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pi05-arena-gr1-microwave
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View details ↗PiD
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View details ↗PixelDiT-1300M-1024px
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View details ↗PointWorld_models
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View details ↗Privasis-Cleaner-0.6B
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View details ↗Privasis-Cleaner-4B
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View details ↗This is a multi-headed model which classifies English text prompts across task types and complexity dimensions. Tasks are classified across 11 common categories. Complexity is evaluated across 6 dimensions and ensembled to create an overall complexity score. Further information on the taxonomies can be found below. **Task types:** * Open QA: A question where the response is based on general knowledge * Closed QA: A question where the response is based on text/data provided with the prompt * Summarization * Text Generation * Code Generation * Chatbot * Classification * Rewrite * Brainstorming * Extraction * Other **Complexity dimensions:** * Overall Complexity Score: The weighted sum of the complexity dimensions. Calculated as 0.35\*CreativityScore + 0.25\*ReasoningScore + 0.15\*ConstraintScore + 0.15\*DomainKnowledgeScore + 0.05\*ContextualKnowledgeScore + 0.05\*NumberOfFewShots * Creativity: The level of creativity needed to respond to a prompt. Score range of 0-1, with a higher score indicating more creativity. * Reasoning: The extent of logical or cognitive effort required to respond to a prompt. Score range of 0-1, with a higher score indicating more reasoning * Contextual Knowledge: The background information necessary to respond to a prompt. Score range of 0-1, with a higher score indicating more contextual knowledge required outside of prompt. * Domain Knowledge: The amount of specialized knowledge or expertise within a specific subject area needed to respond to a prompt. Score range of 0-1, with a higher score indicating more domain knowledge is required. * Constraints: The number of constraints or conditions provided with the prompt. Score range of 0-1, with a higher score indicating more constraints in the prompt. * Number of Few Shots: The number of examples provided with the prompt. Score range of 0-n, with a higher score indicating more examples provided in the prompt.
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View details ↗PS3-1.5K-C-RADIOv2
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View details ↗PS3-1.5K-SigLIP
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View details ↗PS3-1.5K-SigLIP2
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View details ↗PS3-4K-C-RADIOv2
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View details ↗PS3-4K-SigLIP2
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View details ↗PS3_Lang-1.5K-SigLIP2
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View details ↗PS3_Lang-4K-SigLIP2
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View details ↗qdqbert-base-uncased
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View details ↗QLIP-B-16-256
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View details ↗QLIP-B-8-256
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View details ↗QLIP-L-14-392
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View details ↗This is a text classification model that can enable qualitative data annotation, creation of quality-specific blends and addition of metadata tags. The model classifies documents into one of three classes based on the quality of the document: "High", "Medium", "Low" The model was trained using data annotated by human annotators, who considered quality factors such as content accuracy, clarity, coherence, grammar, depth of information, and overall usefulness of the document. This model is used in the NVIDIA NeMo Curator as part of the qualitative filtering module. The model architecture is Deberta V3 Base Context length is 1024 tokens The training set is 22828 Common Crawl text samples, labeled as "High", "Medium", "Low". Here are some examples: 1. Input:
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