This is a sentence-transformers model finetuned from unsloth/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: unsloth/embeddinggemma-300m
Maximum Sequence Length: 1024 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': 1024, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'})
  (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("sentence_transformers_model_id")
# Run inference
queries = [
    "insomnia akut",
]
documents = [
    'Psychiatry',
    'Dermatology (Skin)',
    'General Surgery',
]
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([[0.6914, 0.5889, 0.4946]], dtype=torch.float16)

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0465
cosine_accuracy@3	0.1395
cosine_accuracy@5	0.186
cosine_accuracy@10	0.2791
cosine_precision@1	0.0465
cosine_precision@3	0.0465
cosine_precision@5	0.0372
cosine_precision@10	0.0279
cosine_recall@1	0.0465
cosine_recall@3	0.1395
cosine_recall@5	0.186
cosine_recall@10	0.2791
cosine_ndcg@10	0.1513
cosine_mrr@10	0.112
cosine_map@100	0.1278

Training Details

Training Dataset

Unnamed Dataset

Size: 151 training samples
Columns: query and expected_target
Approximate statistics based on the first 151 samples:
query expected_target
type string string
details
min: 3 tokens
mean: 9.07 tokens
max: 44 tokens
min: 4 tokens
mean: 6.17 tokens
max: 11 tokens

Samples:

query	expected_target
`nyeri tangan sangat lama, spesialis ortopedik di siloam denpasar`	`Orthopaedics (Bone)`
`mau cek jantung soalnya sering deg-degan`	`Cardiology (Heart)`
`jadwal dokter bedah di Siloam Semanggi kapan ya?`	`General Surgery`

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

Evaluation Dataset

Unnamed Dataset

Size: 19 evaluation samples
Columns: query and expected_target
Approximate statistics based on the first 19 samples:
query expected_target
type string string
details
min: 4 tokens
mean: 7.0 tokens
max: 14 tokens
min: 4 tokens
mean: 6.11 tokens
max: 8 tokens
Samples:
query expected_target
anak saya demam tinggi Pediatrics
interventional cardiology Cardiology (Heart)
tulang Orthopaedics (Bone)
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

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
gradient_accumulation_steps: 2
learning_rate: 2e-05
num_train_epochs: 10
max_steps: 240
warmup_ratio: 0.03
prompts: {'query': 'task: search result | query: {query}', 'expected_target': 'title: none | text: {expected_target}'}
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-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: 240
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.03
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: {'query': 'task: search result | query: {query}', 'expected_target': 'title: none | text: {expected_target}'}
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_ndcg@10
-1	-1	-	-	0.1513
2.6667	5	2.4	1.5137	-
5.0	10	1.7563	1.2100	-
7.6667	15	1.1973	1.1016	-
10.0	20	0.6759	1.0469	-
12.6667	25	0.4918	0.9702	-
15.0	30	0.194	1.0283	-
17.6667	35	0.061	1.1143	-
20.0	40	0.0436	1.1846	-
22.6667	45	0.0216	1.2246	-
25.0	50	0.0068	0.9980	-
27.6667	55	0.0204	0.9604	-
30.0	60	0.0098	1.0459	-
32.6667	65	0.0043	1.0967	-
35.0	70	0.004	1.0381	-
37.6667	75	0.0029	1.2236	-
40.0	80	0.0062	1.2285	-
42.6667	85	0.0021	1.3682	-
45.0	90	0.0007	1.2969	-
47.6667	95	0.0016	1.3076	-
50.0	100	0.0004	1.4443	-
52.6667	105	0.0006	1.2227	-
55.0	110	0.0007	1.2383	-
57.6667	115	0.0003	1.6729	-
60.0	120	0.0004	1.6797	-
62.6667	125	0.0005	1.6914	-
65.0	130	0.0004	1.6885	-
67.6667	135	0.0002	1.5742	-
70.0	140	0.0001	1.3975	-
72.6667	145	0.0002	1.2949	-
75.0	150	0.0002	1.2549	-
77.6667	155	0.0003	1.2939	-
80.0	160	0.0002	1.3262	-
82.6667	165	0.0002	1.3604	-
85.0	170	0.0002	1.3916	-
87.6667	175	0.0002	1.4180	-
90.0	180	0.0002	1.4395	-
92.6667	185	0.0002	1.4531	-
95.0	190	0.0002	1.4922	-
97.6667	195	0.0002	1.5186	-
100.0	200	0.0001	1.5420	-
102.6667	205	0.0002	1.5576	-
105.0	210	0.0001	1.5713	-
107.6667	215	0.0002	1.5791	-
110.0	220	0.0001	1.5869	-
112.6667	225	0.0002	1.5908	-
115.0	230	0.0001	1.5928	-
117.6667	235	0.0002	1.5947	-
120.0	240	0.0001	1.5947	-

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.2
Transformers: 4.56.2
PyTorch: 2.9.0+cu126
Accelerate: 1.12.0
Datasets: 4.0.0
Tokenizers: 0.22.2

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}
}

	query	expected_target
type	string	string
details	min: 3 tokens mean: 9.07 tokens max: 44 tokens	min: 4 tokens mean: 6.17 tokens max: 11 tokens

query	expected_target
`anak saya demam tinggi`	`Pediatrics`
`interventional cardiology`	`Cardiology (Heart)`
`tulang`	`Orthopaedics (Bone)`

yosriku/gemma300m-specialization-v1

SentenceTransformer

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

yosriku/
gemma300m-specialization-v1