def main(_):
predictor = orqa_model.get_predictor(FLAGS.model_dir)
example_count = 0
correct_count = 0
with tf.io.gfile.GFile(FLAGS.dataset_path) as dataset_file:
for i, line in enumerate(dataset_file):
example = json.loads(line)
question = example["question"]
answers = example["answer"]
predictions = predictor(question)
predicted_answer = six.ensure_text(predictions["answer"],
errors="ignore")
is_correct = eval_utils.is_correct(
answers=[six.ensure_text(a) for a in answers],
prediction=predicted_answer,
is_regex=False)
correct_count += int(is_correct)
example_count += 1
if FLAGS.print_prediction_samples and i & (i - 1) == 0:
logging.info("[%d] '%s' -> '%s'", i, question,
predicted_answer)
logging.info("Accuracy: %.4f (%d/%d)",
correct_count / float(example_count), correct_count,
example_count)
def main(_):
predictor = orqa_model.get_predictor(FLAGS.model_dir)
example_count = 0
correct_count = 0
predictions_path = os.path.join(FLAGS.model_dir, "predictions.jsonl")
with tf.io.gfile.GFile(predictions_path, "w") as predictions_file:
with tf.io.gfile.GFile(FLAGS.dataset_path) as dataset_file:
for line in dataset_file:
example = json.loads(line)
question = example["question"]
answers = example["answer"]
predictions = predictor(question)
predicted_answer = six.ensure_text(predictions["answer"],
errors="ignore")
is_correct = eval_utils.is_correct(
answers=[six.ensure_text(a) for a in answers],
prediction=predicted_answer,
is_regex=False)
predictions_file.write(
json.dumps(
dict(question=question,
prediction=predicted_answer,
predicted_context=six.ensure_text(
predictions["orig_block"], errors="ignore"),
correct=is_correct,
answer=answers)))
predictions_file.write("\n")
correct_count += int(is_correct)
example_count += 1
tf.logging.info("Accuracy: %.4f (%d/%d)",
correct_count / float(example_count), correct_count,
example_count)
def main(_):
predictor = orqa_model.get_predictor(FLAGS.model_dir)
with tf.io.gfile.GFile(FLAGS.predictions_path, "w") as predictions_file:
with tf.io.gfile.GFile(FLAGS.dataset_path) as dataset_file:
for i, line in enumerate(dataset_file):
example = json.loads(line)
question = example["question"]
predictions = predictor(question)
predicted_answer = six.ensure_text(predictions["answer"],
errors="ignore")
example["prediction"] = predicted_answer
predictions_file.write(json.dumps(example))
predictions_file.write("\n")
if FLAGS.print_prediction_samples and i & (i - 1) == 0:
logging.info("[%d] '%s' -> '%s'", i, question,
predicted_answer)
def main(_):
predictor = orqa_model.get_predictor(FLAGS.model_dir)
# Run once to initialize the block records.
predictor("")
web_path = FLAGS.web_path
if not web_path:
web_path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"web")
env = jinja2.Environment(loader=jinja2.FileSystemLoader(web_path))
application = tornado.wsgi.WSGIApplication([(r"/", MainHandler, {
"env": env,
"predictor": predictor,
})])
tf.logging.info("READY!")
server = simple_server.make_server("", FLAGS.port, application)
server.serve_forever()