Merge pull request #174 from abeljim/master

Added Squad 2.0

examples/run_squad2.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  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.
+"""Run BERT on SQuAD 2.0"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import collections
+import logging
+import json
+import math
+import os
+import random
+import pickle
+from tqdm import tqdm, trange
+
+import numpy as np
+import torch
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data.distributed import DistributedSampler
+
+from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer
+from pytorch_pretrained_bert.modeling import BertForQuestionAnswering
+from pytorch_pretrained_bert.optimization import BertAdam
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
+
+logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
+                    datefmt = '%m/%d/%Y %H:%M:%S',
+                    level = logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+class SquadExample(object):
+    """
+    A single training/test example for the Squad dataset.
+    For examples without an answer, the start and end position are -1.
+    """
+
+    def __init__(self,
+                 qas_id,
+                 question_text,
+                 doc_tokens,
+                 orig_answer_text=None,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=None):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.doc_tokens = doc_tokens
+        self.orig_answer_text = orig_answer_text
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        s = ""
+        s += "qas_id: %s" % (self.qas_id)
+        s += ", question_text: %s" % (
+            self.question_text)
+        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+        if self.start_position:
+            s += ", start_position: %d" % (self.start_position)
+        if self.start_position:
+            s += ", end_position: %d" % (self.end_position)
+        if self.start_position:
+            s += ", is_impossible: %r" % (self.is_impossible)
+        return s
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+
+    def __init__(self,
+                 unique_id,
+                 example_index,
+                 doc_span_index,
+                 tokens,
+                 token_to_orig_map,
+                 token_is_max_context,
+                 input_ids,
+                 input_mask,
+                 segment_ids,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=None):
+        self.unique_id = unique_id
+        self.example_index = example_index
+        self.doc_span_index = doc_span_index
+        self.tokens = tokens
+        self.token_to_orig_map = token_to_orig_map
+        self.token_is_max_context = token_is_max_context
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+
+def read_squad_examples(input_file, is_training):
+    """Read a SQuAD json file into a list of SquadExample."""
+    with open(input_file, "r", encoding='utf-8') as reader:
+        source = json.load(reader)
+        input_data = source["data"]
+        version = source["version"]
+
+    def is_whitespace(c):
+        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+            return True
+        return False
+
+    examples = []
+    for entry in input_data:
+        for paragraph in entry["paragraphs"]:
+            paragraph_text = paragraph["context"]
+            doc_tokens = []
+            char_to_word_offset = []
+            prev_is_whitespace = True
+            for c in paragraph_text:
+                if is_whitespace(c):
+                    prev_is_whitespace = True
+                else:
+                    if prev_is_whitespace:
+                        doc_tokens.append(c)
+                    else:
+                        doc_tokens[-1] += c
+                    prev_is_whitespace = False
+                char_to_word_offset.append(len(doc_tokens) - 1)
+
+            for qa in paragraph["qas"]:
+                qas_id = qa["id"]
+                question_text = qa["question"]
+                start_position = None
+                end_position = None
+                orig_answer_text = None
+                is_impossible = False
+                if is_training:
+                    if version == "v2.0":
+                        is_impossible = qa["is_impossible"]
+                    if (len(qa["answers"]) != 1) and (not is_impossible):
+                        raise ValueError(
+                            "For training, each question should have exactly 1 answer.")
+                    if not is_impossible:
+                        answer = qa["answers"][0]
+                        orig_answer_text = answer["text"]
+                        answer_offset = answer["answer_start"]
+                        answer_length = len(orig_answer_text)
+                        start_position = char_to_word_offset[answer_offset]
+                        end_position = char_to_word_offset[answer_offset + answer_length - 1]
+                        # Only add answers where the text can be exactly recovered from the
+                        # document. If this CAN'T happen it's likely due to weird Unicode
+                        # stuff so we will just skip the example.
+                        #
+                        # Note that this means for training mode, every example is NOT
+                        # guaranteed to be preserved.
