---
base_model: Snowflake/snowflake-arctic-embed-m
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: kim był Steve Yzerman?
  sentences:
  - Łazik marsjański Opportunity
  - w jakim kraju jest przyznawany Order Białego Lotosu?
  - do powstania jakich instytucji przyczynił się pierwszy biskup Makau?
- source_sentence: gdzie rośnie bokkonia?
  sentences:
  - jak rozmnażają się Aeolosomatidae?
  - kto 1 stycznia 2011 został gubernatorem Nowego Jorku?
  - w której świątyni koronowany był król jerozolimski Baldwin I?
- source_sentence: Godło Republiki Ałtaju
  sentences:
  - co przedstawia godło Republiki Ałtaju?
  - w którym kraju w noc sylwestrową je się oliebollen?
  - który z członków załogi Międzynarodowej Stacji Kosmicznej nie ma nóg?
- source_sentence: co to jest meszne?
  sentences:
  - co to jest Mammoth Hot Springs?
  - jak przebiegała kariera sportowa Witolda Sikorskiego?
  - do uratowania ilu dzieł sztuki przyczynił się Borys Woźnicki?
- source_sentence: Chłopiec z Nariokotome
  sentences:
  - ile wynosiła objętość mózgu chłopca z Nariokotome?
  - gdzie znajduje się czwarty polski cmentarz katyński?
  - w jakich miejscach stał warszawski pomnik Ignacego Jana Paderewskiego?
model-index:
- name: snowflake-arctic-embed-m-klej-dyk
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.18509615384615385
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4807692307692308
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.625
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7259615384615384
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18509615384615385
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16025641025641024
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.125
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07259615384615384
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18509615384615385
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4807692307692308
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.625
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7259615384615384
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.44786216254546357
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.358972451159951
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3672210078826913
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.17548076923076922
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.47115384615384615
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6129807692307693
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7019230769230769
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.17548076923076922
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15705128205128205
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12259615384615384
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07019230769230768
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17548076923076922
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.47115384615384615
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6129807692307693
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7019230769230769
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.43344535381311455
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3473920177045177
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3563798565478224
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.15625
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4543269230769231
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5649038461538461
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6730769230769231
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.15625
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15144230769230768
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11298076923076923
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0673076923076923
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.15625
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4543269230769231
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5649038461538461
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6730769230769231
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4102597093872519
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.32613324175824177
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3350744652348361
      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: 0.3918269230769231
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5072115384615384
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6057692307692307
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.16346153846153846
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.13060897435897434
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10144230769230769
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06057692307692307
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.16346153846153846
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3918269230769231
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5072115384615384
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6057692307692307
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3757626519143444
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.30273962148962136
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3116992239855167
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.14903846153846154
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3389423076923077
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4182692307692308
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49278846153846156
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14903846153846154
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.11298076923076923
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08365384615384615
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04927884615384615
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14903846153846154
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3389423076923077
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4182692307692308
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49278846153846156
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.31783226267644227
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.26212320665445676
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.27044860532149884
      name: Cosine Map@100
---

# snowflake-arctic-embed-m-klej-dyk

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). 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:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision 2ca412ec9505022eebd7d10286fbbad4b779f6e0 -->
- **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': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): 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 = [
    'Chłopiec z Nariokotome',
    'ile wynosiła objętość mózgu chłopca z Nariokotome?',
    'gdzie znajduje się czwarty polski cmentarz katyński?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation

### Metrics

#### 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   | 0.1851     |
| cosine_accuracy@3   | 0.4808     |
| cosine_accuracy@5   | 0.625      |
| cosine_accuracy@10  | 0.726      |
| cosine_precision@1  | 0.1851     |
| cosine_precision@3  | 0.1603     |
| cosine_precision@5  | 0.125      |
| cosine_precision@10 | 0.0726     |
| cosine_recall@1     | 0.1851     |
| cosine_recall@3     | 0.4808     |
| cosine_recall@5     | 0.625      |
| cosine_recall@10    | 0.726      |
| cosine_ndcg@10      | 0.4479     |
| cosine_mrr@10       | 0.359      |
| **cosine_map@100**  | **0.3672** |

