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name: megatron_t5_finetune_eval
trainer:
devices: 1
num_nodes: 1
accelerator: gpu
precision: 16
logger: False # logger provided by exp_manager
enable_checkpointing: False
replace_sampler_ddp: False
benchmark: False
exp_manager:
explicit_log_dir: null
exp_dir: null
name: megatron_t5_finetune_eval
create_checkpoint_callback: False
model:
restore_from_path: null # Path to a trained T5 .nemo file
pretrained_checkpoint:
checkpoint_dir: null # Path to a folder that contains a .ckpt file
checkpoint_name: null # Name of the .ckpt file within the checkpoint_dir.
hparams_file: null # Path to a .yaml file that contains the hyperparameters of the checkpoint.
gradient_as_bucket_view: True # Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory)
megatron_amp_O2: False # Enable O2 optimization for megatron amp
data:
validation_ds:
src_file_name: null # Path to the txt file corresponding to the source data.
tgt_file_name: null # Path to the txt file corresponding to the target data.
names: null # If src/tgt file names are ListConfigs, the corresponding label is used to log metrics.
global_batch_size: 64
micro_batch_size: 64
shuffle: False
num_workers: 0
pin_memory: True
max_src_seq_length: 512
max_tgt_seq_length: 128
drop_last: False # TODO: Figure out if there is a way to avoid dropping last.
write_predictions_to_file: False
output_file_path_prefix: null # Prefix of the file to write predictions to.
replace_bos_with_pad: False # Replaces bos with pad for both the encoder and decoder. This is necessary when using Google's T5 checkpoints.
add_bos_to_input: False # Adds bos to the input sequence.
add_eos_to_input: False # Adds eos to the input sequence.
metric:
name: "exact_string_match" # Name of the evaluation metric to use.
average: micro # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported.
num_classes: null # Number of classes for the metric. Works only for 'F1', 'accuracy' and 'average_precision' etc. Refer to torchmetrics for metrics where this is supported.
class_labels: null # If the targets in your dataset are strings and not integers/float, you need to provide a list of class labels (size = num_classes) so we can convert from strings to integer categories to compute the metric.
labels_are_strings: True # NOTE: This is only required to properly handle metrics like f1, accuracy, average_precision etc. This does not affect extract_string_match.
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