Internal change

PiperOrigin-RevId: 454382820

official/nlp/finetuning/glue/run_glue.py

@@ -57,7 +57,7 @@ EVAL_METRIC_MAP = {
     'MNLI': 'cls_accuracy',
     'MRPC': 'cls_accuracy',
     'QNLI': 'cls_accuracy',
-    'QQP': 'cls_accuracy',
+    'QQP': 'f1',
     'RTE': 'cls_accuracy',
     'SST-2': 'cls_accuracy',
     'STS-B': 'pearson_spearman_corr',
@@ -93,11 +93,16 @@ def _override_exp_config_by_flags(exp_config, input_meta_data):
         binary_helper.override_sentence_prediction_task_config,
         num_classes=input_meta_data['num_labels'],
         metric_type='matthews_corrcoef')
-  elif FLAGS.task_name in ('MNLI', 'MRPC', 'QNLI', 'QQP', 'RTE', 'SST-2',
+  elif FLAGS.task_name in ('MNLI', 'MRPC', 'QNLI', 'RTE', 'SST-2',
                            'WNLI'):
     override_task_cfg_fn = functools.partial(
         binary_helper.override_sentence_prediction_task_config,
         num_classes=input_meta_data['num_labels'])
+  elif FLAGS.task_name in ('QQP',):
+    override_task_cfg_fn = functools.partial(
+        binary_helper.override_sentence_prediction_task_config,
+        metric_type='f1',
+        num_classes=input_meta_data['num_labels'])
   elif FLAGS.task_name in ('STS-B',):
     override_task_cfg_fn = functools.partial(
         binary_helper.override_sentence_prediction_task_config,

official/nlp/tasks/sentence_prediction.py

@@ -34,7 +34,7 @@ from official.nlp.modeling import models
 from official.nlp.tasks import utils
 
 METRIC_TYPES = frozenset(
-    ['accuracy', 'matthews_corrcoef', 'pearson_spearman_corr'])
+    ['accuracy', 'f1', 'matthews_corrcoef', 'pearson_spearman_corr'])
 
 
 @dataclasses.dataclass
@@ -180,7 +180,7 @@ class SentencePredictionTask(base_task.Task):
           'labels':
               labels[self.label_field],
       })
-    if self.metric_type == 'pearson_spearman_corr':
+    else:
       logs.update({
           'sentence_prediction': outputs,
           'labels': labels[self.label_field],
@@ -202,18 +202,20 @@ class SentencePredictionTask(base_task.Task):
   def reduce_aggregated_logs(self, aggregated_logs, global_step=None):
     if self.metric_type == 'accuracy':
       return None
-    elif self.metric_type == 'matthews_corrcoef':
+
     preds = np.concatenate(aggregated_logs['sentence_prediction'], axis=0)
-      preds = np.reshape(preds, -1)
     labels = np.concatenate(aggregated_logs['labels'], axis=0)
+    if self.metric_type == 'f1':
+      preds = np.argmax(preds, axis=1)
+      return {self.metric_type: 100 * sklearn_metrics.f1_score(labels, preds)}
+    elif self.metric_type == 'matthews_corrcoef':
+      preds = np.reshape(preds, -1)
       labels = np.reshape(labels, -1)
       return {
           self.metric_type: sklearn_metrics.matthews_corrcoef(preds, labels)
       }
     elif self.metric_type == 'pearson_spearman_corr':
-      preds = np.concatenate(aggregated_logs['sentence_prediction'], axis=0)
       preds = np.reshape(preds, -1)
-      labels = np.concatenate(aggregated_logs['labels'], axis=0)
       labels = np.reshape(labels, -1)
       pearson_corr = stats.pearsonr(preds, labels)[0]
       spearman_corr = stats.spearmanr(preds, labels)[0]

official/nlp/tasks/sentence_prediction_test.py

@@ -162,7 +162,8 @@ class SentencePredictionTaskTest(tf.test.TestCase, parameterized.TestCase):
       self.assertLess(loss, 1.0)
 
   @parameterized.parameters(("matthews_corrcoef", 2),
-                            ("pearson_spearman_corr", 1))
+                            ("pearson_spearman_corr", 1),
+                            ("f1", 2))
   def test_np_metrics(self, metric_type, num_classes):
     config = sentence_prediction.SentencePredictionConfig(
         metric_type=metric_type,