diff --git a/official/nlp/bert/run_squad.py b/official/nlp/bert/run_squad.py
index d88aa85f371..7ca62b13f98 100644
--- a/official/nlp/bert/run_squad.py
+++ b/official/nlp/bert/run_squad.py
@@ -19,9 +19,10 @@
 from __future__ import print_function
 
 import json
-
 import os
 import tempfile
+import time
+
 from absl import app
 from absl import flags
 from absl import logging
@@ -126,24 +127,22 @@ def main(_):
   if 'predict' in FLAGS.mode:
     predict_squad(strategy, input_meta_data)
   if 'eval' in FLAGS.mode:
-    if input_meta_data.get('version_2_with_negative', False):
-      logging.error('SQuAD v2 eval is not supported. '
-                    'Falling back to predict mode.')
-      predict_squad(strategy, input_meta_data)
+    eval_metrics = eval_squad(strategy, input_meta_data)
+    f1_score = eval_metrics['final_f1']
+    logging.info('SQuAD eval F1-score: %f', f1_score)
+    if (not strategy) or strategy.extended.should_save_summary:
+      summary_dir = os.path.join(FLAGS.model_dir, 'summaries')
     else:
-      eval_metrics = eval_squad(strategy, input_meta_data)
-      f1_score = eval_metrics['f1']
-      logging.info('SQuAD eval F1-score: %f', f1_score)
-      if (not strategy) or strategy.extended.should_save_summary:
-        summary_dir = os.path.join(FLAGS.model_dir, 'summaries')
-      else:
-        summary_dir = tempfile.mkdtemp()
-      summary_writer = tf.summary.create_file_writer(
-          os.path.join(summary_dir, 'eval'))
-      with summary_writer.as_default():
-        # TODO(lehou): write to the correct step number.
-        tf.summary.scalar('F1-score', f1_score, step=0)
-        summary_writer.flush()
+      summary_dir = tempfile.mkdtemp()
+    summary_writer = tf.summary.create_file_writer(
+        os.path.join(summary_dir, 'eval'))
+    with summary_writer.as_default():
+      # TODO(lehou): write to the correct step number.
+      tf.summary.scalar('F1-score', f1_score, step=0)
+      summary_writer.flush()
+    # Wait for some time, for the depending mldash/tensorboard jobs to finish
+    # exporting the final F1-score.
+    time.sleep(60)
 
 
 if __name__ == '__main__':
diff --git a/official/nlp/bert/run_squad_helper.py b/official/nlp/bert/run_squad_helper.py
index 293a42693b0..52e11800b25 100644
--- a/official/nlp/bert/run_squad_helper.py
+++ b/official/nlp/bert/run_squad_helper.py
@@ -31,6 +31,7 @@
 from official.nlp.bert import model_saving_utils
 from official.nlp.bert import model_training_utils
 from official.nlp.bert import squad_evaluate_v1_1
+from official.nlp.bert import squad_evaluate_v2_0
 from official.nlp.data import squad_lib_sp
 from official.utils.misc import keras_utils
 
@@ -373,16 +374,16 @@ def eval_squad(strategy, input_meta_data, tokenizer, bert_config, squad_lib):
   dump_to_files(all_predictions, all_nbest_json, scores_diff_json, squad_lib,
                 input_meta_data.get('version_2_with_negative', False))
 
+  with tf.io.gfile.GFile(FLAGS.predict_file, 'r') as reader:
+    dataset_json = json.load(reader)
+    pred_dataset = dataset_json['data']
   if input_meta_data.get('version_2_with_negative', False):
-    # TODO(lehou): support in memory evaluation for SQuAD v2.
-    logging.error('SQuAD v2 eval is not supported. Skipping eval')
-    return None
+    eval_metrics = squad_evaluate_v2_0.evaluate(pred_dataset,
+                                                all_predictions,
+                                                scores_diff_json)
   else:
-    with tf.io.gfile.GFile(FLAGS.predict_file, 'r') as reader:
-      dataset_json = json.load(reader)
-      pred_dataset = dataset_json['data']
     eval_metrics = squad_evaluate_v1_1.evaluate(pred_dataset, all_predictions)
-    return eval_metrics
+  return eval_metrics
 
 
 def export_squad(model_export_path, input_meta_data, bert_config):
diff --git a/official/nlp/bert/squad_evaluate_v1_1.py b/official/nlp/bert/squad_evaluate_v1_1.py
index 2495d285aea..c7f4f4de668 100644
--- a/official/nlp/bert/squad_evaluate_v1_1.py
+++ b/official/nlp/bert/squad_evaluate_v1_1.py
@@ -105,4 +105,4 @@ def evaluate(dataset, predictions):
   exact_match = exact_match / total
   f1 = f1 / total
 
