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-small-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: We offer dual motor powertrain vehicles, which use two electric
    motors to maximize traction and performance in an all-wheel-drive configuration,
    as well as vehicle powertrain technology featuring three electric motors for further
    increased performance in certain versions of Model S and Model X, Cybertruck,
    and the Tesla Semi.
  sentences:
  - What is the purpose of The Home Depot Foundation?
  - What are the features of the company's vehicle powertrain technology?
  - Where can public access the company's SEC filings?
- source_sentence: The litigation requests a declaration that the IRA violates Janssen’s
    rights under the First Amendment and the Fifth Amendment to the Constitution.
  sentences:
  - What changes occurred in the valuation of equity warrants from 2021 to 2023?
  - What constitutional rights does Janssen claim the Inflation Reduction Act violates?
  - What was the cash paid for amounts included in the measurement of operating lease
    liabilities for the years 2021, 2022, and 2023?
- source_sentence: After-tax earnings of other energy businesses decreased $332 million
    (24.5%) in 2023 compared to 2022. The decline reflected lower earnings at Northern
    Powergrid due to unfavorable results at a natural gas exploration project, including
    the write-off of capitalized exploration costs and lower gas production volumes
    and prices, as well as from higher deferred income tax expense related to the
    enactment of the Energy Profits Levy income tax in the United Kingdom. The earnings
    decline was also attributable to lower earnings from renewable energy and retail
    services businesses. The decline in renewable energy and retail services earnings
    was primarily due to lower income tax benefits, higher operating expenses, lower
    solar and wind generation at owned projects and the impact of unfavorable changes
    in valuations of derivatives contracts, partially offset by debt extinguishment
    gains.
  sentences:
  - What were the reasons for the decline in after-tax earnings of other energy businesses
    in 2023?
  - What was the main reason for the increase in the company's valuation allowance
    during fiscal 2023?
  - What were the net purchase amounts of treasury shares for the years ended December
    31, 2022, and 2023?
- source_sentence: The Phase 3 OAKTREE trial of obeldesivir in non-hospitalized participants
    without risk factors for developing severe COVID-19 did not meet its primary endpoint
    of improvement in time to symptom alleviation. Obeldesivir was well-tolerated
    in this large study population.
  sentences:
  - How did the P&C combined ratios trend from 2021 to 2023?
  - What are some of the digital tools Walmart uses to improve associate productivity,
    engagement, and performance?
  - What was the result of the Phase 3 OAKTREE trial of obeldesivir conducted by Gilead?
- source_sentence: The issuance of preferred stock could have the effect of restricting
    dividends on the Company’s common stock, diluting the voting power of its common
    stock, impairing the liquidation rights of its common stock, or delaying or preventing
    a change in control.
  sentences:
  - What is the impact of issuing preferred stock according to the Company's description?
  - For how long did Jeffrey P. Bezos serve as President at Amazon?
  - Where in an Annual Report on Form 10-K is 'Note 13 — Commitments and Contingencies
    — Litigation and Other Legal Matters' included?
pipeline_tag: sentence-similarity
model-index:
- name: BGE small Financial Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 384
      type: dim_384
    metrics:
    - type: cosine_accuracy@1
      value: 0.6642857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8242857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8614285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9085714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6642857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2747619047619047
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17228571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09085714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6642857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8242857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8614285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9085714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7905933695158355
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7523809523809522
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7562726267140966
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6657142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8242857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9114285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6657142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2747619047619047
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09114285714285712
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6657142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8242857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9114285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7919632560554437
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7534053287981859
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.756861587821826
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6528571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8071428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8485714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6528571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26904761904761904
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16971428571428568
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6528571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8071428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8485714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.778048727585675
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7388730158730156
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7424840237912022
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6357142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7757142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8128571428571428
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8585714285714285
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6357142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25857142857142856
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16257142857142853
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08585714285714285
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6357142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7757142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8128571428571428
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8585714285714285
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7490553533476035
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7138038548752832
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7189504452927022
      name: Cosine Map@100
---

