README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:400
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: 'QUESTION #2: What percentage of patients in the study reported
    experiencing "chills" and "feverish discomfort"?'
  sentences:
  - "been proven superior. Annual influenza vaccination is recommended for all people\
    \ six months and older who do not have \ncontraindications. ( Am Fam Physician.\
    \ 2019; 100(12):751-758. Copyright © 2019 American Academy of Family Physicians.)\n\
    BEST PRACTICES IN INFECTIOUS DISEASE  \nRecommendations from the Choosing \nWisely\
    \ Campaign\nRecommendation Sponsoring organization\nDo not routinely avoid \n\
    influenza vaccination in \negg-allergic patients.\nAmerican Academy of Allergy,\
    \ \nAsthma, and Immunology\nSource:  For more information on the Choosing Wisely\
    \ Campaign,"
  - 'Review

    722  Vol 5   November 2005

    accompanied by fever and some subjects have a transient

    fall in body temperature during the early stages of

    common cold. In a study of 272 patients with sore throat

    associated with URTIs, the mean aural temperature was

    36·8ºC and around 35% of these patients said they were

    suffering from “chills” and “feverish discomfort”.49 The

    sensation of chilliness may be unrelated to any change in

    skin or body temperature. In a study of human

    volunteers, a sensation of chill still develops on

    administration of exogenous pyrogen even though the'
  - "ered when the results will modify management or when a \npatient with signs or\
    \ symptoms of influenza is hospitalized.19 \nTABLE 2\nComplications of Influenza\n\
    Cardiovascular   26\nCerebrovascular accidents\nIschemic heart disease\nMyocarditis\n\
    Hematologic  26\nHemolytic uremic syndrome\nHemophagocytic syndrome\nThrombotic\
    \ thrombocytope -\nnic purpura\nMusculoskeletal  19,26\nMyositis\nRhabdomyolysis\n\
    Neurologic  26\nAcute disseminated \nencephalomyelitis\nEncephalitis\nGuillain-Barré\
    \ syndrome\nPostinfluenza encephalopathy \n(neurologic symptoms occur -\nring\
    \ after resolution but within"
- source_sentence: How do cytokines interact with the body's systems to influence
    the hypothalamus and affect body temperature?
  sentences:
  - 'interleukin 1, interleukin 6, and tumour necrosis factor

