README.md

---
base_model: sentence-transformers/all-mpnet-base-v2
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:4012
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Extensive messenger RNA editing generates transcript and protein
    diversity in genes involved in neural excitability, as previously described, as
    well as in genes participating in a broad range of other cellular functions. '
  sentences:
  - Do cephalopods use RNA editing less frequently than other species?
  - GV1001 vaccine targets which enzyme?
  - Which event results in the acetylation of S6K1?
- source_sentence: Yes, exposure to household furry pets influences the gut microbiota
    of infants.
  sentences:
  - Can pets affect infant microbiomed?
  - What is the mode of action of Thiazovivin?
  - What are the effects of CAMK4 inhibition?
- source_sentence: "In children with heart failure evidence of the effect of enalapril\
    \ is empirical. Enalapril was clinically safe and effective in 50% to 80% of for\
    \ children with cardiac failure secondary to congenital heart malformations before\
    \ and after cardiac surgery,  impaired ventricular function , valvar regurgitation,\
    \  congestive cardiomyopathy,  , arterial hypertension, life-threatening arrhythmias\
    \ coexisting with circulatory insufficiency.   \nACE inhibitors have shown a transient\
    \ beneficial effect on heart failure due to anticancer drugs and possibly a beneficial\
    \ effect in muscular dystrophy-associated cardiomyopathy, which deserves further\
    \ studies."
  sentences:
  - Which receptors can be evaluated with the [18F]altanserin?
  - In what proportion of children with heart failure has Enalapril been shown to
    be safe and effective?
  - Which major signaling pathways are regulated by RIP1?
- source_sentence: Cellular senescence-associated heterochromatic foci (SAHFS) are
    a novel type of chromatin condensation involving alterations of linker histone
    H1 and linker DNA-binding proteins. SAHFS can be formed by a variety of cell types,
    but their mechanism of action remains unclear.
  sentences:
  - What is the relationship between the X chromosome and a  neutrophil drumstick?
  - Which microRNAs are involved in exercise adaptation?
  - How are SAHFS created?
- source_sentence: Multicluster Pcdh diversity is required for mouse olfactory neural
    circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins
    are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although
    deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss
    of all three clusters (tricluster deletion) led to a severe axonal arborization
    defect and loss of self-avoidance.
  sentences:
  - What are the effects of the deletion of all three Pcdh clusters (tricluster deletion)
    in mice?
  - what is the role of MEF-2 in cardiomyocyte differentiation?
  - How many periods of regulatory innovation led to the evolution of vertebrates?
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: 0.8373408769448374
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9306930693069307
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9448373408769448
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.958981612446959
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8373408769448374
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31023102310231027
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18896746817538893
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09589816124469587
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8373408769448374
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9306930693069307
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9448373408769448
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.958981612446959
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9038566618329213
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8855380436002787
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8867903631779396
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.8373408769448374
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9335219236209336
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9462517680339463
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9603960396039604
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8373408769448374
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31117397454031115
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18925035360678924
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09603960396039603
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8373408769448374
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9335219236209336
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9462517680339463
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9603960396039604
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9045496377971035
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8860549830493253
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8870969130410834
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.8288543140028288
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9222065063649222
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.942008486562942
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9533239038189534
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8288543140028288
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3074021687883074
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18840169731258838
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09533239038189532
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8288543140028288
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9222065063649222
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.942008486562942
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9533239038189534
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8963408137245359
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8774370804427385
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8786914503856871
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.809052333804809
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8995756718528995
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9207920792079208
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9405940594059405
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.809052333804809
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29985855728429983
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18415841584158416
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09405940594059406
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.809052333804809
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8995756718528995
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9207920792079208
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9405940594059405
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8794609712523561
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8593930311398488
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8608652296821839
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.7694483734087695
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8613861386138614
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8868458274398868
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9080622347949081
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7694483734087695
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2871287128712871
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17736916548797735
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09080622347949079
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7694483734087695
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8613861386138614
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8868458274398868
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9080622347949081
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.841605620432732
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8200012348173592
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8223782042287946
      name: Cosine Map@100
---

# BGE base Financial Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
- **Maximum Sequence Length:** 384 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("juanpablomesa/all-mpnet-base-v2-bioasq-matryoshka")
# Run inference
sentences = [
    'Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss of all three clusters (tricluster deletion) led to a severe axonal arborization defect and loss of self-avoidance.',
    'What are the effects of the deletion of all three Pcdh clusters (tricluster deletion) in mice?',
    'How many periods of regulatory innovation led to the evolution of vertebrates?',
]
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

*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   | 0.8373     |
| cosine_accuracy@3   | 0.9307     |
| cosine_accuracy@5   | 0.9448     |
| cosine_accuracy@10  | 0.959      |
| cosine_precision@1  | 0.8373     |
| cosine_precision@3  | 0.3102     |
| cosine_precision@5  | 0.189      |
| cosine_precision@10 | 0.0959     |
| cosine_recall@1     | 0.8373     |
| cosine_recall@3     | 0.9307     |
| cosine_recall@5     | 0.9448     |
| cosine_recall@10    | 0.959      |
| cosine_ndcg@10      | 0.9039     |
| cosine_mrr@10       | 0.8855     |
| **cosine_map@100**  | **0.8868** |

