README.md

---
base_model: BAAI/bge-base-en-v1.5
datasets: []
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:700
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Goodwill arising from the acquisition of Xilinx was valued at $22,784
    million, attributed mainly to increased synergies expected from the integration
    of Xilinx into the Company's Embedded and Data Center segments.
  sentences:
  - What growth strategy does lululemon plan to employ for their operations in China
    Mainland?
  - What was the fair value of the goodwill generated from the acquisition of Xilinx?
  - How did the products gross margin percentage change from 2022 to 2023?
- source_sentence: In 2023, UnitedHealthcare's regulated subsidiaries paid $8.0 billion
    in dividends to their parent companies.
  sentences:
  - What amount did UnitedHealthcare's regulated subsidiaries pay as dividends to
    their parent companies in 2023?
  - What initiative does the Basel, Rotterdam and Stockholm Conventions focus on?
  - What is the primary target of Palantir's customer acquisition strategy?
- source_sentence: These assumptions about future disposition of inventory are inherently
    uncertain and changes in our estimates and assumptions may cause us to realize
    material write-downs in the future.
  sentences:
  - How did the return on average common stockholders’ equity (GAAP) change from 2021
    to 2023?
  - What is the effect of changes in inventory estimates on the company's financial
    statements?
  - What is the principal business experience of David M. Chojnowski before his current
    role as Senior Vice President and Controller?
- source_sentence: During the years ended December 31, 2021, 2022 and 2023, the weighted-average
    fair value of stock options granted under the Plans was $96.50, $79.75 and $65.22
    per share, respectively.
  sentences:
  - What was the weighted-average grant-date fair value of stock options granted in
    2021, 2022, and 2023?
  - What major weather events contributed to the increase in losses reported in 2023?
  - What is the V2MOM, and how is it used within the company?
- source_sentence: During fiscal year 2023, we repurchased 10.4 million shares for
    approximately $1,295 million.
  sentences:
  - How much does Kroger plan to invest in training its associates in 2023?
  - What total amount was spent on share repurchases during fiscal year 2023?
  - What judicial decision occurred in August 2023 regarding the antitrust lawsuits
    against the airlines?
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: 0.6742857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8052380952380952
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8458730158730159
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8933333333333333
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6742857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26841269841269844
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16917460317460317
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08933333333333332
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6742857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8052380952380952
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8458730158730159
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8933333333333333
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7837644898436449
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7486834215167553
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7524444605977678
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.669047619047619
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8023809523809524
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8444444444444444
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.893015873015873
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.669047619047619
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26746031746031745
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1688888888888889
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08930158730158728
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.669047619047619
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8023809523809524
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8444444444444444
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.893015873015873
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7805515576068588
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.744609410430839
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7483879357643801
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6623809523809524
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7933333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8334920634920635
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8831746031746032
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6623809523809524
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2644444444444444
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16669841269841268
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08831746031746031
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6623809523809524
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7933333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8334920634920635
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8831746031746032
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.772554826031694
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7372027588813304
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7413385015201707
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6419047619047619
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7698412698412699
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8131746031746032
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8628571428571429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6419047619047619
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2566137566137566
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16263492063492063
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08628571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6419047619047619
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7698412698412699
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8131746031746032
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8628571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7522219583193863
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7168462459057695
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7216902902285594
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.5901587301587301
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7241269841269842
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7661904761904762
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8185714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5901587301587301
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.24137566137566135
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15323809523809523
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08185714285714285
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5901587301587301
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7241269841269842
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7661904761904762
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8185714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7039266407844053
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6673720710506443
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6731612260450521
      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("IlhamEbdesk/bge-base-financial-matryoshka_test")
# Run inference
sentences = [
    'During fiscal year 2023, we repurchased 10.4 million shares for approximately $1,295 million.',
    'What total amount was spent on share repurchases during fiscal year 2023?',
    'What judicial decision occurred in August 2023 regarding the antitrust lawsuits against the airlines?',
]
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.*
-->

