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: Chevron regularly conducts employee surveys throughout the year
    to assess the health of the company’s culture, allowing them to gain insights
    into employee well-being.
  sentences:
  - What was the net cash provided by operating activities for the year ended December
    31, 2023?
  - How often does Chevron conduct employee surveys to assess the health of its culture?
  - What were the total future minimum lease payments for Comcast's operating leases
    as of December 31, 2023?
- source_sentence: Gross margin for the fiscal year decreased 250 basis points to
    43.5% primarily driven by higher product costs, higher markdowns and unfavorable
    changes in foreign currency exchange rates, partially offset by strategic pricing
    actions.
  sentences:
  - How does the company maintain high standards of product quality and safety?
  - What were the main factors that negatively impacted NIKE's gross margin in fiscal
    2023?
  - What was the growth rate of Visa Inc.'s commercial payments volume internationally
    between 2021 and 2022?
- source_sentence: Mr. Teter holds a B.S. degree in Mechanical Engineering from the
    University of California at Davis and a J.D. degree from Stanford Law School.
  sentences:
  - What degrees does Timothy S. Teter hold and from which institutions?
  - What regulations are in place in Europe regarding interactions between pharmaceutical
    companies and physicians?
  - What economic factors particularly affected Garmin's consumer behavior in 2023?
- source_sentence: Our Office of Diversity, Equity and Inclusion supports our focus
    on associate diversity, supplier diversity, and engagement with our communities.
  sentences:
  - What are the three segments of alcohol ready-to-drink beverages the company is
    focusing on?
  - How much net cash was provided by operating activities in 2023?
  - What is the focus of The Home Depot's Office of Diversity, Equity and Inclusion?
- source_sentence: Net cash used in financing activities totaled $2,614 in 2023, compared
    to $4,283 in 2022.
  sentences:
  - What was the net cash used in financing activities in 2023 and how does it compare
    to 2022?
  - What are Chipotle's key strategies for business growth as discussed in their strategy?
  - What are the primary regulatory authorities that supervise and regulate JPMorgan
    Chase in the U.S.?
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: 0.6971428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.82
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8685714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9057142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6971428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2733333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1737142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09057142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6971428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.82
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8685714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9057142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.803607128355984
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.770687641723356
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.77485834386751
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6957142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8228571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8642857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9042857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6957142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2742857142857143
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17285714285714285
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0904285714285714
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6957142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8228571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8642857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9042857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.802840202489837
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7701360544217687
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7744106258164117
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6871428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8185714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8528571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8985714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6871428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27285714285714285
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17057142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08985714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6871428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8185714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8528571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8985714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.795190594370522
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7619773242630383
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7664081914180308
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6685714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8128571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8428571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8942857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6685714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27095238095238094
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16857142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08942857142857143
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6685714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8128571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8428571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8942857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7840862792892018
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7486655328798184
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7527149388922518
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6471428571428571
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7828571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8242857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8685714285714285
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6471428571428571
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26095238095238094
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16485714285714284
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08685714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6471428571428571
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7828571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8242857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8685714285714285
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7601900384958588
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.725268707482993
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7302983967510448
      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("revtestuser/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Net cash used in financing activities totaled $2,614 in 2023, compared to $4,283 in 2022.',
    'What was the net cash used in financing activities in 2023 and how does it compare to 2022?',
    "What are Chipotle's key strategies for business growth as discussed in their strategy?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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

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

</details>
-->

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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   | 0.6971     |
| cosine_accuracy@3   | 0.82       |
| cosine_accuracy@5   | 0.8686     |
| cosine_accuracy@10  | 0.9057     |
| cosine_precision@1  | 0.6971     |
| cosine_precision@3  | 0.2733     |
| cosine_precision@5  | 0.1737     |
| cosine_precision@10 | 0.0906     |
| cosine_recall@1     | 0.6971     |
| cosine_recall@3     | 0.82       |
| cosine_recall@5     | 0.8686     |
| cosine_recall@10    | 0.9057     |
| cosine_ndcg@10      | 0.8036     |
| cosine_mrr@10       | 0.7707     |
| **cosine_map@100**  | **0.7749** |

