From 085ca0db293d92d5fb99e4b4215b7d0cf5427b95 Mon Sep 17 00:00:00 2001
From: stevezhangz <75925178+stevezhangz@users.noreply.github.com>
Date: Thu, 22 Apr 2021 17:52:27 +0800
Subject: [PATCH] Update README.md

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index c50ad2b..63802a2 100644
--- a/README.md
+++ b/README.md
@@ -20,10 +20,10 @@ run train_demo.py to train
 
 except that, you have to learn about how to run it on your dataset
 
-  -first use "general_transform_text2list" in data_process.py to transform txt or json file to list which defined as "[s1,s2,s3,s4.....]"
-  -then use "generate_vocab_normalway" in data_process.py to transform list file to "sentences, id_sentence, idx2word, word2idx, vocab_size"
-  -Last but not least, use "creat_batch" in data_process.py to transform "sentences, id_sentence, idx2word, word2idx, vocab_size" to a batch.
-  -finally using dataloder in pytorch to load data.
+  - first use "general_transform_text2list" in data_process.py to transform txt or json file to list which defined as "[s1,s2,s3,s4.....]"
+  - then use "generate_vocab_normalway" in data_process.py to transform list file to "sentences, id_sentence, idx2word, word2idx, vocab_size"
+  - Last but not least, use "creat_batch" in data_process.py to transform "sentences, id_sentence, idx2word, word2idx, vocab_size" to a batch.
+  - finally using dataloder in pytorch to load data.
 for example:
 
     #json2list=general_transform_text2list("data/demo.txt",type="txt")
-- 
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