---
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: As of December 30, 2023, about 92% of securities in the Company's
    portfolio were at an unrealized loss position.
  sentences:
  - What additional document is included in the financial document apart from the
    Consolidated Financial Statements?
  - What percentage of the Company's portfolio of securities was in an unrealized
    loss position as of December 30, 2023?
  - What was the total loss the company incurred in association with the sale of the
    eOne Music business in 2021?
- source_sentence: Revenue Recognition Product Sales We recognize revenue from product
    sales when control of the product transfers to the customer, which is generally
    upon shipment or delivery, or in certain cases, upon the corresponding sales by
    our customer to a third party. Revenues are recognized net of estimated rebates
    and chargebacks, patient co-pay assistance, prompt pay discounts, distributor
    fees, sales return provisions and other related deductions. These deductions to
    product sales are referred to as gross-to-net deductions and are estimated and
    recorded in the period in which the related product sales occur.
  sentences:
  - What is the expiration date for the federal research and development tax credits
    as of 2023?
  - How are revenue recognition and Gross-to-Net deductions related in the context
    of product sales?
  - What is the approval status of Tirzepatide (Mounjaro, Zepbound®) for the treatment
    of obesity as of 2023?
- source_sentence: The expected long-term rate of return assumption used in computing
    2023 net periodic benefit income for the U.S. pension plans was 6.75%.
  sentences:
  - What is the expected long-term rate of return on plan assets used in computing
    the 2023 net periodic benefit income for U.S. pension plans?
  - What was the increase in postpaid phone subscribers at AT&T Inc. from 2021 to
    2023?
  - How does Chipotle ensure pay equity among its employees?
- source_sentence: In an Annual Report on Form 10-K, 'Litigation and Other Legal Matters'
    are detailed under 'Note 13 — Commitments and Contingencies' in Part IV, Item
    15 of the consolidated financial statements.
  sentences:
  - What is Apple's commitment to workplace practices and policies concerning harassment
    or discrimination?
  - By what percentage did net income increase in 2023 compared to 2022?
  - In the structure of an Annual Report on Form 10-K, where does one find details
    about 'Litigation and Other Legal Matters'?
- source_sentence: Any such inquiries or investigations (including the IDPC proceedings)
    could subject us to substantial fines and costs, require us to change our business
    practices, divert resources and the attention of management from our business,
    or adversely affect our business.
  sentences:
  - What are some of the potential consequences for Meta Platforms, Inc. from inquiries
    or investigations as noted in the provided text?
  - What was the quarterly dividend declared by Bank of America's board of directors
    on January 31, 2024?
  - What recent technological advancements has the company implemented in set-top
    box (STB) solutions?
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: 0.7357142857142858
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8557142857142858
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8957142857142857
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9285714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7357142857142858
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28523809523809524
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1791428571428571
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09285714285714286
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7357142857142858
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8557142857142858
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8957142857142857
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9285714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8337852464509243
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8032046485260771
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8062343226371107
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7271428571428571
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8628571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.89
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9328571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7271428571428571
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2876190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17799999999999996
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09328571428571426
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7271428571428571
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8628571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.89
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9328571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8315560673246299
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7989370748299317
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.801544102570532
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.73
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8428571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8842857142857142
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9242857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.73
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28095238095238095
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17685714285714282
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09242857142857142
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.73
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8428571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8842857142857142
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9242857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8268873311527957
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7956485260770971
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.798561528530067
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.7157142857142857
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8414285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8671428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9185714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7157142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.28047619047619043
