def bot_func(bot, update, args):
text = " ".join(args)
words = utils.tokenize(text)
seq_1 = data.encode_words(words, emb_dict)
input_seq = model.pack_input(seq_1, net.emb)
enc = net.encode(input_seq)
if prog_args.sample:
_, tokens = net.decode_chain_sampling(enc,
input_seq.data[0:1],
seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
else:
_, tokens = net.decode_chain_argmax(enc,
input_seq.data[0:1],
seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
if tokens[-1] == end_token:
tokens = tokens[:-1]
reply = data.decode_words(tokens, rev_emb_dict)
if reply:
reply_text = utils.untokenize(reply)
bot.send_message(chat_id=update.message.chat_id, text=reply_text)
def bot_func(bot, update, args):
text = " ".join(args)
words = utils.tokenize(text)
seq_1 = data.encode_words(words, emb_dict)
input_seq = model.pack_input(seq_1, net.emb)
enc = net.encode(input_seq)
if prog_args.sample:
_, tokens = net.decode_chain_sampling(enc, input_seq.data[0:1], seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
else:
_, tokens = net.decode_chain_argmax(enc, input_seq.data[0:1], seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
if tokens[-1] == end_token:
tokens = tokens[:-1]
reply = data.decode_words(tokens, rev_emb_dict)
if reply:
reply_text = utils.untokenize(reply)
bot.send_message(chat_id=update.message.chat_id, text=reply_text)
logging.basicConfig(format="%(asctime)-15s %(levelname)s %(message)s", level=logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", required=True, help="Model name to load")
parser.add_argument("-s", "--string", help="String to process, otherwise will loop")
parser.add_argument("--sample", default=False, action="store_true", help="Enable sampling generation instead of argmax")
parser.add_argument("--self", type=int, default=1, help="Enable self-loop mode with given amount of phrases.")
args = parser.parse_args()
emb_dict = data.load_emb_dict(os.path.dirname(args.model))
net = model.PhraseModel(emb_size=model.EMBEDDING_DIM, dict_size=len(emb_dict), hid_size=model.HIDDEN_STATE_SIZE)
net.load_state_dict(torch.load(args.model))
rev_emb_dict = {idx: word for word, idx in emb_dict.items()}
while True:
if args.string:
input_string = args.string
else:
input_string = input(">>> ")
if not input_string:
break
words = utils.tokenize(input_string)
for _ in range(args.self):
words = words_to_words(words, emb_dict, rev_emb_dict, net, use_sampling=args.sample)
print(utils.untokenize(words))
if args.string:
break
pass
args = parser.parse_args()
emb_dict = data.load_emb_dict(os.path.dirname(args.model))
net = model.PhraseModel(emb_size=model.EMBEDDING_DIM,
dict_size=len(emb_dict),
hid_size=model.HIDDEN_STATE_SIZE)
net.load_state_dict(torch.load(args.model))
rev_emb_dict = {idx: word for word, idx in emb_dict.items()}
while True:
if args.string:
input_string = args.string
else:
input_string = input(">>> ")
if not input_string:
break
words = utils.tokenize(input_string)
for _ in range(args.self):
words = words_to_words(words,
emb_dict,
rev_emb_dict,
net,
use_sampling=args.sample)
print(utils.untokenize(words))
if args.string:
break
pass
