---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
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
widget:
- source_sentence: What service does Walmart GoLocal provide?
  sentences:
  - What services does Walmart Connect offer?
  - What is the process for using reinsurers not on the authorized list?
  - What is the principal amount of debt maturing in fiscal year 2023?
- source_sentence: What is the focus of DaVita Venture Group?
  sentences:
  - What is the main business focus of Eli Lilly and Company?
  - By what percentage did AbbVie's Skyrizi net revenues increase in 2023?
  - What were the net catastrophe losses in U.S. dollars in 2023?
- source_sentence: What are Kroger’s four strategic pillars?
  sentences:
  - What is the nature of Kroger's business operations?
  - What interest rates are applicable to the notes issued in April 2022?
  - Proceeds from issuance of long-term debt in 2023 amounted to $872.9 million.
- source_sentence: What was the effective tax rate in 2023?
  sentences:
  - What was the effective income tax rate for the Company in 2023?
  - What major restructuring activities were completed by the end of 2023?
  - What are the primary responsibilities of Chubb's Product Boards?
- source_sentence: The return on equity for 2023 was 27.0%.
  sentences:
  - What was the return on equity for 2023?
  - What was the total net property and equipment as of December 31, 2023?
  - How does CARB enforce its ZEV mandates and what consequence faces non-compliance?
pipeline_tag: sentence-similarity
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: 0.7028571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.84
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8828571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9214285714285714
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7028571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17657142857142855
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09214285714285714
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7028571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.84
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8828571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9214285714285714
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8149004529112371
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7804058956916099
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7839215377734133
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7028571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8385714285714285
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8871428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9257142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7028571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2795238095238095
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1774285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09257142857142854
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7028571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8385714285714285
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8871428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9257142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8152847434616775
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7796893424036281
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7829824429897414
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7057142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8328571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8728571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9114285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7057142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2776190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17457142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09114285714285714
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7057142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8328571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8728571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9114285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8097167926128003
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7770238095238095
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7810667608834199
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.68
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8285714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8671428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.68
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27619047619047615
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1734285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.091
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.68
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8285714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8671428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7972014326060813
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.760804988662131
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7644398940079736
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6614285714285715
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7942857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.83
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8757142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6614285714285715
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26476190476190475
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16599999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08757142857142856
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6614285714285715
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7942857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.83
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8757142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7702103944866356
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7363231292517006
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7414139881512513
      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("uonyeka/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The return on equity for 2023 was 27.0%.',
    'What was the return on equity for 2023?',
    'What was the total net property and equipment as of December 31, 2023?',
]
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   | 0.7029     |
| cosine_accuracy@3   | 0.84       |
| cosine_accuracy@5   | 0.8829     |
| cosine_accuracy@10  | 0.9214     |
| cosine_precision@1  | 0.7029     |
| cosine_precision@3  | 0.28       |
| cosine_precision@5  | 0.1766     |
| cosine_precision@10 | 0.0921     |
| cosine_recall@1     | 0.7029     |
| cosine_recall@3     | 0.84       |
| cosine_recall@5     | 0.8829     |
| cosine_recall@10    | 0.9214     |
| cosine_ndcg@10      | 0.8149     |
| cosine_mrr@10       | 0.7804     |
| **cosine_map@100**  | **0.7839** |

#### 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   | 0.7029    |
| cosine_accuracy@3   | 0.8386    |
| cosine_accuracy@5   | 0.8871    |
| cosine_accuracy@10  | 0.9257    |
| cosine_precision@1  | 0.7029    |
| cosine_precision@3  | 0.2795    |
| cosine_precision@5  | 0.1774    |
| cosine_precision@10 | 0.0926    |
| cosine_recall@1     | 0.7029    |
| cosine_recall@3     | 0.8386    |
| cosine_recall@5     | 0.8871    |
| cosine_recall@10    | 0.9257    |
| cosine_ndcg@10      | 0.8153    |
| cosine_mrr@10       | 0.7797    |
| **cosine_map@100**  | **0.783** |

#### 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   | 0.7057     |
| cosine_accuracy@3   | 0.8329     |
| cosine_accuracy@5   | 0.8729     |
| cosine_accuracy@10  | 0.9114     |
| cosine_precision@1  | 0.7057     |
| cosine_precision@3  | 0.2776     |
| cosine_precision@5  | 0.1746     |
| cosine_precision@10 | 0.0911     |
| cosine_recall@1     | 0.7057     |
| cosine_recall@3     | 0.8329     |
| cosine_recall@5     | 0.8729     |
| cosine_recall@10    | 0.9114     |
| cosine_ndcg@10      | 0.8097     |
| cosine_mrr@10       | 0.777      |
| **cosine_map@100**  | **0.7811** |

#### 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   | 0.68       |
| cosine_accuracy@3   | 0.8286     |
| cosine_accuracy@5   | 0.8671     |
| cosine_accuracy@10  | 0.91       |
| cosine_precision@1  | 0.68       |
| cosine_precision@3  | 0.2762     |
| cosine_precision@5  | 0.1734     |
| cosine_precision@10 | 0.091      |
| cosine_recall@1     | 0.68       |
| cosine_recall@3     | 0.8286     |
| cosine_recall@5     | 0.8671     |
| cosine_recall@10    | 0.91       |
| cosine_ndcg@10      | 0.7972     |
| cosine_mrr@10       | 0.7608     |
| **cosine_map@100**  | **0.7644** |

#### 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   | 0.6614     |
| cosine_accuracy@3   | 0.7943     |
| cosine_accuracy@5   | 0.83       |
| cosine_accuracy@10  | 0.8757     |
| cosine_precision@1  | 0.6614     |
| cosine_precision@3  | 0.2648     |
| cosine_precision@5  | 0.166      |
| cosine_precision@10 | 0.0876     |
| cosine_recall@1     | 0.6614     |
| cosine_recall@3     | 0.7943     |
| cosine_recall@5     | 0.83       |
| cosine_recall@10    | 0.8757     |
| cosine_ndcg@10      | 0.7702     |
| cosine_mrr@10       | 0.7363     |
| **cosine_map@100**  | **0.7414** |

<!--
## 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: 45.2 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.41 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                     | anchor                                                                                                        |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|
  | <code>The cash equities rate per contract (per 100 shares) for NYSE increased by 6%, from $0.045 in 2022 to $0.048 in 2023.</code>                                           | <code>What was the change in the rate per contract for NYSE cash equities from 2022 to 2023?</code>           |
  | <code>Item 3 specifies that the information regarding Legal Proceedings is sourced from Note 19 of the Notes to Consolidated Financial Statements included in Item 8.</code> | <code>What is the content source for the information requested by Item 3 concerning Legal Proceedings?</code> |
  | <code>North America's operating income for the fiscal year ended October 1, 2023, was $5,495.7 million, up from $4,486.5 million in fiscal 2022.</code>                      | <code>What was the increase in North America's operating income from fiscal 2022 to fiscal 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@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122     | 10     | 1.575         | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7437                 | 0.7623                 | 0.7682                 | 0.7114                | 0.7652                 |
| 1.6244     | 20     | 0.697         | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7619                 | 0.7760                 | 0.7824                 | 0.7346                | 0.7826                 |
| 2.4365     | 30     | 0.4724        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7639                 | 0.7808                 | 0.7831                 | 0.7398                | 0.7834                 |
| 3.2487     | 40     | 0.3999        | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7644**             | **0.7811**             | **0.783**              | **0.7414**            | **0.7839**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.30.1
- 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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