#### 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   | 0.1755     |
| cosine_accuracy@3   | 0.4712     |
| cosine_accuracy@5   | 0.613      |
| cosine_accuracy@10  | 0.7019     |
| cosine_precision@1  | 0.1755     |
| cosine_precision@3  | 0.1571     |
| cosine_precision@5  | 0.1226     |
| cosine_precision@10 | 0.0702     |
| cosine_recall@1     | 0.1755     |
| cosine_recall@3     | 0.4712     |
| cosine_recall@5     | 0.613      |
| cosine_recall@10    | 0.7019     |
| cosine_ndcg@10      | 0.4334     |
| cosine_mrr@10       | 0.3474     |
| **cosine_map@100**  | **0.3564** |

#### 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   | 0.1562     |
| cosine_accuracy@3   | 0.4543     |
| cosine_accuracy@5   | 0.5649     |
| cosine_accuracy@10  | 0.6731     |
| cosine_precision@1  | 0.1562     |
| cosine_precision@3  | 0.1514     |
| cosine_precision@5  | 0.113      |
| cosine_precision@10 | 0.0673     |
| cosine_recall@1     | 0.1562     |
| cosine_recall@3     | 0.4543     |
| cosine_recall@5     | 0.5649     |
| cosine_recall@10    | 0.6731     |
| cosine_ndcg@10      | 0.4103     |
| cosine_mrr@10       | 0.3261     |
| **cosine_map@100**  | **0.3351** |

#### 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   | 0.3918     |
| cosine_accuracy@5   | 0.5072     |
| cosine_accuracy@10  | 0.6058     |
| cosine_precision@1  | 0.1635     |
| cosine_precision@3  | 0.1306     |
| cosine_precision@5  | 0.1014     |
| cosine_precision@10 | 0.0606     |
| cosine_recall@1     | 0.1635     |
| cosine_recall@3     | 0.3918     |
| cosine_recall@5     | 0.5072     |
| cosine_recall@10    | 0.6058     |
| cosine_ndcg@10      | 0.3758     |
| cosine_mrr@10       | 0.3027     |
| **cosine_map@100**  | **0.3117** |

#### 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   | 0.149      |
| cosine_accuracy@3   | 0.3389     |
| cosine_accuracy@5   | 0.4183     |
| cosine_accuracy@10  | 0.4928     |
| cosine_precision@1  | 0.149      |
| cosine_precision@3  | 0.113      |
| cosine_precision@5  | 0.0837     |
| cosine_precision@10 | 0.0493     |
| cosine_recall@1     | 0.149      |
| cosine_recall@3     | 0.3389     |
| cosine_recall@5     | 0.4183     |
| cosine_recall@10    | 0.4928     |
| cosine_ndcg@10      | 0.3178     |
| cosine_mrr@10       | 0.2621     |
| **cosine_map@100**  | **0.2704** |

