class Config(Task.Config):
model: SeqNNModel.Config = SeqNNModel.Config()
trainer: Trainer.Config = Trainer.Config()
labels: DocLabelConfig = DocLabelConfig()
data_handler: SeqModelDataHandler.Config = SeqModelDataHandler.Config()
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
class Config(DocumentClassificationTask.Config):
model: NewBertModel.Config = NewBertModel.Config(
inputs=NewBertModel.Config.BertModelInput(
tokens=BERTTensorizer.Config(columns=["text1", "text2"],
max_seq_len=128)))
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config(
text_column_names=["text1", "text2"]))
class Config(Task_Deprecated.Config):
model: SeqNNModel_Deprecated.Config = SeqNNModel_Deprecated.Config()
trainer: Trainer.Config = Trainer.Config()
labels: DocLabelConfig = DocLabelConfig()
data_handler: SeqModelDataHandler.Config = SeqModelDataHandler.Config()
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
exporter: Optional[DenseFeatureExporter.Config] = None
class Config(Task.Config):
model: Union[BaggingDocEnsemble.Config, BaggingIntentSlotEnsemble.Config]
trainer: EnsembleTrainer.Config = EnsembleTrainer.Config()
labels: List[TargetConfigBase]
metric_reporter: Union[
ClassificationMetricReporter.Config, IntentSlotMetricReporter.Config
] = ClassificationMetricReporter.Config()
data_handler: JointModelDataHandler.Config = JointModelDataHandler.Config()
class Config(Task.Config):
model: DocModel.Config = DocModel.Config()
trainer: Trainer.Config = Trainer.Config()
features: DocClassification.ModelInputConfig = (
DocClassification.ModelInputConfig())
labels: DocClassification.TargetConfig = DocClassification.TargetConfig(
)
data_handler: DocClassificationDataHandler.Config = (
DocClassificationDataHandler.Config())
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
class Config(Task.Config):
features: PairClassification.ModelInputConfig = (
PairClassification.ModelInputConfig())
model: PairClassificationModel.Config = PairClassificationModel.Config(
)
data_handler: PairClassificationDataHandler.Config = (
PairClassificationDataHandler.Config())
trainer: Trainer.Config = Trainer.Config()
labels: PairClassification.TargetConfig = PairClassification.TargetConfig(
)
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
class Config(Task_Deprecated.Config):
model: DocModel_Deprecated.Config = DocModel_Deprecated.Config()
trainer: Trainer.Config = Trainer.Config()
features: DocClassification.ModelInputConfig = (
DocClassification.ModelInputConfig())
labels: DocClassification.TargetConfig = DocClassification.TargetConfig(
)
data_handler: DocClassificationDataHandler.Config = (
DocClassificationDataHandler.Config())
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
exporter: Optional[DenseFeatureExporter.Config] = None
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)
class Config(NewTask.Config):
model: BaseModel.Config = DocModel.Config()
metric_reporter: Union[ClassificationMetricReporter.Config,
PureLossMetricReporter.
Config] = ClassificationMetricReporter.Config()
class Config(NewTask.Config):
model: EnsembleModel.Config
trainer: EnsembleTrainer.Config = EnsembleTrainer.Config()
metric_reporter: Union[ClassificationMetricReporter.Config,
IntentSlotMetricReporter.
Config] = ClassificationMetricReporter.Config()
class Config(NewTask.Config):
model: SeqNNModel.Config = SeqNNModel.Config()
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config(
text_column_names=["text_seq"]))
class Config(NewTask.Config):
model: BasePairwiseModel.Config = PairwiseModel.Config()
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config(
text_column_names=["text1", "text2"]))
trace_both_encoders: bool = True
class Config(NewTask.Config):
model: Model.Config = DocModel.Config()
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
class Config(NewTask.Config):
model: BasePairwiseModel.Config = PairwiseModel.Config()
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
class Config(NewTask.Config):
model: BaggingDocEnsemble.Config
trainer: EnsembleTrainer.Config = EnsembleTrainer.Config()
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
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)
