sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:1814 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:mixedbread-ai/deepset-mxbai-embed-de-large-v1 base_model:finetune:mixedbread-ai/deepset-mxbai-embed-de-large-v1 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: mixedbread-ai/deepset-mxbai-embed-de-large-v1
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1814
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: '

    The document you provided seems to be a list of compounds, some of which are well-known
    drugs or drugs used in experimental contexts, and others that don''t appear to
    have recognized applications in medicine or science. The list includes substances
    like cortisol, a hormone, and filopram, which is related to anesthetics or possibly
    a misprint or misclassification. The side effects listed are also a mix, with
    some being plausible reactions to certain medication (like Edema, dysphagia) and
    others being quite unusual for commonly recognized drug interactions (like retinal
    vein occlusion, which is not a typical side effect of most medications).


    It would be useful to have labels or references indicating which of these compounds
    are actually drugs and which are other chemical substances. For instance, cortisol,
    if given its correct context, would typically have side effects associated with
    cortisol therapy such as fluid retention or electrolyte imbalances.


    If you need detailed information on how these substances work or what their possible
    side effects might be, you''ll likely need to refer to a medical compendium or
    a pharmacology resource for accurate data. It''s also important to clarify the
    intended use for this list, whether for educational purposes, research, or another
    context; the provided list appears to be a jumbled amalgamation, which might not
    have clear clinical relevance without additional detail.'
  sentences:
  - Can you suggest medications targeting the GC gene/protein with a proven synergy
    with AVE9633?
  - Could you help identify the gene or protein that facilitates sodium-dependent
    transportation and elimination of organic anions, with a particular emphasis on
    those implicated in the cellular efflux of potentially hazardous organic anions?
    Additionally, I'm interested in understanding if this gene or protein also mediates
    the transport of drugs known to exhibit synergistic pharmacological interactions
    with Ractopamine.
  - Can you list the medications suitable for benign prostatic hyperplasia and tell
    me if any are linked to dysphagia as a side effect?
- source_sentence: '


    The provided information describes a gene that plays a role in multiple biological
    processes and is linked with certain diseases. Here'
  sentences:
  - Which genes or proteins interact with the "Regulation of HSF1-mediated heat shock
    response" pathway and also engage in protein-protein interactions with PRNP?
  - Which anatomical parts lack the expression of genes or proteins involved in the
    L-alanine degradation pathway?
  - What is the name of a disease classified as a variant or subtype of sinoatrial
    node disease in the latest medical disease taxonomy?
- source_sentence: '

    The list you''ve provided contains a variety of medications, including antidepressants,
    antihistamines, anxiolytics, and more. Here''s a breakdown by category:


    ### Antidepressants

    - **Amphetamine**

    - **Cevimeline**

    - **Esmolol**

    - **Bortezomib**

    - **'
  sentences:
  - What are some related conditions to triple-negative breast cancer that could be
    causing persistent fatigue?
  - Which medication is effective against simple Plasmodium falciparum infections
    and functions by engaging with genes or proteins that interact with the minor
    groove of DNA rich in adenine and thymine?
  - Which diseases associated with SRSF2 gene mutations are primarily found in adults
    and the elderly?
- source_sentence: '


    The drug mentioned in the query is "Dabigatran". It belongs to the class of drugs
    known as direct thrombin inhibitors. This class of drugs is used primarily for
    the prevention and treatment of thromboembolic disorders.


    Regarding potential side effects, they include:

    1. Inflammatory abnormality of the skin (Erythema)

    2. Hemolytic anemia (a type of anemia where red blood cells are destroyed prematurely)

    3. Thrombocytopenia (low platelet count)

    4. Pancytopenia (a decrease in the number of all types of blood cells - red, white,
    and platelet cells)

    5. Fever

    6. Pain

    7. Seizure

    8. Headache

    9. Vomiting

    10. Abdominal pain

    11. Hyperactivity

    12. Erythroderma (a type of skin flare characterized by a redness over the trunk
    and limbs)

    13. Vertigo (a sensation of spinning or motion)

    14. Granulocytopenia (low neutrophil count)

    15. Pruritus (severe itching)

    16. Confusion

    17. Eosinophilia (a condition characterized by an increased number of eosinophils,
    a type of white blood cell)

    18. Anaphylactic shock (a serious allergic reaction)

    19. Hyperkinetic movements

    20. Nausea

    21. Acute sinusitis (inflammation of the sinus cavities)

    22. Agitation

    23. Excessive daytime somnolence (excessively sleepy during the day)

    24. Aplastic anemia (a condition where the bone marrow fails to produce enough
    new blood cells)

    25. Increased blood urea nitrogen (BUN) (a marker of kidney function, indicating
    the kidneys are not working properly)

