sentence-transformers safetensors roberta sentence-similarity feature-extraction dataset_size:1K<n<10K loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:sdadas/mmlw-roberta-base base_model:finetune:sdadas/mmlw-roberta-base license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sdadas/mmlw-roberta-base
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?
  - w którym kraju w noc sylwestrową je się oliebollen?
  - Pierwsze bloki mieszkalne powstały pod koniec lat 80.
- source_sentence: Herkules na rozstajach
  sentences:
  - jak zinterpretować wymowę obrazu Herkules na rozstajach?
  - gdzie zginął przedwojenny minister Antoni Olszewski?
  - kiedy konsekrowano katedrę św. Teresy z Avili w Požedze?
- source_sentence: gdzie rośnie bokkonia?
  sentences:
  - gdzie występuje rogownica szerokolistna?
  - Ochrzcił w sierpniu 1982 ich syna księcia Wilhelma.
  - Pośmiertnie został odznaczony Krzyżem Virtuti Militari.
- source_sentence: czym jest Kompas Sztuki?
  sentences:
  - ' Projekt Kompas Sztuki: Galeria m2 (m kwadrat).'
  - 'Do rodzaju Caraipa zaliczanych jest ok. 55 gatunków:'
  - kto jest aktualnym rekordzistą Chorwacji w skoku w dal?
- source_sentence: Dalsze losy relikwii
  sentences:
  - Losy relikwii świętego
  - czemu gra The Saboteur wywołała wiele kontrowersji?
  - kto jest pierwszym rosyjskim kierowcą wyścigowym startującym w Formule 1?
model-index:
- name: mmlw-roberta-base-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: 0.18990384615384615
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5865384615384616
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7692307692307693
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8533653846153846
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18990384615384615
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1955128205128205
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15384615384615383
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08533653846153846
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18990384615384615
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5865384615384616
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7692307692307693
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8533653846153846
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5204892782178483
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4127814026251526
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.418150211843158
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.1875
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5889423076923077
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7596153846153846
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8629807692307693
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1875
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19631410256410253
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15192307692307688
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08629807692307694
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1875
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5889423076923077
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7596153846153846
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8629807692307693
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5204340563935984
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4100885225885227
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4147514658961434
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.19471153846153846
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5649038461538461
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7451923076923077
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8461538461538461
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.19471153846153846
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18830128205128205
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1490384615384615
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08461538461538462
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.19471153846153846
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5649038461538461
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7451923076923077
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8461538461538461
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5144907264607753
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4078373015873016
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.413093644747221
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.18269230769230768
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5192307692307693
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7163461538461539
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8293269230769231
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18269230769230768
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.17307692307692307
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14326923076923076
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08293269230769229
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18269230769230768
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5192307692307693
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7163461538461539
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8293269230769231
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4955346842225082
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.38889652014651993
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.39396452853345754
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.1778846153846154
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4831730769230769
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6514423076923077
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7740384615384616
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1778846153846154
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16105769230769232
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13028846153846152
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07740384615384614
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1778846153846154
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4831730769230769
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6514423076923077
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7740384615384616
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4639263641936578
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.36540083180708166
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3728380879103276
      name: Cosine Map@100
---

# mmlw-roberta-base-klej-dyk-v0.1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sdadas/mmlw-roberta-base](https://huggingface.co/sdadas/mmlw-roberta-base). 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:** [sdadas/mmlw-roberta-base](https://huggingface.co/sdadas/mmlw-roberta-base) <!-- at revision 57e19d8314b983137ebe25ce734880af0dc98a9e -->
- **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: RobertaModel 
  (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})
)
```

## 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 = [
    'Dalsze losy relikwii',
    'Losy relikwii świętego',
