Update README.md

README.md

@@ -1,3 +1,100 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- cnn
+- amazon_reviews
+---
+# Model Card for Model ID
+
+# Downloads
+!pip install contractions
+!pip install textsearch
+!pip install tqdm
+
+import nltk
+nltk.download('punkt')
+
+# Fundamental classes
+import tensorflow as tf
+from tensorflow import keras
+import pandas as pd
+import numpy as np
+
+# Time
+import time
+import datetime
+
+# Preprocessing
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing import sequence
+from sklearn.preprocessing import LabelEncoder
+import contractions
+from bs4 import BeautifulSoup
+import re
+import tqdm
+import unicodedata
+
+seed = 3541
+np.random.seed(seed)
+
+# Define a dummy loss to bypass the error during model loading
+def dummy_loss(y_true, y_pred):
+    return tf.reduce_mean(y_pred - y_true)
+
+# Loading the model Trained on Amazon reviews
+modelAmazon = keras.models.load_model(
+    '/kaggle/input/pre-trained-model-binary-cnn-nlp-amazon-reviews/tensorflow1/pre_trained_sentiment_analysis_cnn_model_amazon_reviews/1/Binary_Classification_86_Amazon_Reviews_CNN.h5',
+    compile=False
+)
+
+# Compile the model with the correct loss function and reduction
+modelAmazon.compile(
+    optimizer='adam',
+    loss=keras.losses.BinaryCrossentropy(reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE),
+    metrics=['accuracy']
+)
+
+# Loading Amazon test data
+dataset_test_Amazon = pd.read_csv('/kaggle/input/amazon-reviews-for-sa-binary-negative-positive-csv/amazon_review_sa_binary_csv/test.csv')
+
+# Loading Amazon train data (to be used on the label encoder)
+dataset_train_Amazon = pd.read_csv('/kaggle/input/amazon-reviews-for-sa-binary-negative-positive-csv/amazon_review_sa_binary_csv/train.csv')
+
+# Shuffling the Test Data
+test_Amazon = dataset_test_Amazon.sample(frac=1)
+train_Amazon = dataset_train_Amazon.sample(frac=1)
+
+# Taking a tiny portion of the database (because it will only be used on the label encoder)
+train_Amazon = dataset_train_Amazon.iloc[:100, :]
+
+# Taking only necessary columns
+y_test_Amazon = test_Amazon['class_index'].values
+X_train_Amazon = train_Amazon['review_text'].values
+y_train_Amazon = train_Amazon['class_index'].values
+
+# Preprocess corpus function
+def pre_process_corpus(corpus):
+    processed_corpus = []
+    for doc in tqdm.tqdm(corpus):
+        doc = contractions.fix(doc)
+        doc = BeautifulSoup(doc, "html.parser").get_text()
+        doc = unicodedata.normalize('NFKD', doc).encode('ascii', 'ignore').decode('utf-8', 'ignore')
+        doc = re.sub(r'[^a-zA-Z\s]', '', doc, re.I|re.A)
+        doc = doc.lower()
+        doc = doc.strip()
+        processed_corpus.append(doc)
+    return processed_corpus
+
+# Preprocessing the Data
+X_test_Amazon = pre_process_corpus(test_Amazon['review_text'].values)
+X_train_Amazon = pre_process_corpus(X_train_Amazon)
+
+# Creating and Fitting the Tokenizer
+etc ...
+
+More info on the Model's page on Kaggle :
+
+https://www.kaggle.com/models/yacharki/pre-trained-model-binary-cnn-nlp-amazon-reviews