---
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@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: As of December 31, 2023, deferred revenues for unsatisfied performance
    obligations consisted of $769 million related to Hilton Honors that will be recognized
    as revenue over approximately the next two years.
  sentences:
  - How many shares of common stock were issued in both 2022 and 2023?
  - What is the projected timeline for recognizing revenue from deferred revenues
    related to Hilton Honors as of December 31, 2023?
  - What acquisitions did CVS Health Corporation complete in 2023 to enhance their
    care delivery strategy?
- source_sentence: If a good or service does not qualify as distinct, it is combined
    with the other non-distinct goods or services within the arrangement and these
    combined goods or services are treated as a single performance obligation for
    accounting purposes. The arrangement's transaction price is then allocated to
    each performance obligation based on the relative standalone selling price of
    each performance obligation.
  sentences:
  - What does the summary table indicate about the company's activities at the end
    of 2023?
  - What governs the treatment of goods or services that are not distinct within a
    contractual arrangement?
  - What is the basis for the Company to determine the Standalone Selling Price (SSP)
    for each distinct performance obligation in contracts with multiple performance
    obligations?
- source_sentence: As of January 2023, the maximum daily borrowing capacity under
    the commercial paper program was approximately $2.75 billion.
  sentences:
  - What is the maximum daily borrowing capacity under the commercial paper program
    as of January 2023?
  - When does the Company's fiscal year end?
  - How much cash did acquisition activities use in 2023?
- source_sentence: Federal Home Loan Bank borrowings had an interest rate of 4.59%
    in 2022, which increased to 5.14% in 2023.
  sentences:
  - By what percentage did the company's capital expenditures increase in fiscal 2023
    compared to fiscal 2022?
  - What is the significance of Note 13 in the context of legal proceedings described
    in the Annual Report on Form 10-K?
  - How much did the Federal Home Loan Bank borrowings increase in terms of interest
    rates from 2022 to 2023?
- source_sentence: The design of the Annual Report, with the consolidated financial
    statements placed immediately after Part IV, enhances the integration of financial
    data by maintaining a coherent structure.
  sentences:
  - How does the structure of the Annual Report on Form 10-K facilitate the integration
    of the consolidated financial statements?
  - Where can one find the Glossary of Terms and Acronyms in Item 8?
  - What part of the annual report contains the consolidated financial statements
    and accompanying notes?
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: 0.6957142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8171428571428572
      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: 0.6957142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2723809523809524
      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: 0.6957142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8171428571428572
      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: 0.7971144469297426
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7641831065759639
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7681728985040082
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6942857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.81
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8514285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6942857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17028571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6942857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.81
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8514285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7951260604161544
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7617998866213151
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7658003405075238
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7014285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7971428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.85
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8885714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7014285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26571428571428574
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08885714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7014285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7971428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.85
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8885714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.793266992460996
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7629580498866213
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7678096436855835
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6957142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8014285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8357142857142857
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8842857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6957142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2671428571428571
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16714285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08842857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6957142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8014285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8357142857142857
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8842857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.787378246207931
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7566984126984126
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7613545312565108
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6571428571428571
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7871428571428571
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8285714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8757142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6571428571428571
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2623809523809524
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1657142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08757142857142856
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6571428571428571
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7871428571428571
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8285714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8757142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7655516319615892
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7303951247165531
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7349875161463472
      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). 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("rbhatia46/bge-base-financial-nvidia-matryoshka")
# Run inference
sentences = [
    'The design of the Annual Report, with the consolidated financial statements placed immediately after Part IV, enhances the integration of financial data by maintaining a coherent structure.',
    'How does the structure of the Annual Report on Form 10-K facilitate the integration of the consolidated financial statements?',
    'Where can one find the Glossary of Terms and Acronyms in Item 8?',
]
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   | 0.6957     |
| cosine_accuracy@3   | 0.8171     |
| cosine_accuracy@5   | 0.8629     |
| cosine_accuracy@10  | 0.9        |
| cosine_precision@1  | 0.6957     |
| cosine_precision@3  | 0.2724     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.09       |
| cosine_recall@1     | 0.6957     |
| cosine_recall@3     | 0.8171     |
| cosine_recall@5     | 0.8629     |
| cosine_recall@10    | 0.9        |
| cosine_ndcg@10      | 0.7971     |
| cosine_mrr@10       | 0.7642     |
| **cosine_map@100**  | **0.7682** |

