---
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: 'The Innovative Medicine segment is focused on the following therapeutic
    areas: Immunology, Infectious diseases, Neuroscience, Oncology, Pulmonary Hypertension,
    and Cardiovascular and Metabolic diseases.'
  sentences:
  - What was the primary reason for the decrease in adjusted operating income in 2023?
  - What therapeutic areas does the Innovative Medicine segment of Johnson & Johnson
    focus on?
  - What was the remaining budget for the September 2022 Repurchase Program as of
    January 28, 2023?
- source_sentence: It may be necessary in the future to seek or renew licenses relating
    to various aspects of the Company’s products, processes and services. While the
    Company has generally been able to obtain such licenses on commercially reasonable
    terms in the past, there is no guarantee that such licenses could be obtained
    in the future on reasonable terms or at all.
  sentences:
  - What was the percentage change in total earning assets from the previous year
    as reported in 2023?
  - What is Apple's approach to licenses for intellectual property owned by third
    parties used in its products and services?
  - Why did the Ontario class action related to the 2017 cybersecurity incident progress
    differently than other cases?
- source_sentence: Assets and liabilities measured at fair value on a nonrecurring
    basis in the consolidated financial statements include items such as property,
    plant and equipment, ROU assets, goodwill and other intangible assets, equity
    and other investments and other assets. These are measured at fair value if determined
    to be impaired.
  sentences:
  - How are assets and liabilities that are measured at fair value on a nonrecurring
    basis identified in the financial statements?
  - How is goodwill reviewed for impairment in a company, and what methods are used
    to determine the fair value of reporting units?
  - What diversity and inclusion goals has Goldman Sachs set for its workforce by
    2025?
- source_sentence: Based on management’s allocation decision, the portion of the Credit
    Facility available to ME&T as of December 31, 2023 was $2.75 billion.
  sentences:
  - What were the components of the increase in costs related to operating channels
    in 2023?
  - What are the goals of American Express’s balance sheet management strategy?
  - How much of the Credit Facility was available to ME&T as of December 31, 2023?
- source_sentence: Item 8 is labeled 'Financial Statements and Supplementary Data.'
  sentences:
  - What section of the document is labeled 'Item 8'?
  - For comprehensive information on a company's legal matters, which part of the
    financial statement should one consult?
  - What was the return on average common stockholders’ equity for 2023?
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: 0.6928571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8257142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8671428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9071428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6928571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2752380952380953
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1734285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0907142857142857
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6928571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8257142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8671428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9071428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8015678007585516
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7675442176870747
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7711683558124478
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6914285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8257142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8671428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9071428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6914285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2752380952380952
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1734285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0907142857142857
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6914285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8257142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8671428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9071428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8009375601369785
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7666672335600906
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7701113420260945
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6871428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8242857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8585714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9028571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6871428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2747619047619047
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1717142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09028571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6871428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8242857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8585714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9028571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7965630325935761
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7623344671201813
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7659656636117955
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6728571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8085714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8485714285714285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8871428571428571
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6728571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26952380952380944
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16971428571428568
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0887142857142857
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6728571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8085714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8485714285714285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8871428571428571
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7820355932651222
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7480856009070294
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7523134135641188
      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: 0.7685714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8128571428571428
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.86
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6357142857142857
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2561904761904762
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16257142857142853
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.086
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6357142857142857
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7685714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8128571428571428
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.86
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7472648621107045
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7111729024943308
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7168773691247933
      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("schawla2/bge-base-financial-matryoshka")
# Run inference
sentences = [
    "Item 8 is labeled 'Financial Statements and Supplementary Data.'",
    "What section of the document is labeled 'Item 8'?",
    "For comprehensive information on a company's legal matters, which part of the financial statement should one consult?",
]
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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You can finetune this model on your own dataset.

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

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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   | 0.6929     | 0.6914     | 0.6871     | 0.6729    | 0.6357     |
| cosine_accuracy@3   | 0.8257     | 0.8257     | 0.8243     | 0.8086    | 0.7686     |
| cosine_accuracy@5   | 0.8671     | 0.8671     | 0.8586     | 0.8486    | 0.8129     |
| cosine_accuracy@10  | 0.9071     | 0.9071     | 0.9029     | 0.8871    | 0.86       |
| cosine_precision@1  | 0.6929     | 0.6914     | 0.6871     | 0.6729    | 0.6357     |
| cosine_precision@3  | 0.2752     | 0.2752     | 0.2748     | 0.2695    | 0.2562     |
| cosine_precision@5  | 0.1734     | 0.1734     | 0.1717     | 0.1697    | 0.1626     |
| cosine_precision@10 | 0.0907     | 0.0907     | 0.0903     | 0.0887    | 0.086      |
| cosine_recall@1     | 0.6929     | 0.6914     | 0.6871     | 0.6729    | 0.6357     |
| cosine_recall@3     | 0.8257     | 0.8257     | 0.8243     | 0.8086    | 0.7686     |
| cosine_recall@5     | 0.8671     | 0.8671     | 0.8586     | 0.8486    | 0.8129     |
| cosine_recall@10    | 0.9071     | 0.9071     | 0.9029     | 0.8871    | 0.86       |
| **cosine_ndcg@10**  | **0.8016** | **0.8009** | **0.7966** | **0.782** | **0.7473** |
| cosine_mrr@10       | 0.7675     | 0.7667     | 0.7623     | 0.7481    | 0.7112     |
| cosine_map@100      | 0.7712     | 0.7701     | 0.766      | 0.7523    | 0.7169     |

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### Recommendations

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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: 4 tokens</li><li>mean: 45.97 tokens</li><li>max: 326 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.57 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
  | positive                                                                                                        | anchor                                                                                                |
  |:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|
  | <code>In 2023, Delta took delivery of 43 aircraft.</code>                                                       | <code>How many new aircraft did Delta Air Lines take delivery of in 2023?</code>                      |
  | <code>Item 8 incorporates pages 44 through 121 of IBM’s 2023 Annual Report to Stockholders by reference.</code> | <code>What sections of IBM's 2023 Annual Report are incorporated into Item 8 of the Form 10-K?</code> |
  | <code>Total borrowings at the end of 2023 were $29.3 billion.</code>                                            | <code>What was the total amount of debt the Company had at the end of 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,
          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
- `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`: 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`: 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 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8122  | 10     | 24.5441       | -                      | -                      | -                      | -                      | -                     |
| 1.0     | 13     | -             | 0.7920                 | 0.7932                 | 0.7878                 | 0.7699                 | 0.7305                |
| 1.5685  | 20     | 10.1942       | -                      | -                      | -                      | -                      | -                     |
| 2.0     | 26     | -             | 0.7998                 | 0.7988                 | 0.7968                 | 0.7818                 | 0.7443                |
| 2.3249  | 30     | 6.8787        | -                      | -                      | -                      | -                      | -                     |
| **3.0** | **39** | **-**         | **0.8014**             | **0.8015**             | **0.7976**             | **0.7825**             | **0.7503**            |
| 3.0812  | 40     | 6.1782        | -                      | -                      | -                      | -                      | -                     |
| 3.7310  | 48     | -             | 0.8016                 | 0.8009                 | 0.7966                 | 0.7820                 | 0.7473                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 3.3.1
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- 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}
}
```

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