<!--
## 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: 6 tokens</li><li>mean: 94.61 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.71 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | anchor                                                                                                                                            |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Marsz Ochotników (chin.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>kto jest kompozytorem chińskiego hymnu narodowego Marsz Ochotników?</code>                                                                  |
  | <code>Wybrane przykłady: Święta Rodzina – Maryja z Dzieciątkiem na ręku, niekiedy obok niej stoi św. Józef Rodzina Marii – przedstawienie w którym pojawia się Święta Rodzina oraz postaci spokrewnione z Marią. Maria w połogu (Maria in puerperio) – leżąca na łożu Maria opiekuje się Dzieciątkiem Maria karmiąca (Maria lactans) – Maria karmiąca swą piersią Dzieciątko Orantka – kobieta modląca się z podniesionymi rękami (częsty motyw ikon wschodnich); Sacra Conversazione – Matka Boska tronująca z Dzieciątkiem, otoczona stojącymi postaciami świętych Pietà – opłakująca Jezusa, trzymając na kolanach jego ciało po śmierci na krzyżu; Hodegetria – ujęcie popiersia Maryi, trzymającej na rękach małego Jezusa, częsty motyw w ikonach Eleusa – formalnie podobne do przedstawienia Hodegetrii lecz Maryja policzkiem przytula się do policzka Jezusa Immaculata – Niepokalane Poczęcie Najświętszej Maryi Panny.</code> | <code>kto zamiast Maryi trzyma nowonarodzonego Jezusa w scenie Bożego Narodzenia przedstawionej na poliptyku z Marią i Dzieciątkiem Jezus?</code> |
  | <code>Pomnik Josepha von Eichendorffa w Brzeziu Pomnik Josepha von Eichendorffa – odtworzony w 2006 roku pomnik znanego niemieckiego poety epoki romantyzmu związanego z ziemią raciborską, Josepha von Eichendorffa.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>po ilu latach odtworzono wysadzony w 1945 roku pomnik Josepha von Eichendorffa w Raciborzu-Brzeziu?</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      | 9.3155        | -                      | -                      | -                      | -                     | -                      |
| 0.1368     | 2      | 9.1788        | -                      | -                      | -                      | -                     | -                      |
| 0.2051     | 3      | 8.8387        | -                      | -                      | -                      | -                     | -                      |
| 0.2735     | 4      | 8.2961        | -                      | -                      | -                      | -                     | -                      |
| 0.3419     | 5      | 8.0242        | -                      | -                      | -                      | -                     | -                      |
| 0.4103     | 6      | 7.2329        | -                      | -                      | -                      | -                     | -                      |
| 0.4786     | 7      | 5.4386        | -                      | -                      | -                      | -                     | -                      |
| 0.5470     | 8      | 6.1186        | -                      | -                      | -                      | -                     | -                      |
| 0.6154     | 9      | 4.9714        | -                      | -                      | -                      | -                     | -                      |
| 0.6838     | 10     | 5.1958        | -                      | -                      | -                      | -                     | -                      |
| 0.7521     | 11     | 5.1135        | -                      | -                      | -                      | -                     | -                      |
| 0.8205     | 12     | 4.6971        | -                      | -                      | -                      | -                     | -                      |
| 0.8889     | 13     | 4.5559        | -                      | -                      | -                      | -                     | -                      |
| 0.9573     | 14     | 3.9357        | 0.2842                 | 0.3098                 | 0.3191                 | 0.2238                | 0.3209                 |
| 1.0256     | 15     | 3.7916        | -                      | -                      | -                      | -                     | -                      |
| 1.0940     | 16     | 3.6393        | -                      | -                      | -                      | -                     | -                      |
| 1.1624     | 17     | 3.7733        | -                      | -                      | -                      | -                     | -                      |
| 1.2308     | 18     | 3.6974        | -                      | -                      | -                      | -                     | -                      |
| 1.2991     | 19     | 3.5964        | -                      | -                      | -                      | -                     | -                      |
| 1.3675     | 20     | 3.4118        | -                      | -                      | -                      | -                     | -                      |
| 1.4359     | 21     | 3.2022        | -                      | -                      | -                      | -                     | -                      |
| 1.5043     | 22     | 2.8133        | -                      | -                      | -                      | -                     | -                      |
| 1.5726     | 23     | 3.0871        | -                      | -                      | -                      | -                     | -                      |
| 1.6410     | 24     | 2.9559        | -                      | -                      | -                      | -                     | -                      |
| 1.7094     | 25     | 2.8192        | -                      | -                      | -                      | -                     | -                      |
| 1.7778     | 26     | 3.462         | -                      | -                      | -                      | -                     | -                      |
| 1.8462     | 27     | 3.1435        | -                      | -                      | -                      | -                     | -                      |
| 1.9145     | 28     | 2.8001        | -                      | -                      | -                      | -                     | -                      |
| 1.9829     | 29     | 2.5643        | 0.3134                 | 0.3359                 | 0.3563                 | 0.2588                | 0.3671                 |
