Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Key Features

• Segment-Level Recurrence: Enables learning dependencies beyond fixed-length contexts, effectively capturing long-term relationships in data.
• Novel Positional Encoding: Maintains temporal coherence across segments, enhancing the model's ability to understand sequence order.
• Dual Framework Support: Implemented in both TensorFlow and PyTorch, facilitating flexibility in deployment and experimentation.
• State-of-the-Art Performance: Achieves superior results on benchmarks such as enwiki8, text8, One Billion Word, WikiText-103, and Penn Treebank.
• Efficient Training and Inference: Optimized for faster evaluation, making it suitable for large-scale language modeling tasks.