Update README colab notebook

README.md

@@ -7,14 +7,16 @@ tags:
 - 文言文
 - ancient
 - classical
+- letter
+- 书信标题
 license: cc-by-nc-sa-4.0
 ---
 
 # BertForSequenceClassification model (Classical Chinese)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jVu2LrNwkLolItPALKGNjeT6iCfzF8Ic?usp=sharing/)
 
-This BertForSequenceClassification Classical Chinese model is intended to predict whether a Classical Chinese sentence is a letter title (书信标题) or not. This model is first inherited from the BERT base Chinese model (MLM), and finetuned using a large corpus of Classical Chinese language (3GB textual dataset), then concatenated with the BertForSequenceClassification architecture to perform a binary classification task.
-
-#### Labels: 0 = non-letter, 1 = letter
+This BertForSequenceClassification Classical Chinese model is intended to predict whether a Classical Chinese sentence is a letter title (书信标题) or not. This model is first inherited from the BERT base Chinese model (MLM), and finetuned using a large corpus of Classical Chinese language (3GB textual dataset), then concatenated with the BertForSequenceClassification architecture to perform a binary classification task. 
+ * Labels: 0 = non-letter, 1 = letter
 
 ## Model description
 
@@ -35,12 +37,13 @@ Note that this model is primiarly aimed at predicting whether a Classical Chines
 
 Here is how to use this model to get the features of a given text in PyTorch:
 
-1. Import model
+1. Import model and packages
 ```python
 from transformers import BertTokenizer
 from transformers import BertForSequenceClassification
 import torch
 from numpy import exp
+import numpy as np
 
 tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
 model = BertForSequenceClassification.from_pretrained('cbdb/ClassicalChineseLetterClassification',