+                        actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
+                        cleaned_answer_text = " ".join(
+                            whitespace_tokenize(orig_answer_text))
+                        if actual_text.find(cleaned_answer_text) == -1:
+                            logger.warning("Could not find answer: '%s' vs. '%s'",
+                                            actual_text, cleaned_answer_text)
+                            continue
+                    else: 
+                        start_position = -1
+                        end_position = -1
+                        orig_answer_text = ""
+
+                example = SquadExample(
+                    qas_id=qas_id,
+                    question_text=question_text,
+                    doc_tokens=doc_tokens,
+                    orig_answer_text=orig_answer_text,
+                    start_position=start_position,
+                    end_position=end_position,
+                    is_impossible=is_impossible)
+                examples.append(example)
+    return examples
+
+
+def convert_examples_to_features(examples, tokenizer, max_seq_length,
+                                 doc_stride, max_query_length, is_training):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    unique_id = 1000000000
+
+    features = []
+    for (example_index, example) in enumerate(examples):
+        query_tokens = tokenizer.tokenize(example.question_text)
+
+        if len(query_tokens) > max_query_length:
+            query_tokens = query_tokens[0:max_query_length]
+
+        tok_to_orig_index = []
+        orig_to_tok_index = []
+        all_doc_tokens = []
+        for (i, token) in enumerate(example.doc_tokens):
+            orig_to_tok_index.append(len(all_doc_tokens))
+            sub_tokens = tokenizer.tokenize(token)
+            for sub_token in sub_tokens:
+                tok_to_orig_index.append(i)
+                all_doc_tokens.append(sub_token)
+
+        tok_start_position = None
+        tok_end_position = None
+        if is_training and example.is_impossible:
+            tok_start_position = -1
+            tok_end_position = -1
+        if is_training and not example.is_impossible:
+            tok_start_position = orig_to_tok_index[example.start_position]
+            if example.end_position < len(example.doc_tokens) - 1:
+                tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
+            else:
+                tok_end_position = len(all_doc_tokens) - 1
+            (tok_start_position, tok_end_position) = _improve_answer_span(
+                all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
+                example.orig_answer_text)
+
+        # The -3 accounts for [CLS], [SEP] and [SEP]
+        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+        # We can have documents that are longer than the maximum sequence length.
+        # To deal with this we do a sliding window approach, where we take chunks
+        # of the up to our max length with a stride of `doc_stride`.
+        _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
+            "DocSpan", ["start", "length"])
+        doc_spans = []
+        start_offset = 0
+        while start_offset < len(all_doc_tokens):
+            length = len(all_doc_tokens) - start_offset
+            if length > max_tokens_for_doc:
+                length = max_tokens_for_doc
+            doc_spans.append(_DocSpan(start=start_offset, length=length))
+            if start_offset + length == len(all_doc_tokens):
+                break
+            start_offset += min(length, doc_stride)
+
+        for (doc_span_index, doc_span) in enumerate(doc_spans):
+            tokens = []
+            token_to_orig_map = {}
+            token_is_max_context = {}
+            segment_ids = []
+            tokens.append("[CLS]")
+            segment_ids.append(0)
+            for token in query_tokens:
+                tokens.append(token)
+                segment_ids.append(0)
+            tokens.append("[SEP]")
+            segment_ids.append(0)
+
+            for i in range(doc_span.length):
+                split_token_index = doc_span.start + i
+                token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
+
+                is_max_context = _check_is_max_context(doc_spans, doc_span_index,
+                                                       split_token_index)
+                token_is_max_context[len(tokens)] = is_max_context
+                tokens.append(all_doc_tokens[split_token_index])
+                segment_ids.append(1)
+            tokens.append("[SEP]")
+            segment_ids.append(1)
+
+            input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+            # The mask has 1 for real tokens and 0 for padding tokens. Only real
+            # tokens are attended to.
+            input_mask = [1] * len(input_ids)
+
+            # Zero-pad up to the sequence length.
+            while len(input_ids) < max_seq_length:
+                input_ids.append(0)
+                input_mask.append(0)
+                segment_ids.append(0)
+
+            assert len(input_ids) == max_seq_length
+            assert len(input_mask) == max_seq_length
+            assert len(segment_ids) == max_seq_length
+
+            start_position = None
+            end_position = None
+            if is_training and not example.is_impossible:
+                # For training, if our document chunk does not contain an annotation
+                # we throw it out, since there is nothing to predict.
