Add my custom Trainer for Question Answering Problem

trainer_qa.py

@@ -0,0 +1,75 @@
+from transformers import Trainer, is_torch_tpu_available
+from transformers.trainer_utils import PredictionOutput
+
+class QuestionAnsweringTrainer(Trainer):
+    def __init__(self, *args, post_process_function = None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.post_process_function = post_process_function
+
+    def evaluate(self, eval_dataset = None, ignore_keys = None, metric_key_prefix: str = "eval"):
+        eval_dataset    = self.eval_dataset if eval_dataset is None else eval_dataset
+        eval_dataloader = self.get_eval_dataloader(eval_dataset)
+
+        # Temporarily disable metric computation, we will do it in the loop here.
+        compute_metrics = self.compute_metrics
+        self.compute_metrics = None
+        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
+        try:
+            output = eval_loop(
+                eval_dataloader,
+                description="Evaluation",
+                # No point gathering the predictions if there are no metrics, otherwise we defer to
+                # self.args.prediction_loss_only
+                prediction_loss_only=True if compute_metrics is None else None,
+                ignore_keys=ignore_keys,
+            )
+        finally:
+            self.compute_metrics = compute_metrics
+
+        if self.post_process_function is not None and self.compute_metrics is not None:
+            eval_preds = self.post_process_function(eval_dataset, self.tokenizer, output.predictions)
+            metrics = self.compute_metrics(eval_preds)
+
+            # Prefix all keys with metric_key_prefix + '_'
+            for key in list(metrics.keys()):
+                if not key.startswith(f"{metric_key_prefix}_"):
+                    metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
+
+            self.log(metrics)
+        else:
+            metrics = {}
+
+        self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics)
+        return metrics
+
+    def predict(self, predict_dataset, ignore_keys=None, metric_key_prefix: str = "test"):
+        predict_dataloader = self.get_test_dataloader(predict_dataset)
+
+        # Temporarily disable metric computation, we will do it in the loop here.
+        compute_metrics = self.compute_metrics
+        self.compute_metrics = None
+        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
+        try:
+            output = eval_loop(
+                predict_dataloader,
+                description="Prediction",
+                # No point gathering the predictions if there are no metrics, otherwise we defer to
+                # self.args.prediction_loss_only
+                prediction_loss_only=True if compute_metrics is None else None,
+                ignore_keys=ignore_keys,
+            )
+        finally:
+            self.compute_metrics = compute_metrics
+
+        if self.post_process_function is None or self.compute_metrics is None:
+            return output
+
+        predictions = self.post_process_function(predict_dataset, self.tokenizer, output.predictions, "predict")
+        metrics = self.compute_metrics(predictions)
+
+        # Prefix all keys with metric_key_prefix + '_'
+        for key in list(metrics.keys()):
+            if not key.startswith(f"{metric_key_prefix}_"):
+                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)
+
+        return  PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=metrics)