diff --git a/globally_normalized_reader/README.md b/globally_normalized_reader/README.md
index 583f53f7519f3a781ea481f87077106a2f329a4a..ca223ac75bc3b7edea5cf69abd88e16ba4d193a9 100644
--- a/globally_normalized_reader/README.md
+++ b/globally_normalized_reader/README.md
@@ -25,10 +25,13 @@ You can also visit https://github.com/baidu-research/GloballyNormalizedReader to
     docker pull paddledev/paddle
     ```
 2. Download all necessary data by running:
-   ```bash
-   cd data && ./download.sh
-   ```
-3. **(TODO) add the preprocess and featurizer scripts.**
+    ```bash
+    cd data && ./download.sh && cd ..
+    ```
+3. Preprocess and featurizer data:
+    ```bash
+    python featurize.py --datadir data --outdir data/featurized  --glove-path data/glove.840B.300d.txt
+    ```
 
 # Training a Model
 
diff --git a/globally_normalized_reader/config.py b/globally_normalized_reader/config.py
index d89fd0e48535cb6c99eb9b679b52024dac00c5dd..849cc693b646bd131a4f53ca500be48febd1d4ea 100644
--- a/globally_normalized_reader/config.py
+++ b/globally_normalized_reader/config.py
@@ -5,7 +5,7 @@ __all__ = ["ModelConfig", "TrainerConfig"]
 
 
 class ModelConfig(object):
-    vocab_size = 104808
+    vocab_size = 104810
     embedding_dim = 300
     embedding_droprate = 0.3
 
diff --git a/globally_normalized_reader/data/download.sh b/globally_normalized_reader/data/download.sh
index 4782dd55590272e29d5723076b5141887a438278..f089284a897478c686853754312720c0c91a5abd 100755
--- a/globally_normalized_reader/data/download.sh
+++ b/globally_normalized_reader/data/download.sh
@@ -1,4 +1,7 @@
 #!/bin/bash
 
-wget --no-check-certificate https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
-wget --no-check-certificate https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
+wget --no-check-certificate https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json -O train.json
+wget --no-check-certificate https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json -O dev.json
+
+wget http://nlp.stanford.edu/data/glove.840B.300d.zip
+unzip glove.840B.300d.zip
diff --git a/globally_normalized_reader/evaluate.py b/globally_normalized_reader/evaluate.py
new file mode 100644
index 0000000000000000000000000000000000000000..c85ae0126d737cdb2d14d618aa07705faa490d90
--- /dev/null
+++ b/globally_normalized_reader/evaluate.py
@@ -0,0 +1,96 @@
+""" Official evaluation script for v1.1 of the SQuAD dataset. """
+from __future__ import print_function
+from collections import Counter
+import string
+import re
+import argparse
+import json
+import sys
+
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+
+    def remove_articles(text):
+        return re.sub(r'\b(a|an|the)\b', ' ', 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 f1_score(prediction, ground_truth):
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def exact_match_score(prediction, ground_truth):
+    return (normalize_answer(prediction) == normalize_answer(ground_truth))
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+
+def evaluate(dataset, predictions):
+    f1 = exact_match = total = 0
+    for article in dataset:
+        for paragraph in article['paragraphs']:
+            for qa in paragraph['qas']:
+                total += 1
+                if qa['id'] not in predictions:
+                    message = 'Unanswered question ' + qa['id'] + \
+                              ' will receive score 0.'
+                    print(message, file=sys.stderr)
+                    continue
+                ground_truths = list(map(lambda x: x['text'], qa['answers']))
+                prediction = predictions[qa['id']]
+                exact_match += metric_max_over_ground_truths(
+                    exact_match_score, prediction, ground_truths)
+                f1 += metric_max_over_ground_truths(f1_score, prediction,
+                                                    ground_truths)
+
+    exact_match = 100.0 * exact_match / total
+    f1 = 100.0 * f1 / total
+
+    return {'exact_match': exact_match, 'f1': f1}
+
+
+if __name__ == '__main__':
+    expected_version = '1.1'
+    parser = argparse.ArgumentParser(
+        description='Evaluation for SQuAD ' + expected_version)
+    parser.add_argument('dataset_file', help='Dataset file')
+    parser.add_argument('prediction_file', help='Prediction File')
+    args = parser.parse_args()
+    with open(args.dataset_file) as dataset_file:
+        dataset_json = json.load(dataset_file)
+        if (dataset_json['version'] != expected_version):
+            print(
+                'Evaluation expects v-' + expected_version +
+                ', but got dataset with v-' + dataset_json['version'],
+                file=sys.stderr)
+        dataset = dataset_json['data']
+    with open(args.prediction_file) as prediction_file:
+        predictions = json.load(prediction_file)
+    print(json.dumps(evaluate(dataset, predictions)))
diff --git a/globally_normalized_reader/featurize.py b/globally_normalized_reader/featurize.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0eb9d626b2b41436b319b7b0a2cac284107ed98
--- /dev/null
+++ b/globally_normalized_reader/featurize.py
@@ -0,0 +1,308 @@
+# -*- coding: utf-8 -*-
+"""
+Convert the raw json data into training and validation examples.
