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---
base_model: TaylorAI/bge-micro-v2
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: "\nBased on the provided information, it seems like you are listing\
\ various substances and the potential side effects associated with them. Here's\
\ a summary:\n\n**Substances and Related Side Effects:**\n\n1. **Amitriptyline**\n\
\ - Hyperkeratosis\n - Muscle weakness\n - Abnormal macular morphology\n\
\ - Visual impairment\n - Anxiety\n - Abnormality of the endocrine system\n\
\ - Hypothyroidism\n - Inflammatory abnormality of the skin\n - Eczema\n\
\ - Skin ulcer\n - Erythema\n - Jaundice\n - Hyperhidrosis\n - Blurred\
\ vision\n - Abnormality of extrapyramidal motor function\n - Hepatic steatosis\n\
\ - Increased body weight\n - Arrhythmia\n - Supraventricular arrhythmia\n\
\ - Congestive heart failure\n - Abnormality of blood and blood-forming tissues\n\
\ - Thrombocytopenia\n - Renal insufficiency\n - Fever\n - Hypoglycemia\n\
\ - Dehydration\n - Pain\n - Esophageal stenosis\n - Gait disturbance\n\
\ "
sentences:
- Can you provide me with a list of medications that could cause loss of appetite
and a smooth tongue sensation as side effects?
- What are the secondary diseases related to breast cancer characterized by gene
expression changes associated with genomic variations affecting cell growth and
division, and present symptoms like pain, fatigue, breathing problems, vomiting,
changes in bowel movements, and wasting, particularly during treatment?
- Which drugs targeting the dopamine transporter encoded by SLC6A3 gene are approved
for managing Major Depressive Disorder, Generalized Anxiety Disorder, neuropathic
pain, osteoarthritis, and stress urinary incontinence?
- source_sentence: '
Alvespimycin, a derivative of Geldanamycin and a Heat Shock Protein 90 (HSP90)
inhibitor, falls under the drug category on DrugBank. It encompasses Amides, HSP90
Heat-Shock Proteins, Lactams, and Quinones. This compound is currently under investigation
for its antineoplastic potential in treating solid tumors, advanced solid tumors,
or acute myeloid leukemia. Alvespimycin''s typical half-life spans from 9.9 to
54.1 hours, with a median duration of 18.2 hours, making it a longer-acting drug
in its pharmacodynamics profile. Alvespimycin functions by inhibiting HSP90, which
consequently disrupts the correct folding and function of oncoproteins derived
from HSP90 client proteins, a critical role in cellular proliferation, and apoptosis
suppression. The medication targets oncogenic kinases like BRAF, inducing their
proteasomal degradation and facilitating depletion of oncoproteins. Notably, Alvespimycin
shows a minimal degree of protein binding and is more selective in its effect
on tumors compared to normal tissues. The drug also aids in increasing the potency
of telomerase inhibition by imetelstat, as demonstrated in pre-clinical models
of human osteosarcoma.'
sentences:
- Could you give me a list of medications that interact with the HSP90AA1 gene or
protein and have a metabolic half-life ranging from 9.9 to 54.1 hours?
- Which diseases are categorized as forerunners or variations of benign cervical
tumors in current medical classifications?
- Can you give me an overview of diseases related to SLC13A5 gene abnormalities
that involve dysregulation of enzyme activity?
- source_sentence: '
The gene DRAXIN, also known by various aliases such as ''AGPA3119'', ''C1orf187'',
''UNQ3119'', and ''neucrin'', is located on chromosome 1 in the genomic region
defined by its start position at 11691710 and end position at 11725857. DRAXIN,
classified as "dorsal inhibitory axon guidance protein", is predicted to play
a role in the inhibition of the canonical Wnt signaling pathway, negative regulation
of neuron projection development, and nervous system development. It is also indicated
to be active in the extracellular region.