| 2.0513     | 30     | 2.4295        | -                      | -                      | -                      | -                     | -                      |
| 2.1197     | 31     | 2.3892        | -                      | -                      | -                      | -                     | -                      |
| 2.1880     | 32     | 2.5228        | -                      | -                      | -                      | -                     | -                      |
| 2.2564     | 33     | 2.4906        | -                      | -                      | -                      | -                     | -                      |
| 2.3248     | 34     | 2.5358        | -                      | -                      | -                      | -                     | -                      |
| 2.3932     | 35     | 2.2806        | -                      | -                      | -                      | -                     | -                      |
| 2.4615     | 36     | 2.0083        | -                      | -                      | -                      | -                     | -                      |
| 2.5299     | 37     | 2.5088        | -                      | -                      | -                      | -                     | -                      |
| 2.5983     | 38     | 2.0628        | -                      | -                      | -                      | -                     | -                      |
| 2.6667     | 39     | 2.193         | -                      | -                      | -                      | -                     | -                      |
| 2.7350     | 40     | 2.4783        | -                      | -                      | -                      | -                     | -                      |
| 2.8034     | 41     | 2.382         | -                      | -                      | -                      | -                     | -                      |
| 2.8718     | 42     | 2.2017        | -                      | -                      | -                      | -                     | -                      |
| 2.9402     | 43     | 1.9739        | 0.3111                 | 0.3392                 | 0.3572                 | 0.2657                | 0.3659                 |
| 3.0085     | 44     | 2.0332        | -                      | -                      | -                      | -                     | -                      |
| 3.0769     | 45     | 1.9983        | -                      | -                      | -                      | -                     | -                      |
| 3.1453     | 46     | 1.8612        | -                      | -                      | -                      | -                     | -                      |
| 3.2137     | 47     | 1.9897        | -                      | -                      | -                      | -                     | -                      |
| 3.2821     | 48     | 2.2514        | -                      | -                      | -                      | -                     | -                      |
| 3.3504     | 49     | 2.0092        | -                      | -                      | -                      | -                     | -                      |
| 3.4188     | 50     | 1.7399        | -                      | -                      | -                      | -                     | -                      |
| 3.4872     | 51     | 1.5825        | -                      | -                      | -                      | -                     | -                      |
| 3.5556     | 52     | 2.1501        | -                      | -                      | -                      | -                     | -                      |
| 3.6239     | 53     | 1.4505        | -                      | -                      | -                      | -                     | -                      |
| 3.6923     | 54     | 1.8575        | -                      | -                      | -                      | -                     | -                      |
| 3.7607     | 55     | 2.3882        | -                      | -                      | -                      | -                     | -                      |
| 3.8291     | 56     | 2.1119        | -                      | -                      | -                      | -                     | -                      |
| 3.8974     | 57     | 1.8992        | -                      | -                      | -                      | -                     | -                      |
| 3.9658     | 58     | 1.8323        | 0.3117                 | 0.3365                 | 0.3558                 | 0.2683                | 0.3670                 |
| 4.0342     | 59     | 1.5938        | -                      | -                      | -                      | -                     | -                      |
| 4.1026     | 60     | 1.552         | -                      | -                      | -                      | -                     | -                      |
| 4.1709     | 61     | 1.907         | -                      | -                      | -                      | -                     | -                      |
| 4.2393     | 62     | 1.8304        | -                      | -                      | -                      | -                     | -                      |
| 4.3077     | 63     | 1.8775        | -                      | -                      | -                      | -                     | -                      |
| 4.3761     | 64     | 1.8654        | -                      | -                      | -                      | -                     | -                      |
| 4.4444     | 65     | 1.7944        | -                      | -                      | -                      | -                     | -                      |
| 4.5128     | 66     | 1.8335        | -                      | -                      | -                      | -                     | -                      |
| 4.5812     | 67     | 1.8823        | -                      | -                      | -                      | -                     | -                      |
| 4.6496     | 68     | 1.6479        | -                      | -                      | -                      | -                     | -                      |
| 4.7179     | 69     | 1.5771        | -                      | -                      | -                      | -                     | -                      |
| **4.7863** | **70** | **2.1911**    | **0.3117**             | **0.3351**             | **0.3564**             | **0.2704**            | **0.3672**             |

* 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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