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Model: AROY76/Embedding-gemma-300M-skills Source: Original Platform
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README.md
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README.md
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---
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- dense
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- generated_from_trainer
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- dataset_size:4992
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- loss:MultipleNegativesRankingLoss
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base_model: google/embeddinggemma-300m
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widget:
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- source_sentence: Client onboarding, implementation, project management, communication,
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Salesforce, G-suite, Asana, Single Sign-On (SSO), SFTP, data analysis
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sentences:
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- A software engineer uses Python and GitHub to automate testing processes.
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- Setting up clients in Salesforce and G-suite efficiently requires strong project
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management and clear communication.
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- Choosing between cloud storage solutions like Dropbox and Google Drive can be
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challenging.
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- source_sentence: SQL, Excel, stakeholder management, product management
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sentences:
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- FastAPI and Flask both enable developers to build robust RESTful APIs efficiently.
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- Data analysis using SQL and Excel for stakeholder updates in product management.
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- Project scheduling and Gantt charts for timeline tracking.
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- source_sentence: Power Platform,Robotic Process Automation,Power Automate Cloud
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& Desktop,Automation Anywhere,PL900,SAP ECC,Generative AI,Power BI
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sentences:
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- SAP ECC and PL900 are essential for financial management systems.
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- Automation Anywhere, Power Platform, and Power Automate Cloud & Desktop are key
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tools for streamlining business processes.
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- Guidewire uses a test automation framework to ensure continuous integration and
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security testing.
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- source_sentence: Critical Care,ICU
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sentences:
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- Guiding students through Java programming basics is crucial for their computer
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engineering education.
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- Intensive Care, ICU unit
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- Pediatric Clinic, outpatient
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- source_sentence: successfactors,algorithms,sap,data analysis,natural language processing,software
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testing,neural networks,development methodologies
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sentences:
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- successfactors offers travel packages and vacation deals through its partnership
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with various hotels.
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- Azure Data Lake and Cosmos DB are key components of the Microsoft data ecosystem.
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- successfactors uses advanced algorithms to enhance sap software testing and improve
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data analysis accuracy.
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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---
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# SentenceTransformer based on google/embeddinggemma-300m
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) on the csv dataset. 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.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
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- **Maximum Sequence Length:** 2048 tokens
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- **Output Dimensionality:** 768 dimensions
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- csv
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
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(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})
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(2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
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(3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
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(4): Normalize()
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("AROY76/Embedding-gemma-300M-skills")
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# Run inference
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queries = [
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"successfactors,algorithms,sap,data analysis,natural language processing,software testing,neural networks,development methodologies",
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]
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documents = [
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'successfactors uses advanced algorithms to enhance sap software testing and improve data analysis accuracy.',
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'successfactors offers travel packages and vacation deals through its partnership with various hotels.',
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'Azure Data Lake and Cosmos DB are key components of the Microsoft data ecosystem.',
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]
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query_embeddings = model.encode_query(queries)
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document_embeddings = model.encode_document(documents)
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print(query_embeddings.shape, document_embeddings.shape)
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# [1, 768] [3, 768]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(query_embeddings, document_embeddings)
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print(similarities)
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# tensor([[0.7328, 0.0418, 0.0872]])
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Training Dataset
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#### csv
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* Dataset: csv
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* Size: 4,992 training samples
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
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* Approximate statistics based on the first 1000 samples:
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| | anchor | positive | negative |
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|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
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| type | string | string | string |
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| details | <ul><li>min: 3 tokens</li><li>mean: 32.16 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.5 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.5 tokens</li><li>max: 76 tokens</li></ul> |
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* Samples:
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| anchor | positive | negative |
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|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| <code>Statistical analysis, SQL, Scripting (Ruby, Python, etc.), Version control (git), Web design/UX, Monte Carlo simulations, RoR, Front-end JS, Growth hacking, Airflow, Pandas</code> | <code>Data analysis, databases, programming languages like Ruby or Python, software versioning, user interface design, probability modeling, Ruby on Rails, JavaScript for interfaces, customer growth strategies, workflow automation, data manipulation tools</code> | <code>Cloud storage, hardware configuration, network security, project management methodologies, graphic design software, database normalization techniques, agile development practices, server administration, marketing analytics, containerization technologies</code> |
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| <code>Graphic Design, digital design, print design, web design, environmental/experiential design, interaction design, brand design, visual design, communication, user research, illustration, digital design systems</code> | <code>Visual design, graphic design, communication, user research, illustration, digital design systems, web design, brand design, interaction design, print design, environmental/experiential design</code> | <code>Project management, software development, networking, cybersecurity, database administration, IT infrastructure, agile methodologies, cloud computing, hardware engineering, quality assurance</code> |
