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propella-1 is a family of small multilingual LLMs that annotate text documents across six categories: core content, classification, quality & value, audience & purpose, safety & compliance, and geographic relevance. The annotations can be used to filter, select, and curate LLM training data at scale. *Disclaimer: This is a research project, not an official ellamind product. For production-ready evaluation solutions, check out elluminate.* propella-1 models evaluate documents across 18 properties organized into six categories: | Category | Property | Short Description | |----------|----------|-------------| | **Core Content** | Content Integrity | Completeness and technical quality of the content | | | Content Ratio | Proportion of content vs. navigation/UI elements | | | Content Length | Amount of substantive content | | **Classification** | One-Sentence Description | Ultra-short neutral description of the document | | | Content Type | Functional structure and purpose | | | Business Sector | Industry domain relevance | | | Technical Content | Type and intensity of specialized knowledge | | **Quality & Value** | Content Quality | Overall writing and presentation quality | | | Information Density | Ratio of valuable information to redundancy | | | Educational Value | Potential for teaching and learning | | | Reasoning Indicators | Presence of logical reasoning and analysis | | **Audience & Purpose** | Audience Level | Target sophistication level | | | Commercial Bias | Commercial influence on objectivity | | | Time-Sensitivity | How content value changes over time | | **Safety & Compliance** | Content Safety | Presence of inappropriate or harmful content | | | PII Presence | Contains personally identifiable information | | **Geographic** | Regional Relevance | Primary regional/cultural context | | | Country Relevance | Specific country relevance |

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propella-1 is a family of small multilingual LLMs that annotate text documents across six categories: core content, classification, quality & value, audience & purpose, safety & compliance, and geographic relevance. The annotations can be used to filter, select, and curate LLM training data at scale. *Disclaimer: This is a research project, not an official ellamind product. For production-ready evaluation solutions, check out elluminate.* propella-1 models evaluate documents across 18 properties organized into six categories: | Category | Property | Short Description | |----------|----------|-------------| | **Core Content** | Content Integrity | Completeness and technical quality of the content | | | Content Ratio | Proportion of content vs. navigation/UI elements | | | Content Length | Amount of substantive content | | **Classification** | One-Sentence Description | Ultra-short neutral description of the document | | | Content Type | Functional structure and purpose | | | Business Sector | Industry domain relevance | | | Technical Content | Type and intensity of specialized knowledge | | **Quality & Value** | Content Quality | Overall writing and presentation quality | | | Information Density | Ratio of valuable information to redundancy | | | Educational Value | Potential for teaching and learning | | | Reasoning Indicators | Presence of logical reasoning and analysis | | **Audience & Purpose** | Audience Level | Target sophistication level | | | Commercial Bias | Commercial influence on objectivity | | | Time-Sensitivity | How content value changes over time | | **Safety & Compliance** | Content Safety | Presence of inappropriate or harmful content | | | PII Presence | Contains personally identifiable information | | **Geographic** | Regional Relevance | Primary regional/cultural context | | | Country Relevance | Specific country relevance |

Category pending
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
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