README.md

---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: What is the Path to Pro program related to?
  sentences:
  - What types of programs are developed to upskill manufacturing employees?
  - What was the overall turnover rate at the company in fiscal year 2023?
  - What was the net interest revenue of The Charles Schwab Corporation in 2023?
- source_sentence: What types of businesses does HPE serve?
  sentences:
  - What types of industries does TTI service?
  - What interest rates are applicable to the notes issued in April 2022?
  - The total unrealized losses on U.S. Treasury securities amounted to $134 million.
- source_sentence: What is the title of Item 6 in the text?
  sentences:
  - Item 6—Reserved
  - The operating income for the year 2023 was reported as -$74.3 million.
  - Commission revenues at Schwab experienced a 10% decrease from 2022 to 2023.
- source_sentence: How is Dynamics' revenue mainly driven?
  sentences:
  - What are the primary sources of revenue for the company mentioned in the text?
  - How many new stores did the company open in Mexico during fiscal 2022?
  - Legal proceedings are discussed in Item 3 of the Annual Report on Form 10-K.
- source_sentence: What is basic earnings per share based on?
  sentences:
  - How is basic net income per share calculated?
  - How did NIKE's fiscal 2023 revenue compare to its fiscal 2022 revenue?
  - What types of vessels are included in Chevron's operated marine fleet?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
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: 0.6828571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.82
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6828571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2733333333333334
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.091
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6828571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.82
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7970675337008412
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7608446712018138
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7643786819951583
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.68
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8185714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8657142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9114285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.68
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27285714285714285
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17314285714285713
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09114285714285712
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.68
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8185714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8657142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9114285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7954799079266202
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7583633786848069
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7618248215296402
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6785714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.81
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8571428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9142857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6785714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1714285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09142857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6785714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.81
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8571428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9142857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7954881703263427
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7577579365079364
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7606385177656011
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6571428571428571
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7957142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8471428571428572
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6571428571428571
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2652380952380952
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16942857142857143
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6571428571428571
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7957142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8471428571428572
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7770789777970544
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7379240362811791
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7420186535175607
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6342857142857142
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7642857142857142
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8057142857142857
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8657142857142858
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6342857142857142
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25476190476190474
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16114285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08657142857142856
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6342857142857142
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7642857142857142
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8057142857142857
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8657142857142858
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7466817341215128
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.70906462585034
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7141559106614794
      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("xiaofengzi/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'What is basic earnings per share based on?',
    'How is basic net income per share calculated?',
    "How did NIKE's fiscal 2023 revenue compare to its fiscal 2022 revenue?",
]
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.*
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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.6829     |
| cosine_accuracy@3   | 0.82       |
| cosine_accuracy@5   | 0.8629     |
| cosine_accuracy@10  | 0.91       |
| cosine_precision@1  | 0.6829     |
| cosine_precision@3  | 0.2733     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.091      |
| cosine_recall@1     | 0.6829     |
| cosine_recall@3     | 0.82       |
| cosine_recall@5     | 0.8629     |
| cosine_recall@10    | 0.91       |
| cosine_ndcg@10      | 0.7971     |
| cosine_mrr@10       | 0.7608     |
| **cosine_map@100**  | **0.7644** |

#### 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   | 0.68       |
| cosine_accuracy@3   | 0.8186     |
| cosine_accuracy@5   | 0.8657     |
| cosine_accuracy@10  | 0.9114     |
| cosine_precision@1  | 0.68       |
| cosine_precision@3  | 0.2729     |
| cosine_precision@5  | 0.1731     |
| cosine_precision@10 | 0.0911     |
| cosine_recall@1     | 0.68       |
| cosine_recall@3     | 0.8186     |
| cosine_recall@5     | 0.8657     |
| cosine_recall@10    | 0.9114     |
| cosine_ndcg@10      | 0.7955     |
| cosine_mrr@10       | 0.7584     |
| **cosine_map@100**  | **0.7618** |