+                doc_start = doc_span.start
+                doc_end = doc_span.start + doc_span.length - 1
+                out_of_span = False 
+                if (example.start_position < doc_start or
+                        example.end_position < doc_start or
+                        example.start_position > doc_end or example.end_position > doc_end):
+                    out_of_span = True
+                if out_of_span:
+                    start_position = 0
+                    end_position = 0
+                else:
+                    doc_offset = len(query_tokens) + 2
+                    start_position = tok_start_position - doc_start + doc_offset
+                    end_position = tok_end_position - doc_start + doc_offset
+
+            if is_training and example.is_impossible:
+                start_position = 0
+                end_position = 0
+
+            if example_index < 20:
+                logger.info("*** Example ***")
+                logger.info("unique_id: %s" % (unique_id))
+                logger.info("example_index: %s" % (example_index))
+                logger.info("doc_span_index: %s" % (doc_span_index))
+                logger.info("tokens: %s" % " ".join(tokens))
+                logger.info("token_to_orig_map: %s" % " ".join([
+                    "%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
+                logger.info("token_is_max_context: %s" % " ".join([
+                    "%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
+                ]))
+                logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+                logger.info(
+                    "input_mask: %s" % " ".join([str(x) for x in input_mask]))
+                logger.info(
+                    "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+                if is_training and example.is_impossible:
+                    logger.info("impossible example")   
+                if is_training and not example.is_impossible:
+                    answer_text = " ".join(tokens[start_position:(end_position + 1)])
+                    logger.info("start_position: %d" % (start_position))
+                    logger.info("end_position: %d" % (end_position))
+                    logger.info(
+                        "answer: %s" % (answer_text))
+
+            features.append(
+                InputFeatures(
+                    unique_id=unique_id,
+                    example_index=example_index,
+                    doc_span_index=doc_span_index,
+                    tokens=tokens,
+                    token_to_orig_map=token_to_orig_map,
+                    token_is_max_context=token_is_max_context,
+                    input_ids=input_ids,
+                    input_mask=input_mask,
+                    segment_ids=segment_ids,
+                    start_position=start_position,
+                    end_position=end_position,
+                    is_impossible=example.is_impossible))
+            unique_id += 1
+
+    return features
+
+
+def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
+                         orig_answer_text):
+    """Returns tokenized answer spans that better match the annotated answer."""
+
+    # The SQuAD annotations are character based. We first project them to
+    # whitespace-tokenized words. But then after WordPiece tokenization, we can
+    # often find a "better match". For example:
+    #
+    #   Question: What year was John Smith born?
+    #   Context: The leader was John Smith (1895-1943).
+    #   Answer: 1895
+    #
+    # The original whitespace-tokenized answer will be "(1895-1943).". However
+    # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
+    # the exact answer, 1895.
+    #
+    # However, this is not always possible. Consider the following:
+    #
+    #   Question: What country is the top exporter of electornics?
+    #   Context: The Japanese electronics industry is the lagest in the world.
+    #   Answer: Japan
+    #
+    # In this case, the annotator chose "Japan" as a character sub-span of
+    # the word "Japanese". Since our WordPiece tokenizer does not split
+    # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
+    # in SQuAD, but does happen.
+    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
+
+    for new_start in range(input_start, input_end + 1):
+        for new_end in range(input_end, new_start - 1, -1):
+            text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
+            if text_span == tok_answer_text:
+                return (new_start, new_end)
+
+    return (input_start, input_end)
+
+
+def _check_is_max_context(doc_spans, cur_span_index, position):
+    """Check if this is the 'max context' doc span for the token."""
+
+    # Because of the sliding window approach taken to scoring documents, a single
+    # token can appear in multiple documents. E.g.
+    #  Doc: the man went to the store and bought a gallon of milk
+    #  Span A: the man went to the
+    #  Span B: to the store and bought
+    #  Span C: and bought a gallon of
+    #  ...
+    #
+    # Now the word 'bought' will have two scores from spans B and C. We only
+    # want to consider the score with "maximum context", which we define as
+    # the *minimum* of its left and right context (the *sum* of left and
+    # right context will always be the same, of course).
+    #
+    # In the example the maximum context for 'bought' would be span C since
+    # it has 1 left context and 3 right context, while span B has 4 left context
+    # and 0 right context.
