README.md

---
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: The U.S. International Trade Commission (ITC) has become a significant
    forum to litigate intellectual property disputes. An adverse result in an ITC
    action can lead to a prohibition on importing infringing products, which, given
    the importance of the U.S. market, could significantly impact a company including
    preventing the importation of many important products or necessitating workarounds
    that may limit certain features of their products.
  sentences:
  - What was the overall impact of foreign currencies on net sales in 2023?
  - What potential consequences could result from intellectual property disputes in
    the U.S. International Trade Commission for the company?
  - What was the total purchase consideration for the VMware acquisition?
- source_sentence: Reinsurance contracts are normally classified as treaty or facultative
    contracts. Treaty reinsurance refers to reinsurance coverage for all or a portion
    of a specified group or class of risks ceded by a direct insurer or reinsurer,
    while facultative reinsurance involves coverage of specific individual underlying
    risks. Reinsurance contracts are further classified as quota-share or excess.
  sentences:
  - What type of information will you find under 'Note 13 — Commitments and Contingencies'
    in an Annual Report on Form 10-K?
  - What type of reinsurance contracts are offered by Berkshire Hathaway Reinsurance
    Group?
  - What are the consequences for a company violating anti-bribery laws in the U.S.?
- source_sentence: Commitments and contingencies related to legal proceedings are
    detailed in Part II, Item 8, under 'Financial Statements and Supplementary Data
    – Note 14'.
  sentences:
  - Where can one find commitments and contingencies related to legal proceedings
    in the context provided?
  - What is discussed in Item 3. Legal Proceedings of a company's report?
  - How are net realized capital gains and losses treated in the financial statements
    according to the Company?
- source_sentence: The “Glossary of Terms and Acronyms” is included on pages 315-321
    in the set of financial documents.
  sentences:
  - What are the principles used in preparing the discussed financial statements?
  - What is the total remaining budget for future common stock repurchases under the
    company's stock repurchase programs as of December 31, 2023?
  - Where is the “Glossary of Terms and Acronyms” located in a set of financial documents?
- source_sentence: The table presents our market risk by asset category for positions
    accounted for at fair value or accounted for at the lower of cost or fair value,
    that are not included in VaR. As of December 2023, equity was at $1,562 million
    and debt was at $2,446 million.
  sentences:
  - What are the market risk values for Goldman Sachs' equity and debt positions not
    included in VaR as of December 2023?
  - What was the conclusion of the Company's review regarding the impact of the American
    Rescue Plan, the Consolidated Appropriations Act, 2021, and related tax provisions
    on its business for the fiscal year ended June 30, 2023?
  - How much did the company's finance lease obligations total as of December 31,
    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: 0.6957142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8371428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8714285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9242857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6957142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27904761904761904
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17428571428571424
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09242857142857142
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6957142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8371428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8714285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9242857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8105294489003092
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7741910430839002
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7773317927980538
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8285714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8671428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9185714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27619047619047615
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1734285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09185714285714283
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8285714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8671428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9185714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8090367290103152
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7740351473922898
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7776494145961331
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6928571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8185714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8585714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6928571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27285714285714285
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17171428571428568
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09099999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6928571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8185714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8585714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8016663265681359
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7669977324263035
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7711841838569463
      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: 0.8071428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8585714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8985714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6871428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26904761904761904
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1717142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08985714285714283
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6871428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8071428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8585714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8985714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7921056491431833
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7580946712018135
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7627063166788922
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6642857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7842857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8257142857142857
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8728571428571429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6642857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26142857142857145
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16514285714285715
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08728571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6642857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7842857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8257142857142857
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8728571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7689727571743198
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7358214285714282
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7406658506857838
      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("akashmaggon/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The table presents our market risk by asset category for positions accounted for at fair value or accounted for at the lower of cost or fair value, that are not included in VaR. As of December 2023, equity was at $1,562 million and debt was at $2,446 million.',
    "What are the market risk values for Goldman Sachs' equity and debt positions not included in VaR as of December 2023?",
    "What was the conclusion of the Company's review regarding the impact of the American Rescue Plan, the Consolidated Appropriations Act, 2021, and related tax provisions on its business for the fiscal year ended June 30, 2023?",
]
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.*
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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   | 0.6957     |
| cosine_accuracy@3   | 0.8371     |
| cosine_accuracy@5   | 0.8714     |
| cosine_accuracy@10  | 0.9243     |
| cosine_precision@1  | 0.6957     |
| cosine_precision@3  | 0.279      |
| cosine_precision@5  | 0.1743     |
| cosine_precision@10 | 0.0924     |
| cosine_recall@1     | 0.6957     |
| cosine_recall@3     | 0.8371     |
| cosine_recall@5     | 0.8714     |
| cosine_recall@10    | 0.9243     |
| cosine_ndcg@10      | 0.8105     |
| cosine_mrr@10       | 0.7742     |
| **cosine_map@100**  | **0.7773** |

