license: apache-2.0
tags:
- generated_from_trainer
datasets:
- Graphcore/gqa-lxmert
metrics:
- accuracy
model-index:
- name: gqa
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: Graphcore/gqa-lxmert
type: Graphcore/gqa-lxmert
args: gqa
metrics:
- name: Accuracy
type: accuracy
value: 0.5933514030612245
Graphcore/lxmert-gqa-uncased
BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabeled texts. It enables easy and fast fine-tuning for different downstream task such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM.
It was trained with two objectives in pretraining : Masked language modeling(MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations.
It reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.
Model description
LXMERT is a transformer model for learning vision-and-language cross-modality representations. It has a Transformer model that has three encoders: object relationship encoder, a language encoder, and a cross-modality encoder. It is pretrained via a combination of masked language modelling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modelling, and visual-question answering objectives. It achieves the state-of-the-art results on VQA anad GQA.
Paper link : LXMERT: Learning Cross-Modality Encoder Representations from Transformers
Intended uses & limitations
This model is a fine-tuned version of unc-nlp/lxmert-base-uncased on the Graphcore/gqa-lxmert dataset. It achieves the following results on the evaluation set:
- Loss: 1.9326
- Accuracy: 0.5934
Training and evaluation data
- Graphcore/gqa-lxmert dataset
Training procedure
Trained on 16 Graphcore Mk2 IPUs using optimum-graphcore.
Command line:
python examples/question-answering/run_vqa.py \
--model_name_or_path unc-nlp/lxmert-base-uncased \
--ipu_config_name Graphcore/lxmert-base-ipu \
--dataset_name Graphcore/gqa-lxmert \
--do_train \
--do_eval \
--max_seq_length 512 \
--per_device_train_batch_size 1 \
--num_train_epochs 4 \
--dataloader_num_workers 64 \
--logging_steps 5 \
--learning_rate 1e-5 \
--lr_scheduler_type linear \
--loss_scaling 16384 \
--weight_decay 0.01 \
--warmup_ratio 0.1 \
--output_dir /tmp/gqa/ \
--dataloader_drop_last \
--replace_qa_head \
--pod_type pod16
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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
***** train metrics *****
"epoch": 4.0,
"train_loss": 0.6123406731570221,
"train_runtime": 29986.2288,
"train_samples": 943000,
"train_samples_per_second": 125.791,
"train_steps_per_second": 1.965
***** eval metrics *****
"eval_accuracy": 0.5933514030612245,
"eval_loss": 1.9326171875,
"eval_samples": 12576,
Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6