+    best_score = None
+    best_span_index = None
+    for (span_index, doc_span) in enumerate(doc_spans):
+        end = doc_span.start + doc_span.length - 1
+        if position < doc_span.start:
+            continue
+        if position > end:
+            continue
+        num_left_context = position - doc_span.start
+        num_right_context = end - position
+        score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
+        if best_score is None or score > best_score:
+            best_score = score
+            best_span_index = span_index
+
+    return cur_span_index == best_span_index
+
+
+
+RawResult = collections.namedtuple("RawResult",
+                                   ["unique_id", "start_logits", "end_logits"])
+
+
+def write_predictions(all_examples, all_features, all_results, n_best_size,
+                      max_answer_length, do_lower_case, output_prediction_file,
+                      output_nbest_file, output_null_log_odds_file, verbose_logging, is_version2, null_score_diff_threshold):
+    """Write final predictions to the json file and log-odds of null if needed."""
+    logger.info("Writing predictions to: %s" % (output_prediction_file))
+    logger.info("Writing nbest to: %s" % (output_nbest_file))
+
+    example_index_to_features = collections.defaultdict(list)
+    for feature in all_features:
+        example_index_to_features[feature.example_index].append(feature)
+
+    unique_id_to_result = {}
+    for result in all_results:
+        unique_id_to_result[result.unique_id] = result
+
+    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+        "PrelimPrediction",
+        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    scores_diff_json = collections.OrderedDict()
+
+    for (example_index, example) in enumerate(all_examples):
+        features = example_index_to_features[example_index]
+
+        prelim_predictions = []
+        # keep track of the minimum score of null start+end of position 0
+        score_null = 1000000  # large and positive
+        min_null_feature_index = 0  # the paragraph slice with min mull score
+        null_start_logit = 0  # the start logit at the slice with min null score
+        null_end_logit = 0  # the end logit at the slice with min null score
+
+        for (feature_index, feature) in enumerate(features):
+            result = unique_id_to_result[feature.unique_id]
+            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+            # if we could have irrelevant answers, get the min score of irrelevant
+            if is_version2:
+                feature_null_score = result.start_logits[0] + result.end_logits[0]
+                if feature_null_score < score_null:
+                    score_null = feature_null_score
+                    min_null_feature_index = feature_index
+                    null_start_logit = result.start_logits[0]
+                    null_end_logit = result.end_logits[0]
+
+            for start_index in start_indexes:
+                for end_index in end_indexes:
+                    # We could hypothetically create invalid predictions, e.g., predict
+                    # that the start of the span is in the question. We throw out all
+                    # invalid predictions.
+                    if start_index >= len(feature.tokens):
+                        continue
+                    if end_index >= len(feature.tokens):
+                        continue
+                    if start_index not in feature.token_to_orig_map:
+                        continue
+                    if end_index not in feature.token_to_orig_map:
+                        continue
+                    if not feature.token_is_max_context.get(start_index, False):
+                        continue
+                    if end_index < start_index:
+                        continue
+                    length = end_index - start_index + 1
+                    if length > max_answer_length:
+                        continue
+                    prelim_predictions.append(
+                        _PrelimPrediction(
+                            feature_index=feature_index,
+                            start_index=start_index,
+                            end_index=end_index,
+                            start_logit=result.start_logits[start_index],
+                            end_logit=result.end_logits[end_index]))
+
+        if is_version2:
+            prelim_predictions.append(
+                _PrelimPrediction(
+                    feature_index=min_null_feature_index,
+                    start_index=0,
+                    end_index=0,
+                    start_logit=null_start_logit,
+                    end_logit=null_end_logit))
+
+        prelim_predictions = sorted(
+            prelim_predictions,
+            key=lambda x: (x.start_logit + x.end_logit),
+            reverse=True)
+
+        _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+            "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+        seen_predictions = {}
+        nbest = []
+        for pred in prelim_predictions:
+            if len(nbest) >= n_best_size:
+                break
+            feature = features[pred.feature_index]
+            if pred.start_index > 0:
+                tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
+                orig_doc_start = feature.token_to_orig_map[pred.start_index]
+                orig_doc_end = feature.token_to_orig_map[pred.end_index]
+                orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
+                tok_text = " ".join(tok_tokens)
+
+                # De-tokenize WordPieces that have been split off.
