feat:add dockerfile

Issue-ID: USECASEUI-525
Signed-off-by: wangy122 <wangy122@chinatelecom.cn>
Change-Id: Ifca8abdfff479216bb0ea6b84d2c61fb640039f5

1 files changed, 746 insertions, 0 deletions

diff --git a/scripts/create_squad_features.py b/scripts/create_squad_features.py
new file mode 100644
index 0000000..e779b9e
--- /dev/null
+++ b/scripts/create_squad_features.py
@@ -0,0 +1,746 @@
+
+    #!/usr/bin/env python
+    # coding: utf-8
+
+    # auther = 'liuzhiyong'
+    # date = 20201204
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import json
+import datetime
+import threading
+import time
+from flask import Flask, abort, request, jsonify
+from concurrent.futures import ThreadPoolExecutor
+
+import collections
+import math
+import os
+import random
+import modeling
+import optimization
+import tokenization
+import six
+import tensorflow as tf
+import sys
+import requests
+
+from global_setting import *
+
+version_2_with_negative = True
+
+def get_squad_feature_result(title,text,tokenizer,question, url):
+
+    def make_json(title, text, question):
+        res = {}
+        res['data'] = []
+        data_inside = {}
+
+        data_inside['title'] = title
+        data_inside['paragraphs'] = []
+        paragraphs_inside = {}
+        paragraphs_inside['context'] = text
+        paragraphs_inside['qas'] = []
+        for ques in question:
+            qas_inside = {}
+            qas_inside['answers'] = []
+
+            answer_inside = {}
+
+            qas_inside['id'] = ques
+            qas_inside['question'] = ques
+            qas_inside['answers'].append(answer_inside.copy())
+            paragraphs_inside['qas'].append(qas_inside.copy())
+        data_inside['paragraphs'].append(paragraphs_inside.copy())
+
+        res['data'].append(data_inside.copy())
+        return json.dumps(res)
+
+
+    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 get_final_text(pred_text, orig_text, do_lower_case):
+  
+        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 = tokenization.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 0:
+                tf.logging.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 0:
+                tf.logging.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 six.iteritems(tok_ns_to_s_map):
+            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 0:
+                tf.logging.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 0:
+                tf.logging.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):
+      
+        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
+
+    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):
+        """Write final predictions to the json file and log-odds of null if needed."""
+
+        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 version_2_with_negative:
+                    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 version_2_with_negative:
+                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:  # this is a non-null prediction
+                    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)
+                    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 version_2_with_negative:
+                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="", 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 version_2_with_negative:
+                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 > 0:
+                    all_predictions[example.qas_id] = ""
+                else:
+                    all_predictions[example.qas_id] = best_non_null_entry.text
+
+            all_nbest_json[example.qas_id] = nbest_json
+        return all_predictions
+
+
+    def create_int_feature(values):
+
+        feature = tf.train.Feature(
+            int64_list=tf.train.Int64List(value=list(values)))
+        return feature
+
+
+    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 _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
+
+    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
+        result = []
+
+        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 not (tok_start_position >= doc_start and
+                            tok_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:
+                    tf.logging.info("*** Example ***")
+                    tf.logging.info("unique_id: %s" % (unique_id))
+                    tf.logging.info("example_index: %s" % (example_index))
+                    tf.logging.info("doc_span_index: %s" % (doc_span_index))
+                    tf.logging.info("tokens: %s" % " ".join(
+                        [tokenization.printable_text(x) for x in tokens]))
+                    tf.logging.info("token_to_orig_map: %s" % " ".join(
+                        ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
+                    tf.logging.info("token_is_max_context: %s" % " ".join([
+                        "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
+                    ]))
+                    tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+                    tf.logging.info(
+                        "input_mask: %s" % " ".join([str(x) for x in input_mask]))
+                    tf.logging.info(
+                        "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+                    if is_training and example.is_impossible:
+                        tf.logging.info("impossible example")
+                    if is_training and not example.is_impossible:
+                        answer_text = " ".join(tokens[start_position:(end_position + 1)])
+                        tf.logging.info("start_position: %d" % (start_position))
+                        tf.logging.info("end_position: %d" % (end_position))
+                        tf.logging.info(
+                            "answer: %s" % (tokenization.printable_text(answer_text)))
+
+                feature = 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)
+
+                # Run callback
+                
+                result.append(feature)
+                unique_id += 1
+        return result
+
+    class SquadExample(object):
+
+
+        def __init__(self,
+                    qas_id,
+                    question_text,
+                    doc_tokens,
+                    orig_answer_text=None,
+                    start_position=None,
+                    end_position=None,
+                    is_impossible=False):
+            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" % (tokenization.printable_text(self.qas_id))
+            s += ", question_text: %s" % (
+                tokenization.printable_text(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
+
+
+
+    def read_squad_examples(input_file, is_training):
+        """Read a SQuAD json file into a list of SquadExample."""
+
+        input_data = json.loads(input_file)["data"]
+
+        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 (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(
+                                tokenization.whitespace_tokenize(orig_answer_text))
+                            if actual_text.find(cleaned_answer_text) == -1:
+                                tf.logging.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 get_result(title,text,question,url):
+     
+        data = make_json(title,text,question)
+      
+
+        examples = read_squad_examples(data,False)
+
+
+        predict_files = convert_examples_to_features(
+                examples=examples,
+                tokenizer=tokenizer,
+                max_seq_length=512,
+                doc_stride=128,
+                max_query_length=100,
+                is_training=False,
+        )
+       
+        headers = {"content-type": "application/json"}
+        all_results = []
+        for predict_file in predict_files: 
+            features = {}
+            features["unique_ids"] = predict_file.unique_id
+            features["input_mask"] = predict_file.input_mask
+            features["segment_ids"] = predict_file.segment_ids
+            features["input_ids"] = predict_file.input_ids
+            data_list = []
+            data_list.append(features)
+        
+            data = json.dumps({"instances": data_list})
+    
+            json_response = requests.post(url, data=data, headers=headers)
+
+       
+            x = json.loads(json_response.text)
+          
+            all_results.append(
+                RawResult(
+                    unique_id=predict_file.unique_id,
+                    start_logits=x['predictions'][0]['start_logits'],
+                    end_logits=x['predictions'][0]['end_logits']))
+       
+        result = write_predictions(examples, predict_files, all_results,20, 64,True)
+        return result
+
+    return get_result(title, text, question, url)
+