sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:6300 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
datasets: []
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
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: AutoZone, Inc. began operations in 1979.
  sentences:
  - What types of products and markets does the company cater to in the semiconductor
    industry?
  - When did AutoZone, Inc. begin its operations?
  - How much did general and administrative expenses related to merger, acquisition,
    and other costs change from 2022 to 2023?
- source_sentence: See Note 14 to the consolidated financial statements in Item 8
    of this Annual regarding legal proceedings.
  sentences:
  - What is the source to find detailed information about legal proceedings in the
    Annual Report?
  - Where in the Annual Report can one find a description of certain legal matters
    and their impact on the company?
  - What strategic actions is Hershey taking to maintain its leadership in the U.S.
    confectionery market?
- source_sentence: ICE Bonds focuses on increasing efficiency in fixed income markets
    by offering electronic markets that support trading protocols including click-to-trade,
    request for quotation (RFQ), and auctions.
  sentences:
  - What services does the ICE Bonds platform provide and what is its focus?
  - What was the percentage increase in the generic dispensing rate of the Health
    Services segment from 2022 to 2023?
  - How many shares of Class A common stock were repurchased and retired in 2023,
    and what was the total cost including excise tax accruals?
- source_sentence: Subject to various United States and foreign laws and regulations,
    including those related to intellectual property, data privacy and security, cybersecurity,
    tax, employment, competition and antitrust, anti-corruption, anti-bribery, and
    AI. Compliance with these laws has no current material adverse impact on capital
    expenditures, results of operations or competitive position.
  sentences:
  - How much did the total loans and lending commitments amount to as of December
    2023?
  - What types of laws and regulations does the company need to comply with?
  - Where are the consolidated financial statements listed in the Annual Report on
    Form 10-K located?
- source_sentence: CMS made significant changes to the structure of the hierarchical
    condition category model in version 28, which may impact risk adjustment factor
    scores for a larger percentage of Medicare Advantage beneficiaries and could result
    in changes to beneficiary RAF scores with or without a change in the patient’s
    health status.
  sentences:
  - How does Tesla reduce costs and promote renewable power at their Supercharger
    stations?
  - What is the primary method by which the company manages its cash, cash equivalents,
    and marketable securities?
  - What significant regulatory change did CMS make to the hierarchical condition
    category model in its version 28?
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: 0.6985714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8442857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8814285714285715
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9271428571428572
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6985714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2814285714285714
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17628571428571424
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09271428571428571
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6985714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8442857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8814285714285715
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9271428571428572
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8156553778675095
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7796054421768707
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7822282461868646
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.71
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8457142857142858
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8785714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9271428571428572
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.71
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2819047619047619
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17571428571428568
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09271428571428571
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.71
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8457142857142858
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8785714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9271428571428572
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8194766272347418
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7848673469387758
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7873446316370609
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7085714285714285
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8342857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8642857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9142857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7085714285714285
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27809523809523806
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17285714285714282
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09142857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7085714285714285
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8342857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8642857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9142857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8116052646620258
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.77881462585034
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7821002568762089
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.69
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8271428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.86
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.69
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2757142857142857
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.172
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09099999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.69
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8271428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.86
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8013750432226047
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7664954648526079
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7698726210622817
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6657142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.79
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8285714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8857142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6657142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2633333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1657142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08857142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6657142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.79
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8285714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8857142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7732501027431213
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7375017006802721
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7416822153678694
      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("dpokhrel/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'CMS made significant changes to the structure of the hierarchical condition category model in version 28, which may impact risk adjustment factor scores for a larger percentage of Medicare Advantage beneficiaries and could result in changes to beneficiary RAF scores with or without a change in the patient’s health status.',
    'What significant regulatory change did CMS make to the hierarchical condition category model in its version 28?',
    'What is the primary method by which the company manages its cash, cash equivalents, and marketable securities?',
]
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   | 0.6986     |
| cosine_accuracy@3   | 0.8443     |
| cosine_accuracy@5   | 0.8814     |
| cosine_accuracy@10  | 0.9271     |
| cosine_precision@1  | 0.6986     |
| cosine_precision@3  | 0.2814     |
| cosine_precision@5  | 0.1763     |
| cosine_precision@10 | 0.0927     |
| cosine_recall@1     | 0.6986     |
| cosine_recall@3     | 0.8443     |
| cosine_recall@5     | 0.8814     |
| cosine_recall@10    | 0.9271     |
| cosine_ndcg@10      | 0.8157     |
| cosine_mrr@10       | 0.7796     |
| **cosine_map@100**  | **0.7822** |