+                tok_text = tok_text.replace(" ##", "")
+                tok_text = tok_text.replace("##", "")
+
+                # Clean whitespace
+                tok_text = tok_text.strip()
+                tok_text = " ".join(tok_text.split())
+                orig_text = " ".join(orig_tokens)
+
+                final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
+                if final_text in seen_predictions:
+                    continue
+                seen_predictions[final_text] = True
+            else:
+                final_text = ""
+                seen_predictions[final_text] = True
+
+            nbest.append(
+                _NbestPrediction(
+                    text=final_text,
+                    start_logit=pred.start_logit,
+                    end_logit=pred.end_logit))
+
+        # if we didn't inlude the empty option in the n-best, inlcude it
+        if is_version2:
+            if "" not in seen_predictions:
+                nbest.append(
+                    _NbestPrediction(
+                        text="", start_logit=null_start_logit,
+                        end_logit=null_end_logit))
+
+        # In very rare edge cases we could have no valid predictions. So we
+        # just create a nonce prediction in this case to avoid failure.
+        if not nbest:
+            nbest.append(
+                _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+
+        assert len(nbest) >= 1
+
+        total_scores = []
+        best_non_null_entry = None
+        for entry in nbest:
+            total_scores.append(entry.start_logit + entry.end_logit)
+            if not best_non_null_entry:
+                if entry.text:
+                    best_non_null_entry = entry
+
+        probs = _compute_softmax(total_scores)
+
+        nbest_json = []
+        for (i, entry) in enumerate(nbest):
+            output = collections.OrderedDict()
+            output["text"] = entry.text
+            output["probability"] = probs[i]
+            output["start_logit"] = entry.start_logit
+            output["end_logit"] = entry.end_logit
+            nbest_json.append(output)
+
+        assert len(nbest_json) >= 1
+
+        
+        if not is_version2:
+            all_predictions[example.qas_id] = nbest_json[0]["text"]
+        else:
+            # predict "" iff the null score - the score of best non-null > threshold
+            score_diff = score_null - best_non_null_entry.start_logit - (
+                best_non_null_entry.end_logit)
+            scores_diff_json[example.qas_id] = score_diff
+            if score_diff > null_score_diff_threshold:
+                all_predictions[example.qas_id] = ""
+            else:
+                all_predictions[example.qas_id] = best_non_null_entry.text
+        all_nbest_json[example.qas_id] = nbest_json
+
+    with open(output_prediction_file, "w") as writer:
+        writer.write(json.dumps(all_predictions, indent=4) + "\n")
+
+    with open(output_nbest_file, "w") as writer:
+        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+    
+    if is_version2:
+        with open(output_null_log_odds_file, "w") as writer:
+            writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
+
+
+def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
+    """Project the tokenized prediction back to the original text."""
+
+    # When we created the data, we kept track of the alignment between original
+    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
+    # now `orig_text` contains the span of our original text corresponding to the
+    # span that we predicted.
+    #
+    # However, `orig_text` may contain extra characters that we don't want in
+    # our prediction.
+    #
+    # For example, let's say:
+    #   pred_text = steve smith
+    #   orig_text = Steve Smith's
+    #
+    # We don't want to return `orig_text` because it contains the extra "'s".
+    #
+    # We don't want to return `pred_text` because it's already been normalized
+    # (the SQuAD eval script also does punctuation stripping/lower casing but
+    # our tokenizer does additional normalization like stripping accent
+    # characters).
+    #
+    # What we really want to return is "Steve Smith".
+    #
+    # Therefore, we have to apply a semi-complicated alignment heruistic between
+    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
+    # can fail in certain cases in which case we just return `orig_text`.
+
+    def _strip_spaces(text):
+        ns_chars = []
+        ns_to_s_map = collections.OrderedDict()
+        for (i, c) in enumerate(text):
+            if c == " ":
+                continue
+            ns_to_s_map[len(ns_chars)] = i
+            ns_chars.append(c)
+        ns_text = "".join(ns_chars)
+        return (ns_text, ns_to_s_map)
+
+    # We first tokenize `orig_text`, strip whitespace from the result
+    # and `pred_text`, and check if they are the same length. If they are
+    # NOT the same length, the heuristic has failed. If they are the same
+    # length, we assume the characters are one-to-one aligned.
+    tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
+
+    tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+    start_position = tok_text.find(pred_text)
+    if start_position == -1:
+        if verbose_logging:
+            logger.info(
+                "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
+        return orig_text
+    end_position = start_position + len(pred_text) - 1
+
+    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+    if len(orig_ns_text) != len(tok_ns_text):
+        if verbose_logging:
+            logger.info("Length not equal after stripping spaces: '%s' vs '%s'",
+                            orig_ns_text, tok_ns_text)
+        return orig_text
+
+    # We then project the characters in `pred_text` back to `orig_text` using
+    # the character-to-character alignment.
