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
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@10
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 Gross Merchandise Sales (GMS) decreased by 1.2% in 2023 compared
    to 2022.
  sentences:
  - What specific matters did the CFPB investigate concerning Equifax?
  - What was the percentage decline in GMS for the year ended December 31, 2023 compared
    to 2022?
  - What percentage of eBay's 2023 net revenues were attributed to international markets?
- source_sentence: Asset management and administration fees vary with changes in the
    balances of client assets due to market fluctuations and client activity.
  sentences:
  - Why was there a net outflow of cash in financing activities in fiscal 2022?
  - How do asset management and administration fees vary at The Charles Schwab Corporation?
  - What are some key goals of the corporation related to climate change?
- source_sentence: Operating profit margin was 19.3 percent in 2023, compared with
    13.3 percent in 2022.
  sentences:
  - What was the operating profit margin for 2023?
  - How do the studios compete in the entertainment industry?
  - What types of audio products does Garmin's Fusion and JL Audio brands offer?
- source_sentence: Subsequent to 2023, on February 12, 2024, AbbVie borrowed $5.0
    billion under the term loan credit agreement.
  sentences:
  - What percentage of U.S. dialysis patient service revenues in 2023 came from Medicare
    and Medicare Advantage plans?
  - What is Peloton Interactive, Inc. known for in the interactive fitness industry?
  - What was the purpose stated by AbbVie for borrowing $5.0 billion under the term
    loan credit agreement on February 12, 2024?
- source_sentence: Chipotle retains an independent third-party compensation consultant
    each year to conduct a pay equity analysis of its U.S. and Canadian workforce,
    including factors of pay such as grade level, tenure in role, and external market
    conditions like geographic location, to ensure consistency and equitable treatment
    among employees.
  sentences:
  - How does Chipotle ensure pay equity among its employees?
  - How can one locate information on legal proceedings within the Consolidated Financial
    Statements?
  - What criteria did the independent audit use to assess the effectiveness of internal
    control over financial reporting at the company?
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: 0.6985714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8342857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6985714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27809523809523806
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08999999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6985714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8342857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8029099239677612
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.771475056689342
      name: Cosine Mrr@10
    - type: cosine_map@10
      value: 0.7714750566893424
      name: Cosine Map@10
  - 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: 0.8271428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8928571428571429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6842857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2757142857142857
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08928571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6842857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8271428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8928571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7942762197573711
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7620697278911563
      name: Cosine Mrr@10
    - type: cosine_map@10
      value: 0.7620697278911566
      name: Cosine Map@10
  - 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: 0.8157142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8614285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8928571428571429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6871428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27190476190476187
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17228571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08928571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6871428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8157142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8614285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8928571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7935865448697424
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7613917233560088
      name: Cosine Mrr@10
    - type: cosine_map@10
      value: 0.7613917233560091
      name: Cosine Map@10
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6757142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8171428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8514285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8814285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6757142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2723809523809524
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17028571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08814285714285712
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6757142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8171428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8514285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8814285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7842926561068588
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7525731292517003
      name: Cosine Mrr@10
    - type: cosine_map@10
      value: 0.7525731292517006
      name: Cosine Map@10
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.64
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.79
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8271428571428572
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.87
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.64
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2633333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1654285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.087
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.64
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.79
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8271428571428572
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.87
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7594704472459967
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7236507936507934
      name: Cosine Mrr@10
    - type: cosine_map@10
      value: 0.7236507936507937
      name: Cosine Map@10
---

# 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). 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:** 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': 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("Sailesh9999/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Chipotle retains an independent third-party compensation consultant each year to conduct a pay equity analysis of its U.S. and Canadian workforce, including factors of pay such as grade level, tenure in role, and external market conditions like geographic location, to ensure consistency and equitable treatment among employees.',
    'How does Chipotle ensure pay equity among its employees?',
    'How can one locate information on legal proceedings within the Consolidated Financial Statements?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
-->

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

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

## 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   | 0.6986     |
| cosine_accuracy@3   | 0.8343     |
| cosine_accuracy@5   | 0.8629     |
| cosine_accuracy@10  | 0.9        |
| cosine_precision@1  | 0.6986     |
| cosine_precision@3  | 0.2781     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.09       |
| cosine_recall@1     | 0.6986     |
| cosine_recall@3     | 0.8343     |
| cosine_recall@5     | 0.8629     |
| cosine_recall@10    | 0.9        |
| cosine_ndcg@10      | 0.8029     |
| cosine_mrr@10       | 0.7715     |
| **cosine_map@10**   | **0.7715** |

