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: Workforce Solutions is our largest reportable segment, contributing
    44% of total operating revenue for 2023.
  sentences:
  - How much did GameStop Corp's valuation allowances increase during fiscal 2022?
  - What percentage of total operating revenue for 2023 was represented by the Workforce
    Solutions segment?
  - Where are the majority of NIKE's footwear and apparel products manufactured?
- source_sentence: The effects of actual results differing from our assumptions and
    the effects of changing assumptions are considered actuarial gains or losses.
    We utilize a mark-to-market approach in recognizing actuarial gains or losses
    immediately through earnings upon the annual remeasurement in the fourth quarter,
    or on an interim basis as triggering events warrant remeasurement.
  sentences:
  - How are the company's postretirement benefit plan actuarial gains or losses recognized?
  - What specific procedures did the auditors perform related to the Critical Audit
    Matter of medical care services Incurred but not Reported (IBNR)?
  - What strategies does the company use to manage product costs and supply?
- source_sentence: To improve the in-store shopping experience, the company invested
    in wayfinding signage, store refresh packages, self-service lockers, and enhanced
    checkout areas, aiming to provide easier navigation and increased convenience.
  sentences:
  - What are the expectations the company has for its employees in aligning with the
    Code of Conduct?
  - What strategies are employed to improve the in-store shopping experience?
  - Where does the 10-K filing direct readers for specifics on legal proceedings involving
    the company?
- source_sentence: In 2023, under pre-approved share repurchase programs, The Hershey
    Company repurchased shares valued at $27.4 million.
  sentences:
  - What is the value of shares repurchased under the pre-approved program as stated
    in The Hershey Company's 2023 Form 10-K, for the year 2023?
  - What critical accounting estimates were identified as having the greatest potential
    impact on the financial statements?
  - What was the total net sales in fiscal 2022?
- source_sentence: During September 2023, the Company entered into a third amended
    and restated revolving credit agreement with Bank of America, N.A., as administrative
    agent, swing line lender and a letter of credit issuer and lender and certain
    other financial institutions, as lenders thereto (the 'Amended Revolving Credit
    Agreement'), which provides the Company with commitments having a maximum aggregate
    principal amount of $1.25 billion, effective as of September 5, 2023. The Amended
    Revolving Credit Agreement also provides for a potential additional incremental
    commitment increase of up to $500.0 million subject to agreement of the lenders.
    The Amended Revolving Credit Agreement contains certain financial covenants setting
    forth leverage and coverage requirements, and certain other limitations typical
    of an investment grade facility, including with respect to liens, mergers and
    incurrence of indebtedness. The Amended Revolving Credit Agreement extends through
    September 5, 2028.
  sentences:
  - What constitutes the largest expense in the company's various expense categories?
  - What is the function of the amended revolving credit agreement that the Company
    entered into with Bank of America in September 2023?
  - What position does Brad D. Smith currently hold?
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: 0.6617460317460317
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7933333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8365079365079365
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8850793650793651
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6617460317460317
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2644444444444444
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1673015873015873
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08850793650793651
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6617460317460317
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7933333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8365079365079365
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8850793650793651
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7731048434378245
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.737306437389771
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7413478623467549
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.660952380952381
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7880952380952381
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8352380952380952
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8834920634920634
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.660952380952381
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2626984126984127
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16704761904761903
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08834920634920633
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.660952380952381
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7880952380952381
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8352380952380952
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8834920634920634
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7712996524525622
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7355047871000246
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7396551248138244
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6507936507936508
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7795238095238095
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.823968253968254
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.873968253968254
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6507936507936508
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2598412698412698
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16479365079365077
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08739682539682538
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6507936507936508
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7795238095238095
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.823968253968254
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.873968253968254
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7614205489576108
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7255282186948864
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.729844180658852
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.6217460317460317
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7541269841269841
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7987301587301587
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8546031746031746
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6217460317460317
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25137566137566136
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15974603174603175
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08546031746031746
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6217460317460317
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7541269841269841
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7987301587301587
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8546031746031746
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7368786132926283
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6994103048626867
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.704308796361143
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.5647619047619048
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7026984126984127
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7477777777777778
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8012698412698412
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5647619047619048
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2342328042328042
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14955555555555555
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08012698412698412
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5647619047619048
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7026984126984127
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7477777777777778
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8012698412698412
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6817715934378692
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6436686192995734
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6495479778469232
      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")
# Run inference
sentences = [
    "During September 2023, the Company entered into a third amended and restated revolving credit agreement with Bank of America, N.A., as administrative agent, swing line lender and a letter of credit issuer and lender and certain other financial institutions, as lenders thereto (the 'Amended Revolving Credit Agreement'), which provides the Company with commitments having a maximum aggregate principal amount of $1.25 billion, effective as of September 5, 2023. The Amended Revolving Credit Agreement also provides for a potential additional incremental commitment increase of up to $500.0 million subject to agreement of the lenders. The Amended Revolving Credit Agreement contains certain financial covenants setting forth leverage and coverage requirements, and certain other limitations typical of an investment grade facility, including with respect to liens, mergers and incurrence of indebtedness. The Amended Revolving Credit Agreement extends through September 5, 2028.",
    'What is the function of the amended revolving credit agreement that the Company entered into with Bank of America in September 2023?',
    'What position does Brad D. Smith currently hold?',
]
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
* 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   | 0.6617     |
| cosine_accuracy@3   | 0.7933     |
| cosine_accuracy@5   | 0.8365     |
| cosine_accuracy@10  | 0.8851     |
| cosine_precision@1  | 0.6617     |
| cosine_precision@3  | 0.2644     |
| cosine_precision@5  | 0.1673     |
| cosine_precision@10 | 0.0885     |
| cosine_recall@1     | 0.6617     |
| cosine_recall@3     | 0.7933     |
| cosine_recall@5     | 0.8365     |
| cosine_recall@10    | 0.8851     |
| cosine_ndcg@10      | 0.7731     |
| cosine_mrr@10       | 0.7373     |
| **cosine_map@100**  | **0.7413** |