#### 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   | 0.6943     |
| cosine_accuracy@3   | 0.81       |
| cosine_accuracy@5   | 0.8514     |
| cosine_accuracy@10  | 0.9        |
| cosine_precision@1  | 0.6943     |
| cosine_precision@3  | 0.27       |
| cosine_precision@5  | 0.1703     |
| cosine_precision@10 | 0.09       |
| cosine_recall@1     | 0.6943     |
| cosine_recall@3     | 0.81       |
| cosine_recall@5     | 0.8514     |
| cosine_recall@10    | 0.9        |
| cosine_ndcg@10      | 0.7951     |
| cosine_mrr@10       | 0.7618     |
| **cosine_map@100**  | **0.7658** |

#### 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   | 0.7014     |
| cosine_accuracy@3   | 0.7971     |
| cosine_accuracy@5   | 0.85       |
| cosine_accuracy@10  | 0.8886     |
| cosine_precision@1  | 0.7014     |
| cosine_precision@3  | 0.2657     |
| cosine_precision@5  | 0.17       |
| cosine_precision@10 | 0.0889     |
| cosine_recall@1     | 0.7014     |
| cosine_recall@3     | 0.7971     |
| cosine_recall@5     | 0.85       |
| cosine_recall@10    | 0.8886     |
| cosine_ndcg@10      | 0.7933     |
| cosine_mrr@10       | 0.763      |
| **cosine_map@100**  | **0.7678** |

#### 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   | 0.6957     |
| cosine_accuracy@3   | 0.8014     |
| cosine_accuracy@5   | 0.8357     |
| cosine_accuracy@10  | 0.8843     |
| cosine_precision@1  | 0.6957     |
| cosine_precision@3  | 0.2671     |
| cosine_precision@5  | 0.1671     |
| cosine_precision@10 | 0.0884     |
| cosine_recall@1     | 0.6957     |
| cosine_recall@3     | 0.8014     |
| cosine_recall@5     | 0.8357     |
| cosine_recall@10    | 0.8843     |
| cosine_ndcg@10      | 0.7874     |
| cosine_mrr@10       | 0.7567     |
| **cosine_map@100**  | **0.7614** |

#### 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   | 0.6571    |
| cosine_accuracy@3   | 0.7871    |
| cosine_accuracy@5   | 0.8286    |
| cosine_accuracy@10  | 0.8757    |
| cosine_precision@1  | 0.6571    |
| cosine_precision@3  | 0.2624    |
| cosine_precision@5  | 0.1657    |
| cosine_precision@10 | 0.0876    |
| cosine_recall@1     | 0.6571    |
| cosine_recall@3     | 0.7871    |
| cosine_recall@5     | 0.8286    |
| cosine_recall@10    | 0.8757    |
| cosine_ndcg@10      | 0.7656    |
| cosine_mrr@10       | 0.7304    |
| **cosine_map@100**  | **0.735** |

<!--
## 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: 6 tokens</li><li>mean: 45.53 tokens</li><li>max: 222 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.3 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
  | positive                                                                                                                   | anchor                                                                                                         |
  |:---------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|
  | <code>Acquisition activity used cash of $765 million in 2023, primarily related to a Beauty acquisition.</code>            | <code>How much cash did acquisition activities use in 2023?</code>                                             |
  | <code>In a financial report, Part IV Item 15 includes Exhibits and Financial Statement Schedules as mentioned.</code>      | <code>What content can be expected under Part IV Item 15 in a financial report?</code>                         |
  | <code>we had more than 8.3 million fiber consumer wireline broadband customers, adding 1.1 million during the year.</code> | <code>How many fiber consumer wireline broadband customers did the company have at the end of the 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": [
          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@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122     | 10     | 1.5751        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | -                      | -                      | -                      | -                     | 0.7580                 |
| 0.8122     | 10     | 0.6362        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7503                 | 0.7576                 | 0.7653                 | 0.7282                | 0.7638                 |
| 1.6244     | 20     | 0.4426        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7544                 | 0.7662                 | 0.7640                 | 0.7311                | 0.7676                 |
| 2.4365     | 30     | 0.3217        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7608                 | 0.7684                 | 0.7662                 | 0.7341                | 0.7686                 |
| 3.2487     | 40     | 0.2761        | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7614**             | **0.7678**             | **0.7658**             | **0.735**             | **0.7682**             |

* 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: 0.31.0
- 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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