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: The Parisian Macao saw its occupancy rate increase significantly
    from 37.9% in 2022 to 93.0% in 2023.
  sentences:
  - Who is the Chief People Officer of this company and what are their responsibilities?
  - What was the occupancy rate change at The Parisian Macao from 2022 to 2023?
  - What components make up the mall-related expenses?
- source_sentence: According to Item 8, the Financial Statement Schedule is located
    on page S-1 of IBM’s Form 10-K.
  sentences:
  - In IBM’s Form 10-K filing, where is the Financial Statement Schedule located?
  - What was the total amount repurchased by the company in fiscal years 2022 and
    2023 under the share repurchase program?
  - What was the net cash position change due to exchange rate effects during the
    year in the financial data?
- source_sentence: A hypothetical 50% decrease in short-term interest rates would
    decrease our annual pre-tax earnings by $15 million as of December 31, 2023, assuming
    no change in the amount or composition of our cash and cash equivalents and short-term
    and long-term restricted cash and cash equivalents.
  sentences:
  - What financial impact would a 50% decrease in short-term interest rates have on
    the company's annual pre-tax earnings as of December 31, 2023?
  - What are the typical higher sales quarters for companies due to seasonal and holiday-related
    sales patterns?
  - What triggers the company to accrue for the cost of product recalls and corrective
    actions?
- 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 was the percentage change in impairment of goodwill for Hewlett Packard Enterprise
    between fiscal 2022 and 2023?
  - What are Kroger’s four strategic pillars?
  - How much did the foreclosed properties decrease in value during 2023?
- source_sentence: The Inflation Reduction Act of 2022 has and will continue to have
    a significant impact on how drugs are covered and paid for under the Medicare
    program, including through the creation of financial penalties for drugs whose
    price increases outpace inflation, the redesign of Medicare Part D benefits to
    shift a greater portion of the costs to manufacturers, and through government
    price-setting for certain Medicare Part B and Part D drugs.
  sentences:
  - What was the total depreciation and amortization expense for the company in 2023?
  - What overall context does Item 3. Legal Proceedings offer regarding the company?
  - How does the Inflation Reduction Act of 2022 impact AbbVie's drug pricing under
    Medicare?
model-index:
- name: BGE base Financial Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.7357142857142858
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8728571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9014285714285715
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9271428571428572
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7357142857142858
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29095238095238096
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18028571428571427
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09271428571428571
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7357142857142858
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8728571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9014285714285715
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9271428571428572
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8376503331859739
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8083339002267572
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8113126406613911
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7328571428571429
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.87
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8985714285714286
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9271428571428572
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7328571428571429
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1797142857142857
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09271428571428571
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7328571428571429
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.87
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8985714285714286
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9271428571428572
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8361443346673566
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8063117913832198
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.80919006196483
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.7228571428571429
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8642857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8971428571428571
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9214285714285714
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7228571428571429
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2880952380952381
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1794285714285714
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09214285714285714
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7228571428571429
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8642857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8971428571428571
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9214285714285714
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8293858846039718
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7991496598639453
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.802093445052298
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.7142857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.85
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.88
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7142857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2833333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.176
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09099999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7142857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.85
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.88
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8170618027193949
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7867528344671202
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7900596429177168
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.6614285714285715
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8157142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.85
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8928571428571429
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6614285714285715
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27190476190476187
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08928571428571427
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6614285714285715
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8157142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.85
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8928571428571429
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7826677679629053
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7468339002267574
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7512201171926934
      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("KristupasC/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'The Inflation Reduction Act of 2022 has and will continue to have a significant impact on how drugs are covered and paid for under the Medicare program, including through the creation of financial penalties for drugs whose price increases outpace inflation, the redesign of Medicare Part D benefits to shift a greater portion of the costs to manufacturers, and through government price-setting for certain Medicare Part B and Part D drugs.',
    "How does the Inflation Reduction Act of 2022 impact AbbVie's drug pricing under Medicare?",
    'What overall context does Item 3. Legal Proceedings offer regarding the company?',
]
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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### Direct Usage (Transformers)

