π Upgrade README with comprehensive documentation
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README.md
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---
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license: apache-2.0
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base_model_type: llama
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tags:
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- animal-liberation
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- animal-advocacy
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- open-paws
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- ethics
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- alignment
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language:
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- en
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pipeline_tag: text-generation
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widget:
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- text: "How can we effectively advocate for farm animal welfare?"
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- text: "Explain the ethical issues with factory farming"
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- text: "What are the benefits of plant-based diets for animals?"
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---
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# Open Paws Level Of Rationality Prediction Shortform
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πΎ **Specialized model for scoring and ranking content based on animal advocacy principles**
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## Overview
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This model is part of the Open Paws initiative to develop AI systems aligned with animal liberation and advocacy principles. Designed to support advocates, educators, and researchers working toward a more compassionate world for all animals.
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## Model Details
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- **Model Type**: Ranking Model
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- **Model Size**: Compact (under 1B parameters)
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- **Architecture**: Transformer-based
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- **Training Focus**: Animal advocacy and ethical reasoning
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- **Organization**: [Open Paws](https://huggingface.co/open-paws)
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- **License**: Apache 2.0
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## Intended Use
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### Primary Applications
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- Content quality assessment for animal advocacy
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- Message effectiveness scoring
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- Preference modeling for advocacy strategies
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- Performance evaluation of educational materials
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### Ethical Guidelines
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- β
Supporting animal welfare and rights advocacy
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- β
Educational content about animal liberation
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- β
Ethical decision-making frameworks
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- β Content that promotes animal exploitation
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- β Justifying harm to sentient beings
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## Usage
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### Installation
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```bash
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pip install transformers torch
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```
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### Basic Usage
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```python
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from transformers import AutoModel, AutoTokenizer
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import torch
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# Load model and tokenizer
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model_name = "open-paws/level_of_rationality_prediction_shortform"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModel.from_pretrained(model_name)
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# Score content for animal advocacy alignment
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content = "Plant-based diets reduce animal suffering significantly"
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inputs = tokenizer(content, return_tensors="pt")
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score = model(**inputs).logits
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print(f"Advocacy alignment score: {score.item():.3f}")
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```
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## Training Data
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This model was trained on carefully curated data focused on:
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- Animal liberation philosophy and ethics
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- Advocacy strategies and messaging
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- Educational content about animal welfare
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- Ethical reasoning frameworks
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All training data underwent rigorous filtering to ensure alignment with animal liberation principles.
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## Limitations and Considerations
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- **Scope**: Optimized for animal advocacy and ethical reasoning contexts
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- **Bias**: Intentionally aligned towards animal liberation perspectives
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- **Domain**: May perform differently on topics outside animal advocacy
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- **Updates**: Regularly improved based on community feedback
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## Community and Contributions
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- **Organization**: [Open Paws](https://huggingface.co/open-paws) - Making AI an ally to animals
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- **Website**: [openpaws.ai](https://www.openpaws.ai/)
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- **Community**: Join our mission to use AI for animal liberation
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- **Issues**: Report issues via HuggingFace discussions
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## Citation
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```bibtex
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@model{open_paws_level_of_rationality_prediction_shortform,
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title={Open Paws Level_Of_Rationality_Prediction_Shortform},
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author={Open Paws},
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year={2025},
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url={https://huggingface.co/open-paws/level_of_rationality_prediction_shortform},
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organization={Open Paws},
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note={AI model for animal liberation and advocacy}
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}
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```
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## Model Card Contact
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For questions about this model, please reach out via:
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- **HuggingFace Discussions**: [open-paws/level_of_rationality_prediction_shortform](https://huggingface.co/open-paws/level_of_rationality_prediction_shortform/discussions)
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- **Organization Page**: [Open Paws](https://huggingface.co/open-paws)
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---
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*Built with πΎ for animal liberation and AI alignment*
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