    'czemu gra The Saboteur wywołała wiele kontrowersji?',
]
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]
```

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### Direct Usage (Transformers)

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
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<!--
### 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   | 0.1899     |
| cosine_accuracy@3   | 0.5865     |
| cosine_accuracy@5   | 0.7692     |
| cosine_accuracy@10  | 0.8534     |
| cosine_precision@1  | 0.1899     |
| cosine_precision@3  | 0.1955     |
| cosine_precision@5  | 0.1538     |
| cosine_precision@10 | 0.0853     |
| cosine_recall@1     | 0.1899     |
| cosine_recall@3     | 0.5865     |
| cosine_recall@5     | 0.7692     |
| cosine_recall@10    | 0.8534     |
| cosine_ndcg@10      | 0.5205     |
| cosine_mrr@10       | 0.4128     |
| **cosine_map@100**  | **0.4182** |

#### 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   | 0.1875     |
| cosine_accuracy@3   | 0.5889     |
| cosine_accuracy@5   | 0.7596     |
| cosine_accuracy@10  | 0.863      |
| cosine_precision@1  | 0.1875     |
| cosine_precision@3  | 0.1963     |
| cosine_precision@5  | 0.1519     |
| cosine_precision@10 | 0.0863     |
| cosine_recall@1     | 0.1875     |
| cosine_recall@3     | 0.5889     |
| cosine_recall@5     | 0.7596     |
| cosine_recall@10    | 0.863      |
| cosine_ndcg@10      | 0.5204     |
| cosine_mrr@10       | 0.4101     |
| **cosine_map@100**  | **0.4148** |

#### 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   | 0.1947     |
| cosine_accuracy@3   | 0.5649     |
| cosine_accuracy@5   | 0.7452     |
| cosine_accuracy@10  | 0.8462     |
| cosine_precision@1  | 0.1947     |
| cosine_precision@3  | 0.1883     |
| cosine_precision@5  | 0.149      |
| cosine_precision@10 | 0.0846     |
| cosine_recall@1     | 0.1947     |
| cosine_recall@3     | 0.5649     |
| cosine_recall@5     | 0.7452     |
| cosine_recall@10    | 0.8462     |
| cosine_ndcg@10      | 0.5145     |
| cosine_mrr@10       | 0.4078     |
| **cosine_map@100**  | **0.4131** |

#### 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   | 0.1827    |
| cosine_accuracy@3   | 0.5192    |
| cosine_accuracy@5   | 0.7163    |
| cosine_accuracy@10  | 0.8293    |
| cosine_precision@1  | 0.1827    |
| cosine_precision@3  | 0.1731    |
| cosine_precision@5  | 0.1433    |
| cosine_precision@10 | 0.0829    |
| cosine_recall@1     | 0.1827    |
| cosine_recall@3     | 0.5192    |
| cosine_recall@5     | 0.7163    |
| cosine_recall@10    | 0.8293    |
| cosine_ndcg@10      | 0.4955    |
| cosine_mrr@10       | 0.3889    |
| **cosine_map@100**  | **0.394** |

#### 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   | 0.1779     |
| cosine_accuracy@3   | 0.4832     |
| cosine_accuracy@5   | 0.6514     |
| cosine_accuracy@10  | 0.774      |
| cosine_precision@1  | 0.1779     |
| cosine_precision@3  | 0.1611     |
| cosine_precision@5  | 0.1303     |
| cosine_precision@10 | 0.0774     |
| cosine_recall@1     | 0.1779     |
| cosine_recall@3     | 0.4832     |
| cosine_recall@5     | 0.6514     |
| cosine_recall@10    | 0.774      |
| cosine_ndcg@10      | 0.4639     |
| cosine_mrr@10       | 0.3654     |
| **cosine_map@100**  | **0.3728** |

<!--
## Bias, Risks and Limitations

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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: 5 tokens</li><li>mean: 50.1 tokens</li><li>max: 466 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.62 tokens</li><li>max: 49 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
- `gradient_accumulation_steps`: 8
- `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`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `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
<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       | 0       | -             | 0.3475                 | 0.3675                 | 0.3753                 | 0.2982                | 0.3798                 |
| 0.0171  | 1       | 2.6683        | -                      | -                      | -                      | -                     | -                      |
| 0.0342  | 2       | 3.2596        | -                      | -                      | -                      | -                     | -                      |
| 0.0513  | 3       | 3.4541        | -                      | -                      | -                      | -                     | -                      |
| 0.0684  | 4       | 2.4201        | -                      | -                      | -                      | -                     | -                      |
| 0.0855  | 5       | 3.5911        | -                      | -                      | -                      | -                     | -                      |
| 0.1026  | 6       | 3.0902        | -                      | -                      | -                      | -                     | -                      |
| 0.1197  | 7       | 2.5999        | -                      | -                      | -                      | -                     | -                      |
| 0.1368  | 8       | 2.892         | -                      | -                      | -                      | -                     | -                      |
| 0.1538  | 9       | 2.8722        | -                      | -                      | -                      | -                     | -                      |
| 0.1709  | 10      | 2.3703        | -                      | -                      | -                      | -                     | -                      |
| 0.1880  | 11      | 2.6833        | -                      | -                      | -                      | -                     | -                      |
| 0.2051  | 12      | 1.9814        | -                      | -                      | -                      | -                     | -                      |
| 0.2222  | 13      | 1.6643        | -                      | -                      | -                      | -                     | -                      |
| 0.2393  | 14      | 1.8493        | -                      | -                      | -                      | -                     | -                      |
| 0.2564  | 15      | 1.5136        | -                      | -                      | -                      | -                     | -                      |
| 0.2735  | 16      | 1.9726        | -                      | -                      | -                      | -                     | -                      |
| 0.2906  | 17      | 1.1505        | -                      | -                      | -                      | -                     | -                      |
| 0.3077  | 18      | 1.3834        | -                      | -                      | -                      | -                     | -                      |
| 0.3248  | 19      | 1.2244        | -                      | -                      | -                      | -                     | -                      |
| 0.3419  | 20      | 1.2107        | -                      | -                      | -                      | -                     | -                      |