#### 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   | 0.71       |
| cosine_accuracy@3   | 0.8457     |
| cosine_accuracy@5   | 0.8786     |
| cosine_accuracy@10  | 0.9271     |
| cosine_precision@1  | 0.71       |
| cosine_precision@3  | 0.2819     |
| cosine_precision@5  | 0.1757     |
| cosine_precision@10 | 0.0927     |
| cosine_recall@1     | 0.71       |
| cosine_recall@3     | 0.8457     |
| cosine_recall@5     | 0.8786     |
| cosine_recall@10    | 0.9271     |
| cosine_ndcg@10      | 0.8195     |
| cosine_mrr@10       | 0.7849     |
| **cosine_map@100**  | **0.7873** |

#### 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   | 0.7086     |
| cosine_accuracy@3   | 0.8343     |
| cosine_accuracy@5   | 0.8643     |
| cosine_accuracy@10  | 0.9143     |
| cosine_precision@1  | 0.7086     |
| cosine_precision@3  | 0.2781     |
| cosine_precision@5  | 0.1729     |
| cosine_precision@10 | 0.0914     |
| cosine_recall@1     | 0.7086     |
| cosine_recall@3     | 0.8343     |
| cosine_recall@5     | 0.8643     |
| cosine_recall@10    | 0.9143     |
| cosine_ndcg@10      | 0.8116     |
| cosine_mrr@10       | 0.7788     |
| **cosine_map@100**  | **0.7821** |

#### 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   | 0.69       |
| cosine_accuracy@3   | 0.8271     |
| cosine_accuracy@5   | 0.86       |
| cosine_accuracy@10  | 0.91       |
| cosine_precision@1  | 0.69       |
| cosine_precision@3  | 0.2757     |
| cosine_precision@5  | 0.172      |
| cosine_precision@10 | 0.091      |
| cosine_recall@1     | 0.69       |
| cosine_recall@3     | 0.8271     |
| cosine_recall@5     | 0.86       |
| cosine_recall@10    | 0.91       |
| cosine_ndcg@10      | 0.8014     |
| cosine_mrr@10       | 0.7665     |
| **cosine_map@100**  | **0.7699** |

#### 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   | 0.6657     |
| cosine_accuracy@3   | 0.79       |
| cosine_accuracy@5   | 0.8286     |
| cosine_accuracy@10  | 0.8857     |
| cosine_precision@1  | 0.6657     |
| cosine_precision@3  | 0.2633     |
| cosine_precision@5  | 0.1657     |
| cosine_precision@10 | 0.0886     |
| cosine_recall@1     | 0.6657     |
| cosine_recall@3     | 0.79       |
| cosine_recall@5     | 0.8286     |
| cosine_recall@10    | 0.8857     |
| cosine_ndcg@10      | 0.7733     |
| cosine_mrr@10       | 0.7375     |
| **cosine_map@100**  | **0.7417** |

<!--
## 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: 46.37 tokens</li><li>max: 248 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.57 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                  | anchor                                                                                                    |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|
  | <code>Scenario analysis is used to quantify the impact of a specified event, including how the event impacts multiple risk factors simultaneously. For example, for sovereign stress testing, it calculates potential exposure related to sovereign positions as well as the corresponding debt, equity, and currency exposures that may be impacted by sovereign distress.</code>        | <code>How does Goldman Sachs utilize scenario analysis in its risk management strategy?</code>            |
  | <code>The company is involved in various other legal proceedings incidental to the conduct of our business, including, but not limited to, claims and allegations related to wage and hour violations, unlawful termination, employment practices, product liability, privacy and cybersecurity, environmental matters, and intellectual property rights or regulatory compliance.</code> | <code>What types of legal proceedings is the company currently involved in?</code>                        |
  | <code>In 2023, $505 million was utilized for common stock repurchases.</code>                                                                                                                                                                                                                                                                                                             | <code>How much cash was utilized for common stock repurchases in the year ended December 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
- `half_precision_backend`: cpu_amp
- `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
- `torch_empty_cache_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`: cpu_amp
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: 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     | 1.5241        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7486                 | 0.7656                 | 0.7662                 | 0.7108                | 0.7679                 |
| 1.6244     | 20     | 0.658         | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7656                 | 0.7793                 | 0.7843                 | 0.7348                | 0.7798                 |
| 2.4365     | 30     | 0.4743        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7683                 | 0.7814                 | 0.7859                 | 0.7400                | 0.7812                 |
| 3.2487     | 40     | 0.4241        | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7699**             | **0.7821**             | **0.7873**             | **0.7417**            | **0.7822**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.5
- Sentence Transformers: 3.0.1
- Transformers: 4.43.4
- PyTorch: 2.4.0.dev20240607+cu118
- Accelerate: 0.32.0
- Datasets: 2.20.0
- 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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