    alpha, as well as the anti-inflammatory cytokines

    interleukin-1 receptor antagonist and interleukin 10

    have been investigated for their pyrogenic or antipyretic

    action.17 Interleukin 1 and interleukin 6 are believed to

    be the most important cytokines that induce fever. 55

    Cytokines are believed to cross the blood–brain barrier

    or interact with the vagus nerve endings to signal the

    temperature control centre of the hypothalamus to

    increase the thermal set point.55,56 The hypothalamus

    then initiates shivering, constriction of skin blood'
  - "mended human dose; possible \nrisk of embryo-fetal toxicity with \ncontinuous\
    \ intravenous infusion \nbased on limited animal data\nBaloxavir (Xofluza), \n\
    available as oral \ntablets\nNA ($160) Adults and children 12 years \nand older:\
    \  \n88 to 174 lb (40 to 79 kg):  \nsingle dose of 40 mg  \n≥ 175 lb (80 kg):\
    \  single dose \nof 80 mg\nTreatment of uncom-\nplicated acute \ninfluenza in\
    \ patients \n12 years and older who \nhave been symptom -\natic for no more than\
    \ \n48 hours\nContraindicated in people with \na history of hypersensitivity to\
    \ \nbaloxavir or any component of the \nproduct"
  - "CME  This clinical content conforms to AAFP criteria for con-\ntinuing medical\
    \ education (CME). See CME Quiz on page 271.\nAuthor disclosure:     No relevant\
    \ financial affiliations.\nPatient information:    Handouts on this topic, written\
    \ by the \nauthors of this article, are available at https://  www.aafp.org/\n\
    afp/2019/0901/p281-s1.html and https://  www.aafp.org/\nafp/2019/0901/p281-s2.html.\n\
    Acute upper respiratory tract infections are extremely common in adults and children,\
    \ but only a few safe and effective treat-"
- source_sentence: What are the limitations of using adamantanes (amantadine and rimantadine)
    for influenza treatment according to the context?
  sentences:
  - "December 15, 2019 ◆ Volume 100, Number 12 www.aafp.org/afp  American Family Physician\
    \ 755\nINFLUENZA\nClinicians caring for high-risk patients can also be consid\
    \ -\nered for treatment.28\nFour antiviral drugs have been approved for the treat\
    \ -\nment of influenza (Table 4):  the NA inhibitors oseltamivir \n(Tamiflu),\
    \ zanamivir (Relenza), and peramivir (Rapivab), \nand the cap-dependent endonuclease\
    \ inhibitor baloxa -\nvir (Xofluza). 18,37 Any of these agents can be used in\
    \ age-  \nappropriate, otherwise healthy outpatients with uncom -\nplicated influenza\
    \ and no contraindications. 18 Baloxavir is"
  - "756 American Family Physician www.aafp.org/afp  Volume 100, Number 12 ◆ December\
    \ 15, 2019\nINFLUENZA\nthe risk of bronchospasm. 18,28 Adamantanes (amantadine\
    \ \nand rimantadine [Flumadine]) are approved for influenza \ntreatment but are\
    \ not currently recommended. These med -\nications are not active against influenza\
    \ B, and most influ -\nenza A strains have shown adamantane resistance for the\
    \ \npast 10 years.18\nThere is no demonstrated benefit to treating patients \n\
    with more than one antiviral agent or using higher than \nrecommended dosages.\
    \ 28 However, extended treatment"
  - "distress syndrome\nDiffuse alveolar \nhemorrhage\nHypoxic respiratory \nfailure\n\
    Primary viral pneumonia\nSecondary bacterial \npneumonia\nRenal 26\nAcute kidney\
    \ injury  \n(e.g., acute tubulo- \ninterstitial nephritis, \nglomerulonephritis,\
    \ \nminimal change disease)\nMultiorgan failure\nInformation from references 8,\
    \ 19, and 25-27.\nSORT:  KEY RECOMMENDATIONS FOR PRACTICE\nClinical recommendation\n\
    Evidence \nrating Comments\nAnnual influenza vaccination is recommended for all\
    \ people 6 months and older. 15,16 A Reports of expert committees"
- source_sentence: Which symptoms of colds and flu are now better understood due to
    new knowledge in molecular biology?
  sentences:
  - 'mechanisms that generate the familiar symptoms is poor compared with the amount
    of knowledge available on the

    molecular biology of the viruses involved. New knowledge of the effects of cytokines
    in human beings now helps to

    explain some of the symptoms of colds and flu that were previously in the realm
    of folklore rather than medicine—

    eg, fever, anorexia, malaise, chilliness, headache, and muscle aches and pains.
    The mechanisms of symptoms of

    sore throat, rhinorrhoea, sneezing, nasal congestion, cough, watery eyes, and
    sinus pain are discussed, since these'
  - 'medicines such as ipratropium. These studies have

    demonstrated that nasal secretions in the first 4 days of a

    common cold are inhibited by intranasal administration

    of ipratropium.25 The nasal discharge also consists of a

    protein-rich plasma exudate derived from subepithelial

    capillaries,28 which may explain why anticholinergics

    only partly inhibit nasal discharge associated with

    URTIs.27

    The colour of nasal discharge and sputum is often

    used as a clinical marker to determine whether or not to

    prescribe antibiotics but there is no evidence from the'
  - "ing diffuse alveolar hemorrhage in immunocompetent patients:  a state-\nof-the-art\
    \ review. Lung. 2013; 191(1): 9-18.\n 28.  Uyeki TM, Bernstein HH, Bradley JS,\
    \ et al. Clinical practice guidelines by \nthe Infectious Diseases Society of\
    \ America:  2018 update on diagnosis, \ntreatment, chemoprophylaxis, and institutional\
    \ outbreak management \nof seasonal influenza. Clin Infect Dis. 2019; 68(6): 895-902.\n\
    \ 29.  Ebell MH, Afonso AM, Gonzales R, et al. Development and validation of \n\
    a clinical decision rule for the diagnosis of influenza. J Am Board Fam \nMed.\
    \ 2012; 25(1): 55-62."
- source_sentence: 'QUESTION #2: How does the sneeze centre in the brainstem coordinate
    the actions involved in sneezing?'
  sentences:
  - "stroke, seizure disorder, dementia)\nAsthma or other chronic pulmonary disease\n\
    Chronic kidney disease\nChronic liver disease\nHeart disease (acquired or congenital)\n\
    Immunosuppression (e.g., HIV infection, cancer, transplant \nrecipients, use of\
    \ immunosuppressive medications)\nLong-term aspirin therapy in patients younger\
    \ than 19 years\nMetabolic disorders (acquired [e.g., diabetes mellitus] or \n\
    inherited [e.g., mitochondrial disorders])\nMorbid obesity\nSickle cell anemia\
    \ and other hemoglobinopathies\nSpecial groups\nAdults 65 years and older\nAmerican\
    \ Indians and Alaska Natives"
  - 'causes sneezing.23 The trigeminal nerves relay