| 0.3590  | 21      | 0.8936        | -                      | -                      | -                      | -                     | -                      |
| 0.3761  | 22      | 0.8144        | -                      | -                      | -                      | -                     | -                      |
| 0.3932  | 23      | 0.8353        | -                      | -                      | -                      | -                     | -                      |
| 0.4103  | 24      | 1.572         | -                      | -                      | -                      | -                     | -                      |
| 0.4274  | 25      | 0.9257        | -                      | -                      | -                      | -                     | -                      |
| 0.4444  | 26      | 0.8405        | -                      | -                      | -                      | -                     | -                      |
| 0.4615  | 27      | 0.5621        | -                      | -                      | -                      | -                     | -                      |
| 0.4786  | 28      | 0.4241        | -                      | -                      | -                      | -                     | -                      |
| 0.4957  | 29      | 0.6171        | -                      | -                      | -                      | -                     | -                      |
| 0.5128  | 30      | 0.5989        | -                      | -                      | -                      | -                     | -                      |
| 0.5299  | 31      | 0.2767        | -                      | -                      | -                      | -                     | -                      |
| 0.5470  | 32      | 0.5599        | -                      | -                      | -                      | -                     | -                      |
| 0.5641  | 33      | 0.5964        | -                      | -                      | -                      | -                     | -                      |
| 0.5812  | 34      | 0.9778        | -                      | -                      | -                      | -                     | -                      |
| 0.5983  | 35      | 0.772         | -                      | -                      | -                      | -                     | -                      |
| 0.6154  | 36      | 1.0341        | -                      | -                      | -                      | -                     | -                      |
| 0.6325  | 37      | 0.3503        | -                      | -                      | -                      | -                     | -                      |
| 0.6496  | 38      | 0.8229        | -                      | -                      | -                      | -                     | -                      |
| 0.6667  | 39      | 0.969         | -                      | -                      | -                      | -                     | -                      |
| 0.6838  | 40      | 1.7993        | -                      | -                      | -                      | -                     | -                      |
| 0.7009  | 41      | 0.5542        | -                      | -                      | -                      | -                     | -                      |
| 0.7179  | 42      | 1.332         | -                      | -                      | -                      | -                     | -                      |
| 0.7350  | 43      | 1.1516        | -                      | -                      | -                      | -                     | -                      |
| 0.7521  | 44      | 1.3183        | -                      | -                      | -                      | -                     | -                      |
| 0.7692  | 45      | 1.0865        | -                      | -                      | -                      | -                     | -                      |
| 0.7863  | 46      | 0.6204        | -                      | -                      | -                      | -                     | -                      |
| 0.8034  | 47      | 0.7541        | -                      | -                      | -                      | -                     | -                      |
| 0.8205  | 48      | 0.9362        | -                      | -                      | -                      | -                     | -                      |
| 0.8376  | 49      | 0.3979        | -                      | -                      | -                      | -                     | -                      |
| 0.8547  | 50      | 0.7187        | -                      | -                      | -                      | -                     | -                      |
| 0.8718  | 51      | 0.9217        | -                      | -                      | -                      | -                     | -                      |
| 0.8889  | 52      | 0.4866        | -                      | -                      | -                      | -                     | -                      |
| 0.9060  | 53      | 0.355         | -                      | -                      | -                      | -                     | -                      |
| 0.9231  | 54      | 0.7172        | -                      | -                      | -                      | -                     | -                      |
| 0.9402  | 55      | 0.6007        | -                      | -                      | -                      | -                     | -                      |
| 0.9573  | 56      | 1.1547        | -                      | -                      | -                      | -                     | -                      |
| 0.9744  | 57      | 0.5713        | -                      | -                      | -                      | -                     | -                      |
| 0.9915  | 58      | 0.9089        | 0.3985                 | 0.4164                 | 0.4264                 | 0.3642                | 0.4255                 |
| 1.0085  | 59      | 0.594         | -                      | -                      | -                      | -                     | -                      |
| 1.0256  | 60      | 0.6554        | -                      | -                      | -                      | -                     | -                      |
| 1.0427  | 61      | 0.2794        | -                      | -                      | -                      | -                     | -                      |
| 1.0598  | 62      | 0.8654        | -                      | -                      | -                      | -                     | -                      |
| 1.0769  | 63      | 0.9698        | -                      | -                      | -                      | -                     | -                      |
| 1.0940  | 64      | 1.4827        | -                      | -                      | -                      | -                     | -                      |
| 1.1111  | 65      | 0.3159        | -                      | -                      | -                      | -                     | -                      |
