sentence-transformers safetensors bert sentence-similarity feature-extraction dataset_size:1K<n<10K loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What is the title of Item 6 in the text?
  sentences:
  - What is the title of Item 8 in the document?
  - What was the total premiums revenue for the Insurance segment in 2023?
  - How much were the net cash flows from investing activities in 2023 and 2022?
- source_sentence: Which markets does Garmin primarily serve?
  sentences:
  - What types of products are offered in Garmin's Fitness segment?
  - In 2023, AbbVie's net revenue in the United States was $41,883 million.
  - As of December 31, 2023, the total deferred income tax asset was $1,157,486.
- source_sentence: What was the effective tax rate in 2023?
  sentences:
  - What was the effective tax rate for 2023 and how did it compare to 2022?
  - What are the various diversity, equity, and inclusion councils at AMC?
  - What is the title of the section that discusses legal issues in the document?
- source_sentence: What begins on page 105 of this report?
  sentences:
  - Where can one find the details pertaining to Legal Proceedings in the report?
  - What are the technological features of the GeForce RTX 40 Series GPUs?
  - Changes in foreign exchange rates reduced cost of sales by $254 million in 2023.
- source_sentence: How is Costco's fiscal year structured?
  sentences:
  - How many weeks did the fiscal years 2023 and 2022 include?
  - What is the process for using reinsurers not on the authorized list?
  - What contributed to the increase in Google Services' operating income in 2023?
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: 0.6814285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8128571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.85
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9028571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6814285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27095238095238094
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16999999999999996
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09028571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6814285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8128571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.85
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9028571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7916721734405803
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7562692743764173
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7609992859917654
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6842857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8114285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8528571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8985714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6842857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2704761904761905
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17057142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08985714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6842857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8114285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8528571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8985714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7909210075399126
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.756487528344671
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.761586340523296
      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: 0.8085714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8428571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8942857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6785714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2695238095238095
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16857142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08942857142857143
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6785714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8085714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8428571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8942857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7866298497982406
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.752303287981859
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7571741668436585
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6714285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7857142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8257142857142857
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8814285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6714285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2619047619047619
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16514285714285715
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08814285714285713
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6714285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7857142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8257142857142857
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8814285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7742856481999635
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.740471655328798
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.745692801681558
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6371428571428571
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7685714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8071428571428572
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8614285714285714
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6371428571428571
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2561904761904762
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16142857142857142
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08614285714285713
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6371428571428571
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7685714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8071428571428572
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8614285714285714
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7500703607138253
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7145918367346937
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7198995734568113
      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("gK29382231121/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "How is Costco's fiscal year structured?",
    'How many weeks did the fiscal years 2023 and 2022 include?',
    'What is the process for using reinsurers not on the authorized list?',
]
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   | 0.6814    |
| cosine_accuracy@3   | 0.8129    |
| cosine_accuracy@5   | 0.85      |
| cosine_accuracy@10  | 0.9029    |
| cosine_precision@1  | 0.6814    |
| cosine_precision@3  | 0.271     |
| cosine_precision@5  | 0.17      |
| cosine_precision@10 | 0.0903    |
| cosine_recall@1     | 0.6814    |
| cosine_recall@3     | 0.8129    |
| cosine_recall@5     | 0.85      |
| cosine_recall@10    | 0.9029    |
| cosine_ndcg@10      | 0.7917    |
| cosine_mrr@10       | 0.7563    |
| **cosine_map@100**  | **0.761** |

#### 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   | 0.6843     |
| cosine_accuracy@3   | 0.8114     |
| cosine_accuracy@5   | 0.8529     |
| cosine_accuracy@10  | 0.8986     |
| cosine_precision@1  | 0.6843     |
| cosine_precision@3  | 0.2705     |
| cosine_precision@5  | 0.1706     |
| cosine_precision@10 | 0.0899     |
| cosine_recall@1     | 0.6843     |
| cosine_recall@3     | 0.8114     |
| cosine_recall@5     | 0.8529     |
| cosine_recall@10    | 0.8986     |
| cosine_ndcg@10      | 0.7909     |
| cosine_mrr@10       | 0.7565     |
| **cosine_map@100**  | **0.7616** |

