def train(
self,
training_data: DataLoader,
eval_data: DataLoader,
model: Model,
optimizer: Optimizer,
label_names: List[str],
scheduler: Scheduler = None,
sparsifier: Sparsifier = None,
metric_reporter: MetricReporter = None,
train_config: PyTextConfig = None,
rank: int = 0,
) -> Tuple[torch.nn.Module, Any]:
# temp workaround to minimize changes to TaskTrainer
if not train_config:
train_config = PyTextConfig(
task=NewTask.Config(model=RoBERTa.Config), version=20)
if scheduler:
self.scheduler = scheduler
if sparsifier:
self.sparsifier = sparsifier
state = TrainingState(
model=model,
optimizer=optimizer,
scheduler=self.scheduler,
sparsifier=self.sparsifier,
rank=rank,
)
metric_reporter_config = ClassificationMetricReporter.Config(
output_path="/tmp/test_out.txt",
pep_format=False,
model_select_metric=ComparableClassificationMetric.
ACCURACY, # in json: "accuracy"
target_label=None,
text_column_names=["text"],
additional_column_names=[],
recall_at_precision_thresholds=[0.2, 0.4, 0.6, 0.8, 0.9],
)
metric_reporter = ClassificationMetricReporter.from_config_and_label_names(
config=metric_reporter_config, label_names=label_names)
return self.train_from_state(state, training_data, eval_data,
metric_reporter, train_config)
def create_metric_reporter(cls, config: Config,
tensorizers: Dict[str, Tensorizer]):
return ClassificationMetricReporter.from_config_and_label_names(
config.metric_reporter, list(tensorizers["labels"].labels))
def classification_metric_reporter_config_expand(label_names: List[str],
**kwargs):
classification_metric_reporter_config = ClassificationMetricReporter.Config(
**kwargs)
return ClassificationMetricReporter.from_config_and_label_names(
classification_metric_reporter_config, label_names)
