sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:6300 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: The consolidated financial statements and accompanying notes listed
    in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included elsewhere
    in this Annual Report on Form 10-K.
  sentences:
  - What is the carrying value of the indefinite-lived intangible assets related to
    the Certificate of Needs and Medicare licenses as of December 31, 2023?
  - What sections of the Annual Report on Form 10-K contain the company's financial
    statements?
  - What was the effective tax rate excluding discrete net tax benefits for the year
    2022?
- source_sentence: Consumers are served through Amazon's online and physical stores
    with an emphasis on selection, price, and convenience.
  sentences:
  - What decision did the European Commission make on July 10, 2023 regarding the
    United States?
  - What are the primary offerings to consumers through Amazon's online and physical
    stores?
  - What activities are included in the services and other revenue segment of General
    Motors Company?
- source_sentence: Visa has traditionally referred to their structure of facilitating
    secure, reliable, and efficient money movement among consumers, issuing and acquiring
    financial institutions, and merchants as the 'four-party' model.
  sentences:
  - What model does Visa traditionally refer to regarding their transaction process
    among consumers, financial institutions, and merchants?
  - What percentage of Meta's U.S. workforce in 2023 were represented by people with
    disabilities, veterans, and members of the LGBTQ+ community?
  - What are the revenue sources for the Company’s Health Care Benefits Segment?
- source_sentence: 'In addition to LinkedIn’s free services, LinkedIn offers monetized
    solutions: Talent Solutions, Marketing Solutions, Premium Subscriptions, and Sales
    Solutions. Talent Solutions provide insights for workforce planning and tools
    to hire, nurture, and develop talent. Talent Solutions also includes Learning
    Solutions, which help businesses close critical skills gaps in times where companies
    are having to do more with existing talent.'
  sentences:
  - What were the major factors contributing to the increased expenses excluding interest
    for Investor Services and Advisor Services in 2023?
  - What were the pre-tax earnings of the manufacturing sector in 2023, 2022, and
    2021?
  - What does LinkedIn's Talent Solutions include?
- source_sentence: Management assessed the effectiveness of the company’s internal
    control over financial reporting as of December 31, 2023. In making this assessment,
    we used the criteria set forth by the Committee of Sponsoring Organizations of
    the Treadway Commission (COSO) in Internal Control—Integrated Framework (2013).
  sentences:
  - What criteria did Caterpillar Inc. use to assess the effectiveness of its internal
    control over financial reporting as of December 31, 2023?
  - What are the primary components of U.S. sales volumes for Ford?
  - What was the percentage increase in Schwab's common stock dividend in 2022?
model-index:
- name: BGE base Financial Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.6914285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8228571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8585714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9057142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6914285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2742857142857143
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1717142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09057142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6914285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8228571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8585714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9057142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7997899233189547
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7658004535147391
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.769363404870108
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6842857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8185714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8642857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6842857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27285714285714285
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17285714285714285
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.091
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6842857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8185714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8642857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7969832750649838
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.760829365079365
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7638509876949151
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6885714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8171428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8557142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8957142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6885714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2723809523809524
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17114285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08957142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6885714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8171428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8557142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8957142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7929258434953915
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7598605442176868
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7636845384252359
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6657142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8042857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.84
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8771428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6657142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2680952380952381
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16799999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0877142857142857
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6657142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8042857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.84
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8771428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7734083364453213
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7398321995464853
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7442919584899765
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6442857142857142
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.78
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8171428571428572
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8628571428571429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6442857142857142
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1634285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08628571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6442857142857142
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.78
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8171428571428572
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8628571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7538226273270404
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7189291383219955
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7234331075852384
      name: Cosine Map@100
---

# BGE base Financial Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Rajagurumurugan/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Management assessed the effectiveness of the company’s internal control over financial reporting as of December 31, 2023. In making this assessment, we used the criteria set forth by the Committee of Sponsoring Organizations of the Treadway Commission (COSO) in Internal Control—Integrated Framework (2013).',
    'What criteria did Caterpillar Inc. use to assess the effectiveness of its internal control over financial reporting as of December 31, 2023?',
    'What are the primary components of U.S. sales volumes for Ford?',
]
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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</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

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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   | 0.6914     |
| cosine_accuracy@3   | 0.8229     |
| cosine_accuracy@5   | 0.8586     |
| cosine_accuracy@10  | 0.9057     |
| cosine_precision@1  | 0.6914     |
| cosine_precision@3  | 0.2743     |
| cosine_precision@5  | 0.1717     |
| cosine_precision@10 | 0.0906     |
| cosine_recall@1     | 0.6914     |
| cosine_recall@3     | 0.8229     |
| cosine_recall@5     | 0.8586     |
| cosine_recall@10    | 0.9057     |
| cosine_ndcg@10      | 0.7998     |
| cosine_mrr@10       | 0.7658     |
| **cosine_map@100**  | **0.7694** |

