sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:6300 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: The Health Services segment's revenues are primarily generated
    from the sale and managing of prescription drugs to eligible members in benefit
    plans maintained by clients.
  sentences:
  - What online platforms does The Home Depot operate for its product offerings?
  - How does the Company's Health Services segment generate most of its revenue?
  - What are the various diversity, equity, and inclusion councils at AMC?
- source_sentence: Product gross margin decreased to 75.9% in 2023, compared to 2022.
  sentences:
  - What organizations do the cybersecurity leaders hold memberships in?
  - How much did product gross margin decrease from 2022 to 2023?
  - What items are included under Item 8 in the financial report?
- source_sentence: Our apparel assortment includes items such as pants, shorts, tops,
    and jackets designed for a healthy lifestyle including athletic activities such
    as yoga, running, training, and most other activities.
  sentences:
  - What types of activities are lululemon's athletic apparels designed for?
  - How are the capital adequacy ratios of American Express and AENB determined under
    current federal banking regulations?
  - What was the company's worldwide effective income tax rate in 2023?
- source_sentence: Rest of Asia Pacific net sales increased 1% or $240 million during
    2023 compared to 2022. The weakness in foreign currencies relative to the U.S.
    dollar had a significantly unfavorable year-over-year impact on Rest of Asia Pacific
    net sales. The net sales increase consisted of higher net sales of iPhone and
    Services, partially offset by lower net sales of Mac and iPad.
  sentences:
  - What primarily drove the increase in service fees and other revenue associated
    with Card Member cross-currency spending?
  - What was the balance of net cash used in financing activities for Costco for the
    52 weeks ended August 28, 2022?
  - How did Rest of Asia Pacific net sales change in 2023 compared to 2022?
- source_sentence: Cardiovascular/Metabolism/Other products sales were $3.7 billion,
    a decline of 5.5% as compared to the prior year.
  sentences:
  - What was the revenue decline percentage for Cardiovascular/Metabolism/Other products
    in 2023?
  - How is a membership's territory determined according to the description?
  - What informs the ESG disclosures mentioned in the text?
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: 0.68
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8185714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8614285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9057142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.68
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27285714285714285
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17228571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09057142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.68
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8185714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8614285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9057142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7935961173570086
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7574988662131514
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.761336548534802
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6742857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.81
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8585714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9128571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6742857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1717142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09128571428571428
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6742857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.81
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8585714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9128571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7923231259157938
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7537448979591835
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7569742717128601
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6728571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8028571428571428
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.85
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9042857142857142
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6728571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2676190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09042857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6728571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8028571428571428
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.85
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9042857142857142
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7878292403814693
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7507125850340136
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7544072051783587
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6642857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8442857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8957142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6642857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16885714285714284
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08957142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6642857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8442857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8957142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7803597349541593
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7434574829931975
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.747244951206714
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6414285714285715
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7871428571428571
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8185714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8728571428571429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6414285714285715
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2623809523809524
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1637142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08728571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6414285714285715
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7871428571428571
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8185714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8728571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7591433790735056
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7226213151927439
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7273040677676369
      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("JulioSanchezD/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Cardiovascular/Metabolism/Other products sales were $3.7 billion, a decline of 5.5% as compared to the prior year.',
    'What was the revenue decline percentage for Cardiovascular/Metabolism/Other products in 2023?',
    "How is a membership's territory determined according to the description?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### Out-of-Scope Use

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## 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   | 0.68       | 0.6743     | 0.6729     | 0.6643     | 0.6414     |
| cosine_accuracy@3   | 0.8186     | 0.81       | 0.8029     | 0.8        | 0.7871     |
| cosine_accuracy@5   | 0.8614     | 0.8586     | 0.85       | 0.8443     | 0.8186     |
| cosine_accuracy@10  | 0.9057     | 0.9129     | 0.9043     | 0.8957     | 0.8729     |
| cosine_precision@1  | 0.68       | 0.6743     | 0.6729     | 0.6643     | 0.6414     |
| cosine_precision@3  | 0.2729     | 0.27       | 0.2676     | 0.2667     | 0.2624     |
| cosine_precision@5  | 0.1723     | 0.1717     | 0.17       | 0.1689     | 0.1637     |
| cosine_precision@10 | 0.0906     | 0.0913     | 0.0904     | 0.0896     | 0.0873     |
| cosine_recall@1     | 0.68       | 0.6743     | 0.6729     | 0.6643     | 0.6414     |
| cosine_recall@3     | 0.8186     | 0.81       | 0.8029     | 0.8        | 0.7871     |
| cosine_recall@5     | 0.8614     | 0.8586     | 0.85       | 0.8443     | 0.8186     |
| cosine_recall@10    | 0.9057     | 0.9129     | 0.9043     | 0.8957     | 0.8729     |
| **cosine_ndcg@10**  | **0.7936** | **0.7923** | **0.7878** | **0.7804** | **0.7591** |
| cosine_mrr@10       | 0.7575     | 0.7537     | 0.7507     | 0.7435     | 0.7226     |
| cosine_map@100      | 0.7613     | 0.757      | 0.7544     | 0.7472     | 0.7273     |

<!--
## 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

#### 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: 11 tokens</li><li>mean: 45.6 tokens</li><li>max: 288 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.54 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                             | anchor                                                                                                                                                        |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Operating Expenses Our operating expenses consisted of the following: | Year Ended December 31, | 2023 | | 2022 | | Percent Change | (Dollars in millions) Casino | $ | 4,152 | | | $ | 1,792 | | | 131.7 | %</code>                                                                                                           | <code>What was the percentage increase in casino expenses at Las Vegas Sands Corp. for the year ended December 31, 2023 compared to the previous year?</code> |
  | <code>Increases in yield, discount rate, capitalization rate or duration used in the valuation of level 3 investments would have resulted in a lower fair value measurement, while increases in recovery rate or multiples would have resulted in a higher fair value measurement as of both December 2023 and December 2022.</code> | <code>What was the impact on the fair value measurement of level 3 investments when the yield, discount rate, and capitalization rate were increased?</code>  |
  | <code>At December 31, 2023, Ford Credit’s liquidity sources, including cash, committed asset-backed facilities, and unsecured credit facilities, totaled $56.2 billion, up $5.2 billion from year-end 2022.</code>                                                                                                                   | <code>What sources contribute to Ford Credit’s liquidity as of December 31, 2023, and what was their total value?</code>                                      |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `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
- `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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8122     | 10     | 1.5473        | -                      | -                      | -                      | -                      | -                     |
| 0.9746     | 12     | -             | 0.7821                 | 0.7814                 | 0.7723                 | 0.7543                 | 0.7229                |
| 1.6244     | 20     | 0.6848        | -                      | -                      | -                      | -                      | -                     |
| 1.9492     | 24     | -             | 0.7906                 | 0.7877                 | 0.7824                 | 0.7729                 | 0.7519                |
| 2.4365     | 30     | 0.5164        | -                      | -                      | -                      | -                      | -                     |
| 2.9239     | 36     | -             | 0.7921                 | 0.7924                 | 0.7887                 | 0.7778                 | 0.7587                |
| 3.2487     | 40     | 0.4455        | -                      | -                      | -                      | -                      | -                     |
| **3.8985** | **48** | **-**         | **0.7936**             | **0.7923**             | **0.7878**             | **0.7804**             | **0.7591**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.9
- Sentence Transformers: 3.4.1
- Transformers: 4.41.2
- PyTorch: 2.6.0+cu126
- Accelerate: 1.4.0
- 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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