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: Teams across Delta have worked together to make an impact through
    enhanced landing procedures, optimizations to flight routing and speed, and weight
    reduction initiatives, saving over 20 million gallons of jet fuel in 2022 and
    2023.
  sentences:
  - What was the percentage increase in Services net sales from 2022 to 2023?
  - How much jet fuel did Delta Air Lines save between 2022 and 2023 through optimizations
    in aircraft operations?
  - How did Ford Pro's EBIT in 2023 compare to the previous year, and what contributed
    to this change?
- source_sentence: On February 14, 2022, the State of Texas filed a lawsuit against
    us in Texas state court (Texas v. Meta Platforms, Inc.) alleging that "tag suggestions"
    and other uses of facial recognition technology violated the Texas Capture or
    Use of Biometric Identifiers Act and the Texas Deceptive Trade Practices-Consumer
    Protection Act, and seeking statutory damages and injunctive relief.
  sentences:
  - What did the auditor’s report dated February 9, 2024, state about the effectiveness
    of Enphase Energy’s internal control over financial reporting as of December 31,
    2023?
  - What legal action did the State of Texas initiate against Meta Platforms, Inc.
    on February 14, 2022?
  - What caused the pretax loss in the Corporate & Other segment to increase in 2023
    compared to 2022?
- source_sentence: Our two operating segments are "Compute & Networking" and "Graphics."
    Refer to Note 17 of the Notes to the Consolidated Financial Statements in Part
    IV, Item 15 of this Annual Report on Form 10-K for additional information.
  sentences:
  - What are the two operating segments of NVIDIA as mentioned in the text?
  - How much did the gross margin increase in 2023 compared to 2022?
  - What is the total assets and shareholders' equity of Chubb Limited as of December
    31, 2023?
- source_sentence: The increase in marketing and sales expenses in fiscal year 2023
    was mainly due to higher advertising and promotional spending related to Apex
    Legends Mobile and the FIFA franchise.
  sentences:
  - What are included in Part IV, Item 15(a)(1) of the Annual Report on Form 10-K?
  - What was the net income reported for the fiscal year ending in August 2023?
  - What was the primary cause of the increase in marketing and sales expenses in
    fiscal year 2023?
- source_sentence: 'Information on legal proceedings is included in Contact Email  PRIOR
    HISTORY: None PLACEHOLDER FOR ARBITRATION.'
  sentences:
  - Where can information about legal proceedings be found in the financial statements?
  - What remaining authorization amount was available for share repurchases as of
    January 28, 2023?
  - What is the total amount authorized for the repurchase of common stock up to December
    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: 0.71
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8428571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8771428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9142857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.71
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28095238095238095
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1754285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09142857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.71
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8428571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8771428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9142857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8151955748060781
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.783174603174603
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7866554834362436
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7028571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8457142857142858
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.88
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9157142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7028571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2819047619047619
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.176
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09157142857142857
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7028571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8457142857142858
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.88
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9157142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8131832672898918
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7799625850340134
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7833067978748278
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6985714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8457142857142858
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8785714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9071428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6985714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2819047619047619
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17571428571428568
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0907142857142857
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6985714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8457142857142858
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8785714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9071428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8072080679843728
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7746224489795912
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7782328948106179
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6914285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8428571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8714285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9057142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6914285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28095238095238095
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17428571428571427
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09057142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6914285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8428571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8714285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9057142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.80532196181792
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7725623582766435
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7764353709024747
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6757142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8114285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.85
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8842857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6757142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2704761904761904
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08842857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6757142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8114285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.85
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8842857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7835900962247281
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7508775510204081
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7557906355020412
      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("NickyNicky/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Information on legal proceedings is included in Contact Email  PRIOR HISTORY: None PLACEHOLDER FOR ARBITRATION.',
    'Where can information about legal proceedings be found in the financial statements?',
    'What remaining authorization amount was available for share repurchases as of January 28, 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   | 0.71       |
| cosine_accuracy@3   | 0.8429     |
| cosine_accuracy@5   | 0.8771     |
| cosine_accuracy@10  | 0.9143     |
| cosine_precision@1  | 0.71       |
| cosine_precision@3  | 0.281      |
| cosine_precision@5  | 0.1754     |
| cosine_precision@10 | 0.0914     |
| cosine_recall@1     | 0.71       |
| cosine_recall@3     | 0.8429     |
| cosine_recall@5     | 0.8771     |
| cosine_recall@10    | 0.9143     |
| cosine_ndcg@10      | 0.8152     |
| cosine_mrr@10       | 0.7832     |
| **cosine_map@100**  | **0.7867** |