#### 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   | 0.661      |
| cosine_accuracy@3   | 0.7881     |
| cosine_accuracy@5   | 0.8352     |
| cosine_accuracy@10  | 0.8835     |
| cosine_precision@1  | 0.661      |
| cosine_precision@3  | 0.2627     |
| cosine_precision@5  | 0.167      |
| cosine_precision@10 | 0.0883     |
| cosine_recall@1     | 0.661      |
| cosine_recall@3     | 0.7881     |
| cosine_recall@5     | 0.8352     |
| cosine_recall@10    | 0.8835     |
| cosine_ndcg@10      | 0.7713     |
| cosine_mrr@10       | 0.7355     |
| **cosine_map@100**  | **0.7397** |

#### 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   | 0.6508     |
| cosine_accuracy@3   | 0.7795     |
| cosine_accuracy@5   | 0.824      |
| cosine_accuracy@10  | 0.874      |
| cosine_precision@1  | 0.6508     |
| cosine_precision@3  | 0.2598     |
| cosine_precision@5  | 0.1648     |
| cosine_precision@10 | 0.0874     |
| cosine_recall@1     | 0.6508     |
| cosine_recall@3     | 0.7795     |
| cosine_recall@5     | 0.824      |
| cosine_recall@10    | 0.874      |
| cosine_ndcg@10      | 0.7614     |
| cosine_mrr@10       | 0.7255     |
| **cosine_map@100**  | **0.7298** |

#### 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   | 0.6217     |
| cosine_accuracy@3   | 0.7541     |
| cosine_accuracy@5   | 0.7987     |
| cosine_accuracy@10  | 0.8546     |
| cosine_precision@1  | 0.6217     |
| cosine_precision@3  | 0.2514     |
| cosine_precision@5  | 0.1597     |
| cosine_precision@10 | 0.0855     |
| cosine_recall@1     | 0.6217     |
| cosine_recall@3     | 0.7541     |
| cosine_recall@5     | 0.7987     |
| cosine_recall@10    | 0.8546     |
| cosine_ndcg@10      | 0.7369     |
| cosine_mrr@10       | 0.6994     |
| **cosine_map@100**  | **0.7043** |

#### 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   | 0.5648     |
| cosine_accuracy@3   | 0.7027     |
| cosine_accuracy@5   | 0.7478     |
| cosine_accuracy@10  | 0.8013     |
| cosine_precision@1  | 0.5648     |
| cosine_precision@3  | 0.2342     |
| cosine_precision@5  | 0.1496     |
| cosine_precision@10 | 0.0801     |
| cosine_recall@1     | 0.5648     |
| cosine_recall@3     | 0.7027     |
| cosine_recall@5     | 0.7478     |
| cosine_recall@10    | 0.8013     |
| cosine_ndcg@10      | 0.6818     |
| cosine_mrr@10       | 0.6437     |
| **cosine_map@100**  | **0.6495** |

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## Bias, Risks and Limitations

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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 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     | 0.6707                 | 0.7045                 | 0.7171                 | 0.6067                | 0.7188                 |
| 1.4545     | 2     | 0.6912                 | 0.7205                 | 0.7302                 | 0.6313                | 0.7327                 |
| **2.9091** | **4** | **0.7043**             | **0.7298**             | **0.7397**             | **0.6495**            | **0.7413**             |

* 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}
}
```

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