (Sewilla 1999)</code> | <code>kto zaprojektował medale, które będą wręczane podczas tegorocznych mistrzostw Europy juniorów w lekkoatletyce?</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`: 5
- `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`: 5
- `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
| 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      | 9.112         | -                      | -                      | -                      | -                     | -                      |
| 0.1368     | 2      | 9.5133        | -                      | -                      | -                      | -                     | -                      |
| 0.2051     | 3      | 9.0946        | -                      | -                      | -                      | -                     | -                      |
| 0.2735     | 4      | 8.9744        | -                      | -                      | -                      | -                     | -                      |
| 0.3419     | 5      | 7.9039        | -                      | -                      | -                      | -                     | -                      |
| 0.4103     | 6      | 8.1973        | -                      | -                      | -                      | -                     | -                      |
| 0.4786     | 7      | 6.8979        | -                      | -                      | -                      | -                     | -                      |
| 0.5470     | 8      | 7.0324        | -                      | -                      | -                      | -                     | -                      |
| 0.6154     | 9      | 6.6472        | -                      | -                      | -                      | -                     | -                      |
| 0.6838     | 10     | 6.3009        | -                      | -                      | -                      | -                     | -                      |
| 0.7521     | 11     | 6.8778        | -                      | -                      | -                      | -                     | -                      |
| 0.8205     | 12     | 5.9809        | -                      | -                      | -                      | -                     | -                      |
| 0.8889     | 13     | 5.3054        | -                      | -                      | -                      | -                     | -                      |
| 0.9573     | 14     | 5.706         | 0.2868                 | 0.3280                 | 0.3522                 | 0.2415                | 0.3477                 |
| 1.0256     | 15     | 5.0592        | -                      | -                      | -                      | -                     | -                      |
| 1.0940     | 16     | 4.7655        | -                      | -                      | -                      | -                     | -                      |
| 1.1624     | 17     | 4.9682        | -                      | -                      | -                      | -                     | -                      |
| 1.2308     | 18     | 5.1226        | -                      | -                      | -                      | -                     | -                      |
| 1.2991     | 19     | 4.8655        | -                      | -                      | -                      | -                     | -                      |
| 1.3675     | 20     | 4.2008        | -                      | -                      | -                      | -                     | -                      |
| 1.4359     | 21     | 5.0281        | -                      | -                      | -                      | -                     | -                      |
| 1.5043     | 22     | 4.3074        | -                      | -                      | -                      | -                     | -                      |
| 1.5726     | 23     | 4.3163        | -                      | -                      | -                      | -                     | -                      |
| 1.6410     | 24     | 3.9344        | -                      | -                      | -                      | -                     | -                      |
| 1.7094     | 25     | 4.6567        | -                      | -                      | -                      | -                     | -                      |
| 1.7778     | 26     | 4.5145        | -                      | -                      | -                      | -                     | -                      |
| 1.8462     | 27     | 4.1319        | -                      | -                      | -                      | -                     | -                      |
| 1.9145     | 28     | 3.8768        | -                      | -                      | -                      | -                     | -                      |
| 1.9829     | 29     | 3.5525        | 0.2986                 | 0.3330                 | 0.3483                 | 0.2590                | 0.3534                 |
| 2.0513     | 30     | 3.8693        | -                      | -                      | -                      | -                     | -                      |
| 2.1197     | 31     | 3.4675        | -                      | -                      | -                      | -                     | -                      |
| 2.1880     | 32     | 4.0598        | -                      | -                      | -                      | -                     | -                      |
| 2.2564     | 33     | 4.2429        | -                      | -                      | -                      | -                     | -                      |
| 2.3248     | 34     | 3.3686        | -                      | -                      | -                      | -                     | -                      |
| 2.3932     | 35     | 3.2663        | -                      | -                      | -                      | -                     | -                      |
| 2.4615     | 36     | 3.8585        | -                      | -                      | -                      | -                     | -                      |
| 2.5299     | 37     | 3.1157        | -                      | -                      | -                      | -                     | -                      |
| 2.5983     | 38     | 3.5254        | -                      | -                      | -                      | -                     | -                      |
| 2.6667     | 39     | 3.2782        | -                      | -                      | -                      | -                     | -                      |
| 2.7350     | 40     | 4.3151        | -                      | -                      | -                      | -                     | -                      |
| 2.8034     | 41     | 3.4567        | -                      | -                      | -                      | -                     | -                      |
| 2.8718     | 42     | 3.3976        | -                      | -                      | -                      | -                     | -                      |
| 2.9402     | 43     | 3.3945        | 0.3014                 | 0.3343                 | 0.3522                 | 0.2626                | 0.3593                 |
| 3.0085     | 44     | 3.4487        | -                      | -                      | -                      | -                     | -                      |
| 3.0769     | 45     | 3.0021        | -                      | -                      | -                      | -                     | -                      |
| 3.1453     | 46     | 3.2332        | -                      | -                      | -                      | -                     | -                      |
| 3.2137     | 47     | 3.3012        | -                      | -                      | -                      | -                     | -                      |
| 3.2821     | 48     | 3.2735        | -                      | -                      | -                      | -                     | -                      |
| 3.3504     | 49     | 2.5335        | -                      | -                      | -                      | -                     | -                      |
| 3.4188     | 50     | 3.7025        | -                      | -                      | -                      | -                     | -                      |
| 3.4872     | 51     | 2.8596        | -                      | -                      | -                      | -                     | -                      |
| 3.5556     | 52     | 3.1108        | -                      | -                      | -                      | -                     | -                      |
| 3.6239     | 53     | 3.2807        | -                      | -                      | -                      | -                     | -                      |
| 3.6923     | 54     | 3.1604        | -                      | -                      | -                      | -                     | -                      |
| 3.7607     | 55     | 3.7179        | -                      | -                      | -                      | -                     | -                      |
| 3.8291     | 56     | 3.3418        | -                      | -                      | -                      | -                     | -                      |
| 3.8974     | 57     | 2.9735        | -                      | -                      | -                      | -                     | -                      |
| 3.9658     | 58     | 3.2755        | 0.3066                 | 0.3409                 | 0.3546                 | 0.2653                | 0.3626                 |
| 4.0342     | 59     | 3.1444        | -                      | -                      | -                      | -                     | -                      |
| 4.1026     | 60     | 3.0212        | -                      | -                      | -                      | -                     | -                      |
| 4.1709     | 61     | 3.1298        | -                      | -                      | -                      | -                     | -                      |
| 4.2393     | 62     | 3.3195        | -                      | -                      | -                      | -                     | -                      |
| 4.3077     | 63     | 2.996         | -                      | -                      | -                      | -                     | -                      |
| 4.3761     | 64     | 2.4636        | -                      | -                      | -                      | -                     | -                      |
| 4.4444     | 65     | 3.2388        | -                      | -                      | -                      | -                     | -                      |
| 4.5128     | 66     | 2.747         | -                      | -                      | -                      | -                     | -                      |
| 4.5812     | 67     | 2.8715        | -                      | -                      | -                      | -                     | -                      |
| 4.6496     | 68     | 3.1402        | -                      | -                      | -                      | -                     | -                      |
| 4.7179     | 69     | 3.547         | -                      | -                      | -                      | -                     | -                      |
| **4.7863** | **70** | **3.6094**    | **0.3067**             | **0.3427**             | **0.3548**             | **0.2651**            | **0.3624**             |

* The bold row denotes the saved checkpoint.

### 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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