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 retrieval.

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
Supported Modality: Text

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Gemma3TextModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
  (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("kevin-rice/embeddinggemma-ticket-similarity")
# Run inference
queries = [
    'Misaligned Template Section Fields and Inconsistent Invoice Layout Compared to UPS Orders',
]
documents = [
    'PDF export button icon appears similar to Excel icon in Inventory Master List report',
    'Inventory Master List Displays Active/Inactive Products While Manage Products Uses Different Status Visibility Logic',
    'SKU Toggle Prints Commodity Code (CC) Instead of SKU in Location Labels',
]
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([[0.3035, 0.6568, 0.2639]])

Evaluation

Metrics

Semantic Similarity

Dataset: ticket-similarity-eval
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8735
spearman_cosine	0.8192

Training Details

Training Dataset

Unnamed Dataset

Size: 315 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 315 samples:
sentence1 sentence2 score
type string string float
details
min: 10 tokens
mean: 16.62 tokens
max: 27 tokens
min: 10 tokens
mean: 16.83 tokens
max: 27 tokens
min: 0.0
mean: 0.57
max: 1.0

Samples:

sentence1	sentence2	score
`View button icon under Action column is not displayed properly`	`Unable to Search and Select Product in Select Product Catalog During In-House Replenishment`	`1.0`
`Pick Assignment Throws Replenishment Error Even When Primary Location Has Available Stock`	`Cycle Count Variance report not fetching latest cycle count data dynamically`	`0.8`
`Move Items UI should auto-hide location selection when only one Primary location exists`	`Primary Location not populated when product is fetched using Scan/Search Barcode in In-House Replenishment`	`0.8`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss", 3 "cos_score_transformation": "torch.nn.modules.linear.Identity" 4}

Evaluation Dataset

Unnamed Dataset

Size: 79 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 79 samples:
sentence1 sentence2 score
type string string float
details
min: 10 tokens
mean: 16.51 tokens
max: 25 tokens
min: 11 tokens
mean: 17.03 tokens
max: 26 tokens
min: 0.0
mean: 0.63
max: 1.0

Samples:

sentence1	sentence2	score
`Update Packing Slip date format to MM-DD-YYYY`	`Accounting Template data is not fetching under Template column in Sales History By Item report`	`0.8`
`Update Comments Section Format and Merge Herman ID / Employee ID Field`	`Order With Quantity Exceeding Available Primary Stock Is Marked Delivered Instead of Back Order and Creates Negative Stock`	`0.0`
`Default distribution center comment is not displayed in Comments section`	`Update Packing Slip date format to MM-DD-YYYY`	`0.8`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss", 3 "cos_score_transformation": "torch.nn.modules.linear.Identity" 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 4
learning_rate: 2e-05
warmup_steps: 0.1
fp16: True
per_device_eval_batch_size: 4

All Hyperparameters

Click to expand

per_device_train_batch_size: 4
num_train_epochs: 3
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: False
fp16: True
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 4
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
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
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	ticket-similarity-eval_spearman_cosine
0.0633	5	0.1581	-	-
0.1266	10	0.1581	-	-
0.1899	15	0.1117	-	-
0.2532	20	0.0869	0.0750	0.6907
0.3165	25	0.0651	-	-
0.3797	30	0.0590	-	-
0.4430	35	0.0580	-	-
0.5063	40	0.0698	0.1141	0.5602
0.5696	45	0.1079	-	-
0.6329	50	0.0932	-	-
0.6962	55	0.0762	-	-
0.7595	60	0.0938	0.0637	0.7089
0.8228	65	0.1259	-	-
0.8861	70	0.0735	-	-
0.9494	75	0.0276	-	-
1.0127	80	0.0551	0.0607	0.7692
1.0759	85	0.0788	-	-
1.1392	90	0.0807	-	-
1.2025	95	0.0334	-	-
1.2658	100	0.0508	0.0687	0.7471
1.3291	105	0.0719	-	-
1.3924	110	0.0404	-	-
1.4557	115	0.0143	-	-
1.5190	120	0.0740	0.0630	0.7372
1.5823	125	0.0410	-	-
1.6456	130	0.0483	-	-
1.7089	135	0.0629	-	-
1.7722	140	0.0513	0.0483	0.7610
1.8354	145	0.0175	-	-
1.8987	150	0.0397	-	-
1.9620	155	0.0341	-	-
2.0253	160	0.0223	0.0478	0.7755
2.0886	165	0.0167	-	-
2.1519	170	0.0230	-	-
2.2152	175	0.0600	-	-
2.2785	180	0.0357	0.0412	0.8031
2.3418	185	0.0479	-	-
2.4051	190	0.0172	-	-
2.4684	195	0.0183	-	-
2.5316	200	0.0213	0.0399	0.8162
2.5949	205	0.0115	-	-
2.6582	210	0.0305	-	-
2.7215	215	0.0101	-	-
2.7848	220	0.0189	0.0388	0.8229
2.8481	225	0.0249	-	-
2.9114	230	0.0104	-	-
2.9747	235	0.0099	-	-
3.0	237	-	0.0391	0.8192

Training Time

Training: 46.3 minutes

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.4.1
Transformers: 5.7.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.8.5
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",
}

	sentence1	sentence2	score
type	string	string	float
details	min: 10 tokens mean: 16.62 tokens max: 27 tokens	min: 10 tokens mean: 16.83 tokens max: 27 tokens	min: 0.0 mean: 0.57 max: 1.0