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| <code>problem solving, customer support, writing, grammar</code> | <code>improving writing skills to enhance clarity and grammar in customer support communications</code> | <code>designing website layouts for better user experience</code> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim",
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"gather_across_devices": false
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}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `per_device_train_batch_size`: 16
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- `learning_rate`: 2e-05
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- `num_train_epochs`: 2
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- `warmup_ratio`: 0.1
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- `prompts`: task: sentence similarity | query:
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: no
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 8
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `torch_empty_cache_steps`: None
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- `learning_rate`: 2e-05
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`: 2
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.1
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 42
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- `data_seed`: None
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- `jit_mode_eval`: False
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- `use_ipex`: False
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- `bf16`: False
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- `fp16`: False
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- `fp16_opt_level`: O1
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- `half_precision_backend`: auto
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- `bf16_full_eval`: False
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- `fp16_full_eval`: False
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- `tf32`: None
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- `local_rank`: 0
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- `ddp_backend`: None
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- `tpu_num_cores`: None
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- `tpu_metrics_debug`: False
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- `debug`: []
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- `dataloader_drop_last`: False
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- `dataloader_num_workers`: 0
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- `dataloader_prefetch_factor`: None
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- `past_index`: -1
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- `disable_tqdm`: False
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- `remove_unused_columns`: True
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- `label_names`: None
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- `load_best_model_at_end`: False
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- `ignore_data_skip`: False
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- `fsdp`: []
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- `fsdp_min_num_params`: 0
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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- `fsdp_transformer_layer_cls_to_wrap`: None
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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- `parallelism_config`: None
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- `deepspeed`: None
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- `label_smoothing_factor`: 0.0
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- `optim`: adamw_torch_fused
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- `optim_args`: None
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- `adafactor`: False
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- `group_by_length`: False
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- `length_column_name`: length
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- `ddp_find_unused_parameters`: None
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- `ddp_bucket_cap_mb`: None
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- `ddp_broadcast_buffers`: False
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- `dataloader_pin_memory`: True
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- `dataloader_persistent_workers`: False
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- `skip_memory_metrics`: True
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- `use_legacy_prediction_loop`: False
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- `push_to_hub`: False
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- `resume_from_checkpoint`: None
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- `hub_model_id`: None
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- `hub_strategy`: every_save
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- `hub_private_repo`: None
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- `hub_always_push`: False
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- `hub_revision`: None
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- `gradient_checkpointing`: False
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- `gradient_checkpointing_kwargs`: None
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- `include_inputs_for_metrics`: False
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- `include_for_metrics`: []
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- `eval_do_concat_batches`: True
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- `fp16_backend`: auto
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- `push_to_hub_model_id`: None
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- `push_to_hub_organization`: None
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- `mp_parameters`:
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- `auto_find_batch_size`: False
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- `full_determinism`: False
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- `torchdynamo`: None
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- `ray_scope`: last
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- `ddp_timeout`: 1800
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- `torch_compile`: False
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- `torch_compile_backend`: None
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- `torch_compile_mode`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `optim_target_modules`: None
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- `batch_eval_metrics`: False
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- `eval_on_start`: False
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- `use_liger_kernel`: False
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- `liger_kernel_config`: None
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- `eval_use_gather_object`: False
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- `average_tokens_across_devices`: False
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- `prompts`: task: sentence similarity | query:
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- `batch_sampler`: batch_sampler
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- `multi_dataset_batch_sampler`: proportional
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- `router_mapping`: {}
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- `learning_rate_mapping`: {}
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</details>
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### Framework Versions
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- Python: 3.12.12
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- Sentence Transformers: 5.2.0
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- Transformers: 4.57.0.dev0
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- PyTorch: 2.9.0+cu126
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- Accelerate: 1.12.0
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- Datasets: 4.0.0
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- Tokenizers: 0.22.1
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## Citation
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### BibTeX
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#### Sentence Transformers
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```bibtex
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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publisher = "Association for Computational Linguistics",
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url = "https://arxiv.org/abs/1908.10084",
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}
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```
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#### MultipleNegativesRankingLoss
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```bibtex
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@misc{henderson2017efficient,
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title={Efficient Natural Language Response Suggestion for Smart Reply},
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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},
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year={2017},
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eprint={1705.00652},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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