| # 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][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 | | |
| | [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 | | |
| | [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 | | |
| | [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 | | |
| | [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 | | |
| | [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 | | |
| | [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 | | |
| | [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 | | |
| | [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 | | |
| | [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 | | |
| | [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 | | |
| | [roberta-med-small-1M-3][link-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 | |
| [link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1 | |
| [link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2 | |
| [link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3 | |
| [link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1 | |
| [link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2 | |
| [link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3 | |
| [link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1 | |
| [link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2 | |
| [link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3 | |
| [link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1 | |
| [link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2 | |
| [link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3 | |