#### 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   | 0.8373     |
| cosine_accuracy@3   | 0.9335     |
| cosine_accuracy@5   | 0.9463     |
| cosine_accuracy@10  | 0.9604     |
| cosine_precision@1  | 0.8373     |
| cosine_precision@3  | 0.3112     |
| cosine_precision@5  | 0.1893     |
| cosine_precision@10 | 0.096      |
| cosine_recall@1     | 0.8373     |
| cosine_recall@3     | 0.9335     |
| cosine_recall@5     | 0.9463     |
| cosine_recall@10    | 0.9604     |
| cosine_ndcg@10      | 0.9045     |
| cosine_mrr@10       | 0.8861     |
| **cosine_map@100**  | **0.8871** |

#### 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   | 0.8289     |
| cosine_accuracy@3   | 0.9222     |
| cosine_accuracy@5   | 0.942      |
| cosine_accuracy@10  | 0.9533     |
| cosine_precision@1  | 0.8289     |
| cosine_precision@3  | 0.3074     |
| cosine_precision@5  | 0.1884     |
| cosine_precision@10 | 0.0953     |
| cosine_recall@1     | 0.8289     |
| cosine_recall@3     | 0.9222     |
| cosine_recall@5     | 0.942      |
| cosine_recall@10    | 0.9533     |
| cosine_ndcg@10      | 0.8963     |
| cosine_mrr@10       | 0.8774     |
| **cosine_map@100**  | **0.8787** |

#### 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   | 0.8091     |
| cosine_accuracy@3   | 0.8996     |
| cosine_accuracy@5   | 0.9208     |
| cosine_accuracy@10  | 0.9406     |
| cosine_precision@1  | 0.8091     |
| cosine_precision@3  | 0.2999     |
| cosine_precision@5  | 0.1842     |
| cosine_precision@10 | 0.0941     |
| cosine_recall@1     | 0.8091     |
| cosine_recall@3     | 0.8996     |
| cosine_recall@5     | 0.9208     |
| cosine_recall@10    | 0.9406     |
| cosine_ndcg@10      | 0.8795     |
| cosine_mrr@10       | 0.8594     |
| **cosine_map@100**  | **0.8609** |

#### 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   | 0.7694     |
| cosine_accuracy@3   | 0.8614     |
| cosine_accuracy@5   | 0.8868     |
| cosine_accuracy@10  | 0.9081     |
| cosine_precision@1  | 0.7694     |
| cosine_precision@3  | 0.2871     |
| cosine_precision@5  | 0.1774     |
| cosine_precision@10 | 0.0908     |
| cosine_recall@1     | 0.7694     |
| cosine_recall@3     | 0.8614     |
| cosine_recall@5     | 0.8868     |
| cosine_recall@10    | 0.9081     |
| cosine_ndcg@10      | 0.8416     |
| cosine_mrr@10       | 0.82       |
| **cosine_map@100**  | **0.8224** |

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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 Dataset

#### Unnamed Dataset


* Size: 4,012 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: 3 tokens</li><li>mean: 63.14 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.13 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                      | anchor                                                                                 |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
  | <code>Aberrant patterns of H3K4, H3K9, and H3K27 histone lysine methylation were shown to result in histone code alterations, which induce changes in gene expression, and affect the proliferation rate of cells in medulloblastoma.</code>                                                                                                                                  | <code>What is the implication of histone lysine methylation in medulloblastoma?</code> |
  | <code>STAG1/STAG2 proteins are tumour suppressor proteins that suppress cell proliferation and are essential for differentiation.</code>                                                                                                                                                                                                                                      | <code>What is the role of STAG1/STAG2 proteins in differentiation?</code>              |
  | <code>The association between cell phone use and incident glioblastoma remains unclear. Some studies have reported that cell phone use was associated with incident glioblastoma, and with reduced survival of patients diagnosed with glioblastoma. However, other studies have repeatedly replicated to find an association between cell phone use and glioblastoma.</code> | <code>What is the association between cell phone use and glioblastoma?</code>          |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | 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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8889     | 7      | -             | 0.8540                 | 0.8752                 | 0.8825                 | 0.8050                | 0.8864                 |
| 1.2698     | 10     | 1.2032        | -                      | -                      | -                      | -                     | -                      |
| 1.9048     | 15     | -             | 0.8569                 | 0.8775                 | 0.8850                 | 0.8169                | 0.8840                 |
| 2.5397     | 20     | 0.5051        | -                      | -                      | -                      | -                     | -                      |
| **2.9206** | **23** | **-**         | **0.861**              | **0.8794**             | **0.8866**             | **0.8242**            | **0.8858**             |
| 3.5556     | 28     | -             | 0.8609                 | 0.8787                 | 0.8871                 | 0.8224                | 0.8868                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.5
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- 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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