| 1.1282  | 66      | 0.255         | -                      | -                      | -                      | -                     | -                      |
| 1.1453  | 67      | 0.9819        | -                      | -                      | -                      | -                     | -                      |
| 1.1624  | 68      | 0.7442        | -                      | -                      | -                      | -                     | -                      |
| 1.1795  | 69      | 0.8199        | -                      | -                      | -                      | -                     | -                      |
| 1.1966  | 70      | 0.2647        | -                      | -                      | -                      | -                     | -                      |
| 1.2137  | 71      | 0.4098        | -                      | -                      | -                      | -                     | -                      |
| 1.2308  | 72      | 0.1608        | -                      | -                      | -                      | -                     | -                      |
| 1.2479  | 73      | 0.2092        | -                      | -                      | -                      | -                     | -                      |
| 1.2650  | 74      | 0.1231        | -                      | -                      | -                      | -                     | -                      |
| 1.2821  | 75      | 0.3203        | -                      | -                      | -                      | -                     | -                      |
| 1.2991  | 76      | 0.1435        | -                      | -                      | -                      | -                     | -                      |
| 1.3162  | 77      | 0.2293        | -                      | -                      | -                      | -                     | -                      |
| 1.3333  | 78      | 0.131         | -                      | -                      | -                      | -                     | -                      |
| 1.3504  | 79      | 0.1662        | -                      | -                      | -                      | -                     | -                      |
| 1.3675  | 80      | 0.094         | -                      | -                      | -                      | -                     | -                      |
| 1.3846  | 81      | 0.1454        | -                      | -                      | -                      | -                     | -                      |
| 1.4017  | 82      | 0.3096        | -                      | -                      | -                      | -                     | -                      |
| 1.4188  | 83      | 0.3188        | -                      | -                      | -                      | -                     | -                      |
| 1.4359  | 84      | 0.1156        | -                      | -                      | -                      | -                     | -                      |
| 1.4530  | 85      | 0.0581        | -                      | -                      | -                      | -                     | -                      |
| 1.4701  | 86      | 0.0543        | -                      | -                      | -                      | -                     | -                      |
| 1.4872  | 87      | 0.0427        | -                      | -                      | -                      | -                     | -                      |
| 1.5043  | 88      | 0.07          | -                      | -                      | -                      | -                     | -                      |
| 1.5214  | 89      | 0.0451        | -                      | -                      | -                      | -                     | -                      |
| 1.5385  | 90      | 0.0646        | -                      | -                      | -                      | -                     | -                      |
| 1.5556  | 91      | 0.1152        | -                      | -                      | -                      | -                     | -                      |
| 1.5726  | 92      | 0.1292        | -                      | -                      | -                      | -                     | -                      |
| 1.5897  | 93      | 0.1591        | -                      | -                      | -                      | -                     | -                      |
| 1.6068  | 94      | 0.1194        | -                      | -                      | -                      | -                     | -                      |
| 1.6239  | 95      | 0.0876        | -                      | -                      | -                      | -                     | -                      |
| 1.6410  | 96      | 0.1018        | -                      | -                      | -                      | -                     | -                      |
| 1.6581  | 97      | 0.3309        | -                      | -                      | -                      | -                     | -                      |
| 1.6752  | 98      | 0.2214        | -                      | -                      | -                      | -                     | -                      |
| 1.6923  | 99      | 0.1536        | -                      | -                      | -                      | -                     | -                      |
| 1.7094  | 100     | 0.1543        | -                      | -                      | -                      | -                     | -                      |
| 1.7265  | 101     | 0.3663        | -                      | -                      | -                      | -                     | -                      |
| 1.7436  | 102     | 0.2719        | -                      | -                      | -                      | -                     | -                      |
| 1.7607  | 103     | 0.1379        | -                      | -                      | -                      | -                     | -                      |
| 1.7778  | 104     | 0.0479        | -                      | -                      | -                      | -                     | -                      |
| 1.7949  | 105     | 0.0757        | -                      | -                      | -                      | -                     | -                      |
| 1.8120  | 106     | 0.059         | -                      | -                      | -                      | -                     | -                      |
| 1.8291  | 107     | 0.119         | -                      | -                      | -                      | -                     | -                      |
| 1.8462  | 108     | 0.1295        | -                      | -                      | -                      | -                     | -                      |
| 1.8632  | 109     | 0.115         | -                      | -                      | -                      | -                     | -                      |
| 1.8803  | 110     | 0.142         | -                      | -                      | -                      | -                     | -                      |
