NL
kyzhouhzau/NLPGNN
1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.
336 66 +0/wk
GitHub
albert albert-ner bert bert-cls bert-ner bilstm-attention gan gcn gin gnn gpt2 graph-classfication
Trend
0
Star & Fork Trend (18 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
kyzhouhzau/NLPGNN has +0 stars this period . Velocity data will be available after more historical data is collected.
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| Metric | NLPGNN | HallusionBench | gpt-j-api | DALM |
|---|---|---|---|---|
| Stars | 336 | 336 | 335 | 338 |
| Forks | 66 | 9 | 54 | 46 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | Python | Python | Python | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | BSD-3-Clause | MIT | Apache-2.0 |
Capability Radar vs HallusionBench
NLPGNN
HallusionBench
Maintenance Activity 0
Last code push 602 days ago.
Community Engagement 98
Fork-to-star ratio: 19.6%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.