metadata
tags:
- generated_from_trainer
datasets:
- Graphcore/vqa-lxmert
metrics:
- accuracy
model-index:
- name: vqa
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: Graphcore/vqa-lxmert
type: Graphcore/vqa-lxmert
args: vqa
metrics:
- name: Accuracy
type: accuracy
value: 0.7242196202278137
vqa
This model is a fine-tuned version of unc-nlp/lxmert-base-uncased on the Graphcore/vqa-lxmert dataset. It achieves the following results on the evaluation set:
- Loss: 0.0009
- Accuracy: 0.7242
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Graphcore/vqa-lxmert dataset
Training procedure
Trained on 16 Graphcore Mk2 IPUs using optimum-graphcore.
Command line:
python examples/language-modeling/run_clm.py \
--model_name_or_path gpt2 \
--ipu_config_name Graphcore/gpt2-small-ipu \
--dataset_name wikitext \
--dataset_config_name wikitext-103-raw-v1 \
--do_train \
--do_eval \
--num_train_epochs 10 \
--dataloader_num_workers 64 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 128 \
--output_dir /tmp/clm_output \
--logging_steps 5 \
--learning_rate 1e-5 \
--lr_scheduler_type linear \
--loss_scaling 16384 \
--weight_decay 0.01 \
--warmup_ratio 0.1 \
--ipu_config_overrides="embedding_serialization_factor=4,optimizer_state_offchip=true,inference_device_iterations=5" \
--dataloader_drop_last \
--pod_type pod16
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: IPU
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- training precision: Mixed Precision
Training results
Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6