## 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   | 0.6743     |
| cosine_accuracy@3   | 0.8052     |
| cosine_accuracy@5   | 0.8459     |
| cosine_accuracy@10  | 0.8933     |
| cosine_precision@1  | 0.6743     |
| cosine_precision@3  | 0.2684     |
| cosine_precision@5  | 0.1692     |
| cosine_precision@10 | 0.0893     |
| cosine_recall@1     | 0.6743     |
| cosine_recall@3     | 0.8052     |
| cosine_recall@5     | 0.8459     |
| cosine_recall@10    | 0.8933     |
| cosine_ndcg@10      | 0.7838     |
| cosine_mrr@10       | 0.7487     |
| **cosine_map@100**  | **0.7524** |

#### 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   | 0.669      |
| cosine_accuracy@3   | 0.8024     |
| cosine_accuracy@5   | 0.8444     |
| cosine_accuracy@10  | 0.893      |
| cosine_precision@1  | 0.669      |
| cosine_precision@3  | 0.2675     |
| cosine_precision@5  | 0.1689     |
| cosine_precision@10 | 0.0893     |
| cosine_recall@1     | 0.669      |
| cosine_recall@3     | 0.8024     |
| cosine_recall@5     | 0.8444     |
| cosine_recall@10    | 0.893      |
| cosine_ndcg@10      | 0.7806     |
| cosine_mrr@10       | 0.7446     |
| **cosine_map@100**  | **0.7484** |

#### 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   | 0.6624     |
| cosine_accuracy@3   | 0.7933     |
| cosine_accuracy@5   | 0.8335     |
| cosine_accuracy@10  | 0.8832     |
| cosine_precision@1  | 0.6624     |
| cosine_precision@3  | 0.2644     |
| cosine_precision@5  | 0.1667     |
| cosine_precision@10 | 0.0883     |
| cosine_recall@1     | 0.6624     |
| cosine_recall@3     | 0.7933     |
| cosine_recall@5     | 0.8335     |
| cosine_recall@10    | 0.8832     |
| cosine_ndcg@10      | 0.7726     |
| cosine_mrr@10       | 0.7372     |
| **cosine_map@100**  | **0.7413** |

#### 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   | 0.6419     |
| cosine_accuracy@3   | 0.7698     |
| cosine_accuracy@5   | 0.8132     |
| cosine_accuracy@10  | 0.8629     |
| cosine_precision@1  | 0.6419     |
| cosine_precision@3  | 0.2566     |
| cosine_precision@5  | 0.1626     |
| cosine_precision@10 | 0.0863     |
| cosine_recall@1     | 0.6419     |
| cosine_recall@3     | 0.7698     |
| cosine_recall@5     | 0.8132     |
| cosine_recall@10    | 0.8629     |
| cosine_ndcg@10      | 0.7522     |
| cosine_mrr@10       | 0.7168     |
| **cosine_map@100**  | **0.7217** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.5902     |
| cosine_accuracy@3   | 0.7241     |
| cosine_accuracy@5   | 0.7662     |
| cosine_accuracy@10  | 0.8186     |
| cosine_precision@1  | 0.5902     |
| cosine_precision@3  | 0.2414     |
| cosine_precision@5  | 0.1532     |
| cosine_precision@10 | 0.0819     |
| cosine_recall@1     | 0.5902     |
| cosine_recall@3     | 0.7241     |
| cosine_recall@5     | 0.7662     |
| cosine_recall@10    | 0.8186     |
| cosine_ndcg@10      | 0.7039     |
| cosine_mrr@10       | 0.6674     |
| **cosine_map@100**  | **0.6732** |

<!--
## 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### 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
- `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`: 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_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  | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.7273     | 1     | 0.6718                 | 0.7044                 | 0.7160                 | 0.6086                | 0.7194                 |
| 1.4545     | 2     | 0.6897                 | 0.7192                 | 0.7298                 | 0.6329                | 0.7314                 |
| **2.9091** | **4** | **0.7051**             | **0.7292**             | **0.7387**             | **0.6504**            | **0.7409**             |
| 0.7273     | 1     | 0.7051                 | 0.7292                 | 0.7387                 | 0.6504                | 0.7409                 |
| 1.4545     | 2     | 0.7148                 | 0.7366                 | 0.7446                 | 0.6636                | 0.7473                 |
| **2.9091** | **4** | **0.7217**             | **0.7413**             | **0.7484**             | **0.6732**            | **0.7524**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->