README.md

---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
- source_sentence: The company hedges foreign currency exchange-based cash flow variability
    of certain fees using forward contracts designated as hedging instruments. It
    also holds short-term forward contracts to offset exposure to fluctuations in
    certain of its foreign currency denominated cash balances and intercompany financing
    arrangements, without designating these forward contracts as hedging instruments.
  sentences:
  - What was the total stockholders' equity at Amazon.com, Inc. as of December 31,
    2021?
  - How does the company manage fluctuations in foreign currency exchange rates?
  - What are some of the potential consequences for Meta Platforms, Inc. from inquiries
    or investigations as noted in the provided text?
- source_sentence: The Financial Statement Schedule is located on page S-1 of IBM’s
    2023 Form 10-K.
  sentences:
  - How is Hewlett Packard addressing competition in the enterprise IT infrastructure
    market?
  - Where in IBM’s 2023 Form 10-K can the Financial Statement Schedule be found?
  - What was Intuit's Net Income in fiscal year 2023?
- source_sentence: Sales of DARZALEX in 2023 showed a 22.2% increase over the previous
    year.
  sentences:
  - How much did DARZALEX sales increase in 2023 compared to the previous year?
  - What strategic focus does Etsy have for its marketplace?
  - Since when has Mr. Goodarzi been the President and CEO of Intuit?
- source_sentence: Chubb Limited further advanced their goal of greater product, customer,
    and geographical diversification with incremental purchases that led to a controlling
    majority interest in Huatai Insurance Group Co. Ltd, owning about 76.5 percent
    as of July 1, 2023.
  sentences:
  - What are the primary sources of revenue for Salesforce, Inc. as described in their
    consolidated financial statements?
  - What acquisitions did Hershey complete to expand its snacking portfolio, and when
    did these occur?
  - What percentage of the Huatai Insurance Group Co. Ltd does Chubb Limited own as
    of July 1, 2023?
- source_sentence: The consolidated balance sheets of Visa Inc. as of September 30,
    2023, list the total current assets at $33,532 million.
  sentences:
  - What was the total of Visa Inc.'s current assets as of September 30, 2023?
  - What was Garmin Ltd.'s net income for the fiscal year ended December 30, 2023?
  - By what percentage did online sales grow in fiscal 2022 compared to fiscal 2021?
pipeline_tag: sentence-similarity
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: 0.6885714285714286
      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: 0.9128571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6885714285714286
      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: 0.09128571428571426
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6885714285714286
      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: 0.9128571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8022848173323525
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7666422902494329
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7696751281834099
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6928571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8228571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8642857142857143
      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: 0.27428571428571424
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17285714285714285
      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: 0.8228571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8642857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8016907244180009
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7668412698412699
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.770110214157224
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6871428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8185714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9014285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6871428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27285714285714285
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09014285714285712
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6871428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8185714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9014285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7962767797304091
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7623021541950112
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7656765331908582
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6742857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8057142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8528571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8942857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6742857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26857142857142857
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17057142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08942857142857143
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6742857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8057142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8528571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8942857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7861958176742697
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7513151927437639
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7548627394954026
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6428571428571429
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7971428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8185714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8685714285714285
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6428571428571429
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26571428571428574
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1637142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08685714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6428571428571429
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7971428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8185714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8685714285714285
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7590638034734002
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7236972789115643
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7282650681776726
      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("WaheedLone/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The consolidated balance sheets of Visa Inc. as of September 30, 2023, list the total current assets at $33,532 million.',
    "What was the total of Visa Inc.'s current assets as of September 30, 2023?",
    "What was Garmin Ltd.'s net income for the fiscal year ended December 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>
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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   | 0.6886     |
| cosine_accuracy@3   | 0.8286     |
| cosine_accuracy@5   | 0.8671     |
| cosine_accuracy@10  | 0.9129     |
| cosine_precision@1  | 0.6886     |
| cosine_precision@3  | 0.2762     |
| cosine_precision@5  | 0.1734     |
| cosine_precision@10 | 0.0913     |
| cosine_recall@1     | 0.6886     |
| cosine_recall@3     | 0.8286     |
| cosine_recall@5     | 0.8671     |
| cosine_recall@10    | 0.9129     |
| cosine_ndcg@10      | 0.8023     |
| cosine_mrr@10       | 0.7666     |
| **cosine_map@100**  | **0.7697** |

