def train_gpt2(csv_path='gpt2_train_data/bertffn_crossentropy.csv',
steps=100000,
batch_size=1):
tf.compat.v1.disable_v2_behavior()
if not os.path.exists('models/117M'):
gpt2.download_gpt2()
sess = gpt2.start_tf_sess()
gpt2.finetune(sess, csv_path, steps=steps, batch_size=batch_size)
def __init__(self,
pretrained_path='pubmed_pmc_470k/',
ffn_weight_file=None,
bert_ffn_weight_file='models/bertffn_crossentropy/bertffn',
embedding_file='qa_embeddings/bertffn_crossentropy.pkl'):
super(GenerateQADoc, self).__init__()
tf.compat.v1.disable_eager_execution()
self.sess = gpt2.start_tf_sess()
gpt2.load_gpt2(self.sess)
self.embed_sess = gpt2.start_tf_sess()
with self.embed_sess.as_default():
self.qa_embed = QAEmbed(pretrained_path=pretrained_path,
ffn_weight_file=ffn_weight_file,
bert_ffn_weight_file=bert_ffn_weight_file,
with_answer=False,
load_pretrain=False)
self.faiss_topk = FaissTopK(embedding_file)
import tensorflow.compat.v1 as tf
from gpt_2 import gpt2
tf.disable_eager_execution()
csv_path = 'data/GPT2_data_FFNN.csv'
sess = gpt2.start_tf_sess()
gpt2.load_gpt2(sess)
gpt2.generate(
sess, prefix="`QUESTION: What is the best treatment for the flu? `ANSWER:")