Model Type | transformers, language model, causal language modeling |
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Use Cases |
Areas: | research, text generation |
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Applications: | text generation, language modeling |
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Primary Use Cases: | generating texts from prompts |
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Limitations: | Cannot distinguish fact from fiction, Potential bias in outputs |
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Considerations: | Ensure deployment readiness with an understanding of biases. |
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Supported Languages | |
Training Details |
Data Sources: | Reddit outbound links with 3+ karma |
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Data Volume: | Over 40 GB (WebText dataset) |
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Methodology: | Self-supervised training with causal language modeling |
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Context Length: | |
Hardware Used: | |
Model Architecture: | Transformers architecture with 50,257-token vocabulary |
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Safety Evaluation |
Ethical Considerations: | Includes biases inherent to training data; caution advised for sensitive use-cases. |
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Responsible Ai Considerations |
Fairness: | Model reflects biases present in training data; conduct studies on bias in intended use cases. |
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Transparency: | OpenAI released a model card highlighting limitations and ethical considerations. |
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Accountability: | Deployers are responsible for usage and bias evaluation. |
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Mitigation Strategies: | Approach deployment with caution in bias-sensitive applications; consider fine-tuning carefully. |
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Input Output |
Input Format: | Continuous text sequences |
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Accepted Modalities: | |
Output Format: | |
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