SentenceTransformer based on google/embeddinggemma-300m

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yasserrmd/oncology-gemma-300m-emb")
# Run inference
queries = [
    "What are the current standard treatments for glioblastoma multiforme (GBM) and why is recurrence almost unavoidable?\n",
]
documents = [
    'The current standard treatment for GBM includes surgery, radiotherapy, and chemotherapy. However, complete surgical resection is not possible, and GBM is resistant to chemotherapy, including the commonly used drug temozolomide (TMZ). This resistance and the inability to completely remove the tumor during surgery contribute to the high recurrence rate of GBM.',
    'The overexpression of GALNT2 in oral squamous cell carcinoma (OSCC) cells can promote their invasive potential. GALNT2 modifies the O-glycosylation of proteins and increases the activity of epidermal growth factor receptor (EGFR), which plays a crucial role in the invasive behavior of OSCC cells. This suggests that GALNT2 may be involved in the occurrence and development of OSCC.',
    'The main mechanisms responsible for oncogene-mediated drug resistance in ovarian cancer include deregulation of apoptosis, altered phosphorylation (intracellular signaling), and metabolic pathways. Activation of the PI3K/AKT cell survival pathway, as well as deregulation of growth factor receptors mediated by NF-kB and STAT3, plays a pivotal role in drug resistance. Additionally, alterations in DNA damage and repair mechanisms, impaired apoptotic machinery, and epithelial-to-mesenchymal transition (EMT) have been implicated in drug resistance. Wnt signaling, particularly the β-catenin-independent pathway via Wnt5a/ROR1/ROR2, is also involved in EMT and chemoresistance. Targeting these pathways may offer potential means to overcome drug resistance in ovarian cancer.',
]
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([[ 0.7010,  0.0508, -0.0444]])

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: 22.55 tokens
max: 51 tokens

min: 18 tokens
mean: 91.28 tokens
max: 219 tokens

	sentence_0	sentence_1
type	string	string
details	min: 10 tokens mean: 22.55 tokens max: 51 tokens	min: 18 tokens mean: 91.28 tokens max: 219 tokens

Samples:

sentence_0	sentence_1
`Is there a way to prevent PTLD in high-risk patients?`	`Currently, there is no convincing data for the prophylaxis of PTLD. However, the case mentioned suggests that early use of rituximab after HSCT (Hematopoietic Stem Cell Transplantation) could be a good way to prevent PTLD in high-risk patients, especially those who are serum EBV (Epstein-Barr Virus) positive. Early recognition of PTLD, early lymph node biopsy, and early diagnosis are key factors in the successful treatment of PTLD.`
`How does the 34-gene 'CTC profile' contribute to the prognostic power of breast cancer patients?`	The 34-gene 'CTC profile' has been found to be predictive of CTC status in breast cancer patients. It demonstrated a classification accuracy of 82% in the training cohort and 67% in an independent microarray dataset. Furthermore, it has been shown to be prognostic in both independent datasets, with a hazard ratio (HR) of 10 in the first validation dataset and a HR of 3.2 in the second validation dataset. Importantly, multivariate analysis confirmed that the CTC profile provided prognostic information independent of other clinical variables in both patient cohorts.
`How are beauty care services for cancer patients organized and provided?`	Beauty care services for cancer patients are not standardized or evaluated and vary from one establishment to another. In the case of the IGR, consultations on image advice and socio-aesthetics are provided by a socio-aesthetician who has been trained as a personal image advisor. These consultations are offered to women with breast cancer or young adults and adolescents with cancer who are referred by medical units. The consultations take place in a dedicated area with three rooms: an office, make-up parlor, and beauty care salon. Patients are usually seen multiple times during their treatment period. The socio-aesthetician is paid by the hospital and is part of the Onco-hematology Interdisciplinary Supportive Care Directorate.

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 4
per_device_eval_batch_size: 4
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: 4
per_device_eval_batch_size: 4
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
0.1	500	0.0144
0.2	1000	0.0293
0.3	1500	0.0128
0.4	2000	0.0153
0.5	2500	0.0182
0.6	3000	0.008
0.7	3500	0.0098
0.8	4000	0.0044
0.9	4500	0.0024
1.0	5000	0.0019

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.0
Transformers: 4.56.1
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

Citation

BibTeX

Sentence Transformers

@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

@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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Safetensors

Model size

0.3B params

Tensor type

F32

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oncology-gemma-300m-emb