| 1.8974  | 111     | 0.1064        | -                      | -                      | -                      | -                     | -                      |
| 1.9145  | 112     | 0.0959        | -                      | -                      | -                      | -                     | -                      |
| 1.9316  | 113     | 0.0839        | -                      | -                      | -                      | -                     | -                      |
| 1.9487  | 114     | 0.1762        | -                      | -                      | -                      | -                     | -                      |
| 1.9658  | 115     | 0.1986        | -                      | -                      | -                      | -                     | -                      |
| 1.9829  | 116     | 0.0599        | -                      | -                      | -                      | -                     | -                      |
| 2.0     | 117     | 0.1145        | 0.3869                 | 0.4095                 | 0.4135                 | 0.3664                | 0.4195                 |
| 2.0171  | 118     | 0.0815        | -                      | -                      | -                      | -                     | -                      |
| 2.0342  | 119     | 0.1052        | -                      | -                      | -                      | -                     | -                      |
| 2.0513  | 120     | 0.1348        | -                      | -                      | -                      | -                     | -                      |
| 2.0684  | 121     | 0.255         | -                      | -                      | -                      | -                     | -                      |
| 2.0855  | 122     | 0.251         | -                      | -                      | -                      | -                     | -                      |
| 2.1026  | 123     | 0.3033        | -                      | -                      | -                      | -                     | -                      |
| 2.1197  | 124     | 0.0385        | -                      | -                      | -                      | -                     | -                      |
| 2.1368  | 125     | 0.0687        | -                      | -                      | -                      | -                     | -                      |
| 2.1538  | 126     | 0.1682        | -                      | -                      | -                      | -                     | -                      |
| 2.1709  | 127     | 0.0774        | -                      | -                      | -                      | -                     | -                      |
| 2.1880  | 128     | 0.0944        | -                      | -                      | -                      | -                     | -                      |
| 2.2051  | 129     | 0.036         | -                      | -                      | -                      | -                     | -                      |
| 2.2222  | 130     | 0.0393        | -                      | -                      | -                      | -                     | -                      |
| 2.2393  | 131     | 0.0387        | -                      | -                      | -                      | -                     | -                      |
| 2.2564  | 132     | 0.0273        | -                      | -                      | -                      | -                     | -                      |
| 2.2735  | 133     | 0.056         | -                      | -                      | -                      | -                     | -                      |
| 2.2906  | 134     | 0.0279        | -                      | -                      | -                      | -                     | -                      |
| 2.3077  | 135     | 0.0557        | -                      | -                      | -                      | -                     | -                      |
| 2.3248  | 136     | 0.0197        | -                      | -                      | -                      | -                     | -                      |
| 2.3419  | 137     | 0.0216        | -                      | -                      | -                      | -                     | -                      |
| 2.3590  | 138     | 0.0212        | -                      | -                      | -                      | -                     | -                      |
| 2.3761  | 139     | 0.0239        | -                      | -                      | -                      | -                     | -                      |
| 2.3932  | 140     | 0.0526        | -                      | -                      | -                      | -                     | -                      |
| 2.4103  | 141     | 0.1072        | -                      | -                      | -                      | -                     | -                      |
| 2.4274  | 142     | 0.0347        | -                      | -                      | -                      | -                     | -                      |
| 2.4444  | 143     | 0.024         | -                      | -                      | -                      | -                     | -                      |
| 2.4615  | 144     | 0.0128        | -                      | -                      | -                      | -                     | -                      |
| 2.4786  | 145     | 0.0089        | -                      | -                      | -                      | -                     | -                      |
| 2.4957  | 146     | 0.0101        | -                      | -                      | -                      | -                     | -                      |
| 2.5128  | 147     | 0.0124        | -                      | -                      | -                      | -                     | -                      |
| 2.5299  | 148     | 0.011         | -                      | -                      | -                      | -                     | -                      |
| 2.5470  | 149     | 0.0182        | -                      | -                      | -                      | -                     | -                      |
| 2.5641  | 150     | 0.0379        | -                      | -                      | -                      | -                     | -                      |
| 2.5812  | 151     | 0.0395        | -                      | -                      | -                      | -                     | -                      |
| 2.5983  | 152     | 0.0372        | -                      | -                      | -                      | -                     | -                      |
| 2.6154  | 153     | 0.031         | -                      | -                      | -                      | -                     | -                      |
| 2.6325  | 154     | 0.0136        | -                      | -                      | -                      | -                     | -                      |
| 2.6496  | 155     | 0.0355        | -                      | -                      | -                      | -                     | -                      |
