load bert model from keras_nlp:

- problems with hub keras layers google-research/bert#1401

bert_scoring.py

@@ -0,0 +1,23 @@
+import pandas as pd
+import numpy as np
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.models import Sequential 
+from tensorflow.keras.layers import  Dense, Dropout
+import keras_nlp
+
+import tensorflow_text as text
+import tensorflow_hub as hub
+
+from sklearn.model_selection import train_test_split
+
+data = pd.read_csv("learning-agency-lab-automated-essay-scoring-2/train.csv")
+
+X_train, X_val, y_train, y_val = train_test_split(data['full_text'], data['score'], test_size=0.2, random_state=42)
+print(X_train.shape, X_val.shape, y_train.shape, y_val.shape )
+
+text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
+preprocessor = keras_nlp.models.BertPreprocessor.from_preset("bert_base_en_uncased",trainable=True)
+
+encoder_inputs = preprocessor(text_input)