---
language:
- en
license: apache-2.0
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
widget:
- source_sentence: Health Care Benefits revenue is principally derived from insurance
    premiums and fees billed to customers.
  sentences:
  - How much was the cumulative impairment and downward adjustments for observable
    price changes for the equity investments without readily determinable fair values
    as of December 31, 2023?
  - What are the revenue sources for the Company’s Health Care Benefits Segment?
  - What types of legal issues are generally categorized under Commitments and Contingencies
    in a Form 10-K?
- source_sentence: Total net sales increased by 7% during the fiscal year ending December
    30, 2023 compared to the previous fiscal year.
  sentences:
  - What was the percentage increase in Data Center revenue for fiscal year 2023 compared
    to the previous year?
  - What was the percentage increase in total net sales during the fiscal year ending
    December 30, 2023 compared to the previous fiscal year?
  - What were the expenses related to the fair value of restricted stock units (RSUs)
    and stock options for the years 2022, 2021, and 2020?
- source_sentence: The laws and regulations of the jurisdictions in which our insurance
    and reinsurance subsidiaries are domiciled require among other things that these
    subsidiaries maintain minimum levels of statutory capital, surplus, and liquidity,
    meet solvency standards, and submit to periodic examinations of their financial
    condition.
  sentences:
  - What statutory requirements must insurance and reinsurance subsidiaries meet in
    their domiciled jurisdictions?
  - What activities has the federal government used the FCA to prosecute?
  - How are self-insurance reserves computed and presented in financial statements?
- source_sentence: Services net sales increased 9% or $7.1 billion during 2023 compared
    to 2022 due to higher net sales across all lines of business.
  sentences:
  - What is the leverage ratio requirement under the company's financial covenant
    as of January 28, 2023?
  - What are the enrollment periods for Medicare Advantage and stand-alone prescription
    drug plans?
  - What was the percentage increase in Services net sales from 2022 to 2023?
- source_sentence: Certain vendors have been impacted by volatility in the supply
    chain financing market.
  sentences:
  - How have certain vendors been impacted in the supply chain financing market?
  - What was the total value of the company's cash commitments as of December 31,
    2023?
  - What are the key components used to define free cash flow in financial evaluations?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
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: 0.6871428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8171428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8485714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9085714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6871428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2723809523809524
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16971428571428568
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09085714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6871428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8171428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8485714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9085714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7960378752604689
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7603769841269836
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7640840138316877
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6828571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8114285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8528571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9085714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6828571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2704761904761904
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17057142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09085714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6828571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8114285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8528571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9085714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7936620196836198
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7572222222222219
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7609298999926937
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.68
      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: 0.8957142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.68
      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: 0.08957142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.68
      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: 0.8957142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7883110340362532
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7539733560090701
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7582685695127231
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6585714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7942857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.83
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8842857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6585714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26476190476190475
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16599999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08842857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6585714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7942857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.83
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8842857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7727884715594033
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.737036848072562
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7419081242961935
      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: 0.7628571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8142857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.87
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6357142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2542857142857142
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16285714285714287
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.087
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6357142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7628571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8142857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.87
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7501277228250628
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7121167800453513
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7171110018302509
      name: Cosine Map@100
---

# BGE base Financial Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("MarekMarik/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Certain vendors have been impacted by volatility in the supply chain financing market.',
    'How have certain vendors been impacted in the supply chain financing market?',
    "What was the total value of the company's cash commitments as of December 31, 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.*
-->

## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_768   | dim_512    | dim_256    | dim_128    | dim_64     |
|:--------------------|:----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1   | 0.6871    | 0.6829     | 0.68       | 0.6586     | 0.6357     |
| cosine_accuracy@3   | 0.8171    | 0.8114     | 0.8071     | 0.7943     | 0.7629     |
| cosine_accuracy@5   | 0.8486    | 0.8529     | 0.8486     | 0.83       | 0.8143     |
| cosine_accuracy@10  | 0.9086    | 0.9086     | 0.8957     | 0.8843     | 0.87       |
| cosine_precision@1  | 0.6871    | 0.6829     | 0.68       | 0.6586     | 0.6357     |
| cosine_precision@3  | 0.2724    | 0.2705     | 0.269      | 0.2648     | 0.2543     |
| cosine_precision@5  | 0.1697    | 0.1706     | 0.1697     | 0.166      | 0.1629     |
| cosine_precision@10 | 0.0909    | 0.0909     | 0.0896     | 0.0884     | 0.087      |
| cosine_recall@1     | 0.6871    | 0.6829     | 0.68       | 0.6586     | 0.6357     |
| cosine_recall@3     | 0.8171    | 0.8114     | 0.8071     | 0.7943     | 0.7629     |
| cosine_recall@5     | 0.8486    | 0.8529     | 0.8486     | 0.83       | 0.8143     |
| cosine_recall@10    | 0.9086    | 0.9086     | 0.8957     | 0.8843     | 0.87       |
| **cosine_ndcg@10**  | **0.796** | **0.7937** | **0.7883** | **0.7728** | **0.7501** |
| cosine_mrr@10       | 0.7604    | 0.7572     | 0.754      | 0.737      | 0.7121     |
| cosine_map@100      | 0.7641    | 0.7609     | 0.7583     | 0.7419     | 0.7171     |