Studies have revealed that this protein interacts with another gene (NTN1) and
is associated with a range of diseases including Parkinson disease, juvenile onset
Parkinson disease 19A, early-onset parkinsonism-intellectual disability syndrome,
parkinsonian-pyramidal syndrome, X-linked parkinsonism-spasticity syndrome, hemiparkinsonism-hemiatrophy
syndrome, atypical juvenile parkinsonism, and hereditary late onset Parkinson
disease. It is involved across various biological processes such as Wnt signaling
pathway, negative regulation of the canonical Wnt pathway, negative regulation
of axon extension, and negative regulation of neuron apoptotic process. DRAXIN
is known to have expression in numerous anatomical entities like blood, prefrontal
cortex, female reproductive system, brain, cerebral cortex, uterus, endometrium,
frontal cortex, temporal lobe, amygdala, forebrain, neocortex, Ammon''s horn,
cerebellum, cerebellar cortex, and dorsolateral prefrontal cortex; however, its
expression is absent in colonic mucosa, quadriceps femoris, vastus lateralis,
deltoid, biceps'
sentences:
- What are the common Alzheimer's treatments that could cause chest discomfort,
and can you list those with a duration of effect lasting about three days?
- Which genes or proteins are not expressed in either the small intestinal or colonic
mucosal tissues?
- Identify the Y-linked gene associated with spermatogenic failure that's located
in the Y chromosome's nonrecombining zone and exclusively expressed in testes.
- source_sentence: '
TRMT5, also known by aliases such as COXPD26, KIAA1393, PNSED, and TRM5, is a
gene encoding the tRNA methyltransferase 5. This enzyme is responsible for methylating
the N1 position of guanosine-37 (G37) in specific tRNAs using S-adenosyl methionine.
It plays a role in modifying tRNAs, which contain 13 to 14 nucleotides modified
posttranscriptionally by nucleotide-specific enzymes (Brule'
sentences:
- Which gene or protein is known to interact with the one associated with defective
ABCB11, which leads to PFIC2 and BRIC2, and plays a regulatory role in the expression
of genes critical for liver development and functionality?
- Which genes or proteins are known to interact with tRNA (guanine(37)-N(1))-methyltransferase
activity?
- What is the biosynthetic pathway that falls under 'Creation of C4 and C2 activators'
and also involves the MASP1 gene or its protein product?
- source_sentence: '
Muscular dystrophy is a group of inherited disorders characterized by progressive
muscle weakness and wasting. Here''s a concise overview of the information you''ve
provided:
### Types of Muscular Dystrophy:
- **Duchenne Muscular Dystrophy**: Most common in young boys, characterized by
severe muscle weakness and consequent inability to walk by adolescence.
- **Becker Muscular Dystrophy**: Less severe than Duchenne but still progressive,
affecting males.
- **Facioscapulohumeral Muscular Dystrophy (FSHD)**: Affects the face, shoulder,
and upper arm muscles, common in the teenage to adult years.
- **'
sentences:
- Identify a metabolic pathway that is associated with both glyoxylate metabolism
and glycine degradation and is capable of interacting with a common gene or protein.
- Which gene/protein belonging to the activator 1 small subunit family is involved
in interactions with the gene/protein associated with compromised DNA recombination
inhibition at telomeres resulting from DAXX mutations?
- I need details on a disease linked to the COL6A2 gene, presenting with progressive
muscle weakening in specific groups and worsening muscle strength over time.
