diff --git a/process_data/readme.md b/process_data/readme.md
index e47cd1c..7f43fc9 100644
--- a/process_data/readme.md
+++ b/process_data/readme.md
@@ -12,27 +12,51 @@ cat inst.i.pos.txt | sort -n | uniq > inst.i.pos.txt.clean
 ```
 
 
-### 3. run `create_negtive_examples.py`
+### 3. create_negtive_examples
 We use the next file of the current file as its negative examples, which is apparently rational.
 
 Specifically, for each instruction in the current positive file, we randomly choose a line in its next file and select one of two instructions in the line as its negative example.
 
-generate `inst.i.neg.txt` located at `/home/ming/malware/data/elfasm_inst_pairs`
+`python create_negtive_examples.py`, generating `inst.i.neg.txt` located at `/home/ming/malware/data/elfasm_inst_pairs`
 
-### 4. run `merge_examples_to_json.py`
-We dump the positive and negative examples with their corresponding labels into several json files. 
-Each json file contains 20m lines of examples.
 
-generate `inst.i.{0-127}.json` located at `/home/ming/malware/inst2vec_bert/data/asm_bert`
+### 4. merge all of the files
+We catenate all of the `inst.i.pos.txt.clean` files and remove the possible repeting lines between different files:
+``` shell
+cat inst.*.pos.txt.clean | sort -n | uniq > inst.all.pos.txt.clean
+```
 
-### 5. run `check_length.py`
+We process the files containing negative examples similarly.
+``` shell
+cat inst.*.neg.txt.clean | sort -n | uniq > inst.all.neg.txt.clean
+```
+
+Based on the `inst.all.pos.txt.clean`, we remove the lines from `inst.all.neg.txt.clean` if they also occur in `inst.all.pos.txt.clean`. This can be completed by `python clean.py`.
+
+
+### 5. convert to json format
+We first add labels for positive examples and negative examples
+```shell
+cat inst.all.neg.txt.clean | sed 's/^/0\t&/g' > inst.all.neg.txt.clean.label
+cat inst.all.pos.txt.clean | sed 's/^/1\t&/g' > inst.all.pos.txt.clean.label
+```
+
+We dump the positive and negative examples with their corresponding labels into several json files, using `python merge_examples_to_json.py`.
+
+Generate `inst.all.{0,1}.json` located at `/home/ming/malware/inst2vec_bert/data/asm_bert`.
+
+
+### 6. get the maximum of length in examples
 We will specify the length padded to when we use the tokenizer, `tokenizer.enable_padding(..., length=)`. 
 
 So we need to know the longest sentences in the dataset.
 
-<!-- ### 5. run `count_word_for_vocab.py`
+The result is `28`, so I set `length=32`
+
+
+### 7. get the size of vocab of examples
 Similarly, we also need to specify the size of vocabulary when we train the tokenizer, `WordLevelTrainer(vocab_size=, ...)`. 
 
 So we need to know how many characters in the dataset.
 
-Something is wrong with `p.join()`, so I just set `vocab_size=2`. -->
\ No newline at end of file
+The result is `1016`, so I set `vocab_size=2000`.
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