update README

process_data/readme.md

@@ -2,21 +2,37 @@
 ### 1. run `convert_space_format.py`
 Convert the string `<space>` to `SPACE`
 
-### 2. run `create_negtive_examples.py`
+`linux32_0ixxxx.all -> inst.i.pos.txt` located at `/home/ming/malware/data/elfasm_inst_pairs`
+
+### 2. remove the repete lines in the `inst.i.pos.txt`
+Using python script is too slow. We use the shell instead.
+
+``` shell
+cat inst.i.pos.txt | sort -n | uniq > inst.i.pos.txt.clean
+```
+
+
+### 3. run `create_negtive_examples.py`
 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.
 
-### 3. run `merge_examples_to_json.py`
+generate `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.
 
-### 4. run `check_length.py`
+generate `inst.i.{0-127}.json` located at `/home/ming/malware/inst2vec_bert/data/asm_bert`
+
+### 5. run `check_length.py`
 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`
+<!-- ### 5. run `count_word_for_vocab.py`
 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.
+So we need to know how many characters in the dataset.
+
+Something is wrong with `p.join()`, so I just set `vocab_size=2`. -->