+"""
+from collections import Counter
+import json
+import os
+import io
+import string
+
+import click
+import numpy as np
+import ciseau
+
+from vocab import Vocab
+from evaluate import normalize_answer
+
+# Constants
+UNK = "<UNK>"
+SOS = "<SOS>"
+EOS = "<EOS>"
+PAD = "<PAD>"
+
+splits = ["train", "dev"]
+
+ARTICLES = {"a", "an", "the", "of"}
+
+# Keep the random embedding matrix the same between runs.
+np.random.seed(1234)
+
+
+def data_stream(path):
+    """ Given a path json data in Pranav format, convert it to a stream
+    question/context/answers tuple."""
+    with io.open(path, "r") as handle:
+        raw_data = json.load(handle)["data"]
+    for ex in raw_data:
+        for paragraph in ex["paragraphs"]:
+            context = paragraph["context"]
+            for qa in paragraph["qas"]:
+                question = qa["question"]
+                answers = qa["answers"]
+                if "id" not in qa:
+                    qa_id = -1
+                else:
+                    qa_id = qa["id"]
+                yield question, context, answers, qa_id
+
+
+def build_vocabulary(datadir, outdir, glove_path):
+    """Construct the vocabulary object used throughout."""
+    # We're not going to backprop through the word vectors
+    # both train and dev words end up in the vocab.
+    counter = Counter()
+    for split in splits:
+        datapath = os.path.join(datadir, split + ".json")
+
+        for question, context, _, _ in data_stream(datapath):
+            for word in ciseau.tokenize(question, normalize_ascii=False):
+                counter[normalize(word)] += 1
+            for word in ciseau.tokenize(context, normalize_ascii=False):
+                counter[normalize(word)] += 1
+
+    common_words = [UNK, SOS, EOS, PAD] + [w for w, _ in counter.most_common()]
+
+    vocab_path = os.path.join(outdir, "vocab.txt")
+    with io.open(vocab_path, "w", encoding="utf8") as handle:
+        handle.write("\n".join(common_words))
+
+    return Vocab(outdir)
+
+
+def normalize_answer_tokens(tokens):
+    start = 0
+    end = len(tokens)
+
+    while end - start > 1:
+        first_token = tokens[start].rstrip().lower()
+        if first_token in string.punctuation or first_token in ARTICLES:
+            start += 1
+        else:
+            break
+    while end - start > 1:
+        last_token = tokens[end - 1].rstrip().lower()
+        if last_token in string.punctuation:
+            end -= 1
+        else:
+            break
+    return start, end
+
+
+def tokenize_example(question, context, answers, strip_labels=True):
+    # Q: How should we choose the right answer
+    answer = answers[0]["text"]
+    answer_start = answers[0]["answer_start"]
+
+    if strip_labels:
+        answer_tokens = ciseau.tokenize(answer, normalize_ascii=False)
+        start_offset, end_offset = normalize_answer_tokens(answer_tokens)
+        answer = "".join(answer_tokens[start_offset:end_offset])
+        # add back the piece that was stripped off:
+        answer_start = answer_start + len("".join(answer_tokens[:start_offset]))
+
+    # replace answer string with placeholder
+    placeholder = "XXXX"
+    new_context = context[:answer_start] + placeholder + context[answer_start +
+                                                                 len(answer):]
+
+    token_context = ciseau.sent_tokenize(new_context, keep_whitespace=True)
+    token_question = ciseau.tokenize(question)
+
+    sentence_label = None
+    for sent_idx, sent in enumerate(token_context):
+        answer_start = None
+        for idx, word in enumerate(sent):
+            if placeholder in word:
+                answer_start = idx