model-index:
- name: SentenceTransformer based on TaylorAI/bge-micro-v2
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 384
type: dim_384
metrics:
- type: cosine_accuracy@1
value: 0.41089108910891087
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.49504950495049505
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5346534653465347
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5693069306930693
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.41089108910891087
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.165016501650165
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10693069306930691
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05693069306930693
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.41089108910891087
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.49504950495049505
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5346534653465347
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5693069306930693
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.48660626760149667
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.46036657237152295
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4675921280486482
name: Cosine Map@100
---
# SentenceTransformer based on TaylorAI/bge-micro-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) on the json dataset. It maps sentences & paragraphs to a 384-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:** [TaylorAI/bge-micro-v2](https://huggingface.co/TaylorAI/bge-micro-v2) <!-- at revision 3edf6d7de0faa426b09780416fe61009f26ae589 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 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: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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})
)
```
## 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/TaylorAI_bge-micro-v2_FareedKhan_prime_synthetic_data_2k_10_32")
# Run inference
sentences = [
"\n\nMuscular dystrophy is a group of inherited disorders characterized by progressive muscle weakness and wasting. Here's a concise overview of the information you've provided:\n\n### Types of Muscular Dystrophy:\n- **Duchenne Muscular Dystrophy**: Most common in young boys, characterized by severe muscle weakness and consequent inability to walk by adolescence.\n- **Becker Muscular Dystrophy**: Less severe than Duchenne but still progressive, affecting males.\n- **Facioscapulohumeral Muscular Dystrophy (FSHD)**: Affects the face, shoulder, and upper arm muscles, common in the teenage to adult years.\n- **",
'I need details on a disease linked to the COL6A2 gene, presenting with progressive muscle weakening in specific groups and worsening muscle strength over time.',
'Identify a metabolic pathway that is associated with both glyoxylate metabolism and glycine degradation and is capable of interacting with a common gene or protein.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4109 |
| cosine_accuracy@3 | 0.495 |
| cosine_accuracy@5 | 0.5347 |
| cosine_accuracy@10 | 0.5693 |
| cosine_precision@1 | 0.4109 |
| cosine_precision@3 | 0.165 |
| cosine_precision@5 | 0.1069 |
| cosine_precision@10 | 0.0569 |
| cosine_recall@1 | 0.4109 |
| cosine_recall@3 | 0.495 |
| cosine_recall@5 | 0.5347 |
| cosine_recall@10 | 0.5693 |
| cosine_ndcg@10 | 0.4866 |
| cosine_mrr@10 | 0.4604 |
| **cosine_map@100** | **0.4676** |
<!--
## 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: 2 tokens</li><li>mean: 247.16 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 35.28 tokens</li><li>max: 113 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code><br><br>Hemophilia is an inherited bleeding disorder that occurs when a person's body does not produce enough of certain clotting factors, leading to prolonged bleeding and, in severe cases, spontaneous bleeding into joints and muscles. The disorder is typically associated with mutations in the genes that code for clotting factors VIII (for hemophilia A) and IX (for hemophilia B). It can be categorized based on the specific clotting factor affected and the mode of inheritance.