# BertForSequenceClassification on CNN News Dataset

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.

The model was fine-tuned for four epochs achieving a training loss of 0.077900, a validation loss of 0.190814

 - accuracy : 0.956690.
 - f1 : 0.956144.
 - precision : 0.956393
 - recall : 0.956690

## Model Description


- **Developed by:** [CHERGUELAINE Ayoub](https://www.linkedin.com/in/ayoub-cherguelaine/)
- **Shared by :** HuggingFace
- **Model type:** Language model
- **Language(s) (NLP):** en
- **Finetuned from model :** [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)


## Usage

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.

Here's an example of how to use this model for text classification in Python:

```python
from transformers import AutoTokenizer, BertForSequenceClassification

model_name = "AyoubChLin/bert_cnn_news"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)

text = "This is a news article about politics."
inputs = tokenizer(text, padding=True, truncation=True, return_tensors="tf")

outputs = model(inputs)
predicted_class_id = tf.argmax(outputs.logits, axis=-1).numpy()[0]

labels = ["business", "entertainment", "health", "news", "politics", "sport"]
predicted_label = labels[predicted_class_id]
```

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.

## Contributors

This model was fine-tuned by [CHERGUELAINE Ayoub](https://www.linkedin.com/in/ayoub-cherguelaine/).
{"language": ["en"], "license": "apache-2.0", "datasets": ["AyoubChLin/CNN_News_Articles_2011-2022"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"}
transformers pytorch safetensors bert text-classification en dataset:AyoubChLin/CNN_News_Articles_2011-2022 license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

# BertForSequenceClassification on CNN News Dataset

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.

The model was fine-tuned for four epochs achieving a training loss of 0.077900, a validation loss of 0.190814

 - accuracy : 0.956690.
 - f1 : 0.956144.
 - precision : 0.956393
 - recall : 0.956690

## Model Description


- Developed by: CHERGUELAINE Ayoub
- Shared by : HuggingFace
- Model type: Language model
- Language(s) (NLP): en
- Finetuned from model : distilbert-base-uncased


## Usage

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.

Here's an example of how to use this model for text classification in Python:



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.

## Contributors

This model was fine-tuned by CHERGUELAINE Ayoub.
# BertForSequenceClassification on CNN News Dataset

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.

The model was fine-tuned for four epochs achieving a training loss of 0.077900, a validation loss of 0.190814

 - accuracy : 0.956690.
 - f1 : 0.956144.
 - precision : 0.956393
 - recall : 0.956690 ## Model Description


- Developed by: CHERGUELAINE Ayoub
- Shared by : HuggingFace
- Model type: Language model
- Language(s) (NLP): en
- Finetuned from model : distilbert-base-uncased ## Usage

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.

Here's an example of how to use this model for text classification in Python:



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. ## Contributors

This model was fine-tuned by CHERGUELAINE Ayoub.
TAGS
#transformers #pytorch #safetensors #bert #text-classification #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # BertForSequenceClassification on CNN News Dataset

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.

The model was fine-tuned for four epochs achieving a training loss of 0.077900, a validation loss of 0.190814

 - accuracy : 0.956690.
 - f1 : 0.956144.
 - precision : 0.956393
 - recall : 0.956690 ## Model Description


- Developed by: CHERGUELAINE Ayoub
- Shared by : HuggingFace
- Model type: Language model
- Language(s) (NLP): en
- Finetuned from model : distilbert-base-uncased ## Usage

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.

Here's an example of how to use this model for text classification in Python:



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. ## Contributors

This model was fine-tuned by CHERGUELAINE Ayoub.
TAGS
#transformers #pytorch #safetensors #bert #text-classification #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# BertForSequenceClassification on CNN News Dataset

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.

The model was fine-tuned for four epochs achieving a training loss of 0.077900, a validation loss of 0.190814

 - accuracy : 0.956690.
 - f1 : 0.956144.
 - precision : 0.956393
 - recall : 0.956690## Model Description


- Developed by: CHERGUELAINE Ayoub
- Shared by : HuggingFace
- Model type: Language model
- Language(s) (NLP): en
- Finetuned from model : distilbert-base-uncased## Usage

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.

Here's an example of how to use this model for text classification in Python:



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.## Contributors

This model was fine-tuned by CHERGUELAINE Ayoub.