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 |
|---|---|---|
| Text Classification | 18 | 7.73% |
| Sentiment Analysis | 18 | 7.73% |
| Language Modelling | 17 | 7.30% |
| Classification | 16 | 6.87% |
| Question Answering | 12 | 5.15% |
| Sentence | 9 | 3.86% |
| Quantization | 7 | 3.00% |
| Model Compression | 6 | 2.58% |
| General Classification | 6 | 2.58% |
BERT
