| Model Type | | text-to-text, decoder-only, large language model |
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| Use Cases |
| Areas: | | Content Creation and Communication, Research and Education |
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| Applications: | | Text Generation, Chatbots and Conversational AI, Text Summarization |
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| Primary Use Cases: | | NLP Research, Language Learning Tools, Knowledge Exploration |
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| Limitations: | | Factual Accuracy, Common Sense |
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| Considerations: | | Developers are encouraged to exercise caution and implement appropriate content safety safeguards. |
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| Additional Notes | | Training hardware and Tensor Processing Units (TPUs) highlighted along with sustainability focus. |
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| Supported Languages | | English (primary language) |
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| Training Details |
| Data Sources: | | Web Documents, Code, Mathematics |
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| Data Volume: | |
| Methodology: | |
| Hardware Used: | |
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| Safety Evaluation |
| Methodologies: | | structured evaluations, internal red-teaming |
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| Risk Categories: | | Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm |
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| Ethical Considerations: | | Bias and Fairness, Misinformation and Misuse, Transparency and Accountability |
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| Responsible Ai Considerations |
| Fairness: | | Models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported. |
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| Transparency: | | Model card provides architectural, capabilities, limitations, and evaluation details. |
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| Accountability: | | Google is leading the model development, dissemination, and documenting processes. |
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| Mitigation Strategies: | | Continuous monitoring, content safety safeguards, developer and end-user education. |
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| Input Output |
| Input Format: | | Text string input like a question or document for summarization |
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| Accepted Modalities: | |
| Output Format: | | Generated English-language text |
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| Performance Tips: | | Longer context generally enhances model performance. |
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| Release Notes |
| Version: | |
| Notes: | | Improved model interaction, upgraded conversational capabilities, bug fixes. |
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