-  return {"exact_match": exact_match, "f1": f1}
+  return {"exact_match": exact_match, "final_f1": f1}
diff --git a/official/nlp/bert/squad_evaluate_v2_0.py b/official/nlp/bert/squad_evaluate_v2_0.py
new file mode 100644
index 00000000000..54fb84e993c
--- /dev/null
+++ b/official/nlp/bert/squad_evaluate_v2_0.py
@@ -0,0 +1,252 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Evaluation script for SQuAD version 2.0.
+
+The functions are copied and modified from
+https://raw.githubusercontent.com/white127/SQUAD-2.0-bidaf/master/evaluate-v2.0.py
+
+In addition to basic functionality, we also compute additional statistics and
+plot precision-recall curves if an additional na_prob.json file is provided.
+This file is expected to map question ID's to the model's predicted probability
+that a question is unanswerable.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import re
+import string
+
+from absl import logging
+
+
+def _make_qid_to_has_ans(dataset):
+  qid_to_has_ans = {}
+  for article in dataset:
+    for p in article['paragraphs']:
+      for qa in p['qas']:
+        qid_to_has_ans[qa['id']] = bool(qa['answers'])
+  return qid_to_has_ans
+
+
+def _normalize_answer(s):
+  """Lower text and remove punctuation, articles and extra whitespace."""
+  def remove_articles(text):
+    regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
+    return re.sub(regex, ' ', text)
+  def white_space_fix(text):
+    return ' '.join(text.split())
+  def remove_punc(text):
+    exclude = set(string.punctuation)
+    return ''.join(ch for ch in text if ch not in exclude)
+  def lower(text):
+    return text.lower()
+  return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def _get_tokens(s):
+  if not s: return []
+  return _normalize_answer(s).split()
+
+
+def _compute_exact(a_gold, a_pred):
+  return int(_normalize_answer(a_gold) == _normalize_answer(a_pred))
+
+
+def _compute_f1(a_gold, a_pred):
+  """Compute F1-score."""
+  gold_toks = _get_tokens(a_gold)
+  pred_toks = _get_tokens(a_pred)
+  common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
+  num_same = sum(common.values())
+  if not gold_toks or not pred_toks:
+    # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
+    return int(gold_toks == pred_toks)
+  if num_same == 0:
+    return 0
+  precision = 1.0 * num_same / len(pred_toks)
+  recall = 1.0 * num_same / len(gold_toks)
+  f1 = (2 * precision * recall) / (precision + recall)
+  return f1
+
+
+def _get_raw_scores(dataset, predictions):
+  """Compute raw scores."""
+  exact_scores = {}
+  f1_scores = {}
+  for article in dataset:
+    for p in article['paragraphs']:
+      for qa in p['qas']:
+        qid = qa['id']
+        gold_answers = [a['text'] for a in qa['answers']
+                        if _normalize_answer(a['text'])]
+        if not gold_answers:
+          # For unanswerable questions, only correct answer is empty string
+          gold_answers = ['']
+        if qid not in predictions:
+          logging.error('Missing prediction for %s', qid)
+          continue
+        a_pred = predictions[qid]
+        # Take max over all gold answers
+        exact_scores[qid] = max(_compute_exact(a, a_pred) for a in gold_answers)
+        f1_scores[qid] = max(_compute_f1(a, a_pred) for a in gold_answers)
+  return exact_scores, f1_scores
+
+
+def _apply_no_ans_threshold(
+    scores, na_probs, qid_to_has_ans, na_prob_thresh=1.0):
+  new_scores = {}
+  for qid, s in scores.items():
+    pred_na = na_probs[qid] > na_prob_thresh
+    if pred_na:
+      new_scores[qid] = float(not qid_to_has_ans[qid])
+    else:
+      new_scores[qid] = s
+  return new_scores
+
+
+def _make_eval_dict(exact_scores, f1_scores, qid_list=None):
+  """Make evaluation result dictionary."""
+  if not qid_list:
+    total = len(exact_scores)
+    return collections.OrderedDict([
+        ('exact', 100.0 * sum(exact_scores.values()) / total),
+        ('f1', 100.0 * sum(f1_scores.values()) / total),
+        ('total', total),
+    ])
+  else:
+    total = len(qid_list)
+    return collections.OrderedDict([
+        ('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
+        ('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
+        ('total', total),
+    ])
+
+
+def _merge_eval(main_eval, new_eval, prefix):
