sentence-transformers safetensors new sentence-similarity feature-extraction generated_from_trainer dataset_size:1814 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss custom_code arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:Alibaba-NLP/gte-base-en-v1.5 base_model:finetune:Alibaba-NLP/gte-base-en-v1.5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Alibaba-NLP/gte-base-en-v1.5
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: '

    Rodent diseases, as defined by the MONDO ontology, encompass a variety of illnesses
    affecting the order rodentia, including squirrels, gophers, pouched mice, beavers,
    rats, mice, porcupines, and guinea pigs. Recognized by the UMLS as diseases of
    the aforementioned rodent groups, these afflictions are pertinent to various rodent
    species. Notably, one relation is indicated: "parent-child" with "non-human animal
    disease, murine acquired immunodeficiency syndrome" as a child under the parent
    "rodent disease." This relationship suggests that diseases of rodents extend to
    encompass conditions like murine acquired immunodeficiency syndrome, highlighting
    the interconnectedness between rodent health issues and potentially affecting
    common rodent species.'
  sentences:
  - I'd like to request information on medications that interact synergistically with
    Levetiracetam for concurrent treatment of a particular condition and also possess
    the ability to dampen neural excitation by reducing brain neuron activity.
  - What are the observed effects or phenotypes associated with the subtype of gastritis
    induced by alcohol consumption?
  - What diseases are related to non-human animal illnesses and impact common rodent
    species?
- source_sentence: "\n\nThe query is looking for genes associated with the term \"\
    lymphocytic\" in their description on the Human Protein Atlas platform. The term\
    \ \"lymphocytic\" refers to types of white blood cells, particularly lymphocytes,\
    \ which play a significant role in the immune system. Here are the 195 genes identified:\n\
    \n- AEG1 (Alternative endonucleolytic endonuclease 1)\n- AGR2 (Alpha-1 antitrypsin)\n\
    - ALDH2 (Aldehyde dehydrogenase 2)\n- ANGPTL5 (Angiopoietin-like 5)\n- APC2 (Apc\
    \ homologous protein 2)\n- ARID4A (AT-rich interactive domain-containing protein\
    \ 4A)\n- ARHGEF13 (Rho GDP-dissociation inhibitor family member 3)\n- ASCL1 (ASCL\
    \ homeobox 1)\n- ATF2 (Activating transcription factor 2)\n- ATP5G1 (ATP synthase,\
    \ mitochondrial F1 subunit, gamma polypeptide 1)\n- ATRX (AT-rich region X-specific\
    \ protein)\n- ATP6V1B1 (ATPase, type V, beta 1) \n- ATP6V0A2 (ATPase, type V,\
    \ alpha 2)\n- ATXN1 (Ataxin 1)\n- AXIN2 (Axin 2)\n- BAP1 (BRCA1 and arm Pelargonium\
    \ sidifolium bromylactone 1)\n- BCAS3 (BRCA1/CASC3)\n- BCL2 (B-cell lymphoma 2)\n\
    - BCL2L1 (B-cell lymphoma 2 proto-oncogene, T-cell type)\n- BLK (B lymphocyte\
    \ kinase)\n- BMP3 (Bone morphogenetic protein 3)\n- BPGM (BPGM triose phosphate\
    \ isomerase)\n- CALD1 (Caldesmon 1)\n- CAF1 (Complementation factor A, 1)\n- CARS\
    \ (Cystathionine gamma-lyase)\n- CCND1 (Cyclin D1)\n"
  sentences:
  - Which cell structures interact with genes or proteins that are affected by the
    compound [(2R)-1-[(2S)-2-[[(2S,3S)-1-Chloro-6-(diaminomethylideneamino)-2-hydroxyhexan-3-yl]carbamoyl]pyrrolidin-1-yl]-1-oxo-3-phenylpropan-2-yl]azanium?
  - Which anatomical structures lack expression of genes or proteins crucial for interaction
    with the development of glomerular parietal epithelial cells?
  - Which medication shares a gene or protein transporter with O-Trifluoromethylphenyl
    Anthranilic Acid and also acts as a protein-tyrosine kinase inhibitor?
- source_sentence: '


    The drug mentioned in the context provided is Ritobegron. Ritobegron is a beta-3
    adrenergic receptor agonist primarily used in the treatment of overactive bladder
    (OAB) symptoms. It works by relaxing the bladder muscles and reducing unwanted
    contractions. The compound also has potential for treating benign prostatic hyperplasia
    (BPH) as well.