+                break
+
+        if answer_start is None:
+            continue
+
+        sentence_label = sent_idx
+
+        # deal with cases where the answer is in the middle
+        # of the word
+        answer = word.replace(placeholder, answer)
+        token_answer = ciseau.tokenize(answer)
+
+        answer_end = answer_start + len(token_answer) - 1
+        answer_sent = sent[:answer_start] + token_answer + sent[answer_start +
+                                                                1:]
+        break
+
+    token_context[sentence_label] = answer_sent
+
+    return token_question, token_context, sentence_label, answer_start, answer_end
+
+
+def normalize(word):
+    return word.strip()
+
+
+def same_as_question_feature(question_idxs, context_idxs, vocab):
+    question_words = [vocab.idx_to_word(idx) for idx in question_idxs]
+
+    # remove stop word and puncutation
+    question_words = set([
+        w.strip().lower() for w in question_words
+        if w not in ARTICLES and w not in string.punctuation
+    ])
+
+    features = []
+    for word_idx in context_idxs:
+        word = vocab.idx_to_word(word_idx)
+        features.append(int(word.strip().lower() in question_words))
+
+    return features
+
+
+def repeated_word_features(context_idxs, vocab):
+    context_words = [vocab.idx_to_word(idx) for idx in context_idxs]
+
+    word_counter = {}
+    for word in context_words:
+        canon = word.strip().lower()
+        if canon in word_counter:
+            word_counter[canon] += 1
+        else:
+            word_counter[canon] = 1
+
+    max_occur = max(word_counter.values())
+    min_occur = min(word_counter.values())
+    occur_range = max(1.0, max_occur - min_occur)
+
+    repeated_words = []
+    repeated_word_intensity = []
+
+    for word in context_words:
+        canon = word.strip().lower()
+        count = word_counter[canon]
+        repeated = float(count > 1 and canon not in ARTICLES and
+                         canon not in string.punctuation)
+        intensity = float((count - min_occur) / occur_range)
+
+        repeated_words.append(repeated)
+        repeated_word_intensity.append(intensity)
+
+    return repeated_words, repeated_word_intensity
+
+
+def convert_example_to_indices(example, outfile, vocab):
+    print("Processing {}".format(outfile))
+    question, context, answers, qa_id = example
+
+    tokenized = tokenize_example(question, context, answers, strip_labels=True)
+    token_question, token_context, ans_sent, ans_start, ans_end = tokenized
+
+    # Convert to indices
+    question_idxs = [vocab.word_to_idx(normalize(w)) for w in token_question]
+
+    # + 1 for end of sentence
+    sent_lengths = [len(sent) + 1 for sent in token_context]
+    context_idxs = []
+    for sent in token_context:
+        for w in sent:
+            context_idxs.append(vocab.word_to_idx(normalize(w)))
+        context_idxs.append(vocab.eos)
+
+    same_as_question = same_as_question_feature(question_idxs, context_idxs,
+                                                vocab)
+
+    repeated_words, repeated_intensity = repeated_word_features(context_idxs,
+                                                                vocab)
+
+    features = {
+        "question": question_idxs,
+        "context": context_idxs,
+        "ans_sentence": ans_sent,
+        "ans_start": ans_start,
+        "ans_end": ans_end,
+        "sent_lengths": sent_lengths,
+        "same_as_question_word": same_as_question,
+        "repeated_words": repeated_words,
+        "repeated_intensity": repeated_intensity,
+        "qa_id": qa_id
+    }
+
+    # Hack!: This is not a great way to save indices...
