TIN_Repair

Reparing the raised suspicious issues

Environment

python == 3.7
pytorch == 1.7.1
Transformers == 3.3.0

Run

To run the repairing part of TIN, you need import Repairer from repair.py in your code first.

Then you can execute the following python codes to automatically repair the NER issues raised by the testing part:

MY_REPAIRER = Repairer() 
MY_REPAIRER.bert_init() # Initialize the BERT
MY_REPAIRER.repair_suspicious_issue(input_file, output_file, api)

, where the input_file represents the path for the json file of the suspicious issues, the output_file represents the path of the json file to store the NER repairing result, and api is the function to obtain the NER prediction from the NER systems.

We provide four api functions including flair, flair_OntoNotes, azure and aws, you can import them from the apis.py. In practice, you can invoke the flair and flar_OntoNotes directly, and invoke the azure and aws after you fill your keys in the apis.py. It is also availiable that you write own api function to repair other NER system, but you need ensure the output format of your api function is consistent with the api functions provided.

For example, you can run these codes in the repair.py directly and the program will show the repairing result in the data/suspicious_flair_repair.json

apis = APIS()
NER_repairer = Repairer()
NER_repairer.bert_init()
NER_repairer.repair_suspicious_issue("./data/suspicious_flair.json",
                        "./data/suspicious_flair_repair_json", apis.flair)

We provide a demo of repairing, you can directly run:

python repair.py

Example results

We provide the example results including transformation results and suspicious issues, the example results are in the {ROOT_DIR}/repair_evaluation_result folder.