wehub-resource-sync
42 lines
1.7 KiB
Markdown
42 lines
1.7 KiB
Markdown
### Config Files Explained
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Taking `projects/mfmmlm.yaml` for example, which run pretraining using masked frame model (MFM) and masked language model (MLM) on a single BERT:
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```yaml
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project_dir: mfmmlm # specify the project dir for this baseline.
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run_task:
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- how2.yaml # run pretraining on how2 when launching `projects/taskmfmmlm.yaml`
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- [vtt.yaml, vttcap.yaml, vttqa.yaml, youcook.yaml, youcookcap.yaml, crosstask.yaml, coin.yaml] # run fine-tuning tasks.
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base_dir: task # a global template folder to specify each training task.
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task_group:
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pretrain: # section for pretraining. Most baselines differs in this section.
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task_list:
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- how2.yaml # reconfig `projects/task/how2.yaml`
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dataset:
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aligner: MFMMLMAligner # overwrite the aligner for MFMMLM training task.
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model:
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model_cls: MMFusionMFMMLM # overwrite the model, which constructs negative examples for MFM on-the-fly.
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loss:
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loss_cls: MFMMLM # overwrite the loss as MFMMLM, which combines MFM and MLM together.
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fairseq: # all fairseq args can be expecified under this name.
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dataset:
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batch_size: 128
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finetune: # section for fine-tuning tasks, we don't need to change anything here mostly since we want to see how pretraining can contribute to finetuning.
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task_list: # specify the list of downstream tasks, e.g., copy `projects/task/vtt.yaml` to `projects/mfmmlm`.
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- vtt.yaml
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- vttqa.yaml
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- youcook.yaml
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- youcookcap.yaml
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- crosstask.yaml
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- coin.yaml
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test: # section for testing.
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task_list:
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- test_vtt.yaml
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- test_vttqa.yaml
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- test_youcook.yaml
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- test_youcookcap.yaml
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- test_crosstask.yaml
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- test_crosstask_zs.yaml
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- test_coin.yaml
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```
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