+  for k in new_eval:
+    main_eval['%s_%s' % (prefix, k)] = new_eval[k]
+
+
+def _make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans):
+  """Make evaluation dictionary containing average recision recall."""
+  qid_list = sorted(na_probs, key=lambda k: na_probs[k])
+  true_pos = 0.0
+  cur_p = 1.0
+  cur_r = 0.0
+  precisions = [1.0]
+  recalls = [0.0]
+  avg_prec = 0.0
+  for i, qid in enumerate(qid_list):
+    if qid_to_has_ans[qid]:
+      true_pos += scores[qid]
+    cur_p = true_pos / float(i+1)
+    cur_r = true_pos / float(num_true_pos)
+    if i == len(qid_list) - 1 or na_probs[qid] != na_probs[qid_list[i+1]]:
+      # i.e., if we can put a threshold after this point
+      avg_prec += cur_p * (cur_r - recalls[-1])
+      precisions.append(cur_p)
+      recalls.append(cur_r)
+  return {'ap': 100.0 * avg_prec}
+
+
+def _run_precision_recall_analysis(
+    main_eval, exact_raw, f1_raw, na_probs, qid_to_has_ans):
+  """Run precision recall analysis and return result dictionary."""
+  num_true_pos = sum(1 for v in qid_to_has_ans.values() if v)
+  if num_true_pos == 0:
+    return
+  pr_exact = _make_precision_recall_eval(
+      exact_raw, na_probs, num_true_pos, qid_to_has_ans)
+  pr_f1 = _make_precision_recall_eval(
+      f1_raw, na_probs, num_true_pos, qid_to_has_ans)
+  oracle_scores = {k: float(v) for k, v in qid_to_has_ans.items()}
+  pr_oracle = _make_precision_recall_eval(
+      oracle_scores, na_probs, num_true_pos, qid_to_has_ans)
+  _merge_eval(main_eval, pr_exact, 'pr_exact')
+  _merge_eval(main_eval, pr_f1, 'pr_f1')
+  _merge_eval(main_eval, pr_oracle, 'pr_oracle')
+
+
+def _find_best_thresh(predictions, scores, na_probs, qid_to_has_ans):
+  """Find the best threshold for no answer probability."""
+  num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
+  cur_score = num_no_ans
+  best_score = cur_score
+  best_thresh = 0.0
+  qid_list = sorted(na_probs, key=lambda k: na_probs[k])
+  for qid in qid_list:
+    if qid not in scores: continue
+    if qid_to_has_ans[qid]:
+      diff = scores[qid]
+    else:
+      if predictions[qid]:
+        diff = -1
+      else:
+        diff = 0
+    cur_score += diff
+    if cur_score > best_score:
+      best_score = cur_score
+      best_thresh = na_probs[qid]
+  return 100.0 * best_score / len(scores), best_thresh
+
+
+def _find_all_best_thresh(
+    main_eval, predictions, exact_raw, f1_raw, na_probs, qid_to_has_ans):
+  best_exact, exact_thresh = _find_best_thresh(
+      predictions, exact_raw, na_probs, qid_to_has_ans)
+  best_f1, f1_thresh = _find_best_thresh(
+      predictions, f1_raw, na_probs, qid_to_has_ans)
+  main_eval['final_exact'] = best_exact
+  main_eval['final_exact_thresh'] = exact_thresh
+  main_eval['final_f1'] = best_f1
+  main_eval['final_f1_thresh'] = f1_thresh
+
+
+def evaluate(dataset, predictions, na_probs=None):
+  """Evaluate prediction results."""
+  new_orig_data = []
+  for article in dataset:
+    for p in article['paragraphs']:
+      for qa in p['qas']:
+        if qa['id'] in predictions:
+          new_para = {'qas': [qa]}
+          new_article = {'paragraphs': [new_para]}
+          new_orig_data.append(new_article)
+  dataset = new_orig_data
+
+  if na_probs is None:
+    na_probs = {k: 0.0 for k in predictions}
+  qid_to_has_ans = _make_qid_to_has_ans(dataset)  # maps qid to True/False
+  has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
+  no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
+  exact_raw, f1_raw = _get_raw_scores(dataset, predictions)
+  exact_thresh = _apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans)
+  f1_thresh = _apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans)
+  out_eval = _make_eval_dict(exact_thresh, f1_thresh)
+  if has_ans_qids:
+    has_ans_eval = _make_eval_dict(
+        exact_thresh, f1_thresh, qid_list=has_ans_qids)
+    _merge_eval(out_eval, has_ans_eval, 'HasAns')
+  if no_ans_qids:
+    no_ans_eval = _make_eval_dict(exact_thresh, f1_thresh, qid_list=no_ans_qids)
+    _merge_eval(out_eval, no_ans_eval, 'NoAns')
+
+  _find_all_best_thresh(
+      out_eval, predictions, exact_raw, f1_raw, na_probs, qid_to_has_ans)
+  _run_precision_recall_analysis(
+      out_eval, exact_raw, f1_raw, na_probs, qid_to_has_ans)
+  return out_eval