#### 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   | 0.7029     |
| cosine_accuracy@3   | 0.8457     |
| cosine_accuracy@5   | 0.88       |
| cosine_accuracy@10  | 0.9157     |
| cosine_precision@1  | 0.7029     |
| cosine_precision@3  | 0.2819     |
| cosine_precision@5  | 0.176      |
| cosine_precision@10 | 0.0916     |
| cosine_recall@1     | 0.7029     |
| cosine_recall@3     | 0.8457     |
| cosine_recall@5     | 0.88       |
| cosine_recall@10    | 0.9157     |
| cosine_ndcg@10      | 0.8132     |
| cosine_mrr@10       | 0.78       |
| **cosine_map@100**  | **0.7833** |

#### 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   | 0.6986     |
| cosine_accuracy@3   | 0.8457     |
| cosine_accuracy@5   | 0.8786     |
| cosine_accuracy@10  | 0.9071     |
| cosine_precision@1  | 0.6986     |
| cosine_precision@3  | 0.2819     |
| cosine_precision@5  | 0.1757     |
| cosine_precision@10 | 0.0907     |
| cosine_recall@1     | 0.6986     |
| cosine_recall@3     | 0.8457     |
| cosine_recall@5     | 0.8786     |
| cosine_recall@10    | 0.9071     |
| cosine_ndcg@10      | 0.8072     |
| cosine_mrr@10       | 0.7746     |
| **cosine_map@100**  | **0.7782** |

#### 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   | 0.6914     |
| cosine_accuracy@3   | 0.8429     |
| cosine_accuracy@5   | 0.8714     |
| cosine_accuracy@10  | 0.9057     |
| cosine_precision@1  | 0.6914     |
| cosine_precision@3  | 0.281      |
| cosine_precision@5  | 0.1743     |
| cosine_precision@10 | 0.0906     |
| cosine_recall@1     | 0.6914     |
| cosine_recall@3     | 0.8429     |
| cosine_recall@5     | 0.8714     |
| cosine_recall@10    | 0.9057     |
| cosine_ndcg@10      | 0.8053     |
| cosine_mrr@10       | 0.7726     |
| **cosine_map@100**  | **0.7764** |

#### 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   | 0.6757     |
| cosine_accuracy@3   | 0.8114     |
| cosine_accuracy@5   | 0.85       |
| cosine_accuracy@10  | 0.8843     |
| cosine_precision@1  | 0.6757     |
| cosine_precision@3  | 0.2705     |
| cosine_precision@5  | 0.17       |
| cosine_precision@10 | 0.0884     |
| cosine_recall@1     | 0.6757     |
| cosine_recall@3     | 0.8114     |
| cosine_recall@5     | 0.85       |
| cosine_recall@10    | 0.8843     |
| cosine_ndcg@10      | 0.7836     |
| cosine_mrr@10       | 0.7509     |
| **cosine_map@100**  | **0.7558** |

