README.md

---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
widget:
- source_sentence: In 2023, total government-based programs, including Medicare, Medicaid,
    and other government-based programs, contributed 67% to the U.S. dialysis patient
    service revenues.
  sentences:
  - How does Iron Mountain's reported EPS fully diluted from net income in 2023 compare
    to 2022?
  - What was the total percentage of U.S. dialysis patient service revenues coming
    from government-based programs in 2023?
  - What year did the company introduce multiplex theatres?
- source_sentence: The gross realized losses on sales of AFS debt associated for 2023
    amounted to $514 million, indicating a negative financial outcome from these transactions
    during the year.
  sentences:
  - What were the gross realized losses on sales of AFS debt securities in 2023?
  - How is information about legal proceedings described in the Annual Report on Form
    10-K?
  - What sections are included alongside the Financial Statements in this report?
- source_sentence: Other income, net, changed favorably by $215 million in the year
    ended December 31, 2023 as compared to the year ended December 31, 2022. The favorable
    change was primarily due to fluctuations in foreign currency exchange rates on
    our intercompany balances.
  sentences:
  - What was the monetary change in other income (expense), net, from 2022 to 2023?
  - What strategic actions has Walmart International taken over the last three years?
  - What is described under Item 8 in the context of a financial document?
- source_sentence: Segments The Company manages its business primarily on a geographic
    basis. The Company’s reportable segments consist of the Americas, Europe, Greater
    China, Japan and Rest of Asia Pacific.
  sentences:
  - What is the total debt repayment obligation mentioned in the financial outline?
  - What segments does the Company manage its business on?
  - What is the title of Item 8 which contains page information in a financial document?
- source_sentence: Item 8 typically refers to Financial Statements and Supplementary
    Data in a document.
  sentences:
  - What is the primary function of Etsy's online marketplaces?
  - What are the maximum leverage ratios specified under the Senior Credit Facilities
    for the periods ending fourth quarter of 2023 and first quarter of 2024?
  - What does Item 8 in a document usually represent?
pipeline_tag: sentence-similarity
model-index:
- name: BGE base Financial Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.7057142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8371428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8742857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9128571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7057142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27904761904761904
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17485714285714282
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09128571428571428
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7057142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8371428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8742857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9128571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8114149232737874
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7786632653061224
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7821804400415905
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7057142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8328571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8714285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9128571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7057142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2776190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17428571428571427
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09128571428571428
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7057142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8328571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8714285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9128571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8108495475926208
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7780068027210884
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7816465534941897
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7157142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8342857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.87
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9057142857142857
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7157142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27809523809523806
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.174
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09057142857142855
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7157142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8342857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.87
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9057142857142857
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8123157823677117
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7823004535147391
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7862892219643212
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6928571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8171428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8614285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9028571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6928571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2723809523809524
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17228571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09028571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6928571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8171428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8614285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9028571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7975011441256048
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7638248299319729
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7673061455577762
      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("pavanmantha/bge-base-en-honsec10k-embed")
