---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:315
- loss:CosineSimilarityLoss
base_model: google/embeddinggemma-300m
widget:
- source_sentence: In-House Replenishment Does Not Update Quantities in Product Location
History / Batches After Stock Transfer
sentences:
- Add "Primary Location Quantity" column in Suggestions section of In-House Replenishments
UI
- Stock and Min On Hand Column Sorting Not Working in Manage Products UI
- Previous Surcharge and New Surcharge Displayed as Dollar Amount Instead of Percentage
in Product Price History
- source_sentence: Quantity field in Cart Items is not manually editable
sentences:
- Unable to Search and Select Product in Select Product Catalog During In-House
Replenishment
- Commodity Start At - Commodity End At Filter Not Working in Inventory Master List
Report
- "Customer Orders UI \x96 Display \"Return\" for Return Clone Orders in Type Column"
- source_sentence: Inbound process blocks receiving when Expiration Date or Serial
Number is mandatory
sentences:
- '"Order Received" Button Not Functioning During Product Inbound'
- Product stock becomes negative after POS delivery and stock count displayed as
-1
- Update Packing Slip date format to MM-DD-YYYY
- source_sentence: Cycle Count Variance Report Displays No Data Despite Available
Yearly Inventory Count Records
sentences:
- System allows counted quantity higher than available stock during cycle count
- Incorrect page title, wrong PDF icon, and latest variance data not loading in
Cycle Count Variance Report
- Move "Average Price" Menu From Reports to Warehouse > Manage Inventory
- source_sentence: Misaligned Template Section Fields and Inconsistent Invoice Layout
Compared to UPS Orders
sentences:
- SKU Toggle Prints Commodity Code (CC) Instead of SKU in Location Labels
- Inventory Master List Displays Active/Inactive Products While Manage Products
Uses Different Status Visibility Logic
- PDF export button icon appears similar to Excel icon in Inventory Master List
report
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on google/embeddinggemma-300m
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: ticket similarity eval
type: ticket-similarity-eval
metrics:
- type: pearson_cosine
value: 0.8735255680387198
name: Pearson Cosine
- type: spearman_cosine
value: 0.819178435361286
name: Spearman Cosine
---
# 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 retrieval.
## 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
- **Supported Modality:** Text
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```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("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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss",
"cos_score_transformation": "torch.nn.modules.linear.Identity"
}
```
### 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss",
"cos_score_transformation": "torch.nn.modules.linear.Identity"
}
```
### 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`: {}