    The side effects mentioned might be associated with Ritobegron, however, without
    the complete context, it''s hard to specify which side effects are directly linked
    to this drug. The list of potential effects for a single drug might overlap with
    other drugs in the list, hence it is essential to cross-reference the side effects
    with a reliable medical source or database.


    Ritobegron is also used for other purposes including as a vasodilator in animal
    models, and it may have potential applications in other medical conditions. However,
    the safety and effectiveness of these uses in humans have not been thoroughly
    established.


    It is important to note that the medication should be prescribed and used only
    under the guidance of a healthcare provider, as it can have potential risks including
    but not limited to, disorders of vision, changes in body weight, and cardiac effects.'
  sentences:
  - Can you find which genes and proteins interact with ethanolaminephosphotransferase's
    enzyme activity and are involved in creating glycosylphosphatidylinositol (GPI)?
  - Can you find me an antipsychotic similar to chlorpromazine that can cause oculogyric
    crisis and blocks both D1 and D2 dopamine receptors?
  - What genetic skin disease linked to the ATM gene also presents with neurodegeneration
    in the cerebellum and brainstem?
- source_sentence: '

    Brain stem glioma, identified by the MONDO term and definition, is a neuroglial
    tumor originating from the brain stem. This condition shares symptoms with both
    brain stem astrocytic neoplasm and brain stem ependymoma, including headaches,
    nausea, confusion, memory loss, changes in personality or irritability, difficulty
    with balance, urinary incontinence, vision problems, speech difficulties, seizures,
    and in more severe cases, loss of peripheral vision. The exact cause of gliomas
    is unknown, but factors such as age, exposure to radiation, and a family history
    of glioma can increase the risk. Gliomas are most common in adults aged 45 to
    65 years, and certain types, such as ependymomas and pilocytic astrocytomas, are
    more prevalent in children and young adults. To address symptoms, immediate medical
    attention is advised, prompting individuals with any signs of glioma to seek professional
    evaluation promptly. The multifaceted nature of brain stem glioma underscores
    the importance of early diagnosis and intervention.'
  sentences:
  - Can you find genes or proteins implicated in the digestive pathway that also potentially
    stimulate guanylate cyclase activity?
  - Could you identify a condition that manifests similarly to both brain stem astrocytic
    neoplasm and brain stem ependymoma?
  - What are potential illnesses associated with or resulting from echinostomiasis?
- source_sentence: '

    Vascular myelopathy, identified under the MONDO classification system, is a disease
    primarily associated with the spinal cord. This condition is notably marked by
    its effects extending beyond the spinal cord to impact the skin and internal organs.
    As a type of spinal cord disease, vascular myelopathy highlights the significant
    interconnectedness of neurological functions and their broader physiological repercussions,
    underscoring the need for comprehensive medical intervention and understanding
    of its multifaceted impacts.'
  sentences:
  - Could you recommend a treatment for skin infections that requires minimal dosing,
    possibly an IV or oral medication that I can take just a few times a day? It's
    important for the medication to provide relief for several hours as I'm dealing
    with a persistent and uncomfortable skin problem.
  - Which illness, characterized by harm or degeneration to the cornea— the transparent
    anterior part of the eye—precludes the use of medications prescribed for keratoacanthoma?
  - What could be a disease linked to the spinal cord that primarily affects the skin
    and internal organs?
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.4900990099009901
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5792079207920792
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.599009900990099
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6287128712871287
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4900990099009901
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19306930693069307
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11980198019801978
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06287128712871286
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.4900990099009901
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5792079207920792
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.599009900990099
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6287128712871287
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5606218586022012
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5386393996542511
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5453292129431805
      name: Cosine Map@100
---

# SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision a8e4f3e0ee719c75bc30d12b8eae0f8440502718 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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/Alibaba-NLP_gte-base-en-v1.5_FareedKhan_prime_synthetic_data_2k_10_32")
# Run inference
sentences = [
    '\nVascular myelopathy, identified under the MONDO classification system, is a disease primarily associated with the spinal cord. This condition is notably marked by its effects extending beyond the spinal cord to impact the skin and internal organs. As a type of spinal cord disease, vascular myelopathy highlights the significant interconnectedness of neurological functions and their broader physiological repercussions, underscoring the need for comprehensive medical intervention and understanding of its multifaceted impacts.',
    'What could be a disease linked to the spinal cord that primarily affects the skin and internal organs?',
    "Could you recommend a treatment for skin infections that requires minimal dosing, possibly an IV or oral medication that I can take just a few times a day? It's important for the medication to provide relief for several hours as I'm dealing with a persistent and uncomfortable skin problem.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
-->

