Create README.md

README.md

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 ---
 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.