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1734285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09185714285714283
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7157142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8414285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8671428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9185714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8170171494742537
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.784555555555555
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7871835671545038
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6928571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8171428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8471428571428572
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8928571428571429
      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: 0.16942857142857143
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08928571428571426
      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: 0.8471428571428572
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8928571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7945818011619106
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7630130385487527
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7667826657397622
      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("sintuk/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Any such inquiries or investigations (including the IDPC proceedings) could subject us to substantial fines and costs, require us to change our business practices, divert resources and the attention of management from our business, or adversely affect our business.',
    'What are some of the potential consequences for Meta Platforms, Inc. from inquiries or investigations as noted in the provided text?',
    "What was the quarterly dividend declared by Bank of America's board of directors on January 31, 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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</details>
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<!--
### 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   | 0.7357     | 0.7271     | 0.73       | 0.7157    | 0.6929     |
| cosine_accuracy@3   | 0.8557     | 0.8629     | 0.8429     | 0.8414    | 0.8171     |
| cosine_accuracy@5   | 0.8957     | 0.89       | 0.8843     | 0.8671    | 0.8471     |
| cosine_accuracy@10  | 0.9286     | 0.9329     | 0.9243     | 0.9186    | 0.8929     |
| cosine_precision@1  | 0.7357     | 0.7271     | 0.73       | 0.7157    | 0.6929     |
| cosine_precision@3  | 0.2852     | 0.2876     | 0.281      | 0.2805    | 0.2724     |
| cosine_precision@5  | 0.1791     | 0.178      | 0.1769     | 0.1734    | 0.1694     |
| cosine_precision@10 | 0.0929     | 0.0933     | 0.0924     | 0.0919    | 0.0893     |
| cosine_recall@1     | 0.7357     | 0.7271     | 0.73       | 0.7157    | 0.6929     |
| cosine_recall@3     | 0.8557     | 0.8629     | 0.8429     | 0.8414    | 0.8171     |
| cosine_recall@5     | 0.8957     | 0.89       | 0.8843     | 0.8671    | 0.8471     |
| cosine_recall@10    | 0.9286     | 0.9329     | 0.9243     | 0.9186    | 0.8929     |
| **cosine_ndcg@10**  | **0.8338** | **0.8316** | **0.8269** | **0.817** | **0.7946** |
| cosine_mrr@10       | 0.8032     | 0.7989     | 0.7956     | 0.7846    | 0.763      |
| cosine_map@100      | 0.8062     | 0.8015     | 0.7986     | 0.7872    | 0.7668     |

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## 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: 6 tokens</li><li>mean: 45.64 tokens</li><li>max: 301 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.4 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                | anchor                                                                                                    |
  |:----------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|
  | <code>We later began working with commercial enterprises, who often faced fundamentally similar challenges in working with data.</code> | <code>What type of software solutions did Palantir later provide to commercial enterprises?</code>        |
  | <code>General Motors Company was incorporated as a Delaware corporation in 2009.</code>                                                 | <code>What year was General Motors Company incorporated?</code>                                           |
  | <code>Companies with which we have strategic partnerships in some areas may be competitors in other areas.</code>                       | <code>What is the nature of IBM's relationship with its strategic partners in competitional terms?</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
- `tf32`: False
- `load_best_model_at_end`: True
- `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`: 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
- `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     | 1.6103        | -                      | -                      | -                      | -                      | -                     |
| 0.9746     | 12     | -             | 0.8290                 | 0.8247                 | 0.8184                 | 0.8110                 | 0.7726                |
| 1.6244     | 20     | 0.6597        | -                      | -                      | -                      | -                      | -                     |
| 1.9492     | 24     | -             | 0.8313                 | 0.8290                 | 0.8264                 | 0.8161                 | 0.7849                |
| 2.4365     | 30     | 0.5016        | -                      | -                      | -                      | -                      | -                     |
| 2.9239     | 36     | -             | 0.8340                 | 0.8323                 | 0.8265                 | 0.8170                 | 0.7943                |
| 3.2487     | 40     | 0.4629        | -                      | -                      | -                      | -                      | -                     |
| **3.8985** | **48** | **-**         | **0.8338**             | **0.8316**             | **0.8269**             | **0.817**              | **0.7946**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.9
- Sentence Transformers: 3.4.1
- Transformers: 4.41.2
- PyTorch: 2.2.2
- Accelerate: 1.5.2
- 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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