README.md

---
base_model: mixedbread-ai/mxbai-embed-large-v1
datasets: []
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:3550
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: At the end of 2023, Alphabet Inc. reported total debts amounting
    to $14.2 billion, compared to $10.9 billion at the end of 2022.
  sentences:
  - What was the total debt of Alphabet Inc. as of the end of 2023?
  - What was ExxonMobil's contribution to the energy production in the Energy sector
    during 2020?
  - Describe Amazon's revenue growth in 2023?
- source_sentence: In 2022, Pfizer strategically managed cash flow from investments
    by utilizing operating cash flow, issuing new debt, and through the monetization
    of certain non-core assets. This approach of diversifying the source of funding
    for investments was done to minimize risk and uncertainty in economic conditions.
  sentences:
  - How much capital expenditure did AUX Energy invest in renewable energy projects
    in 2022?
  - What effect did the 2023 market downturn have on Amazon's retail and cloud segments?
  - How did Pfizer manage cash flows from investments in 2022?
- source_sentence: The primary revenue generators for JPMorgan Chase for the fiscal
    year 2023 were the Corporate & Investment Bank (CIB) and the Asset & Wealth Management
    (AWM) sectors. The CIB sector benefited from a rise in merger and acquisition
    activities, while AWM saw large net inflows.
  sentences:
  - What is General Electric's strategic priority for its Aviation business segment?
  - Which sectors contributed the most to the revenue of JPMorgan Chase for FY 2023?
  - What is the principal activity of Apple Inc.?
- source_sentence: For the fiscal year 2023, Microsoft's Intelligent Cloud segment
    generated revenues of $58 billion, demonstrating solid growth fueled by strong
    demand for cloud services and server products.
  sentences:
  - What is the primary strategy of McDonald’s to drive growth in the future?
  - What impact did the increase in gold prices have on Newmont Corporation's revenue
    in 2023?
  - What was the revenue generated by Microsoft's Intelligent Cloud segment for fiscal
    year 2023?
- source_sentence: Microsoft, in their latest press release, revealed that they are
    anticipating a revenue growth of approximately 12% for the fiscal year ending
    in 2024.
  sentences:
  - What is Microsoft's projected revenue growth for fiscal year 2024?
  - What is the fair value of equity method investments of Microsoft in the fiscal
    year 2025?
  - What was the impact of COVID-19 on Zoom's profits?
model-index:
- name: mxbai-embed-large-v1-financial-rag-matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 1024
      type: dim_1024
    metrics:
    - type: cosine_accuracy@1
      value: 0.8455696202531645
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9392405063291139
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9670886075949368
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9898734177215189
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8455696202531645
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31308016877637135
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19341772151898737
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0989873417721519
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8455696202531645
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9392405063291139
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9670886075949368
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9898734177215189
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9212281141643793
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.898873819570022
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8993853803492357
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.8455696202531645
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9392405063291139
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9670886075949368
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9898734177215189
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8455696202531645
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3130801687763713
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1934177215189873
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0989873417721519
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8455696202531645
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9392405063291139
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9670886075949368
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9898734177215189
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9217284365901642
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8994826200522402
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8999494134557425
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.8405063291139241
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9367088607594937
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9645569620253165
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9898734177215189
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8405063291139241
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31223628691983124
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19291139240506328
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0989873417721519
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8405063291139241
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9367088607594937
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9645569620253165
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9898734177215189
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9186273598847787
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8954631303998389
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8958871142668611
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.8455696202531645
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9392405063291139
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9645569620253165
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9898734177215189
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8455696202531645
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3130801687763713
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19291139240506328
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0989873417721519
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8455696202531645
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9392405063291139
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9645569620253165
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9898734177215189
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9201161947922436
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8975597749648381
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8979721416614026
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.8405063291139241
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9417721518987342
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9645569620253165
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9848101265822785
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8405063291139241
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3139240506329114
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19291139240506328
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09848101265822784
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8405063291139241
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9417721518987342
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9645569620253165
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9848101265822785
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9170562815583235
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8948693992364878
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8957325656059834
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.8405063291139241
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9316455696202531
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9569620253164557
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9822784810126582
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8405063291139241
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3105485232067511
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19139240506329114
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09822784810126582
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8405063291139241
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9316455696202531
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9569620253164557
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9822784810126582
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9153318022971121
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8934589109905566
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8943102728098851
      name: Cosine Map@100
---

