RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|---|---|---|---|---|---|
| roberta-base-1B-1 | 1B | BASE | 100K | 512 | 3.93 |
| roberta-base-1B-2 | 1B | BASE | 31K | 1024 | 4.25 |
| roberta-base-1B-3 | 1B | BASE | 31K | 4096 | 3.84 |
| roberta-base-100M-1 | 100M | BASE | 100K | 512 | 4.99 |
| roberta-base-100M-2 | 100M | BASE | 31K | 1024 | 4.61 |
| roberta-base-100M-3 | 100M | BASE | 31K | 512 | 5.02 |
| roberta-base-10M-1 | 10M | BASE | 10K | 1024 | 11.31 |
| roberta-base-10M-2 | 10M | BASE | 10K | 512 | 10.78 |
| roberta-base-10M-3 | 10M | BASE | 31K | 512 | 11.58 |
| roberta-med-small-1M-1 | 1M | MED-SMALL | 100K | 512 | 153.38 |
| roberta-med-small-1M-2 | 1M | MED-SMALL | 10K | 512 | 134.18 |
| roberta-med-small-1M-3 | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|---|---|---|---|---|---|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1