+    with io.open(outfile, "w", encoding="utf8") as handle:
+        handle.write(unicode(json.dumps(features, ensure_ascii=False)))
+
+
+def featurize_example(question, context, vocab):
+    # Convert to indices
+    question_idxs = [
+        vocab.word_to_idx(normalize(w))
+        for w in ciseau.tokenize(question, normalize_ascii=False)
+    ]
+
+    context_sents = ciseau.sent_tokenize(
+        context, keep_whitespace=True, normalize_ascii=False)
+    # + 1 for end of sentence
+    sent_lengths = [len(sent) + 1 for sent in context_sents]
+    context_idxs = []
+    for sent in context_sents:
+        for w in sent:
+            context_idxs.append(vocab.word_to_idx(normalize(w)))
+        context_idxs.append(vocab.eos)
+
+    same_as_question = same_as_question_feature(question_idxs, context_idxs,
+                                                vocab)
+    repeated_words, repeated_intensity = repeated_word_features(context_idxs,
+                                                                vocab)
+
+    return (question_idxs, context_idxs, same_as_question, repeated_words,
+            repeated_intensity, sent_lengths), context_sents
+
+
+def random_sample(data, k, replace=False):
+    indices = np.arange(len(data))
+    chosen_indices = np.random.choice(indices, k, replace=replace)
+    return [data[idx] for idx in chosen_indices]
+
+
+@click.command()
+@click.option("--datadir", type=str, help="Path to raw data")
+@click.option("--outdir", type=str, help="Path to save the result")
+@click.option("--glove-path", default="/mnt/data/jmiller/glove.840B.300d.txt")
+def preprocess(datadir, outdir, glove_path):
+    if not os.path.exists(outdir):
+        os.makedirs(outdir)
+
+    print("Constructing vocabularies...")
+    vocab = build_vocabulary(datadir, outdir, glove_path)
+    print("Finished...")
+
+    print("Building word embedding matrix...")
+    vocab.construct_embedding_matrix(glove_path)
+    print("Finished...")
+
+    # Create training featurizations
+    for split in splits:
+        results_path = os.path.join(outdir, split)
+        os.makedirs(results_path)
+
+        # process each example
+        examples = list(data_stream(os.path.join(datadir, split + ".json")))
+
+        for idx, example in enumerate(examples):
+            outfile = os.path.join(results_path, str(idx) + ".json")
+            convert_example_to_indices(example, outfile, vocab)
+
+    print("Building evaluation featurization...")
+    eval_feats = []
+    for question, context, _, qa_id in data_stream(
+            os.path.join(datadir, "dev.json")):
+        features, tokenized_context = featurize_example(question, context,
+                                                        vocab)
+        eval_feats.append((qa_id, tokenized_context, features))
+
+    with io.open(
+            os.path.join(outdir, "eval.json"), "w", encoding="utf8") as handle:
+        handle.write(unicode(json.dumps(eval_feats, ensure_ascii=False)))
+
+
+if __name__ == "__main__":
+    preprocess()
diff --git a/globally_normalized_reader/index.html b/globally_normalized_reader/index.html
index 2b0768ff173cc581ac558edcc64a78a44874b1ed..c8c23c5bad340c7f043f662e4334e9c0f772e55e 100644
--- a/globally_normalized_reader/index.html
+++ b/globally_normalized_reader/index.html
@@ -67,10 +67,13 @@ You can also visit https://github.com/baidu-research/GloballyNormalizedReader to
     docker pull paddledev/paddle
     ```
 2. Download all necessary data by running:
-   ```bash
-   cd data && ./download.sh
-   ```
-3. **(TODO) add the preprocess and featurizer scripts.**
+    ```bash
+    cd data && ./download.sh && cd ..
+    ```
+3. Preprocess and featurizer data:
+    ```bash
+    python featurize.py --datadir data --outdir data/featurized  --glove-path data/glove.840B.300d.txt
+    ```
 
 # Training a Model
 
diff --git a/globally_normalized_reader/model.py b/globally_normalized_reader/model.py
index db5cccae720e947fcf75164edbc8ef395a6b8105..e2ef2b2355f09976bdf004467aab562dd30e54cc 100644
--- a/globally_normalized_reader/model.py
+++ b/globally_normalized_reader/model.py
@@ -195,7 +195,7 @@ def search_answer(doc_lstm_outs, sentence_idx, start_idx, end_idx, config,
     """Search the answer from the document.
 
     The search process for this layer begins with searching a target sequence
-    from a nested sequence by using paddle.lauer.kmax_seq_score and
+    from a nested sequence by using paddle.layer.kmax_seq_score and
     paddle.layer.sub_nested_seq_layer. In the first search step, top beam size
     sequences with highest scores, indices of these top k sequences in the
     original nested sequence, and the ground truth (also called gold)
diff --git a/globally_normalized_reader/vocab.py b/globally_normalized_reader/vocab.py
new file mode 100644
index 0000000000000000000000000000000000000000..874d01c939be0cdeefac815cb206053a314b63b9
--- /dev/null
+++ b/globally_normalized_reader/vocab.py
@@ -0,0 +1,284 @@
+# -*- coding: utf-8 -*-
+import os
+import io
+import numpy as np
+
+# Constants
+UNK = "<UNK>"
+SOS = "<SOS>"
+EOS = "<EOS>"
+PAD = "<PAD>"
+VOCAB_DIM = 2196017
+EMBEDDING_DIM = 300
+WORD2VEC = None
+
+
+class Vocab(object):
+    """Class to hold the vocabulary for the SquadDataset."""