#### 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   | 0.6957     |
| cosine_accuracy@3   | 0.8229     |
| cosine_accuracy@5   | 0.8643     |
| cosine_accuracy@10  | 0.9043     |
| cosine_precision@1  | 0.6957     |
| cosine_precision@3  | 0.2743     |
| cosine_precision@5  | 0.1729     |
| cosine_precision@10 | 0.0904     |
| cosine_recall@1     | 0.6957     |
| cosine_recall@3     | 0.8229     |
| cosine_recall@5     | 0.8643     |
| cosine_recall@10    | 0.9043     |
| cosine_ndcg@10      | 0.8028     |
| cosine_mrr@10       | 0.7701     |
| **cosine_map@100**  | **0.7744** |

#### 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   | 0.6871     |
| cosine_accuracy@3   | 0.8186     |
| cosine_accuracy@5   | 0.8529     |
| cosine_accuracy@10  | 0.8986     |
| cosine_precision@1  | 0.6871     |
| cosine_precision@3  | 0.2729     |
| cosine_precision@5  | 0.1706     |
| cosine_precision@10 | 0.0899     |
| cosine_recall@1     | 0.6871     |
| cosine_recall@3     | 0.8186     |
| cosine_recall@5     | 0.8529     |
| cosine_recall@10    | 0.8986     |
| cosine_ndcg@10      | 0.7952     |
| cosine_mrr@10       | 0.762      |
| **cosine_map@100**  | **0.7664** |

#### 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   | 0.6686     |
| cosine_accuracy@3   | 0.8129     |
| cosine_accuracy@5   | 0.8429     |
| cosine_accuracy@10  | 0.8943     |
| cosine_precision@1  | 0.6686     |
| cosine_precision@3  | 0.271      |
| cosine_precision@5  | 0.1686     |
| cosine_precision@10 | 0.0894     |
| cosine_recall@1     | 0.6686     |
| cosine_recall@3     | 0.8129     |
| cosine_recall@5     | 0.8429     |
| cosine_recall@10    | 0.8943     |
| cosine_ndcg@10      | 0.7841     |
| cosine_mrr@10       | 0.7487     |
| **cosine_map@100**  | **0.7527** |

#### 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   | 0.6471     |
| cosine_accuracy@3   | 0.7829     |
| cosine_accuracy@5   | 0.8243     |
| cosine_accuracy@10  | 0.8686     |
| cosine_precision@1  | 0.6471     |
| cosine_precision@3  | 0.261      |
| cosine_precision@5  | 0.1649     |
| cosine_precision@10 | 0.0869     |
| cosine_recall@1     | 0.6471     |
| cosine_recall@3     | 0.7829     |
| cosine_recall@5     | 0.8243     |
| cosine_recall@10    | 0.8686     |
| cosine_ndcg@10      | 0.7602     |
| cosine_mrr@10       | 0.7253     |
| **cosine_map@100**  | **0.7303** |

<!--
## 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: 44.91 tokens</li><li>max: 246 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.43 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                    | anchor                                                                                                                                                                                 |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Certain provisions of the final rule become effective on April 1, 2024, but the majority of the final rule’s operative provisions (including the revisions to the definition of “limited purpose bank”) become effective on January 1, 2026, with additional data collection and reporting requirements becoming effective on January 1, 2027.</code> | <code>What are the effective dates for the main provisions and additional data collection and reporting requirements of the final rule impacting AENB's compliance obligations?</code> |
  | <code>Our total revenue for 2023 was $134.90 billion, an increase of 16% compared to 2022.</code>                                                                                                                                                                                                                                                           | <code>What was the total revenue for the year 2023 and the percentage increase from 2022?</code>                                                                                       |
  | <code>As of December 31, 2023, our domestic Chief Medical Officer leads a team of 22 nephrologists in our physician leadership team as part of our domestic Office of the Chief Medical Officer.</code>                                                                                                                                                     | <code>How many physicians are part of the domestic Office of the Chief Medical Officer at DaVita as of 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
- `fp16`: True
- `tf32`: False
- `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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122     | 10     | 1.6288        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7384                 | 0.7485                 | 0.7508                 | 0.7013                | 0.7561                 |
| 1.6244     | 20     | 0.6896        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7499                 | 0.7621                 | 0.7676                 | 0.7220                | 0.7704                 |
| 2.4365     | 30     | 0.4965        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7529                 | 0.7669                 | 0.7739                 | 0.7302                | 0.7754                 |
| 3.2487     | 40     | 0.415         | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7527**             | **0.7664**             | **0.7744**             | **0.7303**            | **0.7749**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- 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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