metadata

license: mit
datasets:
  - HermesPenn/athena_data

News Classifier -- The Evaluation Pipeline

The colab link: https://colab.research.google.com/drive/1OmIHVN0joIgjGgYCdqLu2EO2By4yT5Xd#scrollTo=MsmKRoHuHyIp

Ziao You, Samuel Vara, Surya Sandeep Akella

The codes here are the same as the colab link. It shows how to call our model to evaluate the test set. Please use the colab link for easier usage.

pip install package

!pip install datasets > delete.txt

!!! Load Test Set -- Change the file path of test set

import pandas as pd
df_test = pd.read_csv('/content/test_data.csv',index_col="Unnamed: 0")
df_test.head()

Load Model from Hugging Face Hub (Don't change)

from huggingface_hub import snapshot_download
import keras

# Download model from hugging face
local_path = snapshot_download(repo_id="HermesPenn/athena_model")

# Load model from local
model = keras.saving.load_model(local_path)

Load Training set (Don't change)

from datasets import load_dataset

dataset = load_dataset("HermesPenn/athena_data")
dataset = dataset['train']
data = dataset.to_pandas()
data.head()

Fit_transform label_encoder and tokenizer (Don't change)

from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
# Data preprocessing
le = LabelEncoder()
data['label'] = le.fit_transform(data['source'])
X = data['title']
y = data['label']

# Tokenize and pad text data
tokenizer = Tokenizer(num_words=20000, oov_token="<OOV>")
tokenizer.fit_on_texts(X)
X_seq = tokenizer.texts_to_sequences(X)
X_padded = pad_sequences(X_seq, maxlen=200, padding='post', truncating='post')

Test set Evaluation (Don't change)

from sklearn.metrics import classification_report

X_test = df_test['title']
y_test = df_test['label']


X_test_seq = tokenizer.texts_to_sequences(X_test)
X_test_padded = pad_sequences(X_test_seq, maxlen=200, padding='post', truncating='post')

# Predict the labels using the model
y_pred_probs = model.predict(X_test_padded)
y_pred = (y_pred_probs > 0.5).astype(int)

# Evaluate the model
print("Classification Report:")
print(classification_report(y_test, y_pred))


try:
news_outlets = le.inverse_transform(y_pred.flatten()) # le must be pre-fitted
df_test['Predicted News Outlet'] = news_outlets
except NameError:
df_test['Predicted News Outlet'] = y_pred.flatten()

# Display test set with predictions
print("\nTest Set with Predictions:")

df_test[['title', 'News Outlet', 'Predicted News Outlet']]