+
+    def __init__(self, path):
+        self._id_to_word = []
+        self._word_to_id = {}
+        self._word_ending_tables = {}
+        self._path = path
+        self._pad = -1
+        self._unk = None
+        self._sos = None
+        self._eos = None
+
+        # first read in the base vocab
+        with io.open(os.path.join(path, "vocab.txt"), "r") as f:
+            for idx, line in enumerate(f):
+                word_name = line.strip()
+                if word_name == UNK:
+                    self._unk = idx
+                elif word_name == SOS:
+                    self._sos = idx
+                elif word_name == EOS:
+                    self._eos = idx
+
+                self._id_to_word.append(word_name)
+                self._word_to_id[word_name] = idx
+
+    @property
+    def unk(self):
+        return self._unk
+
+    @property
+    def sos(self):
+        return self._sos
+
+    @property
+    def eos(self):
+        return self._eos
+
+    @property
+    def size(self):
+        return len(self._id_to_word)
+
+    def word_to_idx(self, word):
+        if word in self._word_to_id:
+            return self._word_to_id[word]
+        return self.unk
+
+    def idx_to_word(self, idx):
+        if idx == self._pad:
+            return PAD
+        if idx < self.size:
+            return self._id_to_word[idx]
+        return "ERROR"
+
+    def decode(self, idxs):
+        return " ".join([self.idx_to_word(idx) for idx in idxs])
+
+    def encode(self, sentence):
+        return [self.word_to_idx(word) for word in sentence]
+
+    @property
+    def word_embeddings(self):
+        embedding_path = os.path.join(self._path, "embeddings.npy")
+        embeddings = np.load(embedding_path)
+        return embeddings
+
+    def construct_embedding_matrix(self, glove_path):
+        # Randomly initialize word embeddings
+        embeddings = np.random.randn(self.size,
+                                     EMBEDDING_DIM).astype(np.float32)
+
+        load_word_vectors(
+            param=embeddings,
+            vocab=self._id_to_word,
+            path=glove_path,
+            missing_word_alternative=missing_word_heuristic,
+            missing_word_value=lambda: 0.0)
+        embedding_path = os.path.join(self._path, "embeddings.npy")
+        np.save(embedding_path, embeddings)
+
+
+def missing_word_heuristic(word, word2vec):
+    """
+    propose alternate spellings of a word to match against
+    pretrained word vectors (so that if the original spelling
+    has no pretrained vector, but alternate spelling does,
+    a vector can be retrieved anyways.)
+    """
+    if len(word) > 5:
+        # try to find similar words that share
+        # the same 5 character ending:
+        most_sim = word2vec.words_ending_in(word[-5:])
+
+        if len(most_sim) > 0:
+            most_sim = sorted(
+                most_sim,
+                reverse=True,
+                key=lambda x: (
+                    (word[0].isupper() == x[0].isupper()) +
+                    (word.lower()[:3] == x.lower()[:3]) +
+                    (word.lower()[:4] == x.lower()[:4]) +
+                    (abs(len(word) - len(x)) < 5)
+                )
+            )
+            return most_sim[:1]
+    if all(not c.isalpha() for c in word):
+        # this is a fully numerical answer (and non alpha)
+        return ['13', '9', '100', '2.0']
+
+    return [
+        # add a capital letter
+        word.capitalize(),
+        # see if word has spurious period
+        word.split(".")[0],
+        # see if word has spurious backslash
+        word.split("/")[0],
+        # see if word has spurious parenthesis
+        word.split(")")[0],
+        word.split("(")[0]
+    ]
+
+
+class Word2Vec(object):
+    """
+    Load word2vec result from file
+    """
+
+    def __init__(self, vocab_size, vector_size):
+        self.syn0 = np.zeros((vocab_size, vector_size), dtype=np.float32)
+        self.index2word = []
+        self.vocab_size = vocab_size
+        self.vector_size = vector_size
+
+    def load_word2vec_format(self, path):
+        with io.open(path, "r") as fin:
+            for word_id in range(self.vocab_size):
+                line = fin.readline()
+                parts = line.rstrip("\n").rstrip().split(" ")
+                if len(parts) != self.vector_size + 1:
+                    raise ValueError(
+                        "invalid vector on line {}".format(word_id))
+                word, weights = parts[0], [np.float32(x) for x in parts[1:]]
+                self.syn0[word_id] = weights
+                self.index2word.append(word)
+        return self
+
+
+class FastWord2vec(object):
+    """
+    Load word2vec model, cache the embedding matrix using numpy
+    and memory-map it so that future loads are fast.
