metadata
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: Graphcore/bert-base-uncased-squad
results: []
Graphcore/bert-base-uncased-squad11
This model is a fine-tuned version of Graphcore/bert-base-uncased on the squad dataset.
Intended uses & limitations
More information needed
Training and evaluation data
Training procedure
Model was trained on 16 Graphcore Mk2 IPUs using the optimum-graphcore library.
Command line:
python examples/question-answering/run_qa.py \
--model_name_or_path Graphcore/bert-base-uncased \
--ipu_config_name Graphcore/bert-base-ipu \
--dataset_name squad \
--do_train \
--do_eval \
--num_train_epochs 3 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps 16 \
--pod_type pod16 \
--learning_rate 9e-5 \
--max_seq_length 384 \
--doc_stride 128 \
--seed 42\
--lr_scheduler_type linear \
--loss_scaling 64 \
--weight_decay 0.01 \
--warmup_ratio 0.2 \
--logging_steps 1 \
--save_steps 50 \
--dataloader_num_workers 64 \
--ipu_config_overrides "embedding_serialization_factor=2" \
--output_dir squad_v2_bert_base \
--overwrite_output_dir
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- training precision: Mixed Precision
Training results
{
"epoch": 3.0,
"eval_exact_match": 81.79754020813624,
"eval_f1": 88.84840994541061,
"eval_samples": 10784
}
Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 1.18.4
- Tokenizers 0.11.6