---
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 begins on page 105 of this report?
  sentences:
  - What sections are included alongside the Financial Statements in this report?
  - How did net revenues change from 2021 to 2022 on a FX-Neutral basis?
  - How much did MedTech's sales increase in 2023 compared to 2022?
- source_sentence: When does the Company's fiscal year end?
  sentences:
  - What was the total store count for the company at the end of fiscal 2022?
  - What was the total revenue for all UnitedHealthcare services in 2023?
  - What were the main factors contributing to the increase in net income in 2023?
- source_sentence: AutoZone, Inc. began operations in 1979.
  sentences:
  - When did AutoZone, Inc. begin its operations?
  - Mr. Pleas was named Senior Vice President and Controller during 2007.
  - Which item discusses Financial Statements and Supplementary Data?
- source_sentence: Are the ESG goals guaranteed to be met?
  sentences:
  - What measures is the company implementing to support climate goals?
  - What types of diseases does Gilead Sciences, Inc. focus on treating?
  - Changes in foreign exchange rates reduced cost of sales by $254 million in 2023.
- source_sentence: What was Gilead's total revenue in 2023?
  sentences:
  - What was the total revenue for the year ended December 31, 2023?
  - How much was the impairment related to the CAT loan receivable in 2023?
  - What are some of the critical accounting policies that affect financial statements?
pipeline_tag: sentence-similarity
model-index:
- name: BGE base Financial Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: basline 768
      type: basline_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.7085714285714285
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8514285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8842857142857142
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9271428571428572
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7085714285714285
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2838095238095238
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17685714285714282
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09271428571428571
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7085714285714285
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8514285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8842857142857142
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9271428571428572
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8214972164555796
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7873509070294781
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.790665594958196
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: basline 512
      type: basline_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7114285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.85
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8828571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9228571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7114285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2833333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17657142857142855
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09228571428571428
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7114285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.85
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8828571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9228571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.820942296767774
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7878956916099771
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7915593121031292
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: basline 256
      type: basline_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7057142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8414285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.88
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9228571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7057142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28047619047619043
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.176
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09228571428571428
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7057142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8414285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.88
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9228571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8161680075424235
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7817953514739227
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.785367816349997
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: basline 128
      type: basline_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.7028571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8342857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8742857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9171428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7028571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27809523809523806
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17485714285714282
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09171428571428569
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7028571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8342857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8742857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9171428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8109319521599055
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7768752834467119
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7802736634060462
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: basline 64
      type: basline_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6728571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8171428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8614285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9014285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6728571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2723809523809524
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17228571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09014285714285714
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6728571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8171428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8614285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9014285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7900026049536226
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7539795918367346
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7582240178397145
      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("philschmid/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "What was Gilead's total revenue in 2023?",
    'What was the total revenue for the year ended December 31, 2023?',
    'How much was the impairment related to the CAT loan receivable in 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: `basline_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   | 0.7086     |
| cosine_accuracy@3   | 0.8514     |
| cosine_accuracy@5   | 0.8843     |
| cosine_accuracy@10  | 0.9271     |
| cosine_precision@1  | 0.7086     |
| cosine_precision@3  | 0.2838     |
| cosine_precision@5  | 0.1769     |
| cosine_precision@10 | 0.0927     |
| cosine_recall@1     | 0.7086     |
| cosine_recall@3     | 0.8514     |
| cosine_recall@5     | 0.8843     |
| cosine_recall@10    | 0.9271     |
| cosine_ndcg@10      | 0.8215     |
| cosine_mrr@10       | 0.7874     |
| **cosine_map@100**  | **0.7907** |

#### Information Retrieval
* Dataset: `basline_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   | 0.7114     |
| cosine_accuracy@3   | 0.85       |
| cosine_accuracy@5   | 0.8829     |
| cosine_accuracy@10  | 0.9229     |
| cosine_precision@1  | 0.7114     |
| cosine_precision@3  | 0.2833     |
| cosine_precision@5  | 0.1766     |
| cosine_precision@10 | 0.0923     |
| cosine_recall@1     | 0.7114     |
| cosine_recall@3     | 0.85       |
| cosine_recall@5     | 0.8829     |
| cosine_recall@10    | 0.9229     |
| cosine_ndcg@10      | 0.8209     |
| cosine_mrr@10       | 0.7879     |
| **cosine_map@100**  | **0.7916** |