    information to the sneeze centre in the brainstem and

    cause reflex activation of motor and parasympathetic

    branches of the facial nerve and activate respiratory

    muscles. A model of the sneeze reflex is illustrated in

    figure 1. The sneeze centre coordinates the inspiratory

    and expiratory actions of sneezing via respiratory

    muscles, and lacrimation and nasal congestion via

    parasympathetic branches of the facial nerve. The eyes

    are always closed during sneezing by activation of facial

    muscles, indicating a close relation between the'
  - 'during experimental rhinovirus infections have not

    been able to find any morphological changes in the

    nasal epithelium of infected volunteers, apart from a

    substantial increase in polymorphonuclear leucocytes

    early in the course of the infection.11 The major cell

    monitoring the host for the invasion of pathogens is

    the macrophage, which has the ability to trigger an

    acute phase response when stimulated with

    components of viruses or bacteria—eg, viral RNA and

    bacterial cell wall components.12 The surface of the

    macrophage exhibits toll-like receptors that combine'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.6122448979591837
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8877551020408163
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9387755102040817
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9897959183673469
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6122448979591837
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29591836734693877
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1877551020408163
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09897959183673469
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6122448979591837
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8877551020408163
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9387755102040817
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9897959183673469
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8165441473931409
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7593091998704244
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7600380628441854
      name: Cosine Map@100
    - type: cosine_accuracy@1
      value: 0.61
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.86
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.91
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.98
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.61
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2866666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18199999999999997
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09799999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.61
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.86
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.91
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.98
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8056804227184741
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7489960317460317
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7504795482295481
      name: Cosine Map@100
---

# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("Gonalb/flucold-ft-v2")
# Run inference
sentences = [
    'QUESTION #2: How does the sneeze centre in the brainstem coordinate the actions involved in sneezing?',
    'causes sneezing.23 The trigeminal nerves relay\ninformation to the sneeze centre in the brainstem and\ncause reflex activation of motor and parasympathetic\nbranches of the facial nerve and activate respiratory\nmuscles. A model of the sneeze reflex is illustrated in\nfigure 1. The sneeze centre coordinates the inspiratory\nand expiratory actions of sneezing via respiratory\nmuscles, and lacrimation and nasal congestion via\nparasympathetic branches of the facial nerve. The eyes\nare always closed during sneezing by activation of facial\nmuscles, indicating a close relation between the',
    'stroke, seizure disorder, dementia)\nAsthma or other chronic pulmonary disease\nChronic kidney disease\nChronic liver disease\nHeart disease (acquired or congenital)\nImmunosuppression (e.g., HIV infection, cancer, transplant \nrecipients, use of immunosuppressive medications)\nLong-term aspirin therapy in patients younger than 19 years\nMetabolic disorders (acquired [e.g., diabetes mellitus] or \ninherited [e.g., mitochondrial disorders])\nMorbid obesity\nSickle cell anemia and other hemoglobinopathies\nSpecial groups\nAdults 65 years and older\nAmerican Indians and Alaska Natives',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval

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

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.6122     |
| cosine_accuracy@3   | 0.8878     |
| cosine_accuracy@5   | 0.9388     |
| cosine_accuracy@10  | 0.9898     |
| cosine_precision@1  | 0.6122     |
| cosine_precision@3  | 0.2959     |
| cosine_precision@5  | 0.1878     |
| cosine_precision@10 | 0.099      |
| cosine_recall@1     | 0.6122     |
| cosine_recall@3     | 0.8878     |
| cosine_recall@5     | 0.9388     |
| cosine_recall@10    | 0.9898     |
| **cosine_ndcg@10**  | **0.8165** |
| cosine_mrr@10       | 0.7593     |
| cosine_map@100      | 0.76       |

#### Information Retrieval

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

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.61       |
| cosine_accuracy@3   | 0.86       |
| cosine_accuracy@5   | 0.91       |
| cosine_accuracy@10  | 0.98       |
| cosine_precision@1  | 0.61       |
| cosine_precision@3  | 0.2867     |
| cosine_precision@5  | 0.182      |
| cosine_precision@10 | 0.098      |
| cosine_recall@1     | 0.61       |
| cosine_recall@3     | 0.86       |
| cosine_recall@5     | 0.91       |
| cosine_recall@10    | 0.98       |
| **cosine_ndcg@10**  | **0.8057** |
| cosine_mrr@10       | 0.749      |
| cosine_map@100      | 0.7505     |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

