Prompt
Check a dataset before analysis
Find missing values, inconsistent formats, duplicates, and questions that could invalidate results.
ClaudeChatGPTGeminiData & Analytics
Dave AI
SetFit Few-Shot Skill
SetFit few-shot learning for efficient intent classification with minimal data
instructions.md
--- name: setfit-few-shot-934693865a9e description: SetFit few-shot learning for efficient intent classification with minimal data allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:ml-fine-tuning, skill-area:natural-language-processing] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # SetFit Few-Shot Skill ## Capabilities - Train SetFit models with few examples per class - Configure contrastive learning settings - Implement efficient classification pipelines - Design few-shot training strategies - Set up model evaluation - Deploy lightweight classifiers ## Target Processes - intent-classification-system ## Implementation Details ### SetFit Advantages 1. **Few Examples**: 8-16 examples per class 2. **No Prompts**: No prompt engineering needed 3. **Fast Training**: Minutes vs hours 4. **Small Models**: Sentence transformer base ### Training Process - Contrastive fine-tuning of embeddings - Classification head training - Iterative sampling strategies ### Configuration Options - Base sentence transformer model - Number of training examples - Contrastive learning epochs - Classification head architecture - Evaluation metrics ### Best Practices - Diverse few-shot examples - Balance class examples - Use appropriate base model - Validate on held-out data ### Dependencies - setfit - sentence-transformers--- name: setfit-few-shot-934693865a9e description: SetFit few-shot learning for efficient intent classification with minimal data allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:ml-fine-tuning, skill-area:natural-language-processing] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # SetFit Few-Shot Skill ## Capabilities - Train SetFit models with few examples per class - Configure contrastive learning settings - Implement efficient classification pipelines - Design few-shot training strategies - Set up model evaluation - Deploy lightweight classifiers ## Target Processes - intent-classification-system ## Implementation Details ### SetFit Advantages 1. **Few Examples**: 8-16 examples per class 2. **No Prompts**: No prompt engineering needed 3. **Fast Training**: Minutes vs hours 4. **Small Models**: Sentence transformer base ### Training Process - Contrastive fine-tuning of embeddings - Classification head training - Iterative sampling strategies ### Configuration Options - Base sentence transformer model - Number of training examples - Contrastive learning epochs - Classification head architecture - Evaluation metrics ### Best Practices - Diverse few-shot examples - Balance class examples - Use appropriate base model - Validate on held-out data ### Dependencies - setfit - sentence-transformers
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.