<br><br>### Risk Factors<br>The biggest risk factor for hemophilia is a family history of the disorder. If a family member, particularly a parent or a close relative, has hemophilia, there is an increased risk for the disease due to the genetic predisposition.<br><br>### Genetic Inheritance<br>- **Hemophilia A (Severe)** or **Factor VIII deficiency**: Often affects males due to the inheritance pattern X-linked recessive. A carrier female has a 50% chance of passing the gene to each of her offspring.<br>- **Hemophilia B (Severe)** or **Factor IX deficiency**: Also typically X-linked recessive, mostly affecting males. Carrier females are likely to pass the gene to their male offspring only.<br><br>### Complications and Symptoms<br>- **Abnormal bleeding**: This is the most common symptom, ranging from mild to life-threatening.<br>- **Subcutaneous hemorrhage and intracranial hemorrhage**: These can lead to serious complications and require immediate medical attention.<br>- **Joint damage**: Frequent bleeding into joints can result in arthritis, joint destruction, and limitation of joint mobility.<br>- **Gastrointestinal, genitourinary, and epistaxis**: These are other sites where bleeding can occur, often with minor trauma.<br><br>### Treatment and Management<br>Treatment for hemophilia often involves replacing the missing clotting factors using infused or transfused factors. This can be through Factor VIII concentrate for hemophilia A or Factor IX concentrate for hemophilia B. Prophylactic treatments are often administered to prevent bleeding episodes and maintain normal joint function.<br><br>### Diagnosis<br>Diagnosis of hemophilia is typically made through a series of blood tests to measure clotting times and factor levels. Genetic testing is also recommended in families with a history of hemophilia to identify carriers and those with more severe symptoms.<br><br>### See a Doctor<br>It's important to see a doctor if you or your child shows signs of prolonged bleeding or if there is a family history of hemophilia. Early diagnosis and appropriate treatment can significantly improve outcomes and quality of life.<br><br>### Carrying and Symptoms in Female Carriers<br>While female carriers are usually asymptomatic, they can experience mild symptoms under specific circumstances such as during pregnancy (gastrointestinal bleeding) or menopause (menorrhagia). Genetic testing can confirm an asymptomatic carrier status, which is important for family planning and counseling.<br><br>### In Conclusion<br>Hemophilia is a complex condition that requires careful management to prevent complications and maintain quality of life. Early diagnosis, genetic counseling, and proper treatment are crucial for managing this inherited bleeding disorder effectively.</code> | <code>Which condition should be avoided when prescribing medications for outdated forms of contact dermatitis resulting from poison oak exposure?</code> |
| <code><br><br>Assistant: Diabetes insipidus, a rare but serious condition, can manifest with a series of symptoms and has diverse impacts on various systems of the body. Primarily characterized by increased thirst, significant urination, and dehydration, diabetes insipidus requires prompt medical intervention.<br><br>**Symptoms and Impacts**:<br>1. **Polydipsia** (increased thirst) and **polyuria** (frequent urination) are the primary symptoms, typically exceeding 10 liters of fluid intake and urine output per day.<br>2. **Dehydration** can result from excessive fluid loss unless compensated, causing electrolyte</code> | <code>What medical condition could I have that involves persistent thirst, frequent urination, and unexplained weight loss, and is associated with a familial disorder affecting water balance similar to diabetes insipidus, but not identical, as it involves an inability to concentrate urine? My father has it, and my doctor suggested managing salt intake and water consumption, mentioning that medication may be available to reduce the urination. What is the name of this disease?</code> |
