[Fix] text-classification PL example (#6027)

Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

examples/text-classification/run_pl.sh

@@ -23,7 +23,7 @@ mkdir -p $OUTPUT_DIR
 # Add parent directory to python path to access lightning_base.py
 export PYTHONPATH="../":"${PYTHONPATH}"
 
-python3 run_pl_glue.py --data_dir $DATA_DIR \
+python3 run_pl_glue.py --gpus 1 --data_dir $DATA_DIR \
 --task $TASK \
 --model_name_or_path $BERT_MODEL \
 --output_dir $OUTPUT_DIR \

examples/text-classification/run_pl_glue.py

@@ -3,6 +3,7 @@ import glob
 import logging
 import os
 import time
+from argparse import Namespace
 
 import numpy as np
 import torch
@@ -24,6 +25,8 @@ class GLUETransformer(BaseTransformer):
     mode = "sequence-classification"
 
     def __init__(self, hparams):
+        if type(hparams) == dict:
+            hparams = Namespace(**hparams)
         hparams.glue_output_mode = glue_output_modes[hparams.task]
         num_labels = glue_tasks_num_labels[hparams.task]
 
@@ -41,7 +44,8 @@ class GLUETransformer(BaseTransformer):
         outputs = self(**inputs)
         loss = outputs[0]
 
-        tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
+        # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
+        tensorboard_logs = {"loss": loss}
         return {"loss": loss, "log": tensorboard_logs}
 
     def prepare_data(self):
@@ -71,7 +75,7 @@ class GLUETransformer(BaseTransformer):
                 logger.info("Saving features into cached file %s", cached_features_file)
                 torch.save(features, cached_features_file)
 
-    def load_dataset(self, mode, batch_size):
+    def get_dataloader(self, mode: int, batch_size: int, shuffle: bool) -> DataLoader:
         "Load datasets. Called after prepare data."
 
         # We test on dev set to compare to benchmarks without having to submit to GLUE server