
# DistilBERT for MIX Dataset

This repository contains the code and pre-trained DistilBERT model for Multi-Industry Classification (MXC) Dataset. The model was fine-tuned for 4 epochs on a combination of AG News, CNN News, and 20 News Group (relabeled) datasets.

## Model description

DistilBERT is a smaller and faster version of BERT, a state-of-the-art language model developed by Google. DistilBERT has the same architecture as BERT, but with fewer layers and fewer parameters. This makes it much faster and easier to deploy in production environments.

The pre-trained DistilBERT model was fine-tuned on the MIC dataset, which consists of news articles from three different industries: finance, technology, and sports. The task is to classify each article into one of these three categories.

<!-- Provide a longer summary of what this model is. -->


- **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)


## Requirements

- Python 3.x
- PyTorch 1.8 or later
- Transformers 4.0 or later
- Pandas
- NumPy

## Evaluation

The model was evaluated on two different datasets: AG News and CNN News. Here are the evaluation results:

### AG News

- Loss: 0.16675
- Accuracy: 0.94815789
- Precision: 0.9483508
- Recall: 0.94815789
- F1 score: 0.9482136

### CNN News

- Loss: 0.119858004
- Accuracy: 0.963338028
- Precision: 0.96379391
- Recall: 0.963338028
- F1 score: 0.963524631

## Usage

You can use the pre-trained model to make predictions on new news articles. Here's an example:

```python
from transformers import AutoTokenizer, DistilBertForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/distilbert_ag_cnn")
model = DistilBertForSequenceClassification.from_pretrained("AyoubChLin/distilbert_ag_cnn")

# Replace "input_text" with the actual news article
input_text = "The stock market had a strong performance today, driven by strong earnings reports from technology companies."

# Tokenize the input text
inputs = tokenizer(input_text, return_tensors='pt')

# Make a prediction
outputs = model(**inputs)
predicted_label = torch.argmax(outputs.logits, dim=1)

# Print the predicted label
print(predicted_label)
```

## Credits

- The MIX dataset was collected and annotated by [CHERGUELAINE Ayoub](https://www.linkedin.com/in/ayoub-cherguelaine/)
{"language": ["en"], "license": "apache-2.0", "datasets": ["ag_news", "AyoubChLin/CNN_News_Articles_2011-2022"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"}
transformers pytorch distilbert text-classification en dataset:ag_news dataset:AyoubChLin/CNN_News_Articles_2011-2022 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #en #dataset-ag_news #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# DistilBERT for MIX Dataset

This repository contains the code and pre-trained DistilBERT model for Multi-Industry Classification (MXC) Dataset. The model was fine-tuned for 4 epochs on a combination of AG News, CNN News, and 20 News Group (relabeled) datasets.

## Model description

DistilBERT is a smaller and faster version of BERT, a state-of-the-art language model developed by Google. DistilBERT has the same architecture as BERT, but with fewer layers and fewer parameters. This makes it much faster and easier to deploy in production environments.

The pre-trained DistilBERT model was fine-tuned on the MIC dataset, which consists of news articles from three different industries: finance, technology, and sports. The task is to classify each article into one of these three categories.




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


## Requirements

- Python 3.x
- PyTorch 1.8 or later
- Transformers 4.0 or later
- Pandas
- NumPy

## Evaluation

The model was evaluated on two different datasets: AG News and CNN News. Here are the evaluation results:

### AG News

- Loss: 0.16675
- Accuracy: 0.94815789
- Precision: 0.9483508
- Recall: 0.94815789
- F1 score: 0.9482136

### CNN News

- Loss: 0.119858004
- Accuracy: 0.963338028
- Precision: 0.96379391
- Recall: 0.963338028
- F1 score: 0.963524631

## Usage

You can use the pre-trained model to make predictions on new news articles. Here's an example:



## Credits

- The MIX dataset was collected and annotated by CHERGUELAINE Ayoub
# DistilBERT for MIX Dataset

This repository contains the code and pre-trained DistilBERT model for Multi-Industry Classification (MXC) Dataset. The model was fine-tuned for 4 epochs on a combination of AG News, CNN News, and 20 News Group (relabeled) datasets. ## Model description

DistilBERT is a smaller and faster version of BERT, a state-of-the-art language model developed by Google. DistilBERT has the same architecture as BERT, but with fewer layers and fewer parameters. This makes it much faster and easier to deploy in production environments.

The pre-trained DistilBERT model was fine-tuned on the MIC dataset, which consists of news articles from three different industries: finance, technology, and sports. The task is to classify each article into one of these three categories.




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

- Python 3.x
- PyTorch 1.8 or later
- Transformers 4.0 or later
- Pandas
- NumPy ## Evaluation

The model was evaluated on two different datasets: AG News and CNN News. Here are the evaluation results: ### AG News

- Loss: 0.16675
- Accuracy: 0.94815789
- Precision: 0.9483508
- Recall: 0.94815789
- F1 score: 0.9482136 ### CNN News

- Loss: 0.119858004
- Accuracy: 0.963338028
- Precision: 0.96379391
- Recall: 0.963338028
- F1 score: 0.963524631 ## Usage

You can use the pre-trained model to make predictions on new news articles. Here's an example: ## Credits

- The MIX dataset was collected and annotated by CHERGUELAINE Ayoub
TAGS
#transformers #pytorch #distilbert #text-classification #en #dataset-ag_news #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # DistilBERT for MIX Dataset

This repository contains the code and pre-trained DistilBERT model for Multi-Industry Classification (MXC) Dataset. The model was fine-tuned for 4 epochs on a combination of AG News, CNN News, and 20 News Group (relabeled) datasets. ## Model description

DistilBERT is a smaller and faster version of BERT, a state-of-the-art language model developed by Google. DistilBERT has the same architecture as BERT, but with fewer layers and fewer parameters. This makes it much faster and easier to deploy in production environments.

The pre-trained DistilBERT model was fine-tuned on the MIC dataset, which consists of news articles from three different industries: finance, technology, and sports. The task is to classify each article into one of these three categories.




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

- Python 3.x
- PyTorch 1.8 or later
- Transformers 4.0 or later
- Pandas
- NumPy ## Evaluation

The model was evaluated on two different datasets: AG News and CNN News. Here are the evaluation results: ### AG News

- Loss: 0.16675
- Accuracy: 0.94815789
- Precision: 0.9483508
- Recall: 0.94815789
- F1 score: 0.9482136 ### CNN News

- Loss: 0.119858004
- Accuracy: 0.963338028
- Precision: 0.96379391
- Recall: 0.963338028
- F1 score: 0.963524631 ## Usage

You can use the pre-trained model to make predictions on new news articles. Here's an example: ## Credits

- The MIX dataset was collected and annotated by CHERGUELAINE Ayoub