---
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 types of industries does TTI service?
  sentences:
  - What types of businesses does HPE serve?
  - How much did the company's revenues decrease in 2023 compared to 2022?
  - By what percentage did the quarterly cash dividend increase on January 26, 2023?
- source_sentence: What does ITEM 8 in Form 10-K refer to?
  sentences:
  - ITEM 8 in Form 10-K refers to the Financial Statements and Supplementary Data.
  - UnitedHealth Group reported net earnings of $23,144 million in 2023.
  - What factors contributed to the decrease in automotive leasing revenue in 2023?
- source_sentence: What are consolidated financial statements?
  sentences:
  - The report on the Consolidated Financial Statements is dated February 16, 2024.
  - How much did the foreclosed properties decrease in value during 2023?
  - What was Chipotle Mexican Grill's net income in 2023?
- source_sentence: What were the total product sales in 2023?
  sentences:
  - Total product sales in 2023 amounted to $27,305 million.
  - How does AutoZone manage its foreign operations in terms of currency?
  - What restrictions does the Bank Holding Company Act impose on JPMorgan Chase?
- source_sentence: What is the global presence of Lubrizol?
  sentences:
  - How does The Coca-Cola Company distribute its beverage products globally?
  - What are the two operating segments of NVIDIA as mentioned in the text?
  - How much did Delta Air Lines spend on debt and finance lease obligations in 2023?
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: 0.6957142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8342857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9085714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6957142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2780952380952381
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09085714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6957142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8342857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9085714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8045138729797765
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7709591836734694
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7746687336147619
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8271428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8642857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9157142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2757142857142857
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17285714285714285
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09157142857142857
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8271428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8642857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9157142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.807258910509631
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7726218820861678
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7757170101327764
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6928571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.82
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8585714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9028571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6928571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2733333333333334
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1717142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09028571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6928571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.82
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8585714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9028571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7979490809476271
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7643027210884353
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7684617620062486
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6857142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.81
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8542857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.89
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6857142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17085714285714282
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.089
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6857142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.81
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8542857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.89
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7877753635329912
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7549472789115641
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7596045003108374
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6528571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7571428571428571
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8185714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8685714285714285
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6528571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2523809523809524
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1637142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08685714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6528571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7571428571428571
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8185714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8685714285714285
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7557078446701566
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7201400226757368
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7249497855774768
      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("Andresckamilo/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'What is the global presence of Lubrizol?',
    'How does The Coca-Cola Company distribute its beverage products globally?',
    'What are the two operating segments of NVIDIA as mentioned in the text?',
]
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   | 0.8343     |
| cosine_accuracy@5   | 0.8629     |
| cosine_accuracy@10  | 0.9086     |
| cosine_precision@1  | 0.6957     |
| cosine_precision@3  | 0.2781     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.0909     |
| cosine_recall@1     | 0.6957     |
| cosine_recall@3     | 0.8343     |
| cosine_recall@5     | 0.8629     |
| cosine_recall@10    | 0.9086     |
| cosine_ndcg@10      | 0.8045     |
| cosine_mrr@10       | 0.771      |
| **cosine_map@100**  | **0.7747** |

#### 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   | 0.7        |
| cosine_accuracy@3   | 0.8271     |
| cosine_accuracy@5   | 0.8643     |
| cosine_accuracy@10  | 0.9157     |
| cosine_precision@1  | 0.7        |
| cosine_precision@3  | 0.2757     |
| cosine_precision@5  | 0.1729     |
| cosine_precision@10 | 0.0916     |
| cosine_recall@1     | 0.7        |
| cosine_recall@3     | 0.8271     |
| cosine_recall@5     | 0.8643     |
| cosine_recall@10    | 0.9157     |
| cosine_ndcg@10      | 0.8073     |
| cosine_mrr@10       | 0.7726     |
| **cosine_map@100**  | **0.7757** |

#### 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   | 0.6929     |
| cosine_accuracy@3   | 0.82       |
| cosine_accuracy@5   | 0.8586     |
| cosine_accuracy@10  | 0.9029     |
| cosine_precision@1  | 0.6929     |
| cosine_precision@3  | 0.2733     |
| cosine_precision@5  | 0.1717     |
| cosine_precision@10 | 0.0903     |
| cosine_recall@1     | 0.6929     |
| cosine_recall@3     | 0.82       |
| cosine_recall@5     | 0.8586     |
| cosine_recall@10    | 0.9029     |
| cosine_ndcg@10      | 0.7979     |
| cosine_mrr@10       | 0.7643     |
| **cosine_map@100**  | **0.7685** |

#### 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   | 0.6857     |
| cosine_accuracy@3   | 0.81       |
| cosine_accuracy@5   | 0.8543     |
| cosine_accuracy@10  | 0.89       |
| cosine_precision@1  | 0.6857     |
| cosine_precision@3  | 0.27       |
| cosine_precision@5  | 0.1709     |
| cosine_precision@10 | 0.089      |
| cosine_recall@1     | 0.6857     |
| cosine_recall@3     | 0.81       |
| cosine_recall@5     | 0.8543     |
| cosine_recall@10    | 0.89       |
| cosine_ndcg@10      | 0.7878     |
| cosine_mrr@10       | 0.7549     |
| **cosine_map@100**  | **0.7596** |

#### 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   | 0.6529     |
| cosine_accuracy@3   | 0.7571     |
| cosine_accuracy@5   | 0.8186     |
| cosine_accuracy@10  | 0.8686     |
| cosine_precision@1  | 0.6529     |
| cosine_precision@3  | 0.2524     |
| cosine_precision@5  | 0.1637     |
| cosine_precision@10 | 0.0869     |
| cosine_recall@1     | 0.6529     |
| cosine_recall@3     | 0.7571     |
| cosine_recall@5     | 0.8186     |
| cosine_recall@10    | 0.8686     |
| cosine_ndcg@10      | 0.7557     |
| cosine_mrr@10       | 0.7201     |
| **cosine_map@100**  | **0.7249** |

<!--
## 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: 45.39 tokens</li><li>max: 371 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.23 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                              | anchor                                                                                                            |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|
  | <code>Chubb mitigates exposure to climate change risk by ceding catastrophe risk in our insurance portfolio through both reinsurance and capital markets, and our investment portfolio through the diversification of risk, industry, location, type and duration of security.</code> | <code>How does Chubb respond to the risks associated with climate change?</code>                                  |
  | <code>Item 8 of Part IV in the Annual Report on Form 10-K details the consolidated financial statements and accompanying notes.</code>                                                                                                                                                | <code>What documents are detailed in Item 8 of Part IV of the Annual Report on Form 10-K?</code>                  |
  | <code>While the outcome of this matter cannot be determined at this time, it is not currently expected to have a material adverse impact on our business.</code>                                                                                                                      | <code>Is the outcome of the investigation into Tesla's waste segregation practices currently determinable?</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     | 1.521         | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7434                 | 0.7579                 | 0.7641                 | 0.6994                | 0.7678                 |
| 1.6244     | 20     | 0.6597        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7583                 | 0.7628                 | 0.7726                 | 0.7219                | 0.7735                 |
| 2.4365     | 30     | 0.4472        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7578                 | 0.7661                 | 0.7747                 | 0.7251                | 0.7753                 |
| 3.2487     | 40     | 0.3865        | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7596**             | **0.7685**             | **0.7757**             | **0.7249**            | **0.7747**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- 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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