    26. Prolonged prothrombin time (an indication of an increased risk of bleeding,
    due to a reduction in clotting protein)

    27. Recurrent tonsillitis (repeated inflammation of the tonsils)


    Dabigatran works by inhibiting thrombin (Factor IIa), an enzyme involved in the
    clotting process. If any of these side effects are experienced, it is important
    to seek medical attention or consult with a healthcare provider.'
  sentences:
  - What are the clinical manifestations or phenotypic characteristics associated
    with the subtype of myocardial infarction known as posteroinferior?
  - Could you supply a list of drugs prescribed for respiratory infections that may
    also lead to side effects like hemolytic anemia and nausea?
  - Which diseases are associated with the FAM111A gene that present with both skeletal
    and endocrine system abnormalities?
- source_sentence: '

    The list you provided seems to be a mix of various chemical substances, some of
    which appear to be medications, others are chemical compounds, and a few could
    be substances from other fields (e.g., water treatment, food additives). To be
    more precise, it would be helpful to categorize them properly based on their common
    usage:


    ### Medications and Drugs:

    - **Antibiotics**: Cefoxitin, Tobramycin, Amikacin

    - ** pain and inflammation relievers**: Benoxaprofen, Daptomycin, Ceftolozane,
    Salicylates (Benzydamine, Dexamethasone sodium phosphate)

    - **Intravenous fluids**: Magnesium trisilicate

    - **Antivirals**: Ribavirin, Inotersen

    - **Antibacterial agents**: Epirizole, Floctafenine, Flunixin

    - **Vaccines**: Vaborbactam, Brincidofovir, Adefovir

    - **Neuromodulators**: Cefatrizine, Bumadizone, Alminoprofen

    - **Cancer treatments**: Colistin, Nitrofurantoin, Sisomicin


    ### Chemical Compounds:

    - **Salts and salts of acidity**: Fosfomycin, Azosemide, Mofebutazone

    - **Amino acids**: Phenylalanine, Nitrosalicylic'
  sentences:
  - Is there a regulatory function associated with the epidermal growth factor receptor
    or its interacting proteins in the control of genes or proteins that participate
    in the inactivation of fast sodium channels during Phase 1 of cardiac action potential
    propagation?
  - Which diseases, either as subtypes or complications, should be considered when
    a patient shows symptoms suggesting neoplastic syndromes?
  - Which drugs interact with the SERPINA1 gene/protein as carriers?
model-index:
- name: SentenceTransformer based on mixedbread-ai/deepset-mxbai-embed-de-large-v1
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.3910891089108911
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4752475247524752
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.49504950495049505
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5544554455445545
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3910891089108911
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15841584158415842
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.09900990099009901
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05544554455445544
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3910891089108911
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4752475247524752
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.49504950495049505
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5544554455445545
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4669635292605997
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.439788621719315
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.44615433269461197
      name: Cosine Map@100
---