#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7        |
| cosine_accuracy@3   | 0.8286     |
| cosine_accuracy@5   | 0.8671     |
| cosine_accuracy@10  | 0.9186     |
| cosine_precision@1  | 0.7        |
| cosine_precision@3  | 0.2762     |
| cosine_precision@5  | 0.1734     |
| cosine_precision@10 | 0.0919     |
| cosine_recall@1     | 0.7        |
| cosine_recall@3     | 0.8286     |
| cosine_recall@5     | 0.8671     |
| cosine_recall@10    | 0.9186     |
| cosine_ndcg@10      | 0.809      |
| cosine_mrr@10       | 0.774      |
| **cosine_map@100**  | **0.7776** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.6929     |
| cosine_accuracy@3   | 0.8186     |
| cosine_accuracy@5   | 0.8586     |
| cosine_accuracy@10  | 0.91       |
| cosine_precision@1  | 0.6929     |
| cosine_precision@3  | 0.2729     |
| cosine_precision@5  | 0.1717     |
| cosine_precision@10 | 0.091      |
| cosine_recall@1     | 0.6929     |
| cosine_recall@3     | 0.8186     |
| cosine_recall@5     | 0.8586     |
| cosine_recall@10    | 0.91       |
| cosine_ndcg@10      | 0.8017     |
| cosine_mrr@10       | 0.767      |
| **cosine_map@100**  | **0.7712** |

#### 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   | 0.8071     |
| cosine_accuracy@5   | 0.8586     |
| cosine_accuracy@10  | 0.8986     |
| cosine_precision@1  | 0.6871     |
| cosine_precision@3  | 0.269      |
| cosine_precision@5  | 0.1717     |
| cosine_precision@10 | 0.0899     |
| cosine_recall@1     | 0.6871     |
| cosine_recall@3     | 0.8071     |
| cosine_recall@5     | 0.8586     |
| cosine_recall@10    | 0.8986     |
| cosine_ndcg@10      | 0.7921     |
| cosine_mrr@10       | 0.7581     |
| **cosine_map@100**  | **0.7627** |

#### 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   | 0.6643     |
| cosine_accuracy@3   | 0.7843     |
| cosine_accuracy@5   | 0.8257     |
| cosine_accuracy@10  | 0.8729     |
| cosine_precision@1  | 0.6643     |
| cosine_precision@3  | 0.2614     |
| cosine_precision@5  | 0.1651     |
| cosine_precision@10 | 0.0873     |
| cosine_recall@1     | 0.6643     |
| cosine_recall@3     | 0.7843     |
| cosine_recall@5     | 0.8257     |
| cosine_recall@10    | 0.8729     |
| cosine_ndcg@10      | 0.769      |
| cosine_mrr@10       | 0.7358     |
| **cosine_map@100**  | **0.7407** |

<!--
## 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: 7 tokens</li><li>mean: 44.39 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.64 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                     | anchor                                                                                                                    |
  |:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|
  | <code>Johnson & Johnson reported cash and cash equivalents of $21,859 million as of the end of 2023.</code>                                  | <code>What was the amount of cash and cash equivalents reported by Johnson & Johnson at the end of 2023?</code>           |
  | <code>Johnson & Johnson's consolidated statements of earnings for 2023 reported total net earnings of $35,153 million.</code>                | <code>What was the total net earnings for Johnson & Johnson in 2023?</code>                                               |
  | <code>As of December 31, 2023, short-term investments were valued at $236,118 thousand and long-term investments at $86,676 thousand.</code> | <code>What is the total value of short-term and long-term investments held by the company as of 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
- `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`: 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
- `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     | 1.5779        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7388                 | 0.7509                 | 0.7604                 | 0.7081                | 0.7579                 |
| 1.6244     | 20     | 0.6572        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7612                 | 0.7670                 | 0.7729                 | 0.7269                | 0.7705                 |
| 2.4365     | 30     | 0.4661        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7623                 | 0.7702                 | 0.7771                 | 0.7386                | 0.7758                 |
| 3.2487     | 40     | 0.3774        | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7627**             | **0.7712**             | **0.7776**             | **0.7407**            | **0.7773**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- 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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