diff --git a/.circleci/config.yml b/.circleci/config.yml
index 3963270e..0af9f9a8 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -102,6 +102,7 @@ jobs:
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
             - run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece]
+            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -129,6 +130,7 @@ jobs:
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
             - run: pip install .[sklearn,torch,testing,sentencepiece]
+            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
@@ -210,6 +212,7 @@ jobs:
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
             - run: pip install .[sklearn,torch,testing,sentencepiece]
+            - run: pip install tapas torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
diff --git a/src/transformers/file_utils.py b/src/transformers/file_utils.py
index 8b11f14c..0e345bb2 100644
--- a/src/transformers/file_utils.py
+++ b/src/transformers/file_utils.py
@@ -216,6 +216,15 @@ except ImportError:
     _tokenizers_available = False
 
 
+try:
+    import pandas  # noqa: F401
+
+    _pandas_available = True
+
+except ImportError:
+    _pandas_available = False
+
+
 try:
     import torch_scatter
 
@@ -343,6 +352,10 @@ def is_scatter_available():
     return _scatter_available
 
 
+def is_pandas_available():
+    return _pandas_available
+
+
 def torch_only_method(fn):
     def wrapper(*args, **kwargs):
         if not _torch_available:
diff --git a/src/transformers/models/tapas/tokenization_tapas.py b/src/transformers/models/tapas/tokenization_tapas.py
index 154c2b82..f0c6edf9 100644
--- a/src/transformers/models/tapas/tokenization_tapas.py
+++ b/src/transformers/models/tapas/tokenization_tapas.py
@@ -26,11 +26,11 @@ import unicodedata
 from dataclasses import dataclass
 from typing import Callable, Dict, Generator, List, Optional, Text, Tuple, Union
 
-import pandas as pd
-import torch
+import numpy as np
 
 from transformers import add_end_docstrings
 
+from ...file_utils import is_pandas_available
 from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
 from ...tokenization_utils_base import (
     ENCODE_KWARGS_DOCSTRING,
@@ -45,6 +45,9 @@ from ...tokenization_utils_base import (
 from ...utils import logging
 
 
+if is_pandas_available():
+    import pandas as pd
+
 logger = logging.get_logger(__name__)
 
 
@@ -307,6 +310,9 @@ class TapasTokenizer(PreTrainedTokenizer):
         additional_special_tokens: Optional[List[str]] = None,
         **kwargs
     ):
+        if not is_pandas_available():
+            raise ImportError("Pandas is required for the TAPAS tokenizer.")
+
         if additional_special_tokens is not None:
             if empty_token not in additional_special_tokens:
                 additional_special_tokens.append(empty_token)
@@ -539,7 +545,7 @@ class TapasTokenizer(PreTrainedTokenizer):
     @add_end_docstrings(TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
     def __call__(
         self,
-        table: pd.DataFrame,
+        table: "pd.DataFrame",
         queries: Optional[
             Union[
                 TextInput,
@@ -663,7 +669,7 @@ class TapasTokenizer(PreTrainedTokenizer):
     @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
     def batch_encode_plus(
         self,
-        table: pd.DataFrame,
+        table: "pd.DataFrame",
         queries: Optional[
             Union[
                 List[TextInput],
@@ -812,7 +818,7 @@ class TapasTokenizer(PreTrainedTokenizer):
 
     def _batch_prepare_for_model(
         self,
-        raw_table: pd.DataFrame,
+        raw_table: "pd.DataFrame",
         raw_queries: Union[
             List[TextInput],
             List[PreTokenizedInput],
@@ -884,7 +890,7 @@ class TapasTokenizer(PreTrainedTokenizer):
     @add_end_docstrings(ENCODE_KWARGS_DOCSTRING)
     def encode(
         self,
-        table: pd.DataFrame,
+        table: "pd.DataFrame",
         query: Optional[
             Union[
                 TextInput,
@@ -927,7 +933,7 @@ class TapasTokenizer(PreTrainedTokenizer):
     @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
     def encode_plus(
         self,
-        table: pd.DataFrame,
+        table: "pd.DataFrame",
         query: Optional[
             Union[
                 TextInput,
@@ -1010,7 +1016,7 @@ class TapasTokenizer(PreTrainedTokenizer):
 
