---
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
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# 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 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

More information needed

## Training procedure

### 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