| --- |
| license: apache-2.0 |
| datasets: |
| - AyoubChLin/CNN_News_Articles_2011-2022 |
| language: |
| - en |
| metrics: |
| - accuracy |
| pipeline_tag: text-classification |
| tags: |
| - news classification |
| widget: |
| - text: money in the pocket |
| - text: no one can win this cup in quatar.. |
| --- |
| # Fine-Tuned BART Model for Text Classification on CNN News Articles |
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| 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 |
|
|
| ### Install |
|
|
| ```bash |
| pip install transformers |
| ``` |
|
|
| ### Example Usage |
|
|
| ```python |
| 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) |
| |
| print(predicted_label) |
| ``` |
| ## Evaluation |
|
|
| The model achieved the following performance metrics on the test set: |
|
|
| Accuracy: 0.9591836734693877 |
|
|
| F1-score: 0.958301875401112 |
|
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| Recall: 0.9591836734693877 |
|
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| Precision: 0.9579673040369542 |
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|
