GrafoPropagation offers a cutting-edge solution for text classification with its compact architecture and unique geometric attention mechanisms. Leveraging advancements such as WordNet pre-training and AG News fine-tuning, it delivers impressive performance while maintaining efficiency, making it a strong choice for modern NLP applications.
GrafoPropagation is an innovative text classification architecture characterized by its compact design, featuring approximately 990,000 parameters. It leverages geometric von Mises-Fisher (vMF) attention and integrates WordNet dictionary pre-training alongside AG News fine-tuning to enhance its performance in natural language processing tasks.
GrafoPropagation introduces several cutting-edge components that set it apart from traditional models:
For users interested in leveraging this architecture, GrafoPropagation provides a straightforward API:
from grafopropagation import CFG, GrafoPropagation, run_training, build_or_load_tokenizer
# Default configuration (~990k params)
cfg = CFG()
result = run_training(cfg)
# Scale architecture as needed
cfg = CFG(d_model=128, n_layers=4, n_heads=8, head_dim=32, d_ff=640)
result = run_training(cfg)
# Full configurability via dictionary
cfg = CFG.from_dict({
"d_model": 256,
"n_layers": 6,
"n_heads": 8,
"head_dim": 32,
"d_ff": 1024,
"dict_epochs": 500,
"epochs": 50,
})
print(f"Estimated parameters: {cfg.count_parameters():,}")
result = run_training(cfg)
GrafoPropagation employs a multi-layered approach involving character-seeded token embeddings, global workspace memory, and advanced transformer blocks, all designed to optimize the handling of text data and bolster classification accuracy.
In initial training configurations with approximately 990,000 parameters, GrafoPropagation shows promising results with accuracy metrics improving swiftly over epochs, evidencing its efficacy:
| Epoch | Train Accuracy | Validation Accuracy |
|---|---|---|
| 1 | 67.2% | 25.6% |
| 3 | 90.6% | 84.4% |
| 5 | 92.6% | 91.1% |
| 10 | 94.9% | 92.7% |
| 13 | 95.9% | 93.1% |
| 15 | 96.4% | 93.1% |
GrafoPropagation stands as a significant advancement in text classification architectures, combining efficiency with innovative techniques to push the boundaries of traditional models in natural language processing.
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