#### Information Retrieval
* Dataset: `basline_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   | 0.8414     |
| cosine_accuracy@5   | 0.88       |
| cosine_accuracy@10  | 0.9229     |
| cosine_precision@1  | 0.7057     |
| cosine_precision@3  | 0.2805     |
| cosine_precision@5  | 0.176      |
| cosine_precision@10 | 0.0923     |
| cosine_recall@1     | 0.7057     |
| cosine_recall@3     | 0.8414     |
| cosine_recall@5     | 0.88       |
| cosine_recall@10    | 0.9229     |
| cosine_ndcg@10      | 0.8162     |
| cosine_mrr@10       | 0.7818     |
| **cosine_map@100**  | **0.7854** |

#### Information Retrieval
* Dataset: `basline_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   | 0.7029     |
| cosine_accuracy@3   | 0.8343     |
| cosine_accuracy@5   | 0.8743     |
| cosine_accuracy@10  | 0.9171     |
| cosine_precision@1  | 0.7029     |
| cosine_precision@3  | 0.2781     |
| cosine_precision@5  | 0.1749     |
| cosine_precision@10 | 0.0917     |
| cosine_recall@1     | 0.7029     |
| cosine_recall@3     | 0.8343     |
| cosine_recall@5     | 0.8743     |
| cosine_recall@10    | 0.9171     |
| cosine_ndcg@10      | 0.8109     |
| cosine_mrr@10       | 0.7769     |
| **cosine_map@100**  | **0.7803** |

#### Information Retrieval
* Dataset: `basline_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   | 0.6729     |
| cosine_accuracy@3   | 0.8171     |
| cosine_accuracy@5   | 0.8614     |
| cosine_accuracy@10  | 0.9014     |
| cosine_precision@1  | 0.6729     |
| cosine_precision@3  | 0.2724     |
| cosine_precision@5  | 0.1723     |
| cosine_precision@10 | 0.0901     |
| cosine_recall@1     | 0.6729     |
| cosine_recall@3     | 0.8171     |
| cosine_recall@5     | 0.8614     |
| cosine_recall@10    | 0.9014     |
| cosine_ndcg@10      | 0.79       |
| cosine_mrr@10       | 0.754      |
| **cosine_map@100**  | **0.7582** |

