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license: mit
datasets:
- wikipedia
- bookcorpus
language:
- en
metrics:
- glue
library_name: transformers
---
This is our reproduction using the official HuggingFace `roberta` architecture with a medium size. On the architecture side, RoBERTa is exactly the same as BERT except for its larger vocabulary size.
According to Google's [BERT releases](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8) and [BERT-Medium](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8/blob/main/config.json), a medium sized model should have a config of Layer=8, Hidden=512, #AttnHeads=8, and IntermediateSize=2048. We follow this config to pre-train a RoBERTa-base model for reproduction.
We use the same datasets as BERT (English Wikipedia and Book Corpus) to pre-train for 30k steps with a batch size of 8,192. I also released the reproduction of this dataset [on HuggingFace](https://huggingface.co/datasets/JackBAI/bert_pretrain_datasets).
We utilized DeepSpeed ZeRO-2 for performance optimization.
Other training configuration:
| Parameter | Value |
|----------------------|-----------|
| WARMUP_STEPS | 1800 |
| LR_DECAY | linear |
| ADAM_EPS | 1e-6 |
| ADAM_BETA1 | 0.9 |
| ADAM_BETA2 | 0.98 |
| ADAM_WEIGHT_DECAY | 0.01 |
| PEAK_LR | 1e-3 |
|