tensorflow/models · error · ValueError
Invalid metric_type: {}
Error message
Invalid metric_type: {} What it means
Error "Invalid metric_type: {}" thrown in tensorflow/models.
Source
Thrown at official/projects/fffner/fffner_prediction.py:74
metric_type: str = 'accuracy'
# Defines the concrete model config at instantiation time.
model: FFFNerModelConfig = dataclasses.field(
default_factory=FFFNerModelConfig
)
train_data: cfg.DataConfig = dataclasses.field(default_factory=cfg.DataConfig)
validation_data: cfg.DataConfig = dataclasses.field(
default_factory=cfg.DataConfig
)
@task_factory.register_task_cls(FFFNerPredictionConfig)
class FFFNerTask(base_task.Task):
"""Task object for FFFNer."""
def __init__(self, params: cfg.TaskConfig, logging_dir=None, name=None):
super().__init__(params, logging_dir, name=name)
if params.metric_type not in METRIC_TYPES:
raise ValueError('Invalid metric_type: {}'.format(params.metric_type))
self.metric_type = params.metric_type
self.label_field_is_entity = 'is_entity_label'
self.label_field_entity_type = 'entity_type_label'
def build_model(self):
if self.task_config.hub_module_url and self.task_config.init_checkpoint:
raise ValueError('At most one of `hub_module_url` and '
'`init_checkpoint` can be specified.')
if self.task_config.hub_module_url:
encoder_network = utils.get_encoder_from_hub(
self.task_config.hub_module_url)
else:
encoder_network = encoders.build_encoder(self.task_config.model.encoder)
encoder_cfg = self.task_config.model.encoder.get()
if self.task_config.model.encoder.type == 'xlnet':
assert False, 'Not supported yet'
else:
return fffner_classifier.FFFNerClassifier(View on GitHub (pinned to e006f5f0d5)
When it happens
Trigger: Thrown at official/projects/fffner/fffner_prediction.py:74 when the library encounters an invalid state.
Common situations: See trigger scenarios.
AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24).
Data as JSON: /api/errors/610f430bc0199f06.
Report an issue: GitHub.