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---
tags:
- generated_from_trainer
datasets:
- Graphcore/wikipedia-bert-128
- Graphcore/wikipedia-bert-512
model-index:
- name: Graphcore/bert-base-uncased
results: []
---
# Graphcore/bert-base-uncased
This model is a pre-trained BERT-Base trained in two phases on the [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128) and [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512) datasets.
## Model description
Pre-trained BERT Base model trained on Wikipedia data.
## Intended uses & limitations
More information needed
## Training and evaluation data
Trained on wikipedia datasets:
- [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128)
- [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512)
## Training procedure
Trained MLM and NSP pre-training scheme from [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://arxiv.org/abs/1904.00962).
Trained on 16 Graphcore Mk2 IPUs.
### Training hyperparameters
The following hyperparameters were used during phase 1 training:
- learning_rate: 0.006
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 512
- total_train_batch_size: 65536
- total_eval_batch_size: 128
- optimizer: LAMB
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.28
- training_steps: 10500
- training precision: Mixed Precision
The following hyperparameters were used during phase 2 training:
- learning_rate: 0.002828
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 512
- total_train_batch_size: 16384
- total_eval_batch_size: 128
- optimizer: LAMB
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.128
- training_steps: 2038
- training precision: Mixed Precision
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 1.18.3.dev0
- Tokenizers 0.10.3
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