# Run inference
sentences = [
    'Item 8 typically refers to Financial Statements and Supplementary Data in a document.',
    'What does Item 8 in a document usually represent?',
    'What are the maximum leverage ratios specified under the Senior Credit Facilities for the periods ending fourth quarter of 2023 and first quarter of 2024?',
]
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]
```

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### 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
* 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   | 0.7057     |
| cosine_accuracy@3   | 0.8371     |
| cosine_accuracy@5   | 0.8743     |
| cosine_accuracy@10  | 0.9129     |
| cosine_precision@1  | 0.7057     |
| cosine_precision@3  | 0.279      |
| cosine_precision@5  | 0.1749     |
| cosine_precision@10 | 0.0913     |
| cosine_recall@1     | 0.7057     |
| cosine_recall@3     | 0.8371     |
| cosine_recall@5     | 0.8743     |
| cosine_recall@10    | 0.9129     |
| cosine_ndcg@10      | 0.8114     |
| cosine_mrr@10       | 0.7787     |
| **cosine_map@100**  | **0.7822** |

#### 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   | 0.7057     |
| cosine_accuracy@3   | 0.8329     |
| cosine_accuracy@5   | 0.8714     |
| cosine_accuracy@10  | 0.9129     |
| cosine_precision@1  | 0.7057     |
| cosine_precision@3  | 0.2776     |
| cosine_precision@5  | 0.1743     |
| cosine_precision@10 | 0.0913     |
| cosine_recall@1     | 0.7057     |
| cosine_recall@3     | 0.8329     |
| cosine_recall@5     | 0.8714     |
| cosine_recall@10    | 0.9129     |
| cosine_ndcg@10      | 0.8108     |
| cosine_mrr@10       | 0.778      |
| **cosine_map@100**  | **0.7816** |

#### 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   | 0.7157     |
| cosine_accuracy@3   | 0.8343     |
| cosine_accuracy@5   | 0.87       |
| cosine_accuracy@10  | 0.9057     |
| cosine_precision@1  | 0.7157     |
| cosine_precision@3  | 0.2781     |
| cosine_precision@5  | 0.174      |
| cosine_precision@10 | 0.0906     |
| cosine_recall@1     | 0.7157     |
| cosine_recall@3     | 0.8343     |
| cosine_recall@5     | 0.87       |
| cosine_recall@10    | 0.9057     |
| cosine_ndcg@10      | 0.8123     |
| cosine_mrr@10       | 0.7823     |
| **cosine_map@100**  | **0.7863** |

#### 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   | 0.6929     |
| cosine_accuracy@3   | 0.8171     |
| cosine_accuracy@5   | 0.8614     |
| cosine_accuracy@10  | 0.9029     |
| cosine_precision@1  | 0.6929     |
| cosine_precision@3  | 0.2724     |
| cosine_precision@5  | 0.1723     |
| cosine_precision@10 | 0.0903     |
| cosine_recall@1     | 0.6929     |
| cosine_recall@3     | 0.8171     |
| cosine_recall@5     | 0.8614     |
| cosine_recall@10    | 0.9029     |
| cosine_ndcg@10      | 0.7975     |
| cosine_mrr@10       | 0.7638     |
| **cosine_map@100**  | **0.7673** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 6,300 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                           | anchor                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 44.43 tokens</li><li>max: 248 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.52 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                             | anchor                                                                                     |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------|
  | <code>Net deferred tax liabilities | $ | (304) | | | $ | (279)  The deferred tax accounts at the end of 2023 and 2022 include deferred income tax assets of $491 and $445, included in other long-term assets; and deferred income tax liabilities of $795 and $724, included in other long-term liabilities.</code> | <code>What are the net deferred tax liabilities for the company at the end of 2023?</code> |
  | <code>ITEM 3. LEGAL PROCEEDINGS Please see the legal proceedings described in Note 21. Commitments and Contingencies included in Item 8 of Part II of this report.</code>                                                                                                                                            | <code>In what part and item of the report is Note 21 located?</code>                       |
  | <code>During fiscal year 2023, we repurchased 10.4 million shares for approximately $1,295 million.</code>                                                                                                                                                                                                           | <code>What total amount was spent on share repurchases during fiscal year 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": [
          768,
          512,
          256,
          128
      ],
      "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
- `fp16`: 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`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `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`: 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_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|
| 0.8122     | 10     | 1.1537        | -                      | -                      | -                      | -                      |
| 0.9746     | 12     | -             | 0.7517                 | 0.7620                 | 0.7633                 | 0.7636                 |
| 1.6244     | 20     | 0.4387        | -                      | -                      | -                      | -                      |
| 1.9492     | 24     | -             | 0.7616                 | 0.7802                 | 0.7796                 | 0.7769                 |
| 2.4365     | 30     | 0.3113        | -                      | -                      | -                      | -                      |
| 2.9239     | 36     | -             | 0.7668                 | 0.7837                 | 0.7809                 | 0.7821                 |
| 3.2487     | 40     | 0.2554        | -                      | -                      | -                      | -                      |
| **3.8985** | **48** | **-**         | **0.7673**             | **0.7863**             | **0.7816**             | **0.7822**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- 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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