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1   | 0.7357     | 0.7329     | 0.7229     | 0.7143     | 0.6614     |
| cosine_accuracy@3   | 0.8729     | 0.87       | 0.8643     | 0.85       | 0.8157     |
| cosine_accuracy@5   | 0.9014     | 0.8986     | 0.8971     | 0.88       | 0.85       |
| cosine_accuracy@10  | 0.9271     | 0.9271     | 0.9214     | 0.91       | 0.8929     |
| cosine_precision@1  | 0.7357     | 0.7329     | 0.7229     | 0.7143     | 0.6614     |
| cosine_precision@3  | 0.291      | 0.29       | 0.2881     | 0.2833     | 0.2719     |
| cosine_precision@5  | 0.1803     | 0.1797     | 0.1794     | 0.176      | 0.17       |
| cosine_precision@10 | 0.0927     | 0.0927     | 0.0921     | 0.091      | 0.0893     |
| cosine_recall@1     | 0.7357     | 0.7329     | 0.7229     | 0.7143     | 0.6614     |
| cosine_recall@3     | 0.8729     | 0.87       | 0.8643     | 0.85       | 0.8157     |
| cosine_recall@5     | 0.9014     | 0.8986     | 0.8971     | 0.88       | 0.85       |
| cosine_recall@10    | 0.9271     | 0.9271     | 0.9214     | 0.91       | 0.8929     |
| **cosine_ndcg@10**  | **0.8377** | **0.8361** | **0.8294** | **0.8171** | **0.7827** |
| cosine_mrr@10       | 0.8083     | 0.8063     | 0.7991     | 0.7868     | 0.7468     |
| cosine_map@100      | 0.8113     | 0.8092     | 0.8021     | 0.7901     | 0.7512     |

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## Bias, Risks and Limitations

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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: 9 tokens</li><li>mean: 46.27 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.79 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                          | anchor                                                                                                                                  |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|
  | <code>As of December 31, 2023, we owned 4,325 shares of common stock of Tractor Beverages, Inc. (“Tractor”). Our investment represents ownership of approximately 10.2% of Tractor, and we have invested total cash consideration of $10,000. There were no impairment charges for the year ended December 31, 2023 or 2022 associated with this equity method investment.</code> | <code>What financial performance metrics are reported for equity investments in Tractor Beverages, Inc. as of December 31, 2023?</code> |
  | <code>Sales of Alphagan/Combigan in the United States decreased by 40.1% from $373 million in 2021 to $121 million in 2023.</code>                                                                                                                                                                                                                                                | <code>What was the percentage decrease in sales for Alphagan/Combigan in the United States from 2021 to 2023?</code>                    |
  | <code>For the year ended December 31, 2023, the net cash provided by (used in) investing activities totaled -$49,833 million.</code>                                                                                                                                                                                                                                              | <code>What was the net cash impact from investing activities for the year ended December 31, 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`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `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`: True
- `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
- `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 |
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.2030     | 10      | 1.2662        | -                      | -                      | -                      | -                      | -                     |
| 0.4061     | 20      | 0.7152        | -                      | -                      | -                      | -                      | -                     |
| 0.6091     | 30      | 0.4379        | -                      | -                      | -                      | -                      | -                     |
| 0.8122     | 40      | 0.3736        | -                      | -                      | -                      | -                      | -                     |
| 0.9949     | 49      | -             | 0.8274                 | 0.8270                 | 0.8210                 | 0.8048                 | 0.7694                |
| 1.0152     | 50      | 0.3089        | -                      | -                      | -                      | -                      | -                     |
| 1.2183     | 60      | 0.3021        | -                      | -                      | -                      | -                      | -                     |
| 1.4213     | 70      | 0.2266        | -                      | -                      | -                      | -                      | -                     |
| 1.6244     | 80      | 0.2479        | -                      | -                      | -                      | -                      | -                     |
| 1.8274     | 90      | 0.2192        | -                      | -                      | -                      | -                      | -                     |
| 1.9898     | 98      | -             | 0.8372                 | 0.8346                 | 0.8281                 | 0.8141                 | 0.7859                |
| 2.0305     | 100     | 0.2252        | -                      | -                      | -                      | -                      | -                     |
| 2.2335     | 110     | 0.1724        | -                      | -                      | -                      | -                      | -                     |
| 2.4365     | 120     | 0.1553        | -                      | -                      | -                      | -                      | -                     |
| 2.6396     | 130     | 0.151         | -                      | -                      | -                      | -                      | -                     |
| 2.8426     | 140     | 0.1794        | -                      | -                      | -                      | -                      | -                     |
| 2.9848     | 147     | -             | 0.8368                 | 0.8346                 | 0.8298                 | 0.8157                 | 0.7836                |
| 3.0457     | 150     | 0.1716        | -                      | -                      | -                      | -                      | -                     |
| 3.2487     | 160     | 0.1246        | -                      | -                      | -                      | -                      | -                     |
| 3.4518     | 170     | 0.1698        | -                      | -                      | -                      | -                      | -                     |
| 3.6548     | 180     | 0.1108        | -                      | -                      | -                      | -                      | -                     |
| 3.8579     | 190     | 0.1881        | -                      | -                      | -                      | -                      | -                     |
| **3.9797** | **196** | **-**         | **0.8377**             | **0.8361**             | **0.8294**             | **0.8171**             | **0.7827**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.3.0
- 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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