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: Tesla has implemented various remedial measures, including conducting
    training and audits, and enhancements to its site waste management programs, and
    settlement discussions are ongoing.
  sentences:
  - What regulatory body primarily regulates product safety, efficacy, and other aspects
    in the U.S.?
  - What remedial measures has Tesla implemented in response to the investigation
    of its waste segregation practices?
  - What were the main drivers behind the sales growth of TREMFYA?
- source_sentence: Sales of Alphagan/Combigan in the United States decreased by 40.1%
    from $373 million in 2021 to $121 million in 2023.
  sentences:
  - What were the total revenues from unaffiliated customers in 2021?
  - What was the percentage decrease in sales for Alphagan/Combigan in the United
    States from 2021 to 2023?
  - What percent excess of fair value over carrying value did the Compute reporting
    unit have as of the annual test date in 2023?
- source_sentence: Long-lived and intangible assets are reviewed for impairment based
    on indicators of impairment and the evaluation involves estimating the future
    undiscounted cash flows attributable to the asset groups.
  sentences:
  - How are long-lived and intangible assets evaluated for impairment?
  - What strategies are being adopted to enhance revenue through acquisition according
    to the business plans described?
  - How is impairment evaluated for long-lived assets such as leases, property, and
    equipment?
- source_sentence: Our 2023 operating income was $5.5 billion, an improvement of $1.9
    billion compared to 2022.
  sentences:
  - What was the total unrecognized compensation cost related to unvested stock-based
    awards as of October 29, 2023?
  - What significant financial activity occurred in continuing investing activities
    in 2023?
  - What was the operating income for 2023, and how did it compare to 2022?
- source_sentence: We use raw materials that are subject to price volatility caused
    by weather, supply conditions, political and economic variables and other unpredictable
    factors. We may use futures, options and swap contracts to manage the volatility
    related to the above exposures.
  sentences:
  - What financial instruments does the company use to manage commodity price exposure?
  - What types of legal proceedings is the company currently involved in?
  - What was the net impact of fair value hedging instruments on earnings 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: 0.82
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8614285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8942857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6814285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2733333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17228571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08942857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6814285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.82
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8614285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8942857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7922308461157294
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7589693877551015
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7633405151451278
      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: 0.8214285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8614285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8957142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.68
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2738095238095238
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17228571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08957142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.68
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8214285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8614285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8957142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7914243245771438
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7576258503401355
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7617439775393929
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.69
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8271428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8571428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8928571428571429
      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: 0.1714285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08928571428571426
      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: 0.8571428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8928571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7943028094464931
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7623684807256232
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7661836876217925
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6657142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8042857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8457142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8871428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6657142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2680952380952381
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16914285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08871428571428569
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6657142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8042857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8457142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8871428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7784460550829944
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7434297052154194
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.74745032636981
      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: 0.7771428571428571
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8157142857142857
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8642857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6342857142857142
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.259047619047619
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16314285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08642857142857142
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6342857142857142
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7771428571428571
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8157142857142857
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8642857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7508028784634385
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7143225623582764
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7188596090649563
      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("korruz/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'We use raw materials that are subject to price volatility caused by weather, supply conditions, political and economic variables and other unpredictable factors. We may use futures, options and swap contracts to manage the volatility related to the above exposures.',
    'What financial instruments does the company use to manage commodity price exposure?',
    'What types of legal proceedings is the company currently involved in?',
]
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   | 0.82       |
| cosine_accuracy@5   | 0.8614     |
| cosine_accuracy@10  | 0.8943     |
| cosine_precision@1  | 0.6814     |
| cosine_precision@3  | 0.2733     |
| cosine_precision@5  | 0.1723     |
| cosine_precision@10 | 0.0894     |
| cosine_recall@1     | 0.6814     |
| cosine_recall@3     | 0.82       |
| cosine_recall@5     | 0.8614     |
| cosine_recall@10    | 0.8943     |
| cosine_ndcg@10      | 0.7922     |
| cosine_mrr@10       | 0.759      |
| **cosine_map@100**  | **0.7633** |

