## Training config

```
ModelArguments:
  base_model_name: Name of the model to be downloaded from HF
  sign_input_dim (depricated, it is calculated automatically now): Dimmension of sign features on the model input (to be changed with multimodal input)
  hidden_dropout_prob: Dropout probability of the input layer
  max_length: Max sequence length to be generated by the model

# TrainingArguments are overwritten by not-None arguments
TrainingArguments:
  project_name: Name of wandb project
  model_name: Name of the run name (will affect wandb and checkpoint path)
  output_dir: Output path for checkpoints
  resume_from_checkpoint: Path to checkpoint to be loaded. The path needs to contain model.safetensors file
  load_only_weights: True for loading only weights to use different scheduler or optimizer. False for loading the whole checkpoint
#  Logging and saving
  report_to: "wandb" for wandb report. Otherwise wandb not used
#  Debugging
  max_train_samples: Maximum size of training dataset for debugging. "none" for default size
  max_val_samples: Maximum size of validation dataset for debugging. "none" for default size
#  Data processing
  max_sequence_length: Max input sequence length (decoder context window) - data sequence is cropped by this length
  max_token_length: Dataloader tokenizer max_length
  skip_frames: Use only each n-th frame. "True" for each 2nd frame 

SignDataArguments:
  data_dir: Prefix of the path to data including annotation file and metadatafile. This path is joined by paths bellow.
  annotation_path: Path to train/dev annotation file
  visual_features: Path to train/dev metadata file
    pose:
      enable_input: Whether to use this modality. Our code supports only "pose" so far
      train: Path to train metafile
      dev: Path to validation metafile

SignModelArguments: Dimmensions per each sign-features modality
```


## Predict config

```
ModelArguments:
  base_model_name: Name of the model to be downloaded from HF
  sign_input_dim: Dimmension of sign features on the model input (to be changed with multimodal input)
  max_length: Max sequence length to be generated by the model

EvaluationArguments:
  output_dir: Output path for predictions
  model_name: Only for logging purposes
  skip_frames: Use only each n-th frame. "True" for each 2nd frame 
#  Data processing
  split: test 
  max_sequence_length: Max input sequence length (decoder context window) - data sequence is cropped by this length
  max_token_length: Dataloader tokenizer max_length
#  Generation parameters
  model_dir: Path to checkpoint to be evaluated. The path needs to contain model.safetensors file
#  Debugging
  max_val_samples: Maximum size of validation dataset for debugging. "none" for default size

SignDataArguments:
  data_dir: Prefix of the path to data including annotation file and metadatafile. This path is joined by paths bellow.
  annotation_path: Path to test annotation file
  visual_features: Path to test metadata file
    pose:
      enable_input: Whether to use this modality. Our code supports only "pose" so far
      test: Path to test metafile

SignModelArguments: Dimmensions per each sign-features modality
```