+    tok_s_to_ns_map = {}
+    for (i, tok_index) in tok_ns_to_s_map.items():
+        tok_s_to_ns_map[tok_index] = i
+
+    orig_start_position = None
+    if start_position in tok_s_to_ns_map:
+        ns_start_position = tok_s_to_ns_map[start_position]
+        if ns_start_position in orig_ns_to_s_map:
+            orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+    if orig_start_position is None:
+        if verbose_logging:
+            logger.info("Couldn't map start position")
+        return orig_text
+
+    orig_end_position = None
+    if end_position in tok_s_to_ns_map:
+        ns_end_position = tok_s_to_ns_map[end_position]
+        if ns_end_position in orig_ns_to_s_map:
+            orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+    if orig_end_position is None:
+        if verbose_logging:
+            logger.info("Couldn't map end position")
+        return orig_text
+
+    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+    return output_text
+
+
+def _get_best_indexes(logits, n_best_size):
+    """Get the n-best logits from a list."""
+    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
+
+    best_indexes = []
+    for i in range(len(index_and_score)):
+        if i >= n_best_size:
+            break
+        best_indexes.append(index_and_score[i][0])
+    return best_indexes
+
+
+def _compute_softmax(scores):
+    """Compute softmax probability over raw logits."""
+    if not scores:
+        return []
+
+    max_score = None
+    for score in scores:
+        if max_score is None or score > max_score:
+            max_score = score
+
+    exp_scores = []
+    total_sum = 0.0
+    for score in scores:
+        x = math.exp(score - max_score)
+        exp_scores.append(x)
+        total_sum += x
+
+    probs = []
+    for score in exp_scores:
+        probs.append(score / total_sum)
+    return probs
+
+def warmup_linear(x, warmup=0.002):
+    if x < warmup:
+        return x/warmup
+    return 1.0 - x
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    ## Required parameters
+    parser.add_argument("--bert_model", default=None, type=str, required=True,
+                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
+                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
+    parser.add_argument("--output_dir", default=None, type=str, required=True,
+                        help="The output directory where the model checkpoints and predictions will be written.")
+
+    ## Other parameters
+    parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
+    parser.add_argument("--predict_file", default=None, type=str,
+                        help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
+    parser.add_argument("--max_seq_length", default=384, type=int,
+                        help="The maximum total input sequence length after WordPiece tokenization. Sequences "
+                             "longer than this will be truncated, and sequences shorter than this will be padded.")
+    parser.add_argument("--doc_stride", default=128, type=int,
+                        help="When splitting up a long document into chunks, how much stride to take between chunks.")
+    parser.add_argument("--max_query_length", default=64, type=int,
+                        help="The maximum number of tokens for the question. Questions longer than this will "
+                             "be truncated to this length.")
+    parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.")
+    parser.add_argument("--do_predict", default=False, action='store_true', help="Whether to run eval on the dev set.")
+    parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
+    parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.")
+    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
+    parser.add_argument("--num_train_epochs", default=3.0, type=float,
+                        help="Total number of training epochs to perform.")
+    parser.add_argument("--warmup_proportion", default=0.1, type=float,
+                        help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% "
+                             "of training.")
+    parser.add_argument("--n_best_size", default=20, type=int,
+                        help="The total number of n-best predictions to generate in the nbest_predictions.json "
+                             "output file.")
+    parser.add_argument("--max_answer_length", default=30, type=int,
+                        help="The maximum length of an answer that can be generated. This is needed because the start "
+                             "and end predictions are not conditioned on one another.")
+    parser.add_argument("--verbose_logging", default=False, action='store_true',
+                        help="If true, all of the warnings related to data processing will be printed. "
+                             "A number of warnings are expected for a normal SQuAD evaluation.")
+    parser.add_argument("--no_cuda",
+                        default=False,
+                        action='store_true',
+                        help="Whether not to use CUDA when available")
+    parser.add_argument('--seed',
+                        type=int,
+                        default=42,
+                        help="random seed for initialization")
+    parser.add_argument('--gradient_accumulation_steps',
+                        type=int,
+                        default=1,
+                        help="Number of updates steps to accumulate before performing a backward/update pass.")