#### 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   | 0.8214     |
| cosine_accuracy@5   | 0.8614     |
| cosine_accuracy@10  | 0.8957     |
| cosine_precision@1  | 0.68       |
| cosine_precision@3  | 0.2738     |
| cosine_precision@5  | 0.1723     |
| cosine_precision@10 | 0.0896     |
| cosine_recall@1     | 0.68       |
| cosine_recall@3     | 0.8214     |
| cosine_recall@5     | 0.8614     |
| cosine_recall@10    | 0.8957     |
| cosine_ndcg@10      | 0.7914     |
| cosine_mrr@10       | 0.7576     |
| **cosine_map@100**  | **0.7617** |

#### 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   | 0.69       |
| cosine_accuracy@3   | 0.8271     |
| cosine_accuracy@5   | 0.8571     |
| cosine_accuracy@10  | 0.8929     |
| cosine_precision@1  | 0.69       |
| cosine_precision@3  | 0.2757     |
| cosine_precision@5  | 0.1714     |
| cosine_precision@10 | 0.0893     |
| cosine_recall@1     | 0.69       |
| cosine_recall@3     | 0.8271     |
| cosine_recall@5     | 0.8571     |
| cosine_recall@10    | 0.8929     |
| cosine_ndcg@10      | 0.7943     |
| cosine_mrr@10       | 0.7624     |
| **cosine_map@100**  | **0.7662** |

#### 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   | 0.6657     |
| cosine_accuracy@3   | 0.8043     |
| cosine_accuracy@5   | 0.8457     |
| cosine_accuracy@10  | 0.8871     |
| cosine_precision@1  | 0.6657     |
| cosine_precision@3  | 0.2681     |
| cosine_precision@5  | 0.1691     |
| cosine_precision@10 | 0.0887     |
| cosine_recall@1     | 0.6657     |
| cosine_recall@3     | 0.8043     |
| cosine_recall@5     | 0.8457     |
| cosine_recall@10    | 0.8871     |
| cosine_ndcg@10      | 0.7784     |
| cosine_mrr@10       | 0.7434     |
| **cosine_map@100**  | **0.7475** |

#### 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   | 0.7771     |
| cosine_accuracy@5   | 0.8157     |
| cosine_accuracy@10  | 0.8643     |
| cosine_precision@1  | 0.6343     |
| cosine_precision@3  | 0.259      |
| cosine_precision@5  | 0.1631     |
| cosine_precision@10 | 0.0864     |
| cosine_recall@1     | 0.6343     |
| cosine_recall@3     | 0.7771     |
| cosine_recall@5     | 0.8157     |
| cosine_recall@10    | 0.8643     |
| cosine_ndcg@10      | 0.7508     |
| cosine_mrr@10       | 0.7143     |
| **cosine_map@100**  | **0.7189** |

<!--
## 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: 8 tokens</li><li>mean: 45.15 tokens</li><li>max: 281 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.65 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                         | anchor                                                                                                                                         |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The sale and donation transactions closed in June 2022. Total proceeds from the sale were approximately $6,300 (net of transaction and closing costs), resulting in a loss of $13,568, which was recorded in the SM&A expense caption within the Consolidated Statements of Income.</code> | <code>What were Hershey's total proceeds from the sale of a building portion in June 2022, and what was the resulting financial impact?</code> |
  | <code>Operating income margin increased to 7.9% in fiscal 2022 compared to 6.9% in fiscal 2021.</code>                                                                                                                                                                                           | <code>What was the operating income margin for fiscal year 2022 compared to fiscal year 2021?</code>                                           |
  | <code>iPhone® is the Company’s line of smartphones based on its iOS operating system. The iPhone line includes iPhone 15 Pro, iPhone 15, iPhone 14, iPhone 13 and iPhone SE®.</code>                                                                                                             | <code>What operating system is used for the Company's iPhone line?</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
- `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`: 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
- `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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9697     | 6      | -             | 0.7248                 | 0.7459                 | 0.7534                 | 0.6859                | 0.7549                 |
| 1.6162     | 10     | 2.3046        | -                      | -                      | -                      | -                     | -                      |
| 1.9394     | 12     | -             | 0.7456                 | 0.7601                 | 0.7590                 | 0.7111                | 0.7599                 |
| 2.9091     | 18     | -             | 0.7470                 | 0.7652                 | 0.7618                 | 0.7165                | 0.7622                 |
| 3.2323     | 20     | 1.0018        | -                      | -                      | -                      | -                     | -                      |
| **3.8788** | **24** | **-**         | **0.7475**             | **0.7662**             | **0.7617**             | **0.7189**            | **0.7633**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.21.0
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->