<!--
## 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 6,300 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                           | anchor                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 45.84 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.62 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                          | anchor                                                                                                                                       |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>We adopted SAB 121 during fiscal 2022, with no impact on our consolidated financial statements.</code>                                                                                                                                      | <code>What accounting guidance did the company adopt in fiscal 2022 and what was its impact on the consolidated financial statements?</code> |
  | <code>Mortgage Solutions revenue decreased 18% in 2023 compared to 2022, due to significantly lower mortgage credit inquiry volumes in 2023 compared to the prior year.</code>                                                                    | <code>What caused the 18% decline in Mortgage Solutions revenue in 2023 compared to 2022?</code>                                             |
  | <code>Adoption of SBTi goals would build on our current science-based goals to reduce Scope 1 and 2 carbon emissions by 2.1% per year, to achieve a 40% reduction by the end of fiscal 2030 and a 50% reduction by the end of fiscal 2035.</code> | <code>What is the company's percentage target for reducing Scope 1 and 2 carbon emissions by end of fiscal 2035?</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_eval_batch_size`: 4
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: False
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step    | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.1015  | 10      | 0.614         | -                      | -                      | -                      | -                      | -                     |
| 0.2030  | 20      | 0.5098        | -                      | -                      | -                      | -                      | -                     |
| 0.3046  | 30      | 0.426         | -                      | -                      | -                      | -                      | -                     |
| 0.4061  | 40      | 0.3262        | -                      | -                      | -                      | -                      | -                     |
| 0.5076  | 50      | 0.2131        | -                      | -                      | -                      | -                      | -                     |
| 0.6091  | 60      | 0.1892        | -                      | -                      | -                      | -                      | -                     |
| 0.7107  | 70      | 0.3049        | -                      | -                      | -                      | -                      | -                     |
| 0.8122  | 80      | 0.1617        | -                      | -                      | -                      | -                      | -                     |
| 0.9137  | 90      | 0.1214        | -                      | -                      | -                      | -                      | -                     |
| 1.0     | 99      | -             | 0.7895                 | 0.7919                 | 0.7800                 | 0.7685                 | 0.7361                |
| 1.0102  | 100     | 0.147         | -                      | -                      | -                      | -                      | -                     |
| 1.1117  | 110     | 0.0938        | -                      | -                      | -                      | -                      | -                     |
| 1.2132  | 120     | 0.1406        | -                      | -                      | -                      | -                      | -                     |
| 1.3147  | 130     | 0.1058        | -                      | -                      | -                      | -                      | -                     |
| 1.4162  | 140     | 0.1072        | -                      | -                      | -                      | -                      | -                     |
| 1.5178  | 150     | 0.0352        | -                      | -                      | -                      | -                      | -                     |
| 1.6193  | 160     | 0.0568        | -                      | -                      | -                      | -                      | -                     |
| 1.7208  | 170     | 0.1283        | -                      | -                      | -                      | -                      | -                     |
| 1.8223  | 180     | 0.066         | -                      | -                      | -                      | -                      | -                     |
| 1.9239  | 190     | 0.038         | -                      | -                      | -                      | -                      | -                     |
| 2.0     | 198     | -             | 0.7945                 | 0.7945                 | 0.7860                 | 0.7736                 | 0.7462                |
| 2.0203  | 200     | 0.0544        | -                      | -                      | -                      | -                      | -                     |
| 2.1218  | 210     | 0.0333        | -                      | -                      | -                      | -                      | -                     |
| 2.2234  | 220     | 0.042         | -                      | -                      | -                      | -                      | -                     |
| 2.3249  | 230     | 0.0489        | -                      | -                      | -                      | -                      | -                     |
| 2.4264  | 240     | 0.0498        | -                      | -                      | -                      | -                      | -                     |
| 2.5279  | 250     | 0.0119        | -                      | -                      | -                      | -                      | -                     |
| 2.6294  | 260     | 0.0273        | -                      | -                      | -                      | -                      | -                     |
| 2.7310  | 270     | 0.0719        | -                      | -                      | -                      | -                      | -                     |
| 2.8325  | 280     | 0.0366        | -                      | -                      | -                      | -                      | -                     |
| 2.9340  | 290     | 0.0333        | -                      | -                      | -                      | -                      | -                     |
| **3.0** | **297** | **-**         | **0.7927**             | **0.7952**             | **0.7881**             | **0.7743**             | **0.7477**            |
| 3.0305  | 300     | 0.0193        | -                      | -                      | -                      | -                      | -                     |
| 3.1320  | 310     | 0.0254        | -                      | -                      | -                      | -                      | -                     |
| 3.2335  | 320     | 0.0252        | -                      | -                      | -                      | -                      | -                     |
| 3.3350  | 330     | 0.039         | -                      | -                      | -                      | -                      | -                     |
| 3.4365  | 340     | 0.0224        | -                      | -                      | -                      | -                      | -                     |
| 3.5381  | 350     | 0.0091        | -                      | -                      | -                      | -                      | -                     |
| 3.6396  | 360     | 0.0356        | -                      | -                      | -                      | -                      | -                     |
| 3.7411  | 370     | 0.042         | -                      | -                      | -                      | -                      | -                     |
| 3.8426  | 380     | 0.038         | -                      | -                      | -                      | -                      | -                     |
| 3.9442  | 390     | 0.0088        | -                      | -                      | -                      | -                      | -                     |
| 3.9645  | 392     | -             | 0.7960                 | 0.7937                 | 0.7883                 | 0.7728                 | 0.7501                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->