add docstrings

Author: voorhs

autointent/configs/_transformers.py

@@ -127,7 +127,18 @@ class CrossEncoderConfig(HFModelConfig):
 
 
 class EarlyStoppingConfig(BaseModel):
-    val_fraction: float = 0.2
-    patience: int = 1
-    threshold: float = 0.0
-    metric: Literal[tuple((SCORING_METRICS_MULTILABEL | SCORING_METRICS_MULTICLASS).keys())] | None = "scoring_f1"  # type: ignore[valid-type]
+    val_fraction: float = Field(
+        0.2,
+        description=(
+            "Fraction of train samples to allocate to dev set to monitor quality "
+            "during training and perofrm early stopping if quality doesn't enhances."
+        ),
+    )
+    patience: int = Field(1, description="Maximum number of epoches to wait for quality to enhance.")
+    threshold: float = Field(
+        0.0,
+        description="Minimum quality increment to count it as enhancement. Default: any incremeant is counted",
+    )
+    metric: Literal[tuple((SCORING_METRICS_MULTILABEL | SCORING_METRICS_MULTICLASS).keys())] | None = Field(  # type: ignore[valid-type]
+        "scoring_f1", description="Metric to monitor."
+    )

autointent/modules/scoring/_bert.py

@@ -29,6 +29,48 @@
 
 
 class BertScorer(BaseScorer):
+    """Scoring module for transformer-based classification using BERT models.
+
+    This module uses a transformer model (like BERT) to perform intent classification.
+    It supports both multiclass and multilabel classification tasks, with options for
+    early stopping and various training configurations.
+
+    Args:
+        classification_model_config: Config of the transformer model (HFModelConfig, str, or dict)
+        num_train_epochs: Number of training epochs (default: 3)
+        batch_size: Batch size for training (default: 8)
+        learning_rate: Learning rate for training (default: 5e-5)
+        seed: Random seed for reproducibility (default: 0)
+        report_to: Reporting tool for training logs (e.g., "wandb", "tensorboard")
+        early_stopping_config: Configuration for early stopping during training
+
+    Example:
+    --------
+    .. testcode::
+
+        from autointent.modules import BertScorer
+
+        # Initialize scorer with BERT model
+        scorer = BertScorer(
+            classification_model_config="bert-base-uncased",
+            num_train_epochs=3,
+            batch_size=8,
+            learning_rate=5e-5,
+            seed=42
+        )
+
+        # Training data
+        utterances = ["This is great!", "I didn't like it", "Awesome product", "Poor quality"]
+        labels = [1, 0, 1, 0]
+
+        # Fit the model
+        scorer.fit(utterances, labels)
+
+        # Make predictions
+        test_utterances = ["Good product", "Not worth it"]
+        probabilities = scorer.predict(test_utterances)
+    """
+
     name = "bert"
     supports_multiclass = True
     supports_multilabel = True