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Fine-Tuned BART Model for Text Classification on CNN News Articles

This is a fine-tuned BART (Bidirectional and Auto-Regressive Transformers) model for text classification on CNN news articles. The model was fine-tuned on a dataset of CNN news articles with labels indicating the article topic, using a batch size of 32, learning rate of 6e-5, and trained for one epoch.

How to Use


pip install transformers

Example Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Softechlb/articles_classification")
model = AutoModelForSequenceClassification.from_pretrained("Softechlb/articles_classification")

# Tokenize input text
text = "This is an example CNN news article about politics."
inputs = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors="pt")

# Make prediction
outputs = model(inputs["input_ids"], attention_mask=inputs["attention_mask"])
predicted_label = torch.argmax(outputs.logits)



The model achieved the following performance metrics on the test set:

Accuracy: 0.9591836734693877

F1-score: 0.958301875401112

Recall: 0.9591836734693877

Precision: 0.9579673040369542

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Dataset used to train Softechlb/articles_classification

Space using Softechlb/articles_classification 1