# mxbai-embed-large-v1-financial-rag-matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co./mixedbread-ai/mxbai-embed-large-v1). It maps sentences & paragraphs to a 1024-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:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co./mixedbread-ai/mxbai-embed-large-v1) <!-- at revision 990580e27d329c7408b3741ecff85876e128e203 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### Model Sources

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

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("rbhatia46/mxbai-embed-large-v1-financial-rag-matryoshka")
# Run inference
sentences = [
    'Microsoft, in their latest press release, revealed that they are anticipating a revenue growth of approximately 12% for the fiscal year ending in 2024.',
    "What is Microsoft's projected revenue growth for fiscal year 2024?",
    "What was the impact of COVID-19 on Zoom's profits?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
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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_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8456     |
| cosine_accuracy@3   | 0.9392     |
| cosine_accuracy@5   | 0.9671     |
| cosine_accuracy@10  | 0.9899     |
| cosine_precision@1  | 0.8456     |
| cosine_precision@3  | 0.3131     |
| cosine_precision@5  | 0.1934     |
| cosine_precision@10 | 0.099      |
| cosine_recall@1     | 0.8456     |
| cosine_recall@3     | 0.9392     |
| cosine_recall@5     | 0.9671     |
| cosine_recall@10    | 0.9899     |
| cosine_ndcg@10      | 0.9212     |
| cosine_mrr@10       | 0.8989     |
| **cosine_map@100**  | **0.8994** |

#### 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   | 0.8456     |
| cosine_accuracy@3   | 0.9392     |
| cosine_accuracy@5   | 0.9671     |
| cosine_accuracy@10  | 0.9899     |
| cosine_precision@1  | 0.8456     |
| cosine_precision@3  | 0.3131     |
| cosine_precision@5  | 0.1934     |
| cosine_precision@10 | 0.099      |
| cosine_recall@1     | 0.8456     |
| cosine_recall@3     | 0.9392     |
| cosine_recall@5     | 0.9671     |
| cosine_recall@10    | 0.9899     |
| cosine_ndcg@10      | 0.9217     |
| cosine_mrr@10       | 0.8995     |
| **cosine_map@100**  | **0.8999** |

#### 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   | 0.8405     |
| cosine_accuracy@3   | 0.9367     |
| cosine_accuracy@5   | 0.9646     |
| cosine_accuracy@10  | 0.9899     |
| cosine_precision@1  | 0.8405     |
| cosine_precision@3  | 0.3122     |
| cosine_precision@5  | 0.1929     |
| cosine_precision@10 | 0.099      |
| cosine_recall@1     | 0.8405     |
| cosine_recall@3     | 0.9367     |
| cosine_recall@5     | 0.9646     |
| cosine_recall@10    | 0.9899     |
| cosine_ndcg@10      | 0.9186     |
| cosine_mrr@10       | 0.8955     |
| **cosine_map@100**  | **0.8959** |

#### 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   | 0.8456    |
| cosine_accuracy@3   | 0.9392    |
| cosine_accuracy@5   | 0.9646    |
| cosine_accuracy@10  | 0.9899    |
| cosine_precision@1  | 0.8456    |
| cosine_precision@3  | 0.3131    |
| cosine_precision@5  | 0.1929    |
| cosine_precision@10 | 0.099     |
| cosine_recall@1     | 0.8456    |
| cosine_recall@3     | 0.9392    |
| cosine_recall@5     | 0.9646    |
| cosine_recall@10    | 0.9899    |
| cosine_ndcg@10      | 0.9201    |
| cosine_mrr@10       | 0.8976    |
| **cosine_map@100**  | **0.898** |

