This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 2048 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

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/ophthalmology-gemma-300m-emb")
# Run inference
queries = [
    "How does intravitreal injection of tacrolimus affect the expression of chemokines in the retina?\n",
]
documents = [
    'Intravitreal injection of tacrolimus has been shown to markedly reduce the expression of monocyte chemoattractant protein-1 (MCP-1) and regulated upon activation, normal T cell expressed and secreted (RANTES) mRNA in the retina of immunized rats. MCP-1 and RANTES are potent chemoattractants for T lymphocytes and macrophages, which are infiltrating cells observed in the posterior segment of eyes with ocular inflammation. The suppression of MCP-1 and RANTES gene expression by tacrolimus may lead to a reduced number of infiltrating T lymphocytes and macrophages in the vitreous cavity and retina, thereby reducing the inflammatory reactions associated with ocular inflammation.',
    'Anaplastic OAs are characterized by intensive vascularity. The choice of laser radiation for tumor removal depends on the vascularity observed. For low-grade OAs with minimal vascularity, laser vaporization using radiation with a wavelength of 1.47 μm and power of 7 W is planned. However, for anaplastic OAs with intense vascularity, semiconductor laser radiation with a wavelength of 0.808 μm and power of 30 W is used due to its good coagulating properties.',
    'For patients with severe eye changes and diplopia, high dose prednisone (120 mg/day) can be effective in reducing symptoms. Some clinicians also add azathioprine as a steroid sparing agent during long term treatment. Orbital decompression operations and a combination of orbital radiotherapy and steroids have also shown to be effective in reducing proptosis and diplopia in some patients. Corrective muscle surgery may be undertaken for residual diplopia when the eyes are quiet.',
]
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([[0.6629, 0.0904, 0.0715]])

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: 21.87 tokens
max: 67 tokens
min: 15 tokens
mean: 90.2 tokens
max: 241 tokens

Samples:

sentence_0	sentence_1
`How does OCT contribute to the identification and assessment of vitreoretinal traction in macular holes, and why is bilaterally performing tomographic imaging important for early detection in patients affected by this condition?[object Object]`	OCT contributes significantly to the identification and assessment of vitreoretinal traction in macular holes, enabling the visualization of vitreoretinal interface abnormalities. It plays a crucial role in assessing the risk for hole formation in the fellow eye of patients with a unilateral macular hole, as the probability of developing a macular hole in the contralateral eye is 13% in 48 months. Therefore, it is essential to perform bilateral tomographic imaging in patients affected by this pathology for early detection in the other eye. Considering that surgery in the normal contralateral eye of patients with macular holes is not acceptable, early detection through bilateral imaging is crucial for appropriate clinical management.
`How does corneal refractive therapy, also known as overnight orthokeratology, work to reduce myopia?[object Object]`	`Corneal refractive therapy, or overnight orthokeratology, involves the use of reverse geometry contact lenses to induce temporary flattening in the central corneal curvature. This temporary flattening leads to a short-term reduction in myopia and improved unaided visual acuity. Recent studies have also shown that corneal refractive therapy has the potential to slow down the progression of myopia.`
`What is the significance of contrast sensitivity testing in early age-related macular degeneration (AMD) patients, and how does it relate to the prognosis and treatment of the condition?`	Contrast sensitivity testing is crucial in early AMD patients as it can detect even slight changes in visual system performance, particularly impairment at nighttime with and without glare. This testing provides important prognostic value, as AMD patients with less affected or intact contrast sensitivity have better prognoses and respond more effectively to treatments with vascular endothelial growth factor inhibitors, photodynamic therapy, and laser photocoagulation. Additionally, early diagnosis of AMD using contrast sensitivity testing can aid in the prevention of future blindness.

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

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: {}

Training Logs

Epoch	Step	Training Loss
0.1500	500	0.0239
0.2999	1000	0.0192
0.4499	1500	0.015
0.5999	2000	0.0058
0.7499	2500	0.0071
0.8998	3000	0.0072

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.0
Transformers: 4.56.2
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.1

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",
}

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}
}

	sentence_0	sentence_1
type	string	string
details	min: 10 tokens mean: 21.87 tokens max: 67 tokens	min: 15 tokens mean: 90.2 tokens max: 241 tokens