| 2.6667  | 156     | 0.0296        | -                      | -                      | -                      | -                     | -                      |
| 2.6838  | 157     | 0.0473        | -                      | -                      | -                      | -                     | -                      |
| 2.7009  | 158     | 0.0295        | -                      | -                      | -                      | -                     | -                      |
| 2.7179  | 159     | 0.0576        | -                      | -                      | -                      | -                     | -                      |
| 2.7350  | 160     | 0.0592        | -                      | -                      | -                      | -                     | -                      |
| 2.7521  | 161     | 0.0571        | -                      | -                      | -                      | -                     | -                      |
| 2.7692  | 162     | 0.0221        | -                      | -                      | -                      | -                     | -                      |
| 2.7863  | 163     | 0.0179        | -                      | -                      | -                      | -                     | -                      |
| 2.8034  | 164     | 0.0195        | -                      | -                      | -                      | -                     | -                      |
| 2.8205  | 165     | 0.0291        | -                      | -                      | -                      | -                     | -                      |
| 2.8376  | 166     | 0.024         | -                      | -                      | -                      | -                     | -                      |
| 2.8547  | 167     | 0.0396        | -                      | -                      | -                      | -                     | -                      |
| 2.8718  | 168     | 0.0352        | -                      | -                      | -                      | -                     | -                      |
| 2.8889  | 169     | 0.0431        | -                      | -                      | -                      | -                     | -                      |
| 2.9060  | 170     | 0.0222        | -                      | -                      | -                      | -                     | -                      |
| 2.9231  | 171     | 0.016         | -                      | -                      | -                      | -                     | -                      |
| 2.9402  | 172     | 0.0307        | -                      | -                      | -                      | -                     | -                      |
| 2.9573  | 173     | 0.0439        | -                      | -                      | -                      | -                     | -                      |
| 2.9744  | 174     | 0.0197        | -                      | -                      | -                      | -                     | -                      |
| 2.9915  | 175     | 0.0181        | 0.3928                 | 0.4120                 | 0.4152                 | 0.3717                | 0.4180                 |
| 3.0085  | 176     | 0.03          | -                      | -                      | -                      | -                     | -                      |
| 3.0256  | 177     | 0.0325        | -                      | -                      | -                      | -                     | -                      |
| 3.0427  | 178     | 0.0286        | -                      | -                      | -                      | -                     | -                      |
| 3.0598  | 179     | 0.0746        | -                      | -                      | -                      | -                     | -                      |
| 3.0769  | 180     | 0.0677        | -                      | -                      | -                      | -                     | -                      |
| 3.0940  | 181     | 0.0574        | -                      | -                      | -                      | -                     | -                      |
| 3.1111  | 182     | 0.0158        | -                      | -                      | -                      | -                     | -                      |
| 3.1282  | 183     | 0.0092        | -                      | -                      | -                      | -                     | -                      |
| 3.1453  | 184     | 0.0412        | -                      | -                      | -                      | -                     | -                      |
| 3.1624  | 185     | 0.0308        | -                      | -                      | -                      | -                     | -                      |
| 3.1795  | 186     | 0.022         | -                      | -                      | -                      | -                     | -                      |
| 3.1966  | 187     | 0.0157        | -                      | -                      | -                      | -                     | -                      |
| 3.2137  | 188     | 0.0109        | -                      | -                      | -                      | -                     | -                      |
| 3.2308  | 189     | 0.0059        | -                      | -                      | -                      | -                     | -                      |
| 3.2479  | 190     | 0.0206        | -                      | -                      | -                      | -                     | -                      |
| 3.2650  | 191     | 0.0135        | -                      | -                      | -                      | -                     | -                      |
| 3.2821  | 192     | 0.0199        | -                      | -                      | -                      | -                     | -                      |
| 3.2991  | 193     | 0.0124        | -                      | -                      | -                      | -                     | -                      |
| 3.3162  | 194     | 0.0081        | -                      | -                      | -                      | -                     | -                      |
| 3.3333  | 195     | 0.0052        | -                      | -                      | -                      | -                     | -                      |
| 3.3504  | 196     | 0.006         | -                      | -                      | -                      | -                     | -                      |
| 3.3675  | 197     | 0.0074        | -                      | -                      | -                      | -                     | -                      |
| 3.3846  | 198     | 0.0085        | -                      | -                      | -                      | -                     | -                      |
| 3.4017  | 199     | 0.0273        | -                      | -                      | -                      | -                     | -                      |
| 3.4188  | 200     | 0.0363        | -                      | -                      | -                      | -                     | -                      |
| 3.4359  | 201     | 0.0077        | -                      | -                      | -                      | -                     | -                      |
