---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vqa

This model is a fine-tuned version of [unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) on the [Graphcore/vqa-lxmert](https://huggingface.co/datasets/Graphcore/vqa-lxmert) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0009
- Accuracy: 0.7242

## 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 modeling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives. It acheives the state-of-the-art results on VQA anad GQA. 

Paper link : [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/pdf/1908.07490.pdf) 

## Intended uses & limitations

More information needed

## Training and evaluation data

[Graphcore/vqa-lxmert](https://huggingface.co/datasets/Graphcore/vqa-lxmert) dataset

## Training procedure

Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/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/vqa-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 5e-5 \
  --lr_scheduler_type linear \
  --loss_scaling 16384 \
  --weight_decay 0.01 \
  --warmup_ratio 0.1 \
  --output_dir /tmp/vqa/ \
  --dataloader_drop_last \
  --replace_qa_head \
  --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
```
***** train metrics *****
  "epoch": 4.0,
  "train_loss": 0.0060005393999575125,
  "train_runtime": 13854.802,
  "train_samples": 443757,
  "train_samples_per_second": 128.116,
  "train_steps_per_second": 2.002

***** eval metrics *****
  "eval_accuracy": 0.7242196202278137,
  "eval_loss": 0.0008745193481445312,
  "eval_samples": 214354,
```

### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6