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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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--- |
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# BertForSequenceClassification on CNN News Dataset |
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This repository contains a fine-tuned Bert base model for sequence classification on the CNN News dataset. The model is able to classify news articles into one of six categories: business, entertainment, health, news, politics, and sport. |
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The model was fine-tuned for four epochs achieving a training loss of 0.077900, a validation loss of 0.190814 |
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- accuracy : 0.956690. |
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- f1 : 0.956144. |
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- precision : 0.956393 |
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- recall : 0.956690 |
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## Model Description |
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- **Developed by:** [CHERGUELAINE Ayoub](https://www.linkedin.com/in/ayoub-cherguelaine/) |
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- **Shared by :** HuggingFace |
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- **Model type:** Language model |
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- **Language(s) (NLP):** en |
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- **Finetuned from model :** [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) |
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## Usage |
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You can use this model with the Hugging Face Transformers library for a variety of natural language processing tasks, such as text classification, sentiment analysis, and more. |
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Here's an example of how to use this model for text classification in Python: |
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```python |
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from transformers import AutoTokenizer, BertForSequenceClassification |
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model_name = "AyoubChLin/bert_cnn_news" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = TFAutoModelForSequenceClassification.from_pretrained(model_name) |
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text = "This is a news article about politics." |
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inputs = tokenizer(text, padding=True, truncation=True, return_tensors="tf") |
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outputs = model(inputs) |
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predicted_class_id = tf.argmax(outputs.logits, axis=-1).numpy()[0] |
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labels = ["business", "entertainment", "health", "news", "politics", "sport"] |
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predicted_label = labels[predicted_class_id] |
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``` |
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In this example, we first load the tokenizer and the model using their respective `from_pretrained` methods. We then encode a news article using the tokenizer, pass the inputs through the model, and extract the predicted label using the `argmax` function. Finally, we map the predicted label to its corresponding category using a list of labels. |
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## Contributors |
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This model was fine-tuned by [CHERGUELAINE Ayoub](https://www.linkedin.com/in/ayoub-cherguelaine/). |