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

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 400 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 400 samples:
  |         | sentence_0                                                                        | sentence_1                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               |
  | details | <ul><li>min: 2 tokens</li><li>mean: 23.07 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 122.33 tokens</li><li>max: 296 tokens</li></ul> |
* Samples:
  | sentence_0                                                                           | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
  |:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What should individuals with asthma do if they experience flu symptoms?</code> | <code>People with asthma who get flu symptoms should call their health care provider right<br>away. There are antiviral drugs that can treat flu illness and help prevent serious flu<br>complications.<br>About asthma<br>Asthma is a lung disease that is caused by chronic inflammation of the airways. It is one of the most common long-term diseases among<br>children, but adults can have asthma, too. Asthma attacks occur when the lung airways tighten due to inflammation. Asthma attacks can be</code>                                                                        |
  | <code>What causes asthma attacks to occur in individuals with asthma?</code>         | <code>People with asthma who get flu symptoms should call their health care provider right<br>away. There are antiviral drugs that can treat flu illness and help prevent serious flu<br>complications.<br>About asthma<br>Asthma is a lung disease that is caused by chronic inflammation of the airways. It is one of the most common long-term diseases among<br>children, but adults can have asthma, too. Asthma attacks occur when the lung airways tighten due to inflammation. Asthma attacks can be</code>                                                                        |
  | <code>QUESTION #1: How long are people with RSV typically contagious?</code>         | <code>second birthday. However, repeat infections may occur throughout life.<br>People with RSV are usually contagious for 3 to 8 days and may become contagious a day or two before they start showing signs of illness.<br>However, some infants and people with weakened immune systems can continue to spread the virus for 4 weeks or longer, even after they stop<br>showing symptoms. Children are often exposed to and infected with RSV outside the home, such as in school or childcare centers. They can then<br>transmit the virus to other members of the family.</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`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: None
- `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`: False
- `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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | cosine_ndcg@10 |
|:------:|:----:|:-------------:|:--------------:|
| 1.0    | 40   | -             | 0.8359         |
| 1.25   | 50   | -             | 0.8312         |
| 2.0    | 80   | -             | 0.8304         |
| 2.5    | 100  | -             | 0.8156         |
| 3.0    | 120  | -             | 0.8016         |
| 3.75   | 150  | -             | 0.7952         |
| 4.0    | 160  | -             | 0.7880         |
| 5.0    | 200  | -             | 0.8021         |
| 6.0    | 240  | -             | 0.8215         |
| 6.25   | 250  | -             | 0.8286         |
| 7.0    | 280  | -             | 0.8079         |
| 7.5    | 300  | -             | 0.8043         |
| 8.0    | 320  | -             | 0.8126         |
| 8.75   | 350  | -             | 0.8099         |
| 9.0    | 360  | -             | 0.8126         |
| 10.0   | 400  | -             | 0.8165         |
| 0.6173 | 50   | -             | 0.8138         |
| 1.0    | 81   | -             | 0.8158         |
| 1.2346 | 100  | -             | 0.7932         |
| 1.8519 | 150  | -             | 0.7989         |
| 2.0    | 162  | -             | 0.7866         |
| 2.4691 | 200  | -             | 0.8012         |
| 3.0    | 243  | -             | 0.7803         |
| 3.0864 | 250  | -             | 0.7969         |
| 3.7037 | 300  | -             | 0.8030         |
| 4.0    | 324  | -             | 0.7993         |
| 4.3210 | 350  | -             | 0.7848         |
| 4.9383 | 400  | -             | 0.7852         |
| 5.0    | 405  | -             | 0.7814         |
| 5.5556 | 450  | -             | 0.7975         |
| 6.0    | 486  | -             | 0.7846         |
| 6.1728 | 500  | 0.314         | 0.7925         |
| 6.7901 | 550  | -             | 0.7994         |
| 7.0    | 567  | -             | 0.8069         |
| 7.4074 | 600  | -             | 0.8048         |
| 8.0    | 648  | -             | 0.8063         |
| 8.0247 | 650  | -             | 0.8062         |
| 8.6420 | 700  | -             | 0.7992         |
| 9.0    | 729  | -             | 0.8115         |
| 9.2593 | 750  | -             | 0.8118         |
| 9.8765 | 800  | -             | 0.8057         |
| 10.0   | 810  | -             | 0.8057         |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- 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}
}
```

<!--
## Glossary

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

<!--
## Model Card Authors

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

<!--
## Model Card Contact

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