README.md

---
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
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: In the Annual Report on Form 10-K, the consolidated financial statements
    are included immediately following Part IV and incorporated by reference.
  sentences:
  - What movies contributed to higher revenue in 2023 compared to the previous year?
  - How are the financial statements incorporated in the 10-K report?
  - What was the ending store count for the Family Dollar segment after the fiscal
    year ended January 28, 2023?
- source_sentence: Readers are cautioned not to place undue reliance on forward-looking
    statements, which speak only as of the date they are made. We undertake no obligation
    to update or revise publicly any forward-looking statements, whether because of
    new information, future events, or otherwise.
  sentences:
  - What impact did the IRS deadline extension in 2023 have on Intuit's fiscal results?
  - What risks are associated with relying on forward-looking statements according
    to the provided text?
  - What were the total minimum lease payments and their net amounts after imputed
    interest for operating and finance leases as of January 31, 2023?
- 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:
  - What significant regulatory change did CMS make to the hierarchical condition
    category model in its version 28?
  - Which section of IBM’s 2023 Annual Report is reserved for Financial Statements
    and Supplementary Data?
  - What strategic goals are set for the Printing segment at HP Inc.?
- source_sentence: In December 2023, the FCA published a consultation proposing to
    revise the U.K. commodity derivatives framework. The FSMA 2023 reformed the U.K.’s
    commodity derivatives regulatory regime including revoking the MIFID II position
    limit requirements and transferring the powers to set position limits and controls
    from the FCA to the operator of trading venues. The FCA proposal requires U.K.
    trading venues to set position limits for critical and related contracts, to establish
    accountability thresholds and to report enhanced position data.
  sentences:
  - What was the percentage increase in revenues from aviation services in 2023 compared
    to 2022?
  - What was the impairment loss recognized by the Company due to TDA integration
    and restructuring efforts for the year ending December 31, 2023?
  - What changes did the FCA propose in its December 2023 consultation regarding the
    U.K. commodity derivatives framework?
- source_sentence: Operating cash flow provides the primary source of cash to fund
    operating needs and capital expenditures.
  sentences:
  - What is the primary source of cash used by the company to fund operating needs
    and capital expenditures?
  - What kinds of products and services does the Company provide under the AARP Program?
  - What was the total assets under supervision (AUS) for all categories combined
    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: 0.7128571428571429
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8385714285714285
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8657142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9128571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7128571428571429
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27952380952380956
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17314285714285713
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09128571428571428
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7128571428571429
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8385714285714285
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8657142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9128571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8160752408699454
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7850544217687072
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7883813094771759
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7085714285714285
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8314285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8571428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7085714285714285
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27714285714285714
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1714285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.091
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7085714285714285
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8314285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8571428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.810046642542136
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7782335600907029
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7817400926898996
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7057142857142857
      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: 0.7057142857142857
      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: 0.7057142857142857
      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: 0.803237369609097
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7734654195011333
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7778038646628423
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6871428571428572
      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: 0.6871428571428572
      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: 0.6871428571428572
      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: 0.7913904723614839
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7585782312925171
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.762610071156596
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.66
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7714285714285715
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8085714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8714285714285714
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.66
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2571428571428571
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1617142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08714285714285713
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.66
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7714285714285715
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8085714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8714285714285714
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7614379134484182
