Strengths
- Highly flexible and extensible for research and production use.
- Optimized for large-scale distributed training.
- Supports a wide variety of sequence-to-sequence architectures.
- Strong community and backed by Facebook AI Research.
- Includes numerous pretrained models for quick experimentation.
Limitations
- Steeper learning curve for beginners unfamiliar with PyTorch or sequence modeling.
- Limited official documentation compared to some commercial tools.
- Requires significant compute resources for training large models.
- Primarily focused on research, less out-of-the-box user-friendly for production.