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: Item 8 includes information related to Financial Statements and
    Supplementary Data.
  sentences:
  - What are the included components in shipping costs that contribute to the cost
    of sales?
  - What information is mentioned under Item 8 in a financial document?
  - By how much did domestic distribution revenue excluding the Olympics change from
    2021 to 2023?
- source_sentence: Our products enable global service providers to continue to deploy
    next generation broadband access technologies across multiple standards, including
    G.fast, Data Over Cable Service Interface Specifications (DOCSIS), PON and Wi-Fi
    to provide more bandwidth and faster speeds to consumers.
  sentences:
  - Where is the index to the Financial Statements and Supplementary Data located?
  - How do our products support next-generation broadband access technologies?
  - What type of corporation is The Coca-Cola Company?
- source_sentence: 'Our strategy is focused on growing customer loyalty by delivering
    great value and convenience, and investing in four strategic pillars: Fresh, Our
    Brands, Data & Personalization and Seamless.'
  sentences:
  - What are Kroger’s four strategic pillars?
  - What are some current enterprise applications of VR and AR technologies?
  - What was the amount of cost savings achieved in 2022 and for how many consecutive
    years has this level been maintained?
- source_sentence: We utilize a sourcing strategy that emphasizes global procurement
    of materials and product manufacturing in lower cost regions. We outsource the
    manufacturing of our products to third-party contract manufacturers.
  sentences:
  - What was the percentage increase or decrease in total research and development
    expenses for the company from the previous year?
  - What factors are considered when deciding whether to accrue a loss or disclose
    a loss contingency?
  - What kind of sourcing strategy does Enphase Energy, Inc. employ for its production?
- source_sentence: Total Company matching contributions to the plans were $263 million
    in fiscal year 2023.
  sentences:
  - What was the total company matching contributions for Johnson & Johnson's 401(k)
    savings plans in the fiscal year 2023?
  - What are the liquidity requirements under the Liquidity Coverage Ratio (LCR) rule
    for Charles Schwab?
  - How are financial statement indexes presented in a document?
model-index:
- name: BGE base Financial Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.6871428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.83
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8728571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9114285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6871428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27666666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17457142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09114285714285712
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6871428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.83
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8728571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9114285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8028469518140957
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7677721088435375
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7712439029918364
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6871428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8257142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9085714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6871428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2752380952380952
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09085714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6871428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8257142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9085714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7998672590904344
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7649750566893426
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7686634304273016
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6814285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.83
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8557142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8885714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6814285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27666666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17114285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08885714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6814285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.83
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8557142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8885714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7912941283092169
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7594756235827663
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7644992750510822
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6542857142857142
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8114285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8457142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8828571428571429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6542857142857142
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2704761904761904
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16914285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08828571428571429
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6542857142857142
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8114285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8457142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8828571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7730532143416082
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7374013605442173
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7419005381162616
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6285714285714286
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7742857142857142
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8071428571428572
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.86
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6285714285714286
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2580952380952381
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16142857142857142
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08599999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6285714285714286
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7742857142857142
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8071428571428572
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.86
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7464335989173261
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7099308390022675
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.714898042290808
      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("Habiba9/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Total Company matching contributions to the plans were $263 million in fiscal year 2023.',
    "What was the total company matching contributions for Johnson & Johnson's 401(k) savings plans in the fiscal year 2023?",
    'What are the liquidity requirements under the Liquidity Coverage Ratio (LCR) rule for Charles Schwab?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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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   | 0.6871     | 0.6871     | 0.6814     | 0.6543     | 0.6286     |
| cosine_accuracy@3   | 0.83       | 0.8257     | 0.83       | 0.8114     | 0.7743     |
| cosine_accuracy@5   | 0.8729     | 0.8629     | 0.8557     | 0.8457     | 0.8071     |
| cosine_accuracy@10  | 0.9114     | 0.9086     | 0.8886     | 0.8829     | 0.86       |
| cosine_precision@1  | 0.6871     | 0.6871     | 0.6814     | 0.6543     | 0.6286     |
| cosine_precision@3  | 0.2767     | 0.2752     | 0.2767     | 0.2705     | 0.2581     |
| cosine_precision@5  | 0.1746     | 0.1726     | 0.1711     | 0.1691     | 0.1614     |
| cosine_precision@10 | 0.0911     | 0.0909     | 0.0889     | 0.0883     | 0.086      |
| cosine_recall@1     | 0.6871     | 0.6871     | 0.6814     | 0.6543     | 0.6286     |
| cosine_recall@3     | 0.83       | 0.8257     | 0.83       | 0.8114     | 0.7743     |
| cosine_recall@5     | 0.8729     | 0.8629     | 0.8557     | 0.8457     | 0.8071     |
| cosine_recall@10    | 0.9114     | 0.9086     | 0.8886     | 0.8829     | 0.86       |
| **cosine_ndcg@10**  | **0.8028** | **0.7999** | **0.7913** | **0.7731** | **0.7464** |
| cosine_mrr@10       | 0.7678     | 0.765      | 0.7595     | 0.7374     | 0.7099     |
| cosine_map@100      | 0.7712     | 0.7687     | 0.7645     | 0.7419     | 0.7149     |

<!--
## 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: 8 tokens</li><li>mean: 46.71 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.37 tokens</li><li>max: 42 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | anchor                                                                                                                                       |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>The total research and development expense showed a 6.7% increase compared to the previous year.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>What was the percentage increase or decrease in total research and development expenses for the company from the previous year?</code> |
  | <code>Quality of production processes involves strict control of ingredients, equipment, facilities, manufacturing methods, packaging materials, and labeling. We perform tests at various stages of production processes and on the final product in an effort to ensure that the product meets all applicable regulatory requirements and our internal standards. These tests may involve chemical and physical chemical analyses, microbiological testing, testing in animals, or a combination thereof. Additional assurance of quality is provided by quality assurance groups that audit and monitor all aspects of quality related to pharmaceutical manufacturing procedures and systems in company operations and at third-party suppliers.</code> | <code>What measures does the company take to ensure product quality?</code>                                                                  |
  | <code>Item 8. Financial Statements and Supplementary Data. The Consolidated Financial Statements, together with the Notes thereto and the report thereon dated February 16, 2024, of PricewaterhouseCoopers LLP, the Firm’s independent registered public accounting firm (PCAOB ID 238).</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                            | <code>What type of data does Item 8 in a financial document contain?</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     | 1.533         | -                      | -                      | -                      | -                      | -                     |
| 0.9746     | 12     | -             | 0.7920                 | 0.7876                 | 0.7786                 | 0.7516                 | 0.7262                |
| 1.6244     | 20     | 0.6753        | -                      | -                      | -                      | -                      | -                     |
| 1.9492     | 24     | -             | 0.8033                 | 0.8009                 | 0.7924                 | 0.7661                 | 0.7404                |
| 2.4365     | 30     | 0.4925        | -                      | -                      | -                      | -                      | -                     |
| 2.9239     | 36     | -             | 0.8032                 | 0.7998                 | 0.7908                 | 0.7724                 | 0.7464                |
| 3.2487     | 40     | 0.4458        | -                      | -                      | -                      | -                      | -                     |
| **3.8985** | **48** | **-**         | **0.8028**             | **0.7999**             | **0.7913**             | **0.7731**             | **0.7464**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.4.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- 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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