#### 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   | 0.8086     |
| cosine_accuracy@5   | 0.8429     |
| cosine_accuracy@10  | 0.8943     |
| cosine_precision@1  | 0.6786     |
| cosine_precision@3  | 0.2695     |
| cosine_precision@5  | 0.1686     |
| cosine_precision@10 | 0.0894     |
| cosine_recall@1     | 0.6786     |
| cosine_recall@3     | 0.8086     |
| cosine_recall@5     | 0.8429     |
| cosine_recall@10    | 0.8943     |
| cosine_ndcg@10      | 0.7866     |
| cosine_mrr@10       | 0.7523     |
| **cosine_map@100**  | **0.7572** |

#### 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   | 0.6714     |
| cosine_accuracy@3   | 0.7857     |
| cosine_accuracy@5   | 0.8257     |
| cosine_accuracy@10  | 0.8814     |
| cosine_precision@1  | 0.6714     |
| cosine_precision@3  | 0.2619     |
| cosine_precision@5  | 0.1651     |
| cosine_precision@10 | 0.0881     |
| cosine_recall@1     | 0.6714     |
| cosine_recall@3     | 0.7857     |
| cosine_recall@5     | 0.8257     |
| cosine_recall@10    | 0.8814     |
| cosine_ndcg@10      | 0.7743     |
| cosine_mrr@10       | 0.7405     |
| **cosine_map@100**  | **0.7457** |

#### 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   | 0.6371     |
| cosine_accuracy@3   | 0.7686     |
| cosine_accuracy@5   | 0.8071     |
| cosine_accuracy@10  | 0.8614     |
| cosine_precision@1  | 0.6371     |
| cosine_precision@3  | 0.2562     |
| cosine_precision@5  | 0.1614     |
| cosine_precision@10 | 0.0861     |
| cosine_recall@1     | 0.6371     |
| cosine_recall@3     | 0.7686     |
| cosine_recall@5     | 0.8071     |
| cosine_recall@10    | 0.8614     |
| cosine_ndcg@10      | 0.7501     |
| cosine_mrr@10       | 0.7146     |
| **cosine_map@100**  | **0.7199** |

<!--
## 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.*
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## 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: 8 tokens</li><li>mean: 45.34 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.47 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                       | anchor                                                                              |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | <code>The HP GreenValley edge-to-cloud platform is used for software-defined disaggregated storage services that include HPE GreenLake for Block Storage and HPE GreenLake for File Storage, and it provides unified cloud-based management to simplify how customers manage storage.</code>                                                                                                                                                   | <code>What are the focus areas for the HP GreenLake platform?</code>                |
  | <code>Net income | $ | 1,550,190 | | $ | 854,800 | $ | 695,390 | 81.4 | %</code>                                                                                                                                                                                                                                                                                                                                                               | <code>By how much did net income increase in 2023 compared to 2022?</code>          |
  | <code>Deferred tax assets and deferred tax liabilities included in the Consolidated Balance Sheets as follows: As of October 31, 2023: Deferred tax assets were $3,155 million and Deferred tax liabilities were $44 million. As of October 31, 2022: Deferred tax assets were $2,167 million and Deferred tax liabilities were $121 million. The total net deferred tax assets were $3,111 million in 2023 and $2,046 million in 2022.</code> | <code>What was the change in HP's net deferred tax assets from 2022 to 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   | 1.5361        | -                      | -                      | -                      | -                     | -                      |
| 0.9746 | 12   | -             | 0.7280                 | 0.7414                 | 0.7494                 | 0.6896                | 0.7470                 |
| 1.6244 | 20   | 0.6833        | -                      | -                      | -                      | -                     | -                      |
| 1.9492 | 24   | -             | 0.7426                 | 0.7487                 | 0.7573                 | 0.7138                | 0.7592                 |
| 2.4365 | 30   | 0.4674        | -                      | -                      | -                      | -                     | -                      |
| 2.9239 | 36   | -             | 0.7452                 | 0.7558                 | 0.7624                 | 0.7190                | 0.7623                 |
| 3.2487 | 40   | 0.4038        | -                      | -                      | -                      | -                     | -                      |
| 3.8985 | 48   | -             | 0.7457                 | 0.7572                 | 0.7616                 | 0.7199                | 0.7610                 |


### 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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