Add support for Hugging Face Transformers 4.x, upgrade libraries.

ernie/.flake8

@@ -0,0 +1,3 @@
+[flake8]
+max-line-length = 79
+ignore = W503

ernie/.style.yapf

@@ -0,0 +1,9 @@
+[style]
+based_on_style=pep8
+column_limit=79
+split_before_arithmetic_operator=true
+split_before_logical_operator=true
+split_before_named_assigns=true
+split_before_first_argument=true
+allow_split_before_dict_value=false
+dedent_closing_brackets=true

ernie/__init__.py

@@ -5,7 +5,7 @@
 from tensorflow.python.client import device_lib
 import logging
 
-__version__ = '1.0.1'
+__version__ = '1.2307.0'
 
 logging.getLogger().setLevel(logging.WARNING)
 logging.getLogger("transformers.tokenization_utils").setLevel(logging.ERROR)
@@ -18,7 +18,7 @@
 def _get_cpu_name():
     import cpuinfo
     cpu_info = cpuinfo.get_cpu_info()
-    cpu_name = f"{cpu_info['brand']}, {cpu_info['count']} vCores"
+    cpu_name = f"{cpu_info['brand_raw']}, {cpu_info['count']} vCores"
     return cpu_name
 
 

ernie/aggregation_strategies.py

@@ -6,11 +6,7 @@
 
 class AggregationStrategy:
     def __init__(
-        self,
-        method,
-        max_items=None,
-        top_items=True,
-        sorting_class_index=1
+        self, method, max_items=None, top_items=True, sorting_class_index=1
     ):
         self.method = method
         self.max_items = max_items
@@ -36,35 +32,26 @@ def aggregate(self, softmax_tuples):
 
         softmax_list = []
         for key in softmax_dicts[0].keys():
-            softmax_list.append(self.method(
-                [probabilities[key] for probabilities in softmax_dicts]))
+            softmax_list.append(
+                self.method(
+                    [probabilities[key] for probabilities in softmax_dicts]
+                )
+            )
         softmax_tuple = tuple(softmax_list)
         return softmax_tuple
 
 
 class AggregationStrategies:
     Mean = AggregationStrategy(method=mean)
     MeanTopFiveBinaryClassification = AggregationStrategy(
-        method=mean,
-        max_items=5,
-        top_items=True,
-        sorting_class_index=1
+        method=mean, max_items=5, top_items=True, sorting_class_index=1
     )
     MeanTopTenBinaryClassification = AggregationStrategy(
-        method=mean,
-        max_items=10,
-        top_items=True,
-        sorting_class_index=1
+        method=mean, max_items=10, top_items=True, sorting_class_index=1
     )
     MeanTopFifteenBinaryClassification = AggregationStrategy(
-        method=mean,
-        max_items=15,
-        top_items=True,
-        sorting_class_index=1
+        method=mean, max_items=15, top_items=True, sorting_class_index=1
     )
     MeanTopTwentyBinaryClassification = AggregationStrategy(
-        method=mean,
-        max_items=20,
-        top_items=True,
-        sorting_class_index=1
+        method=mean, max_items=20, top_items=True, sorting_class_index=1
     )