# BGE small Financial Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 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': 384, '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("haophancs/bge-small-financial-matryoshka")
# Run inference
sentences = [
    'The issuance of preferred stock could have the effect of restricting dividends on the Company’s common stock, diluting the voting power of its common stock, impairing the liquidation rights of its common stock, or delaying or preventing a change in control.',
    "What is the impact of issuing preferred stock according to the Company's description?",
    'For how long did Jeffrey P. Bezos serve as President at Amazon?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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_384`
* 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   | 0.8243     |
| cosine_accuracy@5   | 0.8614     |
| cosine_accuracy@10  | 0.9086     |
| cosine_precision@1  | 0.6643     |
| cosine_precision@3  | 0.2748     |
| cosine_precision@5  | 0.1723     |
| cosine_precision@10 | 0.0909     |
| cosine_recall@1     | 0.6643     |
| cosine_recall@3     | 0.8243     |
| cosine_recall@5     | 0.8614     |
| cosine_recall@10    | 0.9086     |
| cosine_ndcg@10      | 0.7906     |
| cosine_mrr@10       | 0.7524     |
| **cosine_map@100**  | **0.7563** |

#### 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   | 0.6657     |
| cosine_accuracy@3   | 0.8243     |
| cosine_accuracy@5   | 0.8629     |
| cosine_accuracy@10  | 0.9114     |
| cosine_precision@1  | 0.6657     |
| cosine_precision@3  | 0.2748     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.0911     |
| cosine_recall@1     | 0.6657     |
| cosine_recall@3     | 0.8243     |
| cosine_recall@5     | 0.8629     |
| cosine_recall@10    | 0.9114     |
| cosine_ndcg@10      | 0.792      |
| cosine_mrr@10       | 0.7534     |
| **cosine_map@100**  | **0.7569** |

#### 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   | 0.6529     |
| cosine_accuracy@3   | 0.8071     |
| cosine_accuracy@5   | 0.8486     |
| cosine_accuracy@10  | 0.9        |
| cosine_precision@1  | 0.6529     |
| cosine_precision@3  | 0.269      |
| cosine_precision@5  | 0.1697     |
| cosine_precision@10 | 0.09       |
| cosine_recall@1     | 0.6529     |
| cosine_recall@3     | 0.8071     |
| cosine_recall@5     | 0.8486     |
| cosine_recall@10    | 0.9        |
| cosine_ndcg@10      | 0.778      |
| cosine_mrr@10       | 0.7389     |
| **cosine_map@100**  | **0.7425** |

#### 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   | 0.6357    |
| cosine_accuracy@3   | 0.7757    |
| cosine_accuracy@5   | 0.8129    |
| cosine_accuracy@10  | 0.8586    |
| cosine_precision@1  | 0.6357    |
| cosine_precision@3  | 0.2586    |
| cosine_precision@5  | 0.1626    |
| cosine_precision@10 | 0.0859    |
| cosine_recall@1     | 0.6357    |
| cosine_recall@3     | 0.7757    |
| cosine_recall@5     | 0.8129    |
| cosine_recall@10    | 0.8586    |
| cosine_ndcg@10      | 0.7491    |
| cosine_mrr@10       | 0.7138    |
| **cosine_map@100**  | **0.719** |

<!--
## 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.*
-->

## 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: 9 tokens</li><li>mean: 45.74 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.77 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                              | anchor                                                                                                                                   |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The company believes that trademarks have significant value for marketing products, e-commerce, stores, and business, with the possibility of indefinite renewal as long as the trademarks are in use.</code>                   | <code>What are the benefits of registering trademarks for the company's business?</code>                                                 |
  | <code>The consolidated financial statements and accompanying notes listed in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included immediately following Part IV hereof and incorporated by reference herein.</code> | <code>How are the consolidated financial statements and accompanying notes incorporated into the Annual Report on Form 10-K?</code>      |
  | <code>During the year ended December 31, 2023, the Company repurchased and subsequently retired 2,029,894 shares of common stock from the open market at an average cost of $103.45 per share for a total of $210.0 million.</code>   | <code>How many shares of common stock did the Company repurchase and subsequently retire during the year ended 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": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `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_384_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8122     | 10     | 1.7741        | -                      | -                      | -                      | -                     |
| 0.9746     | 12     | -             | 0.7042                 | 0.7262                 | 0.7327                 | 0.6639                |
| 1.6244     | 20     | 0.7817        | -                      | -                      | -                      | -                     |
| 1.9492     | 24     | -             | 0.7322                 | 0.7477                 | 0.7498                 | 0.7136                |
| 2.4365     | 30     | 0.5816        | -                      | -                      | -                      | -                     |
| 2.9239     | 36     | -             | 0.7387                 | 0.7563                 | 0.7549                 | 0.7165                |
| 3.2487     | 40     | 0.5121        | -                      | -                      | -                      | -                     |
| **3.8985** | **48** | **-**         | **0.7425**             | **0.7569**             | **0.7563**             | **0.719**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.2
- 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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