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--- |
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license: apache-2.0 |
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datasets: |
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- AyoubChLin/CNN_News_Articles_2011-2022 |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- news classification |
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widget: |
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- text: money in the pocket |
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- text: no one can win this cup in quatar.. |
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--- |
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# 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. |
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## How to Use |
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### Install |
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```bash |
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pip install transformers |
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``` |
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### Example Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("Softechlb/articles_classification") |
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model = AutoModelForSequenceClassification.from_pretrained("Softechlb/articles_classification") |
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# Tokenize input text |
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text = "This is an example CNN news article about politics." |
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inputs = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors="pt") |
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# Make prediction |
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outputs = model(inputs["input_ids"], attention_mask=inputs["attention_mask"]) |
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predicted_label = torch.argmax(outputs.logits) |
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print(predicted_label) |
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``` |
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## Evaluation |
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The model achieved the following performance metrics on the test set: |
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Accuracy: 0.9591836734693877 |
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F1-score: 0.958301875401112 |
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Recall: 0.9591836734693877 |
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Precision: 0.9579673040369542 |
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