Update README.md

README.md

@@ -3,60 +3,49 @@ library_name: transformers
 license: apache-2.0
 datasets:
 - TokenBender/code_instructions_122k_alpaca_style
+- nickrosh/Evol-Instruct-Code-80k-v1
 metrics:
 - accuracy
 language:
 - en
 widget:
-  - text: |
-      ### Instruction: write a neural network complete code in Keras 
-      ### Input: Use cifar dataset 
-      ### Response:
-  
-    output:
-      text: >-
-        from keras.datasets import cifar10
-        import numpy as np
-        from keras.models import Sequential
-        from keras.layers import Dense, Dropout, Activation, Flatten
-        from keras.layers import Conv2D, MaxPooling2D
-        from keras.utils import to_categorical
-        from keras.optimizers import Adam
-        
-        # Load the CIFAR-10 dataset
-        (X_train, y_train), (X_test, y_test) = cifar10.load_data()
-        
-        # Normalize pixel values to be between 0 and 1
-        X_train = X_train.astype('float32') / 255
-        X_test = X_test.astype('float32') / 255
-        
-        # Convert class vectors to binary class matrices
-        y_train = to_categorical(y_train, num_classes=10)
-        y_test = to_categorical(y_test, num_classes=10)
-        
-        # Create the model
-        model = Sequential()
-        model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3)))
-        model.add(Activation('relu'))
-        model.add(MaxPooling2D(pool_size=(2, 2)))
-        
-        model.add(Conv2D(64, (3, 3)))
-        model.add(Activation('relu'))
-        model.add(MaxPooling2D(pool_size=(2, 2)))
-        
-        model.add(Flatten())
-        model.add(Dense(64))
-        model.add(Activation('relu'))
-        model.add(Dropout(0.5))
-        model.add(Dense(10))
-        model.add(Activation('softmax'))
-        
-        # Compile the model
-        model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])
-        
-        # Train the model
-        model.fit(X_train, y_train, batch_size=32, epochs=10, validation_split=0.2)
-  
+- text: |
+    ### Instruction: write a neural network complete code in Keras 
+    ### Input: Use cifar dataset 
+    ### Response:
+  output:
+    text: >-
+      from keras.datasets import cifar10 import numpy as np from keras.models
+      import Sequential from keras.layers import Dense, Dropout, Activation,
+      Flatten from keras.layers import Conv2D, MaxPooling2D from keras.utils
+      import to_categorical from keras.optimizers import Adam
+
+      # Load the CIFAR-10 dataset (X_train, y_train), (X_test, y_test) =
+      cifar10.load_data()
+
+      # Normalize pixel values to be between 0 and 1 X_train =
+      X_train.astype('float32') / 255 X_test = X_test.astype('float32') / 255
+
+      # Convert class vectors to binary class matrices y_train =
+      to_categorical(y_train, num_classes=10) y_test = to_categorical(y_test,
+      num_classes=10)
+
+      # Create the model model = Sequential() model.add(Conv2D(32, (3, 3),
+      input_shape=(32, 32, 3))) model.add(Activation('relu'))
+      model.add(MaxPooling2D(pool_size=(2, 2)))
+
+      model.add(Conv2D(64, (3, 3))) model.add(Activation('relu'))
+      model.add(MaxPooling2D(pool_size=(2, 2)))
+
+      model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu'))
+      model.add(Dropout(0.5)) model.add(Dense(10))
+      model.add(Activation('softmax'))
+
+      # Compile the model model.compile(loss='categorical_crossentropy',
+      optimizer=Adam(), metrics=['accuracy'])
+
+      # Train the model model.fit(X_train, y_train, batch_size=32, epochs=10,
+      validation_split=0.2)
 pipeline_tag: text-generation
 base_model: codellama/CodeLlama-13b-Instruct-hf
 ---