Autoencoding Transformers
DistilBERT
Introduced by Sanh et al. in DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterDistilBERT is a small, fast, cheap and light Transformer model based on the BERT architecture. Knowledge distillation is performed during the pre-training phase to reduce the size of a BERT model by 40%. To leverage the inductive biases learned by larger models during pre-training, the authors introduce a triple loss combining language modeling, distillation and cosine-distance losses.
Source: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Sentiment Analysis | 12 | 7.50% |
| Language Modelling | 12 | 7.50% |
| Classification | 10 | 6.25% |
| Question Answering | 9 | 5.63% |
| Text Classification | 8 | 5.00% |
| Knowledge Distillation | 7 | 4.38% |
| Quantization | 6 | 3.75% |
| General Classification | 6 | 3.75% |
| Model Compression | 4 | 2.50% |
BERT