#### 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   | 0.8271     |
| cosine_accuracy@5   | 0.8629     |
| cosine_accuracy@10  | 0.8929     |
| cosine_precision@1  | 0.6843     |
| cosine_precision@3  | 0.2757     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.0893     |
| cosine_recall@1     | 0.6843     |
| cosine_recall@3     | 0.8271     |
| cosine_recall@5     | 0.8629     |
| cosine_recall@10    | 0.8929     |
| cosine_ndcg@10      | 0.7943     |
| cosine_mrr@10       | 0.7621     |
| **cosine_map@10**   | **0.7621** |

#### 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   | 0.8157     |
| cosine_accuracy@5   | 0.8614     |
| cosine_accuracy@10  | 0.8929     |
| cosine_precision@1  | 0.6871     |
| cosine_precision@3  | 0.2719     |
| cosine_precision@5  | 0.1723     |
| cosine_precision@10 | 0.0893     |
| cosine_recall@1     | 0.6871     |
| cosine_recall@3     | 0.8157     |
| cosine_recall@5     | 0.8614     |
| cosine_recall@10    | 0.8929     |
| cosine_ndcg@10      | 0.7936     |
| cosine_mrr@10       | 0.7614     |
| **cosine_map@10**   | **0.7614** |

#### 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   | 0.6757     |
| cosine_accuracy@3   | 0.8171     |
| cosine_accuracy@5   | 0.8514     |
| cosine_accuracy@10  | 0.8814     |
| cosine_precision@1  | 0.6757     |
| cosine_precision@3  | 0.2724     |
| cosine_precision@5  | 0.1703     |
| cosine_precision@10 | 0.0881     |
| cosine_recall@1     | 0.6757     |
| cosine_recall@3     | 0.8171     |
| cosine_recall@5     | 0.8514     |
| cosine_recall@10    | 0.8814     |
| cosine_ndcg@10      | 0.7843     |
| cosine_mrr@10       | 0.7526     |
| **cosine_map@10**   | **0.7526** |

#### 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   | 0.64       |
| cosine_accuracy@3   | 0.79       |
| cosine_accuracy@5   | 0.8271     |
| cosine_accuracy@10  | 0.87       |
| cosine_precision@1  | 0.64       |
| cosine_precision@3  | 0.2633     |
| cosine_precision@5  | 0.1654     |
| cosine_precision@10 | 0.087      |
| cosine_recall@1     | 0.64       |
| cosine_recall@3     | 0.79       |
| cosine_recall@5     | 0.8271     |
| cosine_recall@10    | 0.87       |
| cosine_ndcg@10      | 0.7595     |
| cosine_mrr@10       | 0.7237     |
| **cosine_map@10**   | **0.7237** |

<!--
## 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* 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: 7 tokens</li><li>mean: 46.55 tokens</li><li>max: 439 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>Americas | $ | 7,631,647 | | | $ | 6,817,454 | | 79.3 | % | 84.1 | %</code>                                                                                  | <code>What was the proportion of Americas' net revenue to the company's total net revenue in 2023, and how did it change from 2022?</code> |
  | <code>Item 1 Business typically includes detailed information about the organization's operations, the nature of the business, and its strategic direction.</code> | <code>What is the title of the section that potentially discusses the operations or nature of a business in a document?</code>             |
  | <code>Operating expenses as a percentage of total revenues decreased to 15.3% in 2023 compared to 15.9% in 2022.</code>                                            | <code>What was the operating expenses as a percentage of total revenues in 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_128_cosine_map@10 | dim_256_cosine_map@10 | dim_512_cosine_map@10 | dim_64_cosine_map@10 | dim_768_cosine_map@10 |
|:----------:|:------:|:-------------:|:---------------------:|:---------------------:|:---------------------:|:--------------------:|:---------------------:|
| 0.8122     | 10     | 1.5638        | -                     | -                     | -                     | -                    | -                     |
| 0.9746     | 12     | -             | 0.7308                | 0.7547                | 0.7547                | 0.7004               | 0.7624                |
| 1.6244     | 20     | 0.6662        | -                     | -                     | -                     | -                    | -                     |
| 1.9492     | 24     | -             | 0.7468                | 0.7586                | 0.7624                | 0.7195               | 0.7655                |
| 2.4365     | 30     | 0.4634        | -                     | -                     | -                     | -                    | -                     |
| 2.9239     | 36     | -             | 0.7525                | 0.7620                | 0.7614                | 0.7237               | 0.7717                |
| 3.2487     | 40     | 0.387         | -                     | -                     | -                     | -                    | -                     |
| **3.8985** | **48** | **-**         | **0.7526**            | **0.7614**            | **0.7621**            | **0.7237**           | **0.7715**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.9.18
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.29.3
- 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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