# SentenceTransformer based on mixedbread-ai/deepset-mxbai-embed-de-large-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [mixedbread-ai/deepset-mxbai-embed-de-large-v1](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1) on the json dataset. 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:** [mixedbread-ai/deepset-mxbai-embed-de-large-v1](https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1) <!-- at revision fe450620a047ac704e100d84aebe7cd3fc137021 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
<!-- - **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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("FareedKhan/mixedbread-ai_deepset-mxbai-embed-de-large-v1_FareedKhan_prime_synthetic_data_2k_3_8")
# Run inference
sentences = [
    '\nThe list you provided seems to be a mix of various chemical substances, some of which appear to be medications, others are chemical compounds, and a few could be substances from other fields (e.g., water treatment, food additives). To be more precise, it would be helpful to categorize them properly based on their common usage:\n\n### Medications and Drugs:\n- **Antibiotics**: Cefoxitin, Tobramycin, Amikacin\n- ** pain and inflammation relievers**: Benoxaprofen, Daptomycin, Ceftolozane, Salicylates (Benzydamine, Dexamethasone sodium phosphate)\n- **Intravenous fluids**: Magnesium trisilicate\n- **Antivirals**: Ribavirin, Inotersen\n- **Antibacterial agents**: Epirizole, Floctafenine, Flunixin\n- **Vaccines**: Vaborbactam, Brincidofovir, Adefovir\n- **Neuromodulators**: Cefatrizine, Bumadizone, Alminoprofen\n- **Cancer treatments**: Colistin, Nitrofurantoin, Sisomicin\n\n### Chemical Compounds:\n- **Salts and salts of acidity**: Fosfomycin, Azosemide, Mofebutazone\n- **Amino acids**: Phenylalanine, Nitrosalicylic',
    'Which drugs interact with the SERPINA1 gene/protein as carriers?',
    'Is there a regulatory function associated with the epidermal growth factor receptor or its interacting proteins in the control of genes or proteins that participate in the inactivation of fast sodium channels during Phase 1 of cardiac action potential propagation?',
]
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
* 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   | 0.3911     |
| cosine_accuracy@3   | 0.4752     |
| cosine_accuracy@5   | 0.495      |
| cosine_accuracy@10  | 0.5545     |
| cosine_precision@1  | 0.3911     |
| cosine_precision@3  | 0.1584     |
| cosine_precision@5  | 0.099      |
| cosine_precision@10 | 0.0554     |
| cosine_recall@1     | 0.3911     |
| cosine_recall@3     | 0.4752     |
| cosine_recall@5     | 0.495      |
| cosine_recall@10    | 0.5545     |
| cosine_ndcg@10      | 0.467      |
| cosine_mrr@10       | 0.4398     |
| **cosine_map@100**  | **0.4462** |

<!--
## 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

#### json

* Dataset: json
* Size: 1,814 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: 267.06 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 39.66 tokens</li><li>max: 120 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | anchor                                                                                                                                             |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code><br><br>Based on the provided information, it appears you are describing a complex biological system involving various molecules, drugs, diseases, and anatomical structures. Here's a breakdown:<br><br>### Key Entities<br>1. **Molecules and Targets**<br>   - Mentioned molecules include metabolites, phenols, and drugs, with specific functional groups related to their chemical properties.<br>   - Targets include enzymes (like acetyl-CoA carboxylase) and diseases causing various health conditions (e.g., Finnish type amyloidosis, lung cancer).<br><br>2. **Functionality and Interactions**<br>   - The molecules and drugs interact with various biological processes, pathways, and bodily systems.<br></code>                                                                      | <code>Identify common genetic targets that interact with both N-(3,5-dibromo-4-hydroxyphenyl)benzamide and 1-Naphthylamine-5-sulfonic acid.</code> |
  | <code><br>The provided list appears to be a collection of gene symbols related to cancer. Gene symbols are used in genetics and molecular biology to identify genes. Each symbol is associated with a specific gene that plays a role in cellular functions, including cancer processes. When studying cancer, researchers often analyze these genes to understand their roles in tumor development, potential as targets for therapy, or as indicators for patient prognosis. For example, some genes listed are known oncogenes or tumor suppressor genes:<br><br>- TP53: A tumor suppressor gene that when mutated can lead to uncontrolled cell growth.<br>- P53, POLD1, PTEN: These are well-known tumor suppressors that help regulate cell division and DNA repair.<br>- BRCA</code>                   | <code>Which anatomical structures lack expression of genes or proteins involved in the homogentisate degradation pathway?</code>                   |
  | <code><br><br>The gene in question appears to have a wide range of functions across various biological processes and body systems. It's involved in several key areas that regulate cellular responses, metabolic processes, and organ development. Here is a summary of its potential roles:<br><br>1. **Cell Growth and Regulation**: The gene contributes to growth control in cells, particularly in smooth muscle cells, and seems to influence cell proliferation, which is essential for tissue repair and development.<br><br>2. **Nerve Function**: It plays a role in functions like signal transduction, neurotrophin signaling, and regulation of neural activity, suggesting it’s involved in neural health and development.<br><br>3. **Metabolic Processes**: There is evidence linking</code> | <code>Identify genes or proteins that interact with angiotensin-converting enzyme 2 (ACE2) and are linked to a common phenotype or effect.</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
      ],
      "matryoshka_weights": [
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `learning_rate`: 1e-05
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: False
- `load_best_model_at_end`: True