     def _encode_plus(
         self,
-        table: pd.DataFrame,
+        table: "pd.DataFrame",
         query: Union[
             TextInput,
             PreTokenizedInput,
@@ -1066,7 +1072,7 @@ class TapasTokenizer(PreTrainedTokenizer):
     @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPAS_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
     def prepare_for_model(
         self,
-        raw_table: pd.DataFrame,
+        raw_table: "pd.DataFrame",
         raw_query: Union[
             TextInput,
             PreTokenizedInput,
@@ -1884,7 +1890,7 @@ class TapasTokenizer(PreTrainedTokenizer):
             col = column_ids[i] - 1
             row = row_ids[i] - 1
             coords_to_probs[(col, row)].append(prob)
-        return {coords: torch.as_tensor(cell_probs).mean() for coords, cell_probs in coords_to_probs.items()}
+        return {coords: np.array(cell_probs).mean() for coords, cell_probs in coords_to_probs.items()}
 
     def convert_logits_to_predictions(self, data, logits, logits_agg=None, cell_classification_threshold=0.5):
         """
@@ -1912,11 +1918,8 @@ class TapasTokenizer(PreTrainedTokenizer):
             of length ``batch_size``: Predicted aggregation operator indices of the aggregation head.
         """
         # compute probabilities from token logits
-        dist_per_token = torch.distributions.Bernoulli(logits=logits)
-        probabilities = dist_per_token.probs * data["attention_mask"].type(torch.float32).to(
-            dist_per_token.probs.device
-        )
-
+        # DO sigmoid here
+        probabilities = 1 / (1 + np.exp(-logits)) * data["attention_mask"]
         token_types = [
             "segment_ids",
             "column_ids",
diff --git a/src/transformers/testing_utils.py b/src/transformers/testing_utils.py
index 80493444..c664664f 100644
--- a/src/transformers/testing_utils.py
+++ b/src/transformers/testing_utils.py
@@ -28,6 +28,7 @@ from .file_utils import (
     _datasets_available,
     _faiss_available,
     _flax_available,
+    _pandas_available,
     _scatter_available,
     _sentencepiece_available,
     _tf_available,
@@ -222,6 +223,19 @@ def require_tokenizers(test_case):
         return test_case
 
 
+def require_pandas(test_case):
+    """
+    Decorator marking a test that requires pandas.
+
+    These tests are skipped when pandas isn't installed.
+
+    """
+    if not _pandas_available:
+        return unittest.skip("test requires pandas")(test_case)
+    else:
+        return test_case
+
+
 def require_scatter(test_case):
     """
     Decorator marking a test that requires PyTorch Scatter. These tests are skipped when PyTorch Scatter isn't
diff --git a/tests/test_tokenization_tapas.py b/tests/test_tokenization_tapas.py
index 64fc51b2..808dfdd2 100644
--- a/tests/test_tokenization_tapas.py
+++ b/tests/test_tokenization_tapas.py
@@ -32,12 +32,13 @@ from transformers.models.tapas.tokenization_tapas import (
     _is_punctuation,
     _is_whitespace,
 )
-from transformers.testing_utils import is_pt_tf_cross_test, require_tokenizers, require_torch, slow
+from transformers.testing_utils import is_pt_tf_cross_test, require_pandas, require_tokenizers, require_torch, slow
 
 from .test_tokenization_common import TokenizerTesterMixin, filter_non_english, merge_model_tokenizer_mappings
 
 
 @require_tokenizers
+@require_pandas
 class TapasTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = TapasTokenizer
     test_rust_tokenizer = False
@@ -3418,3 +3419,7 @@ class TapasTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         self.assertListEqual(segment_ids.tolist(), expected_results["segment_ids"])
         self.assertListEqual(column_ids.tolist(), expected_results["column_ids"])
         self.assertListEqual(row_ids.tolist(), expected_results["row_ids"])
+
+    @unittest.skip("Skip this test while all models are still to be uploaded.")
+    def test_pretrained_model_lists(self):
+        pass