| <code><br><br>The pathway described in this document is titled "p75 NTR receptor-mediated signalling" which suggests that it centers around the activity of the p75 neurotrophin receptor (p75 NTR), a cell surface receptor that plays a crucial role in neuronal development, survival, and function. <br><br>### Key Components and Their Roles:<br><br>- **Neurotrophin (NGF or Nerve Growth Factor)**: This is a ligand that binds to the p75 NTR. Binding of NGF to p75 NTR initiates a cascade of events resulting in various cellular responses.<br><br>- **p75 NTR**: The receptor itself is pivotal, as its binding with ligands like NGF modulates signal transduction in cells, affecting survival, differentiation, and various aspects of cellular metabolism and function.<br><br>- **Sphingomyelinase (SMPD2)**: This gene/protein is implicated in the pathway, with involvement in modulating ceramide production upon NGF Binding to p75 NTR. Sphingomyelinase is activated by the NGF:p75NTR complex, suggesting an integral role in the effector phase of the signaling cascade.<br><br>- **Ceramide**: A lipid derived from sphingomyelin that plays a key role in cellular signaling. Ceramide's production upon ligand-receptor binding can lead to either cell survival or apoptosis depending on the context within specific cell types.<br><br>- **JNK (c-Jun N-terminal kinase)**: This is a serine/threonine kinase that can be activated by ceramide and is involved in various cellular processes including apoptosis, cell cycle regulation, and differentiation.<br><br>### Pathway Description:<br><br>The pathway described includes mechanisms by which ligand binding to p75 NTR leads to ceramide production, which in</code> | <code>Which signaling pathway interacts with both p75 NTR receptor signaling and the nerve growth factor (NGF) gene/protein in a hierarchical manner?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
384
],
"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_384_cosine_map@100 |
|:-------:|:-------:|:-------------:|:----------------------:|
| 0 | 0 | - | 0.4238 |
| 0.1754 | 10 | 1.9916 | - |
| 0.3509 | 20 | 1.8049 | - |
| 0.5263 | 30 | 1.8366 | - |
| 0.7018 | 40 | 1.8585 | - |
| 0.8772 | 50 | 1.7288 | - |
| 1.0 | 57 | - | 0.4326 |
| 1.0526 | 60 | 1.6438 | - |
| 1.2281 | 70 | 1.5404 | - |
| 1.4035 | 80 | 1.6168 | - |
| 1.5789 | 90 | 1.5432 | - |
| 1.7544 | 100 | 1.4976 | - |
| 1.9298 | 110 | 1.5275 | - |
| 2.0 | 114 | - | 0.4422 |
| 2.1053 | 120 | 1.3276 | - |
| 2.2807 | 130 | 1.3629 | - |
| 2.4561 | 140 | 1.4108 | - |
| 2.6316 | 150 | 1.3338 | - |
| 2.8070 | 160 | 1.4043 | - |
| 2.9825 | 170 | 1.4664 | - |
| 3.0 | 171 | - | 0.4487 |
| 3.1579 | 180 | 1.2225 | - |
| 3.3333 | 190 | 1.2557 | - |
| 3.5088 | 200 | 1.3518 | - |
| 3.6842 | 210 | 1.3227 | - |
| 3.8596 | 220 | 1.3391 | - |
| 4.0 | 228 | - | 0.4561 |
| 4.0351 | 230 | 1.2035 | - |
| 4.2105 | 240 | 1.197 | - |
| 4.3860 | 250 | 1.2908 | - |
| 4.5614 | 260 | 1.1738 | - |
| 4.7368 | 270 | 1.1855 | - |
| 4.9123 | 280 | 1.2118 | - |
| 5.0 | 285 | - | 0.4578 |
| 5.0877 | 290 | 1.1835 | - |
| 5.2632 | 300 | 1.1624 | - |
| 5.4386 | 310 | 1.2075 | - |
| 5.6140 | 320 | 1.1771 | - |
| 5.7895 | 330 | 1.0814 | - |
| 5.9649 | 340 | 1.2039 | - |
| **6.0** | **342** | **-** | **0.4584** |
| 6.1404 | 350 | 1.2029 | - |
| 6.3158 | 360 | 1.1043 | - |
| 6.4912 | 370 | 1.2011 | - |
| 6.6667 | 380 | 1.0401 | - |
| 6.8421 | 390 | 1.0732 | - |
| 7.0 | 399 | - | 0.4624 |
| 7.0175 | 400 | 1.1137 | - |
| 7.1930 | 410 | 1.0946 | - |
| 7.3684 | 420 | 1.1581 | - |
| 7.5439 | 430 | 1.0605 | - |
| 7.7193 | 440 | 1.076 | - |
| 7.8947 | 450 | 1.2689 | - |
| 8.0 | 456 | - | 0.4680 |
| 8.0702 | 460 | 1.0004 | - |
| 8.2456 | 470 | 1.1387 | - |
| 8.4211 | 480 | 1.0652 | - |
| 8.5965 | 490 | 1.0879 | - |
| 8.7719 | 500 | 1.1845 | - |
| 8.9474 | 510 | 1.0979 | - |
| 9.0 | 513 | - | 0.4684 |
| 9.1228 | 520 | 1.0588 | - |
| 9.2982 | 530 | 1.2412 | - |
| 9.4737 | 540 | 1.0261 | - |
| 9.6491 | 550 | 1.0919 | - |
| 9.8246 | 560 | 1.129 | - |
| 10.0 | 570 | 1.0425 | 0.4676 |
* 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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