#### 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   | 0.8405     |
| cosine_accuracy@3   | 0.9418     |
| cosine_accuracy@5   | 0.9646     |
| cosine_accuracy@10  | 0.9848     |
| cosine_precision@1  | 0.8405     |
| cosine_precision@3  | 0.3139     |
| cosine_precision@5  | 0.1929     |
| cosine_precision@10 | 0.0985     |
| cosine_recall@1     | 0.8405     |
| cosine_recall@3     | 0.9418     |
| cosine_recall@5     | 0.9646     |
| cosine_recall@10    | 0.9848     |
| cosine_ndcg@10      | 0.9171     |
| cosine_mrr@10       | 0.8949     |
| **cosine_map@100**  | **0.8957** |

#### 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   | 0.8405     |
| cosine_accuracy@3   | 0.9316     |
| cosine_accuracy@5   | 0.957      |
| cosine_accuracy@10  | 0.9823     |
| cosine_precision@1  | 0.8405     |
| cosine_precision@3  | 0.3105     |
| cosine_precision@5  | 0.1914     |
| cosine_precision@10 | 0.0982     |
| cosine_recall@1     | 0.8405     |
| cosine_recall@3     | 0.9316     |
| cosine_recall@5     | 0.957      |
| cosine_recall@10    | 0.9823     |
| cosine_ndcg@10      | 0.9153     |
| cosine_mrr@10       | 0.8935     |
| **cosine_map@100**  | **0.8943** |

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## 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: 3,550 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: 17 tokens</li><li>mean: 44.69 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 18.26 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                              | anchor                                                                                          |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
  | <code>The total revenue for Google as of 2021 stands at approximately $181 billion, primarily driven by the performance of its advertising and cloud segments, hailing from the Information Technology sector.</code> | <code>What is the total revenue of Google as of 2021?</code>                                    |
  | <code>In Q4 2021, Amazon.com Inc. reported a significant increase in net income, reaching $14.3 billion, due to the surge in online shopping during the pandemic.</code>                                              | <code>What was the Net Income of Amazon.com Inc. in Q4 2021?</code>                             |
  | <code>Coca-Cola reported full-year 2021 revenue of $37.3 billion, a rise of 13% compared to $33.0 billion in 2020. This was primarily due to strong volume growth as well as improved pricing and mix.</code>         | <code>How did Coca-Cola's revenue performance in 2021 measure against its previous year?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          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`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 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`: 10
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | dim_1024_cosine_map@100 | 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 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8649     | 6      | -             | 0.8783                  | 0.8651                 | 0.8713                 | 0.8783                 | 0.8439                | 0.8809                 |
| 1.4414     | 10     | 0.7682        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.8739     | 13     | -             | 0.8918                  | 0.8827                 | 0.8875                 | 0.8918                 | 0.8729                | 0.8933                 |
| 2.8829     | 20     | 0.1465        | 0.8948                  | 0.8896                 | 0.8928                 | 0.8961                 | 0.8884                | 0.8953                 |
| 3.8919     | 27     | -             | 0.8930                  | 0.8884                 | 0.8917                 | 0.8959                 | 0.8900                | 0.8945                 |
| 4.3243     | 30     | 0.0646        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9009     | 34     | -             | 0.8972                  | 0.8883                 | 0.8947                 | 0.8955                 | 0.8925                | 0.8970                 |
| 5.7658     | 40     | 0.0397        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.9099     | 41     | -             | 0.8964                  | 0.8915                 | 0.8953                 | 0.8943                 | 0.8926                | 0.8979                 |
| 6.9189     | 48     | -             | 0.8994                  | 0.8930                 | 0.8966                 | 0.8955                 | 0.8932                | 0.8974                 |
| 7.2072     | 50     | 0.0319        | -                       | -                      | -                      | -                      | -                     | -                      |
| 7.9279     | 55     | -             | 0.8998                  | 0.8945                 | 0.8967                 | 0.8961                 | 0.8943                | 0.8999                 |
| **8.6486** | **60** | **0.0296**    | **0.8994**              | **0.8957**             | **0.898**              | **0.8959**             | **0.8943**            | **0.8999**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.6
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- 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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