Learning Rate Schedules
Linear Warmup With Linear Decay
Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Language Modelling | 96 | 11.96% |
| Sentiment Analysis | 42 | 5.23% |
| Text Classification | 39 | 4.86% |
| Retrieval | 34 | 4.23% |
| Question Answering | 31 | 3.86% |
| Classification | 25 | 3.11% |
| Large Language Model | 21 | 2.62% |
| NER | 21 | 2.62% |
| Named Entity Recognition (NER) | 15 | 1.87% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