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7269172335600907
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7319569628864667
      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) on the json dataset. 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:**
    - json
- **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("ValentinaKim/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Operating cash flow provides the primary source of cash to fund operating needs and capital expenditures.',
    'What is the primary source of cash used by the company to fund operating needs and capital expenditures?',
    'What kinds of products and services does the Company provide under the AARP Program?',
]
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]
```

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### Direct Usage (Transformers)

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation

### Metrics

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

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7129     |
| cosine_accuracy@3   | 0.8386     |
| cosine_accuracy@5   | 0.8657     |
| cosine_accuracy@10  | 0.9129     |
| cosine_precision@1  | 0.7129     |
| cosine_precision@3  | 0.2795     |
| cosine_precision@5  | 0.1731     |
| cosine_precision@10 | 0.0913     |
| cosine_recall@1     | 0.7129     |
| cosine_recall@3     | 0.8386     |
| cosine_recall@5     | 0.8657     |
| cosine_recall@10    | 0.9129     |
| cosine_ndcg@10      | 0.8161     |
| cosine_mrr@10       | 0.7851     |
| **cosine_map@100**  | **0.7884** |

#### 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   | 0.7086     |
| cosine_accuracy@3   | 0.8314     |
| cosine_accuracy@5   | 0.8571     |
| cosine_accuracy@10  | 0.91       |
| cosine_precision@1  | 0.7086     |
| cosine_precision@3  | 0.2771     |
| cosine_precision@5  | 0.1714     |
| cosine_precision@10 | 0.091      |
| cosine_recall@1     | 0.7086     |
| cosine_recall@3     | 0.8314     |
| cosine_recall@5     | 0.8571     |
| cosine_recall@10    | 0.91       |
| cosine_ndcg@10      | 0.81       |
| cosine_mrr@10       | 0.7782     |
| **cosine_map@100**  | **0.7817** |

#### 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   | 0.7057     |
| cosine_accuracy@3   | 0.8214     |
| cosine_accuracy@5   | 0.8614     |
| cosine_accuracy@10  | 0.8957     |
| cosine_precision@1  | 0.7057     |
| cosine_precision@3  | 0.2738     |
| cosine_precision@5  | 0.1723     |
| cosine_precision@10 | 0.0896     |
| cosine_recall@1     | 0.7057     |
| cosine_recall@3     | 0.8214     |
| cosine_recall@5     | 0.8614     |
| cosine_recall@10    | 0.8957     |
| cosine_ndcg@10      | 0.8032     |
| cosine_mrr@10       | 0.7735     |
| **cosine_map@100**  | **0.7778** |

#### 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   | 0.6871     |
| cosine_accuracy@3   | 0.8086     |
| cosine_accuracy@5   | 0.8429     |
| cosine_accuracy@10  | 0.8943     |
| cosine_precision@1  | 0.6871     |
| cosine_precision@3  | 0.2695     |
| cosine_precision@5  | 0.1686     |
| cosine_precision@10 | 0.0894     |
| cosine_recall@1     | 0.6871     |
| cosine_recall@3     | 0.8086     |
| cosine_recall@5     | 0.8429     |
| cosine_recall@10    | 0.8943     |
| cosine_ndcg@10      | 0.7914     |
| cosine_mrr@10       | 0.7586     |
| **cosine_map@100**  | **0.7626** |

#### 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   | 0.66      |
| cosine_accuracy@3   | 0.7714    |
| cosine_accuracy@5   | 0.8086    |
| cosine_accuracy@10  | 0.8714    |
| cosine_precision@1  | 0.66      |
| cosine_precision@3  | 0.2571    |
| cosine_precision@5  | 0.1617    |
| cosine_precision@10 | 0.0871    |
| cosine_recall@1     | 0.66      |
| cosine_recall@3     | 0.7714    |
| cosine_recall@5     | 0.8086    |
| cosine_recall@10    | 0.8714    |
| cosine_ndcg@10      | 0.7614    |
| cosine_mrr@10       | 0.7269    |
| **cosine_map@100**  | **0.732** |

<!--
## 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

#### json

* Dataset: json
* 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: 45.81 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.26 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                             | anchor                                                                                             |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
  | <code>For the year ended December 31, 2023, Alphabet Inc. reported a net cash provided by operating activities of $101,746 million.</code>                                                           | <code>What was the net cash provided by operating activities for Alphabet Inc. in 2023?</code>     |
  | <code>Our History In 2000, ICE was founded with the idea of transforming energy markets by creating a network that removed barriers and provided greater transparency, efficiency and access.</code> | <code>When was Intercontinental Exchange, Inc. founded, and what was its initial focus?</code>     |
  | <code>Item 8. Financial Statements and Supplementary Data The index to Financial Statements and Supplementary Data is presented</code>                                                               | <code>What is presented in Item 8 according to Financial Statements and Supplementary Data?</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`: 16
- `gradient_accumulation_steps`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `tf32`: False
- `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`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 32
- `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`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9746     | 6      | -             | 0.7258                 | 0.7501                 | 0.7513                 | 0.6860                | 0.7589                 |
| 1.6244     | 10     | 1.4436        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 12     | -             | 0.7494                 | 0.7733                 | 0.7800                 | 0.7187                | 0.7827                 |
| 2.9239     | 18     | -             | 0.7601                 | 0.7796                 | 0.7813                 | 0.7312                | 0.7897                 |
| 3.2487     | 20     | 0.6159        | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **24** | **-**         | **0.7626**             | **0.7778**             | **0.7817**             | **0.732**             | **0.7884**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- 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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