| Model Type | | Hybrid Transformer-RNN, TransformerXL-T5 with LSTM |
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| Use Cases |
| Areas: | | Text Generation, Causal Language Modeling, Question Answering |
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| Primary Use Cases: | | Text Generation: Generating coherent and contextually relevant text sequences, Causal Language Modeling: Predicting the next word in a sequence |
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| Limitations: | | Not designed for Real-time Conversational AI, Not suitable for multilingual support |
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| Considerations: | | For applications where fairness and bias are critical, human review is recommended. |
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| Supported Languages | |
| Training Details |
| Data Sources: | |
| Methodology: | | Hybrid Transformer-RNN architecture, integration of self-attention (Transformer-XL and T5) with LSTM |
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| Training Time: | | 36 hours on a single NVIDIA V100 GPU |
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| Hardware Used: | |
| Model Architecture: | | Hybrid model combining Transformer-XL, T5, and LSTM layers with multi-head self-attention mechanisms, positional encodings, and RNN layers to process and generate text |
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