#### 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`: 8
- `per_device_eval_batch_size`: 8
- `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`: 1e-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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: 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
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step    | Training Loss | dim_768_cosine_map@100 |
|:-------:|:-------:|:-------------:|:----------------------:|
| 0       | 0       | -             | 0.3930                 |
| 0.0441  | 10      | 1.18          | -                      |
| 0.0881  | 20      | 1.0507        | -                      |
| 0.1322  | 30      | 0.9049        | -                      |
| 0.1762  | 40      | 0.8999        | -                      |
| 0.2203  | 50      | 0.6519        | -                      |
| 0.2643  | 60      | 0.5479        | -                      |
| 0.3084  | 70      | 0.6493        | -                      |
| 0.3524  | 80      | 0.4706        | -                      |
| 0.3965  | 90      | 0.5459        | -                      |
| 0.4405  | 100     | 0.5692        | -                      |
| 0.4846  | 110     | 0.7834        | -                      |
| 0.5286  | 120     | 0.5341        | -                      |
| 0.5727  | 130     | 0.5343        | -                      |
| 0.6167  | 140     | 0.4865        | -                      |
| 0.6608  | 150     | 0.3942        | -                      |
| 0.7048  | 160     | 0.3578        | -                      |
| 0.7489  | 170     | 0.5158        | -                      |
| 0.7930  | 180     | 0.3426        | -                      |
| 0.8370  | 190     | 0.5789        | -                      |
| 0.8811  | 200     | 0.5271        | -                      |
| 0.9251  | 210     | 0.577         | -                      |
| 0.9692  | 220     | 0.5193        | -                      |
| 1.0     | 227     | -             | 0.4354                 |
| 1.0132  | 230     | 0.4598        | -                      |
| 1.0573  | 240     | 0.2735        | -                      |
| 1.1013  | 250     | 0.2919        | -                      |
| 1.1454  | 260     | 0.3206        | -                      |
| 1.1894  | 270     | 0.2851        | -                      |
| 1.2335  | 280     | 0.3899        | -                      |
| 1.2775  | 290     | 0.3279        | -                      |
| 1.3216  | 300     | 0.2155        | -                      |
| 1.3656  | 310     | 0.3471        | -                      |
| 1.4097  | 320     | 0.327         | -                      |
| 1.4537  | 330     | 0.229         | -                      |
| 1.4978  | 340     | 0.2902        | -                      |
| 1.5419  | 350     | 0.3216        | -                      |
| 1.5859  | 360     | 0.2902        | -                      |
| 1.6300  | 370     | 0.4527        | -                      |
| 1.6740  | 380     | 0.1583        | -                      |
| 1.7181  | 390     | 0.3144        | -                      |
| 1.7621  | 400     | 0.2573        | -                      |
| 1.8062  | 410     | 0.2309        | -                      |
| 1.8502  | 420     | 0.3475        | -                      |
| 1.8943  | 430     | 0.3082        | -                      |
| 1.9383  | 440     | 0.3176        | -                      |
| 1.9824  | 450     | 0.2104        | -                      |
| **2.0** | **454** | **-**         | **0.4453**             |
| 2.0264  | 460     | 0.2615        | -                      |
| 2.0705  | 470     | 0.1599        | -                      |
| 2.1145  | 480     | 0.1015        | -                      |
| 2.1586  | 490     | 0.2154        | -                      |
| 2.2026  | 500     | 0.1161        | -                      |
| 2.2467  | 510     | 0.2208        | -                      |
| 2.2907  | 520     | 0.2035        | -                      |
| 2.3348  | 530     | 0.1622        | -                      |
| 2.3789  | 540     | 0.1758        | -                      |
| 2.4229  | 550     | 0.2782        | -                      |
| 2.4670  | 560     | 0.303         | -                      |
| 2.5110  | 570     | 0.1787        | -                      |
| 2.5551  | 580     | 0.2221        | -                      |
| 2.5991  | 590     | 0.1686        | -                      |
| 2.6432  | 600     | 0.2522        | -                      |
| 2.6872  | 610     | 0.1334        | -                      |
| 2.7313  | 620     | 0.1102        | -                      |
| 2.7753  | 630     | 0.2499        | -                      |
| 2.8194  | 640     | 0.2648        | -                      |
| 2.8634  | 650     | 0.1859        | -                      |
| 2.9075  | 660     | 0.2385        | -                      |
| 2.9515  | 670     | 0.2283        | -                      |
| 2.9956  | 680     | 0.1126        | -                      |
| 3.0     | 681     | -             | 0.4462                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.10
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.2.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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