| Model Type | | Transformer-based language model |
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| Use Cases |
| Areas: | | Research, Commercial applications |
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| Applications: | | AI research, Writing assistance, Creative content generation |
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| Primary Use Cases: | | Language understanding and generation |
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| Limitations: | | May reflect inherent biases from training data., Not suitable for fact-distinguishing tasks., Effects of biases on sensitive use cases. |
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| Considerations: | | Users must be aware of model limitations and biases. |
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| Additional Notes | | Significant research into biases and ethical implications. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | Web pages from outbound links on Reddit |
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| Data Volume: | |
| Methodology: | | Causal language modeling (CLM) objective |
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| Context Length: | |
| Model Architecture: | |
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| Responsible Ai Considerations |
| Fairness: | | Research explores bias and fairness issues, e.g., Sheng et al. (2021) and Bender et al. (2021). |
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| Transparency: | | Training data not released for browsing, indicating a lack of transparency in data sources. |
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| Accountability: | | Model may not be suitable for deployment in systems that interact with humans without studying biases first. |
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| Mitigation Strategies: | | Awareness of biases, caution in use cases sensitive to biases. |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Use seed for reproducibility in text generation. |
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