#### 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   | 0.6929     |
| cosine_accuracy@3   | 0.8229     |
| cosine_accuracy@5   | 0.8643     |
| cosine_accuracy@10  | 0.91       |
| cosine_precision@1  | 0.6929     |
| cosine_precision@3  | 0.2743     |
| cosine_precision@5  | 0.1729     |
| cosine_precision@10 | 0.091      |
| cosine_recall@1     | 0.6929     |
| cosine_recall@3     | 0.8229     |
| cosine_recall@5     | 0.8643     |
| cosine_recall@10    | 0.91       |
| cosine_ndcg@10      | 0.8017     |
| cosine_mrr@10       | 0.7668     |
| **cosine_map@100**  | **0.7701** |

#### 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   | 0.6871     |
| cosine_accuracy@3   | 0.8186     |
| cosine_accuracy@5   | 0.8629     |
| cosine_accuracy@10  | 0.9014     |
| cosine_precision@1  | 0.6871     |
| cosine_precision@3  | 0.2729     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.0901     |
| cosine_recall@1     | 0.6871     |
| cosine_recall@3     | 0.8186     |
| cosine_recall@5     | 0.8629     |
| cosine_recall@10    | 0.9014     |
| cosine_ndcg@10      | 0.7963     |
| cosine_mrr@10       | 0.7623     |
| **cosine_map@100**  | **0.7657** |

#### 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   | 0.6743     |
| cosine_accuracy@3   | 0.8057     |
| cosine_accuracy@5   | 0.8529     |
| cosine_accuracy@10  | 0.8943     |
| cosine_precision@1  | 0.6743     |
| cosine_precision@3  | 0.2686     |
| cosine_precision@5  | 0.1706     |
| cosine_precision@10 | 0.0894     |
| cosine_recall@1     | 0.6743     |
| cosine_recall@3     | 0.8057     |
| cosine_recall@5     | 0.8529     |
| cosine_recall@10    | 0.8943     |
| cosine_ndcg@10      | 0.7862     |
| cosine_mrr@10       | 0.7513     |
| **cosine_map@100**  | **0.7549** |

#### 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   | 0.6429     |
| cosine_accuracy@3   | 0.7971     |
| cosine_accuracy@5   | 0.8186     |
| cosine_accuracy@10  | 0.8686     |
| cosine_precision@1  | 0.6429     |
| cosine_precision@3  | 0.2657     |
| cosine_precision@5  | 0.1637     |
| cosine_precision@10 | 0.0869     |
| cosine_recall@1     | 0.6429     |
| cosine_recall@3     | 0.7971     |
| cosine_recall@5     | 0.8186     |
| cosine_recall@10    | 0.8686     |
| cosine_ndcg@10      | 0.7591     |
| cosine_mrr@10       | 0.7237     |
| **cosine_map@100**  | **0.7283** |

<!--
## 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: 6 tokens</li><li>mean: 45.17 tokens</li><li>max: 260 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.38 tokens</li><li>max: 40 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                      | anchor                                                                                                |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|
  | <code>Net revenue for fiscal year 2023 increased by $435 million compared to fiscal year 2022.</code>                                                                         | <code>How did the net revenue for fiscal year 2023 compare to fiscal year 2022?</code>                |
  | <code>Adjusted Free Cash Flow is defined as operating cash flow less capital spending and excluding payments for the transitional tax resulting from the U.S. Tax Act.</code> | <code>How is Adjusted Free Cash Flow defined in the text?</code>                                      |
  | <code>During 2023, the Company’s net sales through its direct and indirect distribution channels accounted for 37% and 63%, respectively, of total net sales.</code>          | <code>During 2023, what percentage of the Company’s net sales came from direct sales channels?</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
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122     | 10     | 1.6399        | -                      | -                      | -                      | -                     | -                      |
| 0.9746     | 12     | -             | 0.7441                 | 0.7580                 | 0.7543                 | 0.7068                | 0.7632                 |
| 1.6244     | 20     | 0.6475        | -                      | -                      | -                      | -                     | -                      |
| 1.9492     | 24     | -             | 0.7530                 | 0.7653                 | 0.7672                 | 0.7244                | 0.7708                 |
| 2.4365     | 30     | 0.4494        | -                      | -                      | -                      | -                     | -                      |
| 2.9239     | 36     | -             | 0.7548                 | 0.7653                 | 0.7683                 | 0.7297                | 0.7679                 |
| 3.2487     | 40     | 0.4089        | -                      | -                      | -                      | -                     | -                      |
| **3.8985** | **48** | **-**         | **0.7549**             | **0.7657**             | **0.7701**             | **0.7283**            | **0.7697**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- 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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