#### 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   | 0.6843     |
| cosine_accuracy@3   | 0.8186     |
| cosine_accuracy@5   | 0.8643     |
| cosine_accuracy@10  | 0.91       |
| cosine_precision@1  | 0.6843     |
| cosine_precision@3  | 0.2729     |
| cosine_precision@5  | 0.1729     |
| cosine_precision@10 | 0.091      |
| cosine_recall@1     | 0.6843     |
| cosine_recall@3     | 0.8186     |
| cosine_recall@5     | 0.8643     |
| cosine_recall@10    | 0.91       |
| cosine_ndcg@10      | 0.797      |
| cosine_mrr@10       | 0.7608     |
| **cosine_map@100**  | **0.7639** |

#### 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   | 0.6886     |
| cosine_accuracy@3   | 0.8171     |
| cosine_accuracy@5   | 0.8557     |
| cosine_accuracy@10  | 0.8957     |
| cosine_precision@1  | 0.6886     |
| cosine_precision@3  | 0.2724     |
| cosine_precision@5  | 0.1711     |
| cosine_precision@10 | 0.0896     |
| cosine_recall@1     | 0.6886     |
| cosine_recall@3     | 0.8171     |
| cosine_recall@5     | 0.8557     |
| cosine_recall@10    | 0.8957     |
| cosine_ndcg@10      | 0.7929     |
| cosine_mrr@10       | 0.7599     |
| **cosine_map@100**  | **0.7637** |

#### 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   | 0.6657     |
| cosine_accuracy@3   | 0.8043     |
| cosine_accuracy@5   | 0.84       |
| cosine_accuracy@10  | 0.8771     |
| cosine_precision@1  | 0.6657     |
| cosine_precision@3  | 0.2681     |
| cosine_precision@5  | 0.168      |
| cosine_precision@10 | 0.0877     |
| cosine_recall@1     | 0.6657     |
| cosine_recall@3     | 0.8043     |
| cosine_recall@5     | 0.84       |
| cosine_recall@10    | 0.8771     |
| cosine_ndcg@10      | 0.7734     |
| cosine_mrr@10       | 0.7398     |
| **cosine_map@100**  | **0.7443** |

#### 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   | 0.6443     |
| cosine_accuracy@3   | 0.78       |
| cosine_accuracy@5   | 0.8171     |
| cosine_accuracy@10  | 0.8629     |
| cosine_precision@1  | 0.6443     |
| cosine_precision@3  | 0.26       |
| cosine_precision@5  | 0.1634     |
| cosine_precision@10 | 0.0863     |
| cosine_recall@1     | 0.6443     |
| cosine_recall@3     | 0.78       |
| cosine_recall@5     | 0.8171     |
| cosine_recall@10    | 0.8629     |
| cosine_ndcg@10      | 0.7538     |
| cosine_mrr@10       | 0.7189     |
| **cosine_map@100**  | **0.7234** |

<!--
## 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

#### json

* Dataset: json
* Size: 6,300 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: 8 tokens</li><li>mean: 44.33 tokens</li><li>max: 289 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.43 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | anchor                                                                                                                                                                             |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The Company defines fair value as the price received to transfer an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date. In accordance with ASC 820, Fair Value Measurements and Disclosures, the Company uses the fair value hierarchy which prioritizes the inputs used to measure fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1), observable inputs other than quoted prices (Level 2), and unobservable inputs (Level 3).</code> | <code>What is the role of Level 1, Level 2, and Level 3 inputs in the fair value hierarchy according to ASC 820?</code>                                                            |
  | <code>In the event of conversion of the Notes, if shares are delivered to the Company under the Capped Call Transactions, they will offset the dilutive effect of the shares that the Company would issue under the Notes.</code>                                                                                                                                                                                                                                                                                                                                                                            | <code>What happens to the dilutive effect of shares issued under the Notes if shares are delivered to the Company under the Capped Call Transactions during the conversion?</code> |
  | <code>Marketing expenses increased $48.8 million to $759.2 million in the year ended December 31, 2023 compared to the year ended December 31, 2022.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                  | <code>How much did the marketing expenses increase in the year ended December 31, 2023?</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`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `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`: 32
- `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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: 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_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8122     | 10     | 1.5604        | -                      | -                      | -                      | -                      | -                     |
| 0.9746     | 12     | -             | 0.7547                 | 0.7556                 | 0.7489                 | 0.7274                 | 0.6903                |
| 1.6244     | 20     | 0.6615        | -                      | -                      | -                      | -                      | -                     |
| 1.9492     | 24     | -             | 0.7646                 | 0.7630                 | 0.7588                 | 0.7422                 | 0.7198                |
| 2.4365     | 30     | 0.4579        | -                      | -                      | -                      | -                      | -                     |
| **2.9239** | **36** | **-**         | **0.7695**             | **0.7648**             | **0.7622**             | **0.7459**             | **0.7237**            |
| 3.2487     | 40     | 0.3996        | -                      | -                      | -                      | -                      | -                     |
| 3.8985     | 48     | -             | 0.7694                 | 0.7639                 | 0.7637                 | 0.7443                 | 0.7234                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.41.2
- PyTorch: 2.2.0a0+6a974be
- Accelerate: 0.27.0
- 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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