+    """
+
+    def __init__(self, path):
+        if not os.path.exists(path + ".npy"):
+            word2vec = Word2Vec(VOCAB_DIM,
+                                EMBEDDING_DIM).load_word2vec_format(path)
+
+            # save as numpy
+            np.save(path + ".npy", word2vec.syn0)
+            # also save the vocab
+            with io.open(path + ".vocab", "w", encoding="utf8") as fout:
+                for word in word2vec.index2word:
+                    fout.write(word + "\n")
+
+        self.syn0 = np.load(path + ".npy", mmap_mode="r")
+        self.index2word = [l.strip("\n") for l in io.open(path + ".vocab", "r")]
+        self.word2index = {word: k for k, word in enumerate(self.index2word)}
+        self._word_ending_tables = {}
+        self._word_beginning_tables = {}
+
+    def __getitem__(self, key):
+        return np.array(self.syn0[self.word2index[key]])
+
+    def __contains__(self, key):
+        return key in self.word2index
+
+    def words_ending_in(self, word_ending):
+        if len(word_ending) == 0:
+            return self.index2word
+        self._build_word_ending_table(len(word_ending))
+        return self._word_ending_tables[len(word_ending)].get(word_ending, [])
+
+    def _build_word_ending_table(self, length):
+        if length not in self._word_ending_tables:
+            table = {}
+            for word in self.index2word:
+                if len(word) >= length:
+                    ending = word[-length:]
+                    if ending not in table:
+                        table[ending] = [word]
+                    else:
+                        table[ending].append(word)
+            self._word_ending_tables[length] = table
+
+    def words_starting_in(self, word_beginning):
+        if len(word_beginning) == 0:
+            return self.index2word
+        self._build_word_beginning_table(len(word_beginning))
+        return self._word_beginning_tables[len(word_beginning)].get(
+            word_beginning, [])
+
+    def _build_word_beginning_table(self, length):
+        if length not in self._word_beginning_tables:
+            table = {}
+            for word in get_progress_bar('building prefix lookup ')(
+                    self.index2word):
+                if len(word) >= length:
+                    ending = word[:length]
+                    if ending not in table:
+                        table[ending] = [word]
+                    else:
+                        table[ending].append(word)
+            self._word_beginning_tables[length] = table
+
+    @staticmethod
+    def get(path):
+        global WORD2VEC
+        if WORD2VEC is None:
+            WORD2VEC = FastWord2vec(path)
+        return WORD2VEC
+
+
+def load_word_vectors(param,
+                      vocab,
+                      path,
+                      verbose=True,
+                      missing_word_alternative=None,
+                      missing_word_value=None):
+    """
+    Add the pre-trained word embeddings stored under path to the parameter
+    matrix `param` that has size `vocab x embedding_dim`.
+    Arguments:
+        param : np.array
+        vocab : list<str>
+        path : str, location of the pretrained word embeddings
+        verbose : (optional) bool, whether to print how
+            many words were recovered
+    """
+    word2vec = FastWord2vec.get(path)
+    missing = 0
+    for idx, word in enumerate(vocab):
+        try:
+            param[idx, :] = word2vec[word]
+        except KeyError:
+            try:
+                param[idx, :] = word2vec[word.lower()]
+            except KeyError:
+                found = False
+                if missing_word_alternative is not None:
+                    alternatives = missing_word_alternative(word, word2vec)
+                    if isinstance(alternatives, str):
+                        alternatives = [alternatives]
+                    assert (isinstance(alternatives, list)), (
+                        "missing_word_alternative should return a list of strings."
+                    )
+                    for alternative in alternatives:
+                        if alternative in word2vec:
+                            param[idx, :] = word2vec[alternative]
+                            found = True
+                            break
+                if not found:
+                    if missing_word_value is not None:
+                        param[idx, :] = missing_word_value()
+                    missing += 1
+    if verbose:
+        print(
+            "Loaded {} words, {} missing".format(len(vocab) - missing, missing))