added params

requirements.txt

@@ -1,7 +1,7 @@
-numpy==1.19.2
-datasets==1.8.0
+numpy==1.21.1
+datasets==1.10.2
 pytorch_lightning==1.3.5
-transformers==4.6.0
+transformers==4.9.0
 torch==1.9.0+cu111
 dagshub==0.1.6
 pandas==1.2.4

src/models/model.py

@@ -1,6 +1,4 @@
-import time
 import torch
-import numpy as np
 import pandas as pd
 from dagshub.pytorch_lightning import DAGsHubLogger
 from transformers import (
@@ -319,7 +317,7 @@ class Summarization:
 
         self.T5Model = LightningModel(
             tokenizer=self.tokenizer, model=self.model, output=outputdir,
-            learning_rate=learning_rate,adam_epsilon=adam_epsilon
+            learning_rate=learning_rate, adam_epsilon=adam_epsilon
         )
 
         MLlogger = MLFlowLogger(experiment_name="Summarization",

src/models/predict_model.py

@@ -1,7 +1,7 @@
-from src.data.make_dataset import make_dataset
 from .model import Summarization
 import pandas as pd
 
+
 def predict_model(text):
     """
     Predict the summary of the given text.
@@ -11,8 +11,8 @@ def predict_model(text):
     pre_summary = model.predict(text)
     return pre_summary
 
-    
+
 if __name__ == '__main__':
     text = pd.load_csv('data/processed/test.csv')['input_text'][0]
     pre_summary = predict_model(text)
-    print(pre_summary)  
+    print(pre_summary)

tox.ini

@@ -1,3 +1,3 @@
 [flake8]
-max-line-length = 79
+max-line-length = 160
 max-complexity = 10