| 3.4530  | 202     | 0.0046        | -                      | -                      | -                      | -                     | -                      |
| 3.4701  | 203     | 0.0067        | -                      | -                      | -                      | -                     | -                      |
| 3.4872  | 204     | 0.0054        | -                      | -                      | -                      | -                     | -                      |
| 3.5043  | 205     | 0.0055        | -                      | -                      | -                      | -                     | -                      |
| 3.5214  | 206     | 0.0052        | -                      | -                      | -                      | -                     | -                      |
| 3.5385  | 207     | 0.004         | -                      | -                      | -                      | -                     | -                      |
| 3.5556  | 208     | 0.0102        | -                      | -                      | -                      | -                     | -                      |
| 3.5726  | 209     | 0.0228        | -                      | -                      | -                      | -                     | -                      |
| 3.5897  | 210     | 0.0315        | -                      | -                      | -                      | -                     | -                      |
| 3.6068  | 211     | 0.0095        | -                      | -                      | -                      | -                     | -                      |
| 3.6239  | 212     | 0.0069        | -                      | -                      | -                      | -                     | -                      |
| 3.6410  | 213     | 0.0066        | -                      | -                      | -                      | -                     | -                      |
| 3.6581  | 214     | 0.0395        | -                      | -                      | -                      | -                     | -                      |
| 3.6752  | 215     | 0.0176        | -                      | -                      | -                      | -                     | -                      |
| 3.6923  | 216     | 0.0156        | -                      | -                      | -                      | -                     | -                      |
| 3.7094  | 217     | 0.0168        | -                      | -                      | -                      | -                     | -                      |
| 3.7265  | 218     | 0.0376        | -                      | -                      | -                      | -                     | -                      |
| 3.7436  | 219     | 0.0149        | -                      | -                      | -                      | -                     | -                      |
| 3.7607  | 220     | 0.0179        | -                      | -                      | -                      | -                     | -                      |
| 3.7778  | 221     | 0.0059        | -                      | -                      | -                      | -                     | -                      |
| 3.7949  | 222     | 0.013         | -                      | -                      | -                      | -                     | -                      |
| 3.8120  | 223     | 0.0081        | -                      | -                      | -                      | -                     | -                      |
| 3.8291  | 224     | 0.0136        | -                      | -                      | -                      | -                     | -                      |
| 3.8462  | 225     | 0.0129        | -                      | -                      | -                      | -                     | -                      |
| 3.8632  | 226     | 0.0132        | -                      | -                      | -                      | -                     | -                      |
| 3.8803  | 227     | 0.0228        | -                      | -                      | -                      | -                     | -                      |
| 3.8974  | 228     | 0.0091        | -                      | -                      | -                      | -                     | -                      |
| 3.9145  | 229     | 0.0112        | -                      | -                      | -                      | -                     | -                      |
| 3.9316  | 230     | 0.0124        | -                      | -                      | -                      | -                     | -                      |
| 3.9487  | 231     | 0.0224        | -                      | -                      | -                      | -                     | -                      |
| 3.9658  | 232     | 0.0191        | -                      | -                      | -                      | -                     | -                      |
| 3.9829  | 233     | 0.0078        | -                      | -                      | -                      | -                     | -                      |
| **4.0** | **234** | **0.0145**    | **0.3959**             | **0.411**              | **0.4154**             | **0.3741**            | **0.4179**             |
| 4.0171  | 235     | 0.0089        | -                      | -                      | -                      | -                     | -                      |
| 4.0342  | 236     | 0.0157        | -                      | -                      | -                      | -                     | -                      |
| 4.0513  | 237     | 0.019         | -                      | -                      | -                      | -                     | -                      |
| 4.0684  | 238     | 0.0315        | -                      | -                      | -                      | -                     | -                      |
| 4.0855  | 239     | 0.0311        | -                      | -                      | -                      | -                     | -                      |
| 4.1026  | 240     | 0.0155        | -                      | -                      | -                      | -                     | -                      |
| 4.1197  | 241     | 0.0078        | -                      | -                      | -                      | -                     | -                      |
| 4.1368  | 242     | 0.0069        | -                      | -                      | -                      | -                     | -                      |
| 4.1538  | 243     | 0.0246        | -                      | -                      | -                      | -                     | -                      |
| 4.1709  | 244     | 0.011         | -                      | -                      | -                      | -                     | -                      |
| 4.1880  | 245     | 0.0169        | -                      | -                      | -                      | -                     | -                      |
| 4.2051  | 246     | 0.0065        | -                      | -                      | -                      | -                     | -                      |
