kiddojazz

# Add a new column named 'Price'
df['Prediction'] = df['labels'].map(label_map)
print(df)

kiddojazz

label_map = {
0: "Business",
1: "Entertainment",
2: "Politics",
3: "Sport",
4: "Tech"
}

kiddojazz

unlabelled_predictions = []
for data in unlabelled_data:
unlabelled_predictions.append(predict_category(data))
prediction_df = pd.DataFrame({
"data": unlabelled_data,
"labels": unlabelled_predictions,
})
prediction_df.to_csv("model_prediction.csv", index=False)

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url_unknown = 'https://github.com/kiddojazz/Multitext-Classification/blob/master/bbc_data.csv?raw=true'
df_unknown = pd.read_csv(url)
print(df_unknown.head(5))

kiddojazz

from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt
import seaborn as sns
confusion = confusion_matrix(test_labels, y_pred)
plt.figure(figsize=(8, 6))
sns.set(font_scale=1.2)
sns.heatmap(confusion, annot=True, fmt="d", cmap="Blues", cbar=False, square=True,
xticklabels=["Business", "Entertainment", "Politics", "Sport", "Tech"], yticklabels=["Business", "Entertainment", "Politics", "Sport", "Tech"])
plt.xlabel('Predicted')

kiddojazz

def predict_category(text):
predict_input = loaded_tokenizer.encode(text,
truncation=True,
padding=True,
return_tensors="tf")
output = loaded_model(predict_input)[0]
prediction_value = tf.argmax(output, axis=1).numpy()[0]
return prediction_value
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
y_pred = []

kiddojazz

predict_input = loaded_tokenizer.encode(test_text,
 truncation=True,
 padding=True,
 return_tensors="tf")
 
 output = loaded_model(predict_input)[0]
 
 prediction_value = tf.argmax(output, axis=1).numpy()[0]
 
 # Convert numeric prediction to category label

kiddojazz

#Business = 0, Entertainment = 1, Politics = 2, Sport = 3, Tech = 4
predict_input = loaded_tokenizer.encode(test_text,
truncation=True,
padding=True,
return_tensors="tf")
 
output = loaded_model(predict_input)[0]
 
prediction_value = tf.argmax(output, axis=1).numpy()[0]
prediction_value

kiddojazz

save_directory = "Multitext_Classification_2"
loaded_tokenizer = DistilBertTokenizer.from_pretrained(save_directory)
loaded_model = TFDistilBertForSequenceClassification.from_pretrained(save_directory)

kiddojazz

from tensorflow.keras.models import load_model
save_directory = "Multitext_Classification_colab" # Change this to your preferred location
 
model.save_pretrained(save_directory)
tokenizer.save_pretrained(save_directory)