<!--
## 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: 4 tokens</li><li>mean: 47.19 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.59 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                | anchor                                                                                          |
  |:----------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
  | <code>For the year ended December 31, 2023, $305 million was recorded as a distribution against retained earnings for dividends.</code> | <code>How much in dividends was recorded against retained earnings in 2023?</code>              |
  | <code>In February 2023, we announced a 10% increase in our quarterly cash dividend to $2.09 per share.</code>                           | <code>By how much did the company increase its quarterly cash dividend in February 2023?</code> |
  | <code>Depreciation and amortization totaled $4,856 as recorded in the financial statements.</code>                                      | <code>How much did depreciation and amortization total to in the financial statements?</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`: 40
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 20
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: 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`: 40
- `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`: 20
- `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`: False
- `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 |
|:-------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9114  | 9    | -             | 0.7124                 | 0.7361                 | 0.7366                 | 0.6672                | 0.7443                 |
| 1.0127  | 10   | 2.0952        | -                      | -                      | -                      | -                     | -                      |
| 1.9241  | 19   | -             | 0.7437                 | 0.7561                 | 0.7628                 | 0.7172                | 0.7653                 |
| 2.0253  | 20   | 1.1175        | -                      | -                      | -                      | -                     | -                      |
| 2.9367  | 29   | -             | 0.7623                 | 0.7733                 | 0.7694                 | 0.7288                | 0.7723                 |
| 3.0380  | 30   | 0.6104        | -                      | -                      | -                      | -                     | -                      |
| 3.9494  | 39   | -             | 0.7723                 | 0.7746                 | 0.7804                 | 0.7405                | 0.7789                 |
| 4.0506  | 40   | 0.4106        | -                      | -                      | -                      | -                     | -                      |
| 4.9620  | 49   | -             | 0.7777                 | 0.7759                 | 0.7820                 | 0.7475                | 0.7842                 |
| 5.0633  | 50   | 0.314         | -                      | -                      | -                      | -                     | -                      |
| 5.9747  | 59   | -             | 0.7802                 | 0.7796                 | 0.7856                 | 0.7548                | 0.7839                 |
| 6.0759  | 60   | 0.2423        | -                      | -                      | -                      | -                     | -                      |
| 6.9873  | 69   | -             | 0.7756                 | 0.7772                 | 0.7834                 | 0.7535                | 0.7818                 |
| 7.0886  | 70   | 0.1962        | -                      | -                      | -                      | -                     | -                      |
| 8.0     | 79   | -             | 0.7741                 | 0.7774                 | 0.7841                 | 0.7551                | 0.7822                 |
| 8.1013  | 80   | 0.1627        | -                      | -                      | -                      | -                     | -                      |
| 8.9114  | 88   | -             | 0.7724                 | 0.7752                 | 0.7796                 | 0.7528                | 0.7816                 |
| 9.1139  | 90   | 0.1379        | -                      | -                      | -                      | -                     | -                      |
| 9.9241  | 98   | -             | 0.7691                 | 0.7782                 | 0.7834                 | 0.7559                | 0.7836                 |
| 10.1266 | 100  | 0.1249        | -                      | -                      | -                      | -                     | -                      |
| 10.9367 | 108  | -             | 0.7728                 | 0.7802                 | 0.7831                 | 0.7536                | 0.7848                 |
| 11.1392 | 110  | 0.1105        | -                      | -                      | -                      | -                     | -                      |
| 11.9494 | 118  | -             | 0.7748                 | 0.7785                 | 0.7814                 | 0.7558                | 0.7851                 |
| 12.1519 | 120  | 0.1147        | -                      | -                      | -                      | -                     | -                      |
| 12.9620 | 128  | -             | 0.7756                 | 0.7788                 | 0.7839                 | 0.7550                | 0.7864                 |
| 13.1646 | 130  | 0.098         | -                      | -                      | -                      | -                     | -                      |
| 13.9747 | 138  | -             | 0.7767                 | 0.7792                 | 0.7828                 | 0.7557                | 0.7873                 |
| 14.1772 | 140  | 0.0927        | -                      | -                      | -                      | -                     | -                      |
| 14.9873 | 148  | -             | 0.7758                 | 0.7804                 | 0.7847                 | 0.7569                | 0.7892                 |
| 15.1899 | 150  | 0.0921        | -                      | -                      | -                      | -                     | -                      |
| 16.0    | 158  | -             | 0.7760                 | 0.7794                 | 0.7831                 | 0.7551                | 0.7873                 |
| 16.2025 | 160  | 0.0896        | -                      | -                      | -                      | -                     | -                      |
| 16.9114 | 167  | -             | 0.7753                 | 0.7799                 | 0.7841                 | 0.7570                | 0.7888                 |
| 17.2152 | 170  | 0.0881        | -                      | -                      | -                      | -                     | -                      |
| 17.9241 | 177  | -             | 0.7763                 | 0.7787                 | 0.7842                 | 0.7561                | 0.7867                 |
| 18.2278 | 180  | 0.0884        | 0.7764                 | 0.7782                 | 0.7833                 | 0.7558                | 0.7867                 |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.2.0+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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