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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Graphcore/gqa-lxmert |
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metrics: |
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- accuracy |
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model-index: |
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- name: gqa |
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results: |
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- task: |
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name: Question Answering |
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type: question-answering |
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dataset: |
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name: Graphcore/gqa-lxmert |
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type: Graphcore/gqa-lxmert |
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args: gqa |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5933514030612245 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# gqa |
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This model is a fine-tuned version of [unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) on the [Graphcore/gqa-lxmert](https://huggingface.co/datasets/Graphcore/gqa-lxmert) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9326 |
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- Accuracy: 0.5934 |
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## Model description |
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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. |
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Paper link : [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/pdf/1908.07490.pdf) |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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[Graphcore/gqa-lxmert](https://huggingface.co/datasets/Graphcore/gqa-lxmert) dataset |
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## Training procedure |
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Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore). |
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Command line: |
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``` |
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python examples/question-answering/run_vqa.py \ |
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--model_name_or_path unc-nlp/lxmert-base-uncased \ |
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--ipu_config_name Graphcore/lxmert-base-ipu \ |
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--dataset_name Graphcore/gqa-lxmert \ |
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--do_train \ |
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--do_eval \ |
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--max_seq_length 512 \ |
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--per_device_train_batch_size 1 \ |
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--num_train_epochs 4 \ |
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--dataloader_num_workers 64 \ |
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--logging_steps 5 \ |
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--learning_rate 1e-5 \ |
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--lr_scheduler_type linear \ |
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--loss_scaling 16384 \ |
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--weight_decay 0.01 \ |
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--warmup_ratio 0.1 \ |
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--output_dir /tmp/gqa/ \ |
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--dataloader_drop_last \ |
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--replace_qa_head \ |
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--pod_type pod16 |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: IPU |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 4.0 |
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- training precision: Mixed Precision |
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### Training results |
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``` |
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***** train metrics ***** |
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"epoch": 4.0, |
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"train_loss": 0.6123406731570221, |
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"train_runtime": 29986.2288, |
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"train_samples": 943000, |
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"train_samples_per_second": 125.791, |
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"train_steps_per_second": 1.965 |
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***** eval metrics ***** |
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"eval_accuracy": 0.5933514030612245, |
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"eval_loss": 1.9326171875, |
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"eval_samples": 12576, |
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``` |
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### Framework versions |
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- Transformers 4.18.0.dev0 |
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- Pytorch 1.10.0+cpu |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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