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

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

## 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   | 0.4901     |
| cosine_accuracy@3   | 0.5792     |
| cosine_accuracy@5   | 0.599      |
| cosine_accuracy@10  | 0.6287     |
| cosine_precision@1  | 0.4901     |
| cosine_precision@3  | 0.1931     |
| cosine_precision@5  | 0.1198     |
| cosine_precision@10 | 0.0629     |
| cosine_recall@1     | 0.4901     |
| cosine_recall@3     | 0.5792     |
| cosine_recall@5     | 0.599      |
| cosine_recall@10    | 0.6287     |
| cosine_ndcg@10      | 0.5606     |
| cosine_mrr@10       | 0.5386     |
| **cosine_map@100**  | **0.5453** |

<!--
## 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: 247.78 tokens</li><li>max: 685 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 35.98 tokens</li><li>max: 135 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | anchor                                                                                                                                                              |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code><br><br>Assistant: Ascariasis, also known as ascaridiasis, is a common parasitic infection that occurs when people ingest eggs of the Ascaris lumbricoides worm, typically contracted from soil contaminated with human feces. Here is a breakdown of various aspects of ascariasis based on the provided information:<br><br>### Risk Factors<br>- **Age**: Most cases affect children aged 10 years or younger due to common play behavior in soil.<br>- **Climate**: More common in warm climates with year-round warm temperatures, particularly in developing countries.<br>- **Sanitation**: Frequent in regions where human feces mix with soil, highlighting poor sanitation as a significant risk factor.<br><br>### Life Cycle<br>1. **Ingestion**: Eggs are ingested through the mouth—either by hand-to-mouth contact or through consumption of contaminated fruits and vegetables.<br>2. **Migration**: Eggs hatch in the small intestine, their larvae migrate through the bloodstream to the lungs, mature there, and then coughed up and swallowed.<br>3. **Maturation**: Worms grow to full size in the intestines, potentially reaching over 15 inches in length, depending on the presence of both male and female worms.<br>4. **Reproduction**: Female worms can produce up to 200,000 eggs daily, with eggs exiting the body in feces.<br>   - Eggs must mature in soil for at least 2-4 weeks before becoming infectious, highlighting soil's role in the parasite's life cycle.<br><br>### Symptoms and Complications<br>- **Mild Cases**: Usually entails no significant complications.<br>- **</code>                                                                                          | <code>Could you list diseases similar to Ascaridida that can be contracted by contact with contaminated soil or water, presented in order of their relation?</code> |
  | <code><br><br>Based on the information provided, it appears that this individual likely has a genetic condition known as Berardinelli-Seip Congenital Lipodystrophy, which is characterized by severe genetic lipodystrophy (a rare condition that causes the body to lose fat) and insulin-resistant diabetes, among other signs and symptoms. The presence of genetic markers such as the specific form of a laminopathy and negative results for other disorders like Cushing's disease, non-classical hyperlipidemia, and familial lipodystrophies, points to this diagnosis. The individual also has a history of insulin resistance and diabetes, and the genetic testing further supports the identification of this rare lipodystrophy syndrome. Given the traits and history of the individual, potential explanations for their condition include genetic mutations related to these conditions, which may be exacerbated by environmental factors such as exposure to certain chemicals or toxins. <br><br>Therefore, their condition could be attributed to genetic mutations associated with Berardinelli-Seip Congenital Lipodystrophy, which could be inherited from their mother (indicating it may be a maternally-inherited trait) and is further complicated by other genetic and environmental factors that contribute to insulin resistance and glucose intolerance. Potential health care considerations would include ongoing management of diabetes, dietary adjustments, and lifestyle modifications, alongside potential therapies aimed at managing insulin resistance. Additionally, long-term surveillance for complications related to diabetes and genetic syndromes might be necessary.</code> | <code>List health conditions linked to the gene HMG20A which should not be managed with the drug Norgestimate.</code>                                               |