| 4.2222  | 247     | 0.0093        | -                      | -                      | -                      | -                     | -                      |
| 4.2393  | 248     | 0.0059        | -                      | -                      | -                      | -                     | -                      |
| 4.2564  | 249     | 0.0072        | -                      | -                      | -                      | -                     | -                      |
| 4.2735  | 250     | 0.0114        | -                      | -                      | -                      | -                     | -                      |
| 4.2906  | 251     | 0.0048        | -                      | -                      | -                      | -                     | -                      |
| 4.3077  | 252     | 0.0099        | -                      | -                      | -                      | -                     | -                      |
| 4.3248  | 253     | 0.0061        | -                      | -                      | -                      | -                     | -                      |
| 4.3419  | 254     | 0.005         | -                      | -                      | -                      | -                     | -                      |
| 4.3590  | 255     | 0.0077        | -                      | -                      | -                      | -                     | -                      |
| 4.3761  | 256     | 0.0057        | -                      | -                      | -                      | -                     | -                      |
| 4.3932  | 257     | 0.0106        | -                      | -                      | -                      | -                     | -                      |
| 4.4103  | 258     | 0.0176        | -                      | -                      | -                      | -                     | -                      |
| 4.4274  | 259     | 0.0085        | -                      | -                      | -                      | -                     | -                      |
| 4.4444  | 260     | 0.0059        | -                      | -                      | -                      | -                     | -                      |
| 4.4615  | 261     | 0.0063        | -                      | -                      | -                      | -                     | -                      |
| 4.4786  | 262     | 0.003         | -                      | -                      | -                      | -                     | -                      |
| 4.4957  | 263     | 0.0041        | -                      | -                      | -                      | -                     | -                      |
| 4.5128  | 264     | 0.0048        | -                      | -                      | -                      | -                     | -                      |
| 4.5299  | 265     | 0.0037        | -                      | -                      | -                      | -                     | -                      |
| 4.5470  | 266     | 0.0052        | -                      | -                      | -                      | -                     | -                      |
| 4.5641  | 267     | 0.0084        | -                      | -                      | -                      | -                     | -                      |
| 4.5812  | 268     | 0.0183        | -                      | -                      | -                      | -                     | -                      |
| 4.5983  | 269     | 0.0065        | -                      | -                      | -                      | -                     | -                      |
| 4.6154  | 270     | 0.0074        | -                      | -                      | -                      | -                     | -                      |
| 4.6325  | 271     | 0.0046        | -                      | -                      | -                      | -                     | -                      |
| 4.6496  | 272     | 0.009         | -                      | -                      | -                      | -                     | -                      |
| 4.6667  | 273     | 0.01          | -                      | -                      | -                      | -                     | -                      |
| 4.6838  | 274     | 0.0158        | -                      | -                      | -                      | -                     | -                      |
| 4.7009  | 275     | 0.0077        | -                      | -                      | -                      | -                     | -                      |
| 4.7179  | 276     | 0.0259        | -                      | -                      | -                      | -                     | -                      |
| 4.7350  | 277     | 0.0204        | -                      | -                      | -                      | -                     | -                      |
| 4.7521  | 278     | 0.0155        | -                      | -                      | -                      | -                     | -                      |
| 4.7692  | 279     | 0.0101        | -                      | -                      | -                      | -                     | -                      |
| 4.7863  | 280     | 0.0062        | -                      | -                      | -                      | -                     | -                      |
| 4.8034  | 281     | 0.0065        | -                      | -                      | -                      | -                     | -                      |
| 4.8205  | 282     | 0.0115        | -                      | -                      | -                      | -                     | -                      |
| 4.8376  | 283     | 0.0088        | -                      | -                      | -                      | -                     | -                      |
| 4.8547  | 284     | 0.0157        | -                      | -                      | -                      | -                     | -                      |
| 4.8718  | 285     | 0.0145        | -                      | -                      | -                      | -                     | -                      |
| 4.8889  | 286     | 0.0122        | -                      | -                      | -                      | -                     | -                      |
| 4.9060  | 287     | 0.007         | -                      | -                      | -                      | -                     | -                      |
| 4.9231  | 288     | 0.0126        | -                      | -                      | -                      | -                     | -                      |
| 4.9402  | 289     | 0.0094        | -                      | -                      | -                      | -                     | -                      |
| 4.9573  | 290     | 0.016         | 0.3940                 | 0.4131                 | 0.4148                 | 0.3728                | 0.4182                 |

* 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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