<!--
## 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: 10 tokens</li><li>mean: 46.11 tokens</li><li>max: 289 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.26 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                          | anchor                                                                                             |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
  | <code>Fiscal 2023 total gross profit margin of 35.1% represents an increase of 1.7 percentage points as compared to the respective prior year period.</code>                                                                                                                                      | <code>What was the total gross profit margin for Hewlett Packard Enterprise in fiscal 2023?</code> |
  | <code>Noninterest expense increased to $65.8 billion in 2023, primarily due to higher investments in people and technology and higher FDIC expense, including $2.1 billion for the estimated special assessment amount arising from the closure of Silicon Valley Bank and Signature Bank.</code> | <code>What was the total noninterest expense for the company in 2023?</code>                       |
  | <code>As of May 31, 2022, FedEx Office had approximately 12,000 employees.</code>                                                                                                                                                                                                                 | <code>How many employees did FedEx Office have as of May 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
- `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
- `sanity_evaluation`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | basline_128_cosine_map@100 | basline_256_cosine_map@100 | basline_512_cosine_map@100 | basline_64_cosine_map@100 | basline_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-------------------------:|:--------------------------:|
| 0.8122     | 10     | 1.5259        | -                          | -                          | -                          | -                         | -                          |
| 0.9746     | 12     | -             | 0.7502                     | 0.7737                     | 0.7827                     | 0.7185                    | 0.7806                     |
| 1.6244     | 20     | 0.6545        | -                          | -                          | -                          | -                         | -                          |
| **1.9492** | **24** | **-**         | **0.7689**                 | **0.7844**                 | **0.7869**                 | **0.7447**                | **0.7909**                 |
| 2.4365     | 30     | 0.4784        | -                          | -                          | -                          | -                         | -                          |
| 2.9239     | 36     | -             | 0.7733                     | 0.7916                     | 0.7904                     | 0.7491                    | 0.7930                     |
| 3.2487     | 40     | 0.3827        | -                          | -                          | -                          | -                         | -                          |
| 3.8985     | 48     | -             | 0.7739                     | 0.7907                     | 0.7900                     | 0.7479                    | 0.7948                     |
| 0.8122     | 10     | 0.2685        | -                          | -                          | -                          | -                         | -                          |
| 0.9746     | 12     | -             | 0.7779                     | 0.7932                     | 0.7948                     | 0.7517                    | 0.7943                     |
| 1.6244     | 20     | 0.183         | -                          | -                          | -                          | -                         | -                          |
| **1.9492** | **24** | **-**         | **0.7784**                 | **0.7929**                 | **0.7963**                 | **0.7575**                | **0.7957**                 |
| 2.4365     | 30     | 0.1877        | -                          | -                          | -                          | -                         | -                          |
| 2.9239     | 36     | -             | 0.7814                     | 0.7914                     | 0.7992                     | 0.7570                    | 0.7974                     |
| 3.2487     | 40     | 0.1826        | -                          | -                          | -                          | -                         | -                          |
| 3.8985     | 48     | -             | 0.7818                     | 0.7916                     | 0.7976                     | 0.7580                    | 0.7960                     |
| 0.8122     | 10     | 0.071         | -                          | -                          | -                          | -                         | -                          |
| 0.9746     | 12     | -             | 0.7810                     | 0.7935                     | 0.7954                     | 0.7550                    | 0.7949                     |
| 1.6244     | 20     | 0.0629        | -                          | -                          | -                          | -                         | -                          |
| **1.9492** | **24** | **-**         | **0.7855**                 | **0.7914**                 | **0.7989**                 | **0.7559**                | **0.7981**                 |
| 2.4365     | 30     | 0.0827        | -                          | -                          | -                          | -                         | -                          |
| 2.9239     | 36     | -             | 0.7893                     | 0.7927                     | 0.7987                     | 0.7539                    | 0.7961                     |
| 3.2487     | 40     | 0.1003        | -                          | -                          | -                          | -                         | -                          |
| 3.8985     | 48     | -             | 0.7903                     | 0.7915                     | 0.7980                     | 0.7530                    | 0.7951                     |
| 0.8122     | 10     | 0.0213        | -                          | -                          | -                          | -                         | -                          |
| 0.9746     | 12     | -             | 0.7786                     | 0.7869                     | 0.7885                     | 0.7566                    | 0.7908                     |
| 1.6244     | 20     | 0.0234        | -                          | -                          | -                          | -                         | -                          |
| **1.9492** | **24** | **-**         | **0.783**                  | **0.7882**                 | **0.793**                  | **0.7551**                | **0.7946**                 |
| 2.4365     | 30     | 0.0357        | -                          | -                          | -                          | -                         | -                          |
| 2.9239     | 36     | -             | 0.7838                     | 0.7892                     | 0.7922                     | 0.7579                    | 0.7907                     |
| 3.2487     | 40     | 0.0563        | -                          | -                          | -                          | -                         | -                          |
| 3.8985     | 48     | -             | 0.7846                     | 0.7887                     | 0.7912                     | 0.7582                    | 0.7901                     |
| 0.8122     | 10     | 0.0075        | -                          | -                          | -                          | -                         | -                          |
| 0.9746     | 12     | -             | 0.7730                     | 0.7816                     | 0.7818                     | 0.7550                    | 0.7868                     |
| 1.6244     | 20     | 0.01          | -                          | -                          | -                          | -                         | -                          |
| **1.9492** | **24** | **-**         | **0.7827**                 | **0.785**                  | **0.7896**                 | **0.7551**                | **0.7915**                 |
| 2.4365     | 30     | 0.0154        | -                          | -                          | -                          | -                         | -                          |
| 2.9239     | 36     | -             | 0.7808                     | 0.7838                     | 0.7921                     | 0.7584                    | 0.7916                     |
| 3.2487     | 40     | 0.0312        | -                          | -                          | -                          | -                         | -                          |
| 3.8985     | 48     | -             | 0.7803                     | 0.7854                     | 0.7916                     | 0.7582                    | 0.7907                     |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.0
- Transformers: 4.42.0.dev0
- PyTorch: 2.1.2+cu121
- Accelerate: 0.29.2
- 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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