#### 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   | 0.6786     |
| cosine_accuracy@3   | 0.81       |
| cosine_accuracy@5   | 0.8571     |
| cosine_accuracy@10  | 0.9143     |
| cosine_precision@1  | 0.6786     |
| cosine_precision@3  | 0.27       |
| cosine_precision@5  | 0.1714     |
| cosine_precision@10 | 0.0914     |
| cosine_recall@1     | 0.6786     |
| cosine_recall@3     | 0.81       |
| cosine_recall@5     | 0.8571     |
| cosine_recall@10    | 0.9143     |
| cosine_ndcg@10      | 0.7955     |
| cosine_mrr@10       | 0.7578     |
| **cosine_map@100**  | **0.7606** |

#### 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   | 0.6571    |
| cosine_accuracy@3   | 0.7957    |
| cosine_accuracy@5   | 0.8471    |
| cosine_accuracy@10  | 0.9       |
| cosine_precision@1  | 0.6571    |
| cosine_precision@3  | 0.2652    |
| cosine_precision@5  | 0.1694    |
| cosine_precision@10 | 0.09      |
| cosine_recall@1     | 0.6571    |
| cosine_recall@3     | 0.7957    |
| cosine_recall@5     | 0.8471    |
| cosine_recall@10    | 0.9       |
| cosine_ndcg@10      | 0.7771    |
| cosine_mrr@10       | 0.7379    |
| **cosine_map@100**  | **0.742** |

#### 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   | 0.6343     |
| cosine_accuracy@3   | 0.7643     |
| cosine_accuracy@5   | 0.8057     |
| cosine_accuracy@10  | 0.8657     |
| cosine_precision@1  | 0.6343     |
| cosine_precision@3  | 0.2548     |
| cosine_precision@5  | 0.1611     |
| cosine_precision@10 | 0.0866     |
| cosine_recall@1     | 0.6343     |
| cosine_recall@3     | 0.7643     |
| cosine_recall@5     | 0.8057     |
| cosine_recall@10    | 0.8657     |
| cosine_ndcg@10      | 0.7467     |
| cosine_mrr@10       | 0.7091     |
| **cosine_map@100**  | **0.7142** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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<!--
### Recommendations

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

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 6,300 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 7 tokens</li><li>mean: 20.44 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 47.22 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | anchor                                                                                      | positive                                                                                                                                                                                                        |
  |:--------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>How did the Energy & Transportation segment's sales and profit change in 2023?</code> | <code>Energy & Transportation's total sales were $28.001 billion in 2023, an increase of $4.249 billion, or 18... and profit was $4.936 billion in 2023, an increase of $1.627 billion, or 49 percent...</code> |
  | <code>In which segments were acquisitions made in 2022?</code>                              | <code>During 2022, acquisitions occurred in Workforce Solutions and USIS operating segments, and the International segment.</code>                                                                              |
  | <code>What are the contents found on pages 163 to 309 in the document?</code>               | <code>The Consolidated Financial Statements, together with the Notes thereto and the report thereon dated February 16, 2024, of PricewaterhouseCoopers LLP, appear on pages 163–309.</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     | 1.5644        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7186                 | 0.7399                 | 0.7414                 | 0.6757                | 0.7445                 |
| 1.6244     | 20     | 0.6502        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7379                 | 0.7544                 | 0.7573                 | 0.7069                | 0.7600                 |
| 2.4365     | 30     | 0.434         | -                      | -                      | -                      | -                     | -                      |
| **2.9239** | **36** | **-**         | **0.7426**             | **0.7614**             | **0.7616**             | **0.7134**            | **0.7634**             |
| 3.2487     | 40     | 0.3627        | -                      | -                      | -                      | -                     | -                      |
| 3.8985     | 48     | -             | 0.7420                 | 0.7606                 | 0.7618                 | 0.7142                | 0.7644                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.4
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.6.0+cu118
- Accelerate: 1.6.0
- 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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