Update README.md

README.md

@@ -1,3 +1,67 @@
----
-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+language:
+- en
+tags:
+- bert
+- question-classification
+- trec
+widget:
+  - text: |
+      Enter your question here to classify its type.
+    example_title: "Classify Question Type"
+---
+
+# BERT-Question-Classifier
+
+The BERT-Question-Classifier is a refined model based on the `bert-base-uncased` architecture. It has been fine-tuned specifically for classifying the types of questions (Description, Entity, Expression, Human, Location, Numeric) using the TREC question classification dataset.
+
+- **Developed by**: phanerozoic
+- **Model type**: BertForSequenceClassification
+- **Source model**: `bert-base-uncased`
+- **License**: cc-by-nc-4.0
+- **Languages**: English
+
+## Model Details
+
+The BERT-Question-Classifier utilizes a self-attention mechanism to assess the relevance of each word in the context of a question, optimized for categorizing question types.
+
+### Configuration
+- **Attention probs dropout prob**: 0.1
+- **Hidden act**: gelu
+- **Hidden size**: 768
+- **Number of attention heads**: 12
+- **Number of hidden layers**: 12
+
+## Training and Evaluation Data
+
+This model is trained on the TREC dataset, which contains a diverse set of question types each labeled under categories such as Description, Entity, Expression, Human, Location, and Numeric.
+
+## Training Procedure
+
+The training process was systematically automated to evaluate various hyperparameters, ensuring the selection of optimal settings for the best model performance.
+
+- **Initial exploratory training**: Various configurations of epochs, batch sizes, and learning rates were tested.
+- **Focused refinement training**: Post initial testing, the model underwent intensive training with selected hyperparameters to ensure consistent performance and generalization.
+
+### Optimal Hyperparameters Identified
+- **Epochs**: 5
+- **Batch size**: 48
+- **Learning rate**: 2e-5
+
+### Performance
+Post-refinement, the model exhibits high efficacy in question type classification:
+- **Accuracy**: 91%
+- **F1 Score**: 92%
+
+## Usage
+
+This model excels in classifying question types in English, ideal for systems needing to interpret and categorize user queries accurately.
+
+## Limitations
+
+The BERT-Question-Classifier performs best on question data similar to that found in the TREC dataset. Performance may vary when applied to different domains or languages.
+
+## Acknowledgments
+
+Special thanks to the developers of the BERT architecture and the contributions from the Hugging Face team, whose tools and libraries were crucial in the