  | <code><br><br>Osteomyelitis is a serious medical condition characterized by an infection of bone tissue. The symptoms can vary depending on the severity of the infection, but typically include:<br><br>**Signs and Symptoms**<br>- **Bone pain or tenderness.** Persistent pain in a specific bone that worsens during activity and at night without reason.<br>- **发热 (fever).** Often accompanied by chills, sweating, and a feeling of being unwell.<br>- **局部红肿.** Redness, swelling, and warmth around the affected bone or joint.<br>- **变动的疼痛 (fluctuant pain).** Pain that moves from one area to another simultaneously.<br>- **体位改变 (position-dependent pain).** Pain that becomes more severe when in certain positions.<br><br>**Causes of Osteomyelitis**<br>Osteomyelitis can be caused by several factors, including:<br><br>1. **Bacterial Infections.** Common bacteria like Staphylococcus aureus can spread into bones through a cut, wound, catheter, or dental procedures.<br>2. **Surgery Complications.** Infection can occur after surgery, when bacteria enter through the incision or from</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | <code>What disease, often starting with fever and bone pain, could be associated with Amikacin treatment?</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
- `per_device_train_batch_size`: 32
- `learning_rate`: 1e-05
- `num_train_epochs`: 10
- `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`: 32
- `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`: 10
- `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       | -             | 0.4481                 |
| 0.1754  | 10      | 1.4654        | -                      |
| 0.3509  | 20      | 1.4231        | -                      |
| 0.5263  | 30      | 1.3712        | -                      |
| 0.7018  | 40      | 1.3637        | -                      |
| 0.8772  | 50      | 1.3106        | -                      |
| 1.0     | 57      | -             | 0.5072                 |
| 1.0526  | 60      | 1.0429        | -                      |
| 1.2281  | 70      | 1.1889        | -                      |
| 1.4035  | 80      | 0.9391        | -                      |
| 1.5789  | 90      | 0.9941        | -                      |
| 1.7544  | 100     | 1.0176        | -                      |
| 1.9298  | 110     | 0.9045        | -                      |
| 2.0     | 114     | -             | 0.5283                 |
| 2.1053  | 120     | 0.8172        | -                      |
| 2.2807  | 130     | 0.6839        | -                      |
| 2.4561  | 140     | 0.6771        | -                      |
| 2.6316  | 150     | 0.743         | -                      |
| 2.8070  | 160     | 0.801         | -                      |
| 2.9825  | 170     | 0.8255        | -                      |
| **3.0** | **171** | **-**         | **0.5435**             |
| 3.1579  | 180     | 0.5296        | -                      |
| 3.3333  | 190     | 0.5701        | -                      |
| 3.5088  | 200     | 0.6061        | -                      |
| 3.6842  | 210     | 0.5169        | -                      |
| 3.8596  | 220     | 0.702         | -                      |
| 4.0     | 228     | -             | 0.5405                 |
| 4.0351  | 230     | 0.5539        | -                      |
| 4.2105  | 240     | 0.4384        | -                      |
| 4.3860  | 250     | 0.4482        | -                      |
| 4.5614  | 260     | 0.4905        | -                      |
| 4.7368  | 270     | 0.5511        | -                      |
| 4.9123  | 280     | 0.4802        | -                      |
| 5.0     | 285     | -             | 0.5402                 |
| 5.0877  | 290     | 0.3825        | -                      |
| 5.2632  | 300     | 0.4112        | -                      |
| 5.4386  | 310     | 0.4047        | -                      |
| 5.6140  | 320     | 0.3606        | -                      |
| 5.7895  | 330     | 0.4008        | -                      |
| 5.9649  | 340     | 0.3424        | -                      |
| 6.0     | 342     | -             | 0.5477                 |
| 6.1404  | 350     | 0.3508        | -                      |
| 6.3158  | 360     | 0.3003        | -                      |
| 6.4912  | 370     | 0.2989        | -                      |
| 6.6667  | 380     | 0.3652        | -                      |
| 6.8421  | 390     | 0.4117        | -                      |
| 7.0     | 399     | -             | 0.5506                 |
| 7.0175  | 400     | 0.3181        | -                      |
| 7.1930  | 410     | 0.3245        | -                      |
| 7.3684  | 420     | 0.2634        | -                      |
| 7.5439  | 430     | 0.2909        | -                      |
| 7.7193  | 440     | 0.2903        | -                      |
| 7.8947  | 450     | 0.268         | -                      |
| 8.0     | 456     | -             | 0.5437                 |
| 8.0702  | 460     | 0.289         | -                      |
| 8.2456  | 470     | 0.2892        | -                      |
| 8.4211  | 480     | 0.2846        | -                      |
| 8.5965  | 490     | 0.2624        | -                      |
| 8.7719  | 500     | 0.2536        | -                      |
| 8.9474  | 510     | 0.2744        | -                      |
| 9.0     | 513     | -             | 0.5453                 |
| 9.1228  | 520     | 0.2064        | -                      |
| 9.2982  | 530     | 0.3322        | -                      |
| 9.4737  | 540     | 0.2654        | -                      |
| 9.6491  | 550     | 0.2352        | -                      |
| 9.8246  | 560     | 0.2504        | -                      |
| 10.0    | 570     | 0.2302        | 0.5453                 |

* 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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