def run_func2(dataset, config):
vocab, rev_vocab = initialize_vocab(config.vocab_path)
q, c, a = zip(*[[_q, _c, _a] for (_q, _c, _a) in dataset])
dataset = [q, c, a]
embed_path = config.embed_path
embeddings = get_trimmed_glove_vectors(embed_path)
encoder = Encoder(config.hidden_state_size)
decoder = Decoder(config.hidden_state_size)
qa = QASystem(encoder, decoder, embeddings, config)
question_uuid_data = [i for i in xrange(len(a))]
with tf.Session() as sess:
qa.initialize_model(sess, config.train_dir)
answers, answers_canonical = generate_answers(sess, qa, dataset,
question_uuid_data,
rev_vocab)
# write to json file to root dir
with io.open('dev-prediction.txt', 'w', encoding='utf-8') as f:
for i in xrange(len(a)):
curr_ans = unicode(answers[i], "utf-8")
f.write("%s\n" % (curr_ans))
def run_func():
config = Config()
# ========= Load Dataset =========
# You can change this code to load dataset in your own way
vocab, rev_vocab = initialize_vocab(config.vocab_path)
dev_path = "data/squad/fuse.json"
dev_dirname = os.path.dirname(os.path.abspath(dev_path))
dev_filename = os.path.basename(dev_path)
context_data, question_data, question_uuid_data = prepare_dev(
dev_dirname, dev_filename, vocab)
ques_len = len(question_data)
answers = [[0, 0] for _ in xrange(ques_len)]
dataset = [question_data, context_data, answers]
embed_path = config.embed_path
embeddings = get_trimmed_glove_vectors(embed_path)
encoder = Encoder(config.hidden_state_size)
decoder = Decoder(config.hidden_state_size)
qa = QASystem(encoder, decoder, embeddings, config)
with tf.Session() as sess:
qa.initialize_model(sess, config.train_dir)
answers, _ = generate_answers(sess, qa, dataset, question_uuid_data,
rev_vocab)
# write to json file to root dir
with io.open('temp/fuse-answer.json', 'w', encoding='utf-8') as f:
f.write(unicode(json.dumps(answers, ensure_ascii=False)))
def run_func():
config = Config()
vocab, rev_vocab = initialize_vocab(config.vocab_path)
dev_path = "download/squad/test.json"
dev_dirname = os.path.dirname(os.path.abspath(dev_path))
dev_filename = os.path.basename(dev_path)
context_data, question_data, question_uuid_data = prepare_dev(
dev_dirname, dev_filename, vocab)
ques_len = len(question_data)
answers = [[0, 0] for _ in xrange(ques_len)]
dataset = [question_data, context_data, answers]
embed_path = config.embed_path
embeddings = get_trimmed_glove_vectors(embed_path)
encoder = Encoder(config.hidden_state_size)
decoder = Decoder(config.hidden_state_size)
qa = QASystem(encoder, decoder, embeddings, config)
data = "Id,Answer\n"
with tf.Session() as sess:
qa.initialize_model(sess, config.train_dir)
answers, _ = generate_answers(sess, qa, dataset, question_uuid_data,
rev_vocab)
for a in answers:
ans = answers[a]
data += a + "," + normalize_answer(ans).replace(" s ", "s ") + "\n"
with open('submission.csv', 'wb') as file:
file.write(data)
def predict(model_name):
qa = QASystem(model_name)
with tf.Session() as sess:
qa.initialize_model(sess)
while True:
question = input("Ask a question: ")
for answer, confidence, doc in answer_question(qa, sess, question, best_n=10):
print('{:.2f}:\t{} ({})'.format(confidence, answer, doc))
def run_func(model_name):
train = SquadData.load(config.SQUAD_TRAIN_PREFIX, size=config.TRAIN_SIZE)
dev = SquadData.load(config.SQUAD_DEV_PREFIX, size=config.EVAL_SIZE)
qa = QASystem(model_name)
with tf.Session() as sess:
# ====== Load a pretrained model if it exists or create a new one if no pretrained available ======
qa.initialize_model(sess)
qa.train(sess, [train, dev])
def run_func():
config = Config()
train = squad_dataset(config.question_train, config.context_train,
config.answer_train)
dev = squad_dataset(config.question_dev, config.context_dev,
config.answer_dev)
# print(config.question_train)
embed_path = config.embed_path
vocab_path = config.vocab_path
# print(config.embed_path, config.vocab_path)
vocab, rev_vocab = initialize_vocab(vocab_path)
embeddings = get_trimmed_glove_vectors(embed_path)
encoder = Encoder(config.hidden_size)
decoder = Decoder(config.hidden_size)
qa = QASystem(encoder, decoder, embeddings, config)
with tf.Session() as sess:
# ====== Load a pretrained model if it exists or create a new one if no pretrained available ======
qa.initialize_model(sess, config.train_dir)
# train process
# qa.train(sess, [train, dev], config.train_dir)
# em = qa.evaluate_model(sess, dev)
# run process
while True:
question = input('please input question: ')
if question == 'exit':
break
raw_context = input('please input context: ')
if raw_context == 'exit':
break
question = [
vocab[x] if x in vocab.keys() else 2 for x in question.split()
]
context = [
vocab[x] if x in vocab.keys() else 2
for x in raw_context.split()
]
test = [[question], [context], [[1, 2]]]
a_s, a_e = qa.answer(sess, test)
if a_e == a_s:
print("answer: ", raw_context.split()[a_s[0]])
else:
print("answer: ",
' '.join(raw_context.split()[a_s[0]:a_e[0] + 1]))
def evaluate(model_name, n=None):
data = []
with open(config.TREC_PATH, 'r') as f:
reader = csv.reader(f, delimiter='\t')
for row in list(reader)[199:]:
data.append((row[2].strip(), row[3].strip()))
if not n:
n = len(data)
qa = QASystem(model_name)
top_count = 0
top_5_count = 0
top_10_count = 0
with tf.Session() as sess:
qa.initialize_model(sess)
with open(os.path.join(config.MODELS_DIR, model_name, 'trec.csv'),
'w') as f:
writer = csv.writer(f)
i = 0
for question, answer_pattern in tqdm(data[:n]):
answers = [
answer for answer, confidence, doc in answer_question(
qa, sess, question, 10)
]
writer.writerow(answers)
correct = [
bool(re.search(answer_pattern, answer))
for answer in answers
]
if True in correct[:1]:
top_count += 1
if True in correct[:5]:
top_5_count += 1
if True in correct[:10]:
top_10_count += 1
i += 1
print('{}: {}, {}, {}'.format(i,
float(top_count) / i,
float(top_5_count) / i,
float(top_10_count) / i))
print('Top match: {}'.format(float(top_count) / n))
print('Top 5 match: {}'.format(float(top_5_count) / n))
print('Top 10 match: {}'.format(float(top_10_count) / n))
def run_func():
config = Config()
train = squad_dataset(config.question_train, config.context_train,
config.answer_train)
dev = squad_dataset(config.question_dev, config.context_dev,
config.answer_dev)
embed_path = config.embed_path
vocab_path = config.vocab_path
vocab, rev_vocab = initialize_vocab(vocab_path)
embeddings = get_trimmed_glove_vectors(embed_path)
encoder = Encoder(config.hidden_state_size)
decoder = Decoder(config.hidden_state_size)
qa = QASystem(encoder, decoder, embeddings, config)
with tf.Session() as sess:
# ====== Load a pretrained model if it exists or create a new one if no pretrained available ======
qa.initialize_model(sess, config.train_dir)
qa.train(sess, [train, dev], config.train_dir)