+    parser.add_argument("--do_lower_case",
+                        default=True,
+                        action='store_true',
+                        help="Whether to lower case the input text. True for uncased models, False for cased models.")
+    parser.add_argument("--local_rank",
+                        type=int,
+                        default=-1,
+                        help="local_rank for distributed training on gpus")
+    parser.add_argument('--fp16',
+                        default=False,
+                        action='store_true',
+                        help="Whether to use 16-bit float precision instead of 32-bit")
+    parser.add_argument('--loss_scale',
+                        type=float, default=0,
+                        help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
+                             "0 (default value): dynamic loss scaling.\n"
+                             "Positive power of 2: static loss scaling value.\n")
+    parser.add_argument('--null_score_diff_threshold',
+                        type=float, default=0.0,
+                        help="If null_score - best_non_null is greater than the threshold predict null.")
+
+    args = parser.parse_args()
+
+    if args.local_rank == -1 or args.no_cuda:
+        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+        n_gpu = torch.cuda.device_count()
+    else:
+        torch.cuda.set_device(args.local_rank)
+        device = torch.device("cuda", args.local_rank)
+        n_gpu = 1
+        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
+        torch.distributed.init_process_group(backend='nccl')
+    logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
+        device, n_gpu, bool(args.local_rank != -1), args.fp16))
+
+    if args.gradient_accumulation_steps < 1:
+        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
+                            args.gradient_accumulation_steps))
+
+    args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+    if not args.do_train and not args.do_predict:
+        raise ValueError("At least one of `do_train` or `do_predict` must be True.")
+
+    if args.do_train:
+        if not args.train_file:
+            raise ValueError(
+                "If `do_train` is True, then `train_file` must be specified.")
+    if args.do_predict:
+        if not args.predict_file:
+            raise ValueError(
+                "If `do_predict` is True, then `predict_file` must be specified.")
+
+    if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
+        raise ValueError("Output directory () already exists and is not empty.")
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    tokenizer = BertTokenizer.from_pretrained(args.bert_model)
+
+    train_examples = None
+    num_train_steps = None
+    if args.do_train:
+        train_examples = read_squad_examples(
+            input_file=args.train_file, is_training=True)
+        num_train_steps = int(
+            len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
+
+    # Prepare model
+    model = BertForQuestionAnswering.from_pretrained(args.bert_model,
+                cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank))
+
+    if args.fp16:
+        model.half()
+    model.to(device)
+    if args.local_rank != -1:
+        try:
+            from apex.parallel import DistributedDataParallel as DDP
+        except ImportError:
+            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
+
+        model = DDP(model)
+    elif n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+
+    # Prepare optimizer
+    param_optimizer = list(model.named_parameters())
+
+    # hack to remove pooler, which is not used
+    # thus it produce None grad that break apex
+    param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
+
+    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
+    optimizer_grouped_parameters = [
+        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
+        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
+        ]
+
+    t_total = num_train_steps
+    if args.local_rank != -1:
+        t_total = t_total // torch.distributed.get_world_size()
+    if args.fp16:
+        try:
+            from apex.optimizers import FP16_Optimizer
+            from apex.optimizers import FusedAdam
+        except ImportError:
+            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
+
+        optimizer = FusedAdam(optimizer_grouped_parameters,
+                              lr=args.learning_rate,
+                              bias_correction=False,
+                              max_grad_norm=1.0)
+        if args.loss_scale == 0:
+            optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
+        else:
+            optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
+    else:
+        optimizer = BertAdam(optimizer_grouped_parameters,
+                             lr=args.learning_rate,
+                             warmup=args.warmup_proportion,
+                             t_total=t_total)
+
+    global_step = 0
+    if args.do_train:
+        cached_train_features_file = args.train_file+'_{0}_{1}_{2}_{3}'.format(
+            args.bert_model, str(args.max_seq_length), str(args.doc_stride), str(args.max_query_length))
+        train_features = None
+        try:
+            with open(cached_train_features_file, "rb") as reader:
+                train_features = pickle.load(reader)
+        except:
+            train_features = convert_examples_to_features(
+                examples=train_examples,
+                tokenizer=tokenizer,
+                max_seq_length=args.max_seq_length,
+                doc_stride=args.doc_stride,
+                max_query_length=args.max_query_length,
+                is_training=True)
+            if args.local_rank == -1 or torch.distributed.get_rank() == 0:
+                logger.info("  Saving train features into cached file %s", cached_train_features_file)
+                with open(cached_train_features_file, "wb") as writer:
+                    pickle.dump(train_features, writer)
+        logger.info("***** Running training *****")
+        logger.info("  Num orig examples = %d", len(train_examples))
+        logger.info("  Num split examples = %d", len(train_features))
+        logger.info("  Batch size = %d", args.train_batch_size)
+        logger.info("  Num steps = %d", num_train_steps)
+        all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
+        all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)
+        all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)
+        all_is_impossibles = torch.tensor([int(f.is_impossible) for f in train_features], dtype=torch.long)
+        train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
+                                   all_start_positions, all_end_positions, all_is_impossibles)
+        if args.local_rank == -1:
+            train_sampler = RandomSampler(train_data)
+        else:
+            train_sampler = DistributedSampler(train_data)
+        train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
+
+        model.train()
+        for _ in trange(int(args.num_train_epochs), desc="Epoch"):
+            for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
+                if n_gpu == 1:
+                    batch = tuple(t.to(device) for t in batch) # multi-gpu does scattering it-self
+                input_ids, input_mask, segment_ids, start_positions, end_positions, _ = batch
+                loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions)
+                if n_gpu > 1:
+                    loss = loss.mean() # mean() to average on multi-gpu.
+                if args.gradient_accumulation_steps > 1:
+                    loss = loss / args.gradient_accumulation_steps
+
+                if args.fp16:
+                    optimizer.backward(loss)
+                else:
+                    loss.backward()
+                if (step + 1) % args.gradient_accumulation_steps == 0:
+                    # modify learning rate with special warm up BERT uses
+                    lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion)
+                    for param_group in optimizer.param_groups:
+                        param_group['lr'] = lr_this_step
+                    optimizer.step()
+                    optimizer.zero_grad()
+                    global_step += 1
+
+    # Save a trained model
+    model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+    output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
+    torch.save(model_to_save.state_dict(), output_model_file)
+
+    # Load a trained model that you have fine-tuned
+    model_state_dict = torch.load(output_model_file)
+    model = BertForQuestionAnswering.from_pretrained(args.bert_model, state_dict=model_state_dict)
+    model.to(device)
+
+    if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        eval_examples = read_squad_examples(
+            input_file=args.predict_file, is_training=False)
+        eval_features = convert_examples_to_features(
+            examples=eval_examples,
+            tokenizer=tokenizer,
+            max_seq_length=args.max_seq_length,
+            doc_stride=args.doc_stride,
+            max_query_length=args.max_query_length,
+            is_training=False)
+
+        logger.info("***** Running predictions *****")
+        logger.info("  Num orig examples = %d", len(eval_examples))
+        logger.info("  Num split examples = %d", len(eval_features))
+        logger.info("  Batch size = %d", args.predict_batch_size)
+
+        all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
+        all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
+        all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
+        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
+        eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
+        # Run prediction for full data
+        eval_sampler = SequentialSampler(eval_data)
+        eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size)
+
+        model.eval()
+        all_results = []
+        logger.info("Start evaluating")
+        for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating"):
+            if len(all_results) % 1000 == 0:
+                logger.info("Processing example: %d" % (len(all_results)))
+            input_ids = input_ids.to(device)
+            input_mask = input_mask.to(device)
+            segment_ids = segment_ids.to(device)
+            with torch.no_grad():
+                batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask)
+            for i, example_index in enumerate(example_indices):
+                start_logits = batch_start_logits[i].detach().cpu().tolist()
+                end_logits = batch_end_logits[i].detach().cpu().tolist()
+                eval_feature = eval_features[example_index.item()]
+                unique_id = int(eval_feature.unique_id)
+                all_results.append(RawResult(unique_id=unique_id,
+                                             start_logits=start_logits,
+                                             end_logits=end_logits))
+        output_prediction_file = os.path.join(args.output_dir, "predictions.json")
+        output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
+        output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json")
+        write_predictions(eval_examples, eval_features, all_results,
+                          args.n_best_size, args.max_answer_length,
+                          args.do_lower_case, output_prediction_file,
+                          output_nbest_file, output_null_log_odds_file, args.verbose_logging, True, args.null_score_diff_threshold)
+
+
+if __name__ == "__main__":
+    main()