diff --git a/experiments/cls_test_wt103_1_f.ipynb b/experiments/cls_test_wt103_1_f.ipynb
index 26a6e2e..ee3fd22 100644
--- a/experiments/cls_test_wt103_1_f.ipynb
+++ b/experiments/cls_test_wt103_1_f.ipynb
@@ -714,9 +714,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:fastaiv1]",
+   "display_name": "fastai-dev",
    "language": "python",
-   "name": "conda-env-fastaiv1-py"
+   "name": "fastai-dev"
   },
   "language_info": {
    "codemirror_mode": {
@@ -728,7 +728,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,
diff --git a/tests/test_text_data.py b/tests/test_text_data.py
index 6b3dabf..1b96079 100644
--- a/tests/test_text_data.py
+++ b/tests/test_text_data.py
@@ -22,38 +22,35 @@ def test_should_load_backwards_lm():
     df = text_df(['neg','pos'])
 
     data = TextLMDataBunch.from_df(path, train_df=df, valid_df=df, label_cols=0, text_cols=["text"], bs=2,
-                                   lm_type=contrib_data.LanguageModelType.BwdLM,
-                                   ld_cls=contrib_data.LanguageModelLoader)
+                                   lm_type=contrib_data.LanguageModelType.BwdLM)
     lml = data.train_dl.dl
     lml.data = lml.batchify(np.concatenate([lml.dataset.x.items[i] for i in range(len(lml.dataset))]))
     batch = lml.get_batch(lml.data, 0, 70)
 
-    assert batch[0].shape == (70, lml.bs)
+    assert batch[0].shape == (lml.bs, 70)
     assert batch[1].shape == (70*lml.bs,)
 
-
-    as_text = [lml.dataset.vocab.itos[x] for x in batch[0][:,0]]
-    np.testing.assert_array_equal(as_text[:5], ["world", "hello", '1', 'xxfld', 'project',])
+    as_text = [lml.dataset.vocab.itos[x] for x in batch[0][0]]
+    np.testing.assert_array_equal(as_text[:5], ["world", "hello", 'xxbos', 'project', 'cool'])
 
 def test_should_load_bi_lm():
     path = untar_data(URLs.IMDB_SAMPLE)
     df = text_df(['neg', 'pos'])
 
     data = TextLMDataBunch.from_df(path, train_df=df, valid_df=df, label_cols=0, text_cols=["text"], bs=2,
-                                   lm_type=contrib_data.LanguageModelType.BiLM,
-                                   ld_cls=contrib_data.LanguageModelLoader)
+                                   lm_type=contrib_data.LanguageModelType.BiLM)
     lml = data.train_dl.dl
     lml.data = lml.batchify(np.concatenate([lml.dataset.x.items[i] for i in range(len(lml.dataset))]))
     batch = lml.get_batch(lml.data, 0, 70)
 
-    assert batch[0].shape == (70, lml.bs, 2)
+    assert batch[0].shape == (lml.bs, 70, 2)
     assert batch[1].shape == (70*lml.bs, 2)
 
-    as_text = [lml.dataset.vocab.itos[x] for x in batch[0][:, 0, 0]]
-    np.testing.assert_array_equal(as_text[:7], "xxfld 1 fast ai is a cool".split())
+    as_text = [lml.dataset.vocab.itos[x] for x in batch[0][0, :, 0]]
+    np.testing.assert_array_equal(as_text[:7], "xxbos fast ai is a cool project".split())
 
-    as_text = [lml.dataset.vocab.itos[x] for x in batch[0][:,0,1]]
-    np.testing.assert_array_equal(as_text[:5], ["world", "hello", '1', 'xxfld', 'project',])
+    as_text = [lml.dataset.vocab.itos[x] for x in batch[0][0, :, 1]]
+    np.testing.assert_array_equal(as_text[:5], ["world", "hello", 'xxbos', 'project', 'cool'])
 
 ###################### NEW CODE