---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:20000
- loss:MultipleNegativesRankingLoss
base_model: google/embeddinggemma-300m
widget:
- source_sentence: 'What is the Dialysis Symptom Index (DSI) and why is it important
for assessing symptoms in hemodialysis patients?
'
sentences:
- Proteinuria in LCDD patients is measured from a 24-hour urine collection. Nephrotic
range proteinuria (NRP) is defined as 24-hour proteinuria equal to or greater
than 3 grams. This indicates significant protein loss in the urine, which is a
characteristic feature of LCDD.
- The Dialysis Symptom Index (DSI) is a self-reported index that assesses the presence
and severity of symptoms in patients with end-stage renal disease undergoing hemodialysis.
It contains 30 items targeting specific physical and emotional symptoms. The DSI
is important for accurately assessing symptoms in hemodialysis patients, both
for research and practice purposes, and to improve the care provided to these
patients.
- A meta-analysis of trials that utilized high-dose NAC found that it was associated
with a lower risk of CIAKI compared to controls. The analysis showed no significant
heterogeneity or publication bias. However, the effectiveness of NAC for the prevention
of CIAKI is still uncertain, and no definitive conclusions can be drawn at the
current time. If a beneficial effect exists, it may be related to the use of higher
doses of NAC. Large clinical trials are needed to better define the clinical utility
of this agent.
- source_sentence: 'What is the role of Doppler ultrasound in kidney transplant recipients?
'
sentences:
- The most common diagnoses for emergency department visits among patients receiving
maintenance in-center hemodialysis are heart failure, throat and chest pain, and
abdominal pain. These conditions account for a significant proportion of emergency
department visits in this patient population.
- 'Complications associated with leflunomide treatment in kidney transplant patients
include leukopenia, thrombocytopenia, hepatotoxicity, and anemia. In this particular
study, 11 out of 28 patients (39%) developed complications while receiving leflunomide. '
- Doppler ultrasound is a non-invasive imaging method commonly used in kidney transplant
recipients. It helps verify the patency of vascular anastomoses and exclude thrombotic
complications in the early period after transplantation. Doppler ultrasound also
measures the spectrum of blood flow within the kidney graft's segmental arteries,
providing parameters of vascular resistance such as pulsatility and resistance
indices. These indices can help detect complications like delayed graft function
(DGF) and predict the severity and duration of acute tubular necrosis (ATN) in
kidney transplant recipients.
- source_sentence: 'What is the natural history of analgesic-associated nephropathy
(AAN)?
'
sentences:
- In glomerulonephritis associated with the nephrotic syndrome, a progressive decline
in proteinuria to less than 2 g/day (or less) is associated with a favorable prognostic
outlook, whether the reduction occurs spontaneously or in response to treatment.
This means that a decrease in proteinuria to a lower level is indicative of a
better prognosis in patients with glomerulonephritis and the nephrotic syndrome.
- Infection is the second-leading cause of death in hemodialysis patients. Hemodialysis
patients have various risk factors that impair the immune system, putting them
at increased risk of infection and its related mortality. Malnutrition is a major
contributor to the development and fatality of infections in CKD patients. Patients
with malnutrition have weakened immune systems, making them more susceptible to
infections and increasing the risk of infection-related mortality. Severe dietary
protein restriction, which is often necessary to manage hyperphosphatemia in CKD
patients, can lead to protein-energy malnutrition. However, the use of phosphate
binders to manage hyperphosphatemia can improve phosphate management with less
risk of malnutrition compared to dietary protein restriction. Therefore, phosphate
binders are expected to allow patients to maintain a better nutritional state
while decreasing the chance of infection, thereby reducing the risk of infection-related
mortality.
- The natural history of AAN is poorly understood, especially since the withdrawal
of phenacetin. However, it is known that AAN can progress slowly and lead to end-stage
chronic renal failure (ESCRF). The incidence of AAN appears to be declining, but
it remains an important and preventable cause of ESRF in many areas.
- source_sentence: What are the treatment options for membranous glomerulonephritis?
sentences:
- Chronic kidney disease (CKD) can impact the management of gout by limiting the
dosage or hampering the use of urate-lowering drugs (ULD), colchicine, and nonsteroidal
anti-inflammatory agents. The frequent prescription of diuretics in CKD patients
can also affect outcomes. Diuretics can increase serum urate levels and interfere
with the effectiveness of allopurinol, a commonly used ULD. This knowledge is
important for managing gout in patients with CKD who are taking diuretics.
- Treatment for membranous glomerulonephritis may include the use of corticosteroids,
such as metacorten or prednisone, to reduce inflammation and proteinuria. However,
in some cases, treatment may not be effective, and the disease may progress to
advanced or chronic stages.
- Some factors that may benefit twice-weekly HD treatment include a longer HD session
time, a higher spKt/V (a measure of dialysis adequacy), the use of high flux dialyzers,
and the use of ultrapure dialysate. These factors can contribute to optimal solute
clearance and improve outcomes for patients undergoing twice-weekly HD.
- source_sentence: How do some participants believe that reimbursement or compensation
for living kidney donors can help minimize disadvantage?
sentences:
- Urinary L-PGDS excretions have been found to be superior to other markers, including
urinary excretions of type-IV collagen, beta-2 microglobulin, and NAG, as well
as serum creatinine levels, in predicting renal injury in type-2 diabetes. Studies
have shown that urinary L-PGDS excretions better predict ≥30 mg/gCr albuminuria
in type-2 diabetes. The use of urinary L-PGDS excretions as a marker for renal
injury in type-2 diabetes is supported by its ability to reflect a slight change
in glomerular permeability and its positive correlation with albuminuria.
- The time in therapeutic range (TTR) of INR (International Normalized Ratio) is
an important factor in determining the risk of hemorrhagic and ischemic events
in hemodialysis patients. If the INR is below 1.5, there is an increased risk
of hemorrhagic events, while an INR above 5 increases the risk of ischemic events.
Maintaining the INR within the therapeutic range is challenging but crucial in
minimizing these risks.
- Some participants believe that reimbursement or compensation can effectively help
donors and recipients who are socioeconomically disadvantaged by removing financial
barriers to donation. They advocate for government subsidies or special paid leave
to support potential donors who may not be able to take leave or afford donation-related
expenses. The goal is to ensure that financial constraints do not penalize individuals
who are willing to donate.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on google/embeddinggemma-300m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). 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:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m)
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(4): 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("yasserrmd/nephrology-gemma-300m-emb")
# Run inference
queries = [
"How do some participants believe that reimbursement or compensation for living kidney donors can help minimize disadvantage?",
]
documents = [
'Some participants believe that reimbursement or compensation can effectively help donors and recipients who are socioeconomically disadvantaged by removing financial barriers to donation. They advocate for government subsidies or special paid leave to support potential donors who may not be able to take leave or afford donation-related expenses. The goal is to ensure that financial constraints do not penalize individuals who are willing to donate.',
'The time in therapeutic range (TTR) of INR (International Normalized Ratio) is an important factor in determining the risk of hemorrhagic and ischemic events in hemodialysis patients. If the INR is below 1.5, there is an increased risk of hemorrhagic events, while an INR above 5 increases the risk of ischemic events. Maintaining the INR within the therapeutic range is challenging but crucial in minimizing these risks.',
'Urinary L-PGDS excretions have been found to be superior to other markers, including urinary excretions of type-IV collagen, beta-2 microglobulin, and NAG, as well as serum creatinine levels, in predicting renal injury in type-2 diabetes. Studies have shown that urinary L-PGDS excretions better predict ≥30 mg/gCr albuminuria in type-2 diabetes. The use of urinary L-PGDS excretions as a marker for renal injury in type-2 diabetes is supported by its ability to reflect a slight change in glomerular permeability and its positive correlation with albuminuria.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.6341, 0.0019, 0.0465]])
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 20,000 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 10 tokens
- mean: 22.05 tokens
- max: 56 tokens
| - min: 20 tokens
- mean: 91.9 tokens
- max: 281 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-----------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| How do the CKD-EPI and Japanese equations compare to Ccr and CGF in estimating renal function in cancer patients?
| The CKD-EPI and Japanese equations provide more accurate estimates of renal function compared to 24-hour Ccr and CGF in cancer patients before and after chemotherapy with cisplatin. These new equations have lower bias and higher precision values, indicating better estimation of glomerular filtration rate (GFR). The CKD-EPI and Japanese equations were developed as better estimates of GFR than Ccr and CGF, which were mostly developed in chronic kidney disease (CKD) patients without cancer. The accuracy of the CKD-EPI and Japanese equations in estimating GFR in cancer patients is consistent with previous studies. Therefore, it is recommended to replace Ccr and CGF with these new equations for the evaluation of renal function in cancer patients undergoing cisplatin-containing chemotherapy. |
| What are the clinical phenotypes of Bartter-like syndrome?
| Bartter-like syndrome can be divided into at least three different clinical phenotypes: classic Bartter syndrome, Gitelman syndrome, and antenatal (neonatal) Bartter syndrome. Classic Bartter syndrome and Gitelman syndrome have renal tubular hypokalemic alkalosis, while antenatal Bartter syndrome also has profound systemic manifestations such as polyhydramnios, premature delivery, severe water and salt wasting, hypokalemic metabolic alkalosis, severe hypercalciuria, and marked growth retardation. |
| What is granulomatous interstitial nephritis (GIN), and how frequently does it occur in patients with sarcoidosis? | Granulomatous interstitial nephritis (GIN) is a form of renal inflammation characterized by the presence of granulomas in the interstitial tissue of the kidneys. In patients with sarcoidosis, GIN is reportedly present in approximately one-third of patients with clinical evidence of renal disease. Post-mortem series have shown that between 7 and 27% of all patients with sarcoidosis may have GIN. It is important to note that GIN can occur in sarcoidosis patients even in the absence of obvious clinical renal disease. |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 6
- `per_device_eval_batch_size`: 6
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 6
- `per_device_eval_batch_size`: 6
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}