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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+Based on the sample dataset and the purpose of your model, here's an updated model card for your fine-tuned DistilBERT model on Hugging Face:
+
+---
+
+## Model Details
+
+- **Model Name**: DistilBERT for Educational Query Classification
+- **Model Architecture**: DistilBERT (base model: `distilbert-base-uncased`)
+- **Language**: English
+- **Model Type**: Transformer-based text classification model
+- **License**: [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+## Overview
+
+This model is a fine-tuned version of [DistilBERT](https://huggingface.co/distilbert-base-uncased) specifically designed for classifying queries as either educational or non-educational. It was trained on a dataset containing a variety of questions and statements, with each entry labeled as either "educational" or "non-educational."
+
+## Intended Use
+
+- **Primary Use Case**: This model is intended to classify text inputs into two categories: "educational" or "non-educational." It is useful for applications that need to filter out or prioritize educational content.
+- **Potential Applications**:
+  - Educational chatbots or virtual assistants
+  - Content moderation for educational platforms
+  - Automated tagging of educational content
+  - Filtering non-educational queries from educational websites or apps
+
+## Training Data
+
+- **Dataset**: The model was fine-tuned on a custom educational dataset. This dataset includes various types of queries that are labeled based on their content as either "educational" or "non-educational."
+- **Dataset Source**: The dataset was manually curated to include a balanced mix of educational questions (covering various academic subjects) and non-educational questions (general queries that do not pertain to educational content).
+
+## Training Procedure
+
+- **Framework**: The model was trained using the [Hugging Face Transformers library](https://huggingface.co/transformers/) with PyTorch.
+- **Fine-Tuning Parameters**:
+  - **Batch Size**: 16
+  - **Learning Rate**: 5e-5
+  - **Epochs**: 3
+  - **Optimizer**: AdamW with weight decay
+- **Hardware**: Fine-tuning was performed on a single NVIDIA V100 GPU.
+
+## Limitations and Bias
+
+While this model has been fine-tuned for classifying queries as educational or non-educational, there are some limitations and potential biases:
+
+- **Bias in Data**: The model may reflect any biases present in the training data, particularly if certain topics or types of educational content are overrepresented or underrepresented.
+- **Binary Classification**: The model categorizes inputs strictly as "educational" or "non-educational." It may not handle nuanced or ambiguous queries effectively.
+- **Not Suitable for Other Classifications**: This model is specifically designed for educational vs. non-educational classification. It may not perform well on other types of classification tasks without further fine-tuning.
+
+## How to Use
+
+You can load the model using the Hugging Face Transformers library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("haider0941/distilbert-base-educationl")
+model = AutoModelForSequenceClassification.from_pretrained("haider0941/distilbert-base-educationl")
+
+input_text = "What is the capital of France?"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model(**inputs)
+```
+
+## Citation
+
+If you use this model, please cite it as follows:
+
+```
+@misc{your-username_2024,
+  title={Fine-Tuned DistilBERT for Educational Query Classification},
+  author={Haider0941},
+  year={2024},
+  howpublished={\url{https://huggingface.co/haider0941/distilbert-base-educationl}},
+}
+```