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: On October 1, 2023, The Hershey Company paid $1.083 per share as
    dividends for Class B common stock.
  sentences:
  - What mechanisms does the company use to hedge foreign currency exposure for its
    Canadian subsidiary?
  - What was the dividends paid per share for Class B common stock of The Hershey
    Company on October 1, 2023?
  - What were the primary reasons for the decline in FedEx Express segment's operating
    income in 2023?
- source_sentence: The Federal Reserve may require JPMorgan Chase & Co. to take appropriate
    action, such as guaranteeing the performance of capital restoration plans, if
    any of its bank subsidiaries become undercapitalized.
  sentences:
  - Is the information about legal proceedings in Item 3 directly provided or referenced
    from another section?
  - What are the consequences for JPMorgan Chase & Co. under the Federal Reserve's
    rules if its bank subsidiaries become undercapitalized?
  - What measures does the company take to ensure supplier compliance with responsible
    sourcing standards?
- source_sentence: 'FDICIA establishes five capital categories for FDIC-insured banks,
    such as GS Bank USA: well-capitalized, adequately capitalized, undercapitalized,
    significantly undercapitalized and critically undercapitalized.'
  sentences:
  - What are the capital categories established under FDICIA for FDIC-insured banks?
  - What types of costs are included in the non-cash amortization related to derivative
    instruments?
  - What did Masimo allege in its complaint before the U.S. International Trade Commission?
- source_sentence: Leasehold improvements are depreciated over the shorter of the
    estimated useful life, based on the estimates above, or the remaining lease term.
  sentences:
  - How much did the after-tax earnings of the real estate brokerage decrease in 2023,
    and what were the primary factors for this decrease?
  - What factors contributed to the increase in operating expenses in the Health Services
    segment in 2023 compared to 2022?
  - How does the company handle leasehold improvements in terms of depreciation?
- source_sentence: Historically, the majority of gift cards are redeemed within one
    year. In addition, a portion of gift cards are not expected to be redeemed and
    will be recognized as breakage over time.
  sentences:
  - What is the estimated redemption rate for Chipotle gift cards?
  - What criterion must companies meet to join the Billion Dollar Roundtable Inc.?
  - How did net sales at The Hershey Company in 2023 compare with those in 2022?
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: 0.6971428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8242857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8642857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9085714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6971428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2747619047619047
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17285714285714285
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09085714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6971428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8242857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8642857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9085714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8047211278959339
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7712590702947846
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7749643014746781
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7042857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8271428571428572
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9114285714285715
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7042857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2757142857142857
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09114285714285712
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7042857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8271428571428572
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9114285714285715
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8083954377602355
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7754263038548752
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7789945459013643
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6928571428571428
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8242857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8671428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6928571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27476190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1734285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09099999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6928571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8242857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8671428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8018496000116092
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7671831065759637
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7706729768881546
      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: 0.8042857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8614285714285714
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8985714285714286
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6728571428571428
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2680952380952381
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17228571428571426
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08985714285714284
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6728571428571428
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8042857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8614285714285714
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8985714285714286
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7858671205003231
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7496071428571424
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7533922486855088
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6342857142857142
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7785714285714286
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8157142857142857
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8642857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6342857142857142
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2595238095238095
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16314285714285712
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08642857142857142
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6342857142857142
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7785714285714286
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8157142857142857
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8642857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7521412897592005
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7160515873015872
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7203641723179117
      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("Dash00/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Historically, the majority of gift cards are redeemed within one year. In addition, a portion of gift cards are not expected to be redeemed and will be recognized as breakage over time.',
    'What is the estimated redemption rate for Chipotle gift cards?',
    'What criterion must companies meet to join the Billion Dollar Roundtable Inc.?',
]
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>
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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   | 0.6971     | 0.7043     | 0.6929     | 0.6729     | 0.6343     |
| cosine_accuracy@3   | 0.8243     | 0.8271     | 0.8243     | 0.8043     | 0.7786     |
| cosine_accuracy@5   | 0.8643     | 0.8629     | 0.8671     | 0.8614     | 0.8157     |
| cosine_accuracy@10  | 0.9086     | 0.9114     | 0.91       | 0.8986     | 0.8643     |
| cosine_precision@1  | 0.6971     | 0.7043     | 0.6929     | 0.6729     | 0.6343     |
| cosine_precision@3  | 0.2748     | 0.2757     | 0.2748     | 0.2681     | 0.2595     |
| cosine_precision@5  | 0.1729     | 0.1726     | 0.1734     | 0.1723     | 0.1631     |
| cosine_precision@10 | 0.0909     | 0.0911     | 0.091      | 0.0899     | 0.0864     |
| cosine_recall@1     | 0.6971     | 0.7043     | 0.6929     | 0.6729     | 0.6343     |
| cosine_recall@3     | 0.8243     | 0.8271     | 0.8243     | 0.8043     | 0.7786     |
| cosine_recall@5     | 0.8643     | 0.8629     | 0.8671     | 0.8614     | 0.8157     |
| cosine_recall@10    | 0.9086     | 0.9114     | 0.91       | 0.8986     | 0.8643     |
| **cosine_ndcg@10**  | **0.8047** | **0.8084** | **0.8018** | **0.7859** | **0.7521** |
| cosine_mrr@10       | 0.7713     | 0.7754     | 0.7672     | 0.7496     | 0.7161     |
| cosine_map@100      | 0.775      | 0.779      | 0.7707     | 0.7534     | 0.7204     |

<!--
## 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: 45.3 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.29 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                 | anchor                                                                                                                             |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
  | <code>Our Employee Opinion Survey is a vehicle for employees to provide confidential feedback on their experience as Salesforce employees. The results are used to assess employee engagement, our company culture and our workplace environment.</code> | <code>What is the purpose of the Employee Opinion Survey at Salesforce?</code>                                                     |
  | <code>Phrases such as 'anticipates', 'believes', 'estimates', 'seeks', 'expects', 'plans', 'intends', 'remains', 'positions', and similar expressions are intended to identify forward-looking statements related to the company or management.</code>   | <code>What are the key phrases used to identify forward-looking statements related to the company or management?</code>            |
  | <code>In 2022, the Reverb and Depop marketplaces generated approximately $942.1 million (7.1% of the total) and $599.6 million (4.6% of the total).</code>                                                                                               | <code>What was the economic contribution by the Reverb and Depop marketplaces in terms of Gross Merchandise Sales for 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 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0     | 7      | -             | 0.7898                 | 0.7887                 | 0.7847                 | 0.7697                 | 0.7298                |
| 1.4848  | 10     | 35.5192       | -                      | -                      | -                      | -                      | -                     |
| 2.0     | 14     | -             | 0.8042                 | 0.8055                 | 0.7987                 | 0.7804                 | 0.7497                |
| 2.9697  | 20     | 16.2448       | -                      | -                      | -                      | -                      | -                     |
| **3.0** | **21** | **-**         | **0.8047**             | **0.8084**             | **0.8017**             | **0.7859**             | **0.7532**            |
| 3.4848  | 24     | -             | 0.8047                 | 0.8084                 | 0.8018                 | 0.7859                 | 0.7521                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.4.0
- 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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