---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-classification
---
# Distil BERT Base Uncased Text Classification Model

This repository contains a Distil BERT Base Uncased model that has been fine-tuned for a custom text classification use case. The model is designed to classify text into nine different classes based on the following categories:

1. **Neutral**: This class is for any other sort of sentences that do not fall into the specific categories below.

2. **Play**: Use this class to classify the intent of the user to listen to music or audio.

3. **Visit**: This class is intended for classifying the user's intent to visit or open a website in a web browser.

4. **ImgReco**: Use this class to make the bot process an image for image-to-text conversion or any image recognition tasks.

5. **Close**: For classifying sentences that indicate the user's intent to close a specific application or software.

6. **Web Search**: This class is designed to identify the user's intent to search the internet for information.

7. **Open**: Use this class for classifying the intent of opening a specific app or software.

8. **ImgGen**: This class is for sentences related to text-to-image processing or image generation tasks.

9. **Math**: Classify sentences that include mathematical equations or expressions using this category.

## Model Details

- **Model Architecture**: Distil BERT Base Uncased
- **Number of Classes**: 9
- **Training Data**: The model was trained on a custom dataset for text classification tasks related to the mentioned categories.

## Usage

You can use this fine-tuned Distil BERT model for text classification in your own applications or projects. Here's a simple example of how to use the model in Python:

```python
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch

# Load the pre-trained model and tokenizer
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
model = DistilBertForSequenceClassification.from_pretrained("your_model_directory_path")

# Prepare the input text
text = "Your input text goes here."

# Tokenize the text and classify it
input_ids = tokenizer.encode(text, truncation=True, padding=True, return_tensors="pt")
output = model(input_ids)
logits = output.logits
probabilities = logits.softmax(dim=1)

# Get the class with the highest probability
predicted_class = torch.argmax(probabilities)

# The predicted_class variable will contain the predicted class index.
```

Remember to replace `"your_model_directory_path"` with the actual path to the fine-tuned model on your system.

## Training

To fine-tune this model for your own custom use case, you can use the Hugging Face Transformers library along with your custom dataset. Refer to the [Hugging Face Transformers documentation](https://huggingface.co/transformers/main_classes/configuration.html) for more details on training and fine-tuning.

## License

Please refer to the licenses associated with the Distil BERT Base Uncased model and any other relevant libraries or datasets you used during fine-tuning.

If you have any questions or need further assistance, please feel free to reach out.