Merge all columns in classification task into first column

This should fix CLS issues.

ulmfit/train_clas.py

@@ -143,6 +143,16 @@ class CLSHyperParams(LMHyperParams):
                               **kwargs)
         return self.databunches(bs, **data)
 
+    def merge_cols(self, df):
+        if len(df.columns) <= 2:
+            return df
+        ndf = df[[0,1]].copy()
+        for i in range(2, len(df.columns)):
+            ndf[1] += ("\n" + FLD + "\n") + df[i].fillna(" ")
+
+        assert ndf[1].isna().sum().sum() == 0, f"You have NaN values in column(s) of your dataframe, please fix it."
+        return ndf
+
     def load_data(self, lang='', **kwargs):
         prefix = '' if lang == '' else lang+'.'
         trn_df = pd.read_csv(self.dataset_path / f'{prefix}train.csv', header=None)
@@ -161,7 +171,10 @@ class CLSHyperParams(LMHyperParams):
             val_len = max(int(len(trn_df) * 0.1), 2)
             trn_len = len(trn_df) - val_len
             trn_df, val_df = trn_df[:trn_len], trn_df[trn_len:]
-
+        trn_df = self.merge_cols(trn_df)
+        val_df = self.merge_cols(val_df)
+        tst_df = self.merge_cols(tst_df)
+        unsup_df = self.merge_cols(unsup_df)
         kwargs.update(dict(trn_df=trn_df, val_df=val_df, tst_df=tst_df, unsup_df=unsup_df))
         return kwargs