README.md

metadata

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 and 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
• 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. 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