#include palm for easier nlp coding
from palm.toolkit.configure import PDConfig
from squad.evaluate_v1 import *
def do_eval(args):
expected_version = "1.1"
with open(args.evaluation_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'])
dataset = dataset_json['data']
with open(args.output_prediction_file) as prediction_file:
predictions = json.load(prediction_file)
print(json.dumps(evaluate(dataset, predictions)))
if __name__ == "__main__":
args = PDConfig(yaml_file="./data/config/squad1.yaml")
args.build()
args.Print()
do_eval(args)
import numpy as np
import paddle
import paddle.fluid as fluid
#include palm for easier nlp coding
from palm.toolkit.configure import PDConfig
# from my_train import do_predict
# from predict import do_predict
from train import do_train
from inference_model import do_save_inference_model
if __name__ == "__main__":
LOG_FORMAT = "[%(asctime)s %(levelname)s %(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(stream=sys.stdout,
level=logging.DEBUG,
format=LOG_FORMAT)
logging.getLogger().setLevel(logging.INFO)
args = PDConfig(yaml_file="./transformer.yaml")
args.build()
args.Print()
if args.do_train:
do_train(args)
if args.do_predict:
do_predict(args)
if args.do_save_inference_model:
do_save_inference_model(args)