Multiple-Relations-Extraction-Only-Look-Once. Just look at the sentence once and extract the multiple pairs of entities and their corresponding relations. 端到端联合多关系抽取模型,可用于 http://lic2019.ccf.org.cn/kg 信息抽取。
非常感谢您的开源精神!
我想利用您的项目尝试单条文本预测?该如何做?
我尝试改了下run_multiple_relations_extraction.py代码,最后再这个函数的时候,对单条文本这里要怎么输入?
d = tf.data.TFRecordDataset(input_file)
def input_fn(params):
"""The actual input function."""
batch_size = params["batch_size"]
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
非常感谢您的开源精神! 我想利用您的项目尝试单条文本预测?该如何做? 我尝试改了下run_multiple_relations_extraction.py代码,最后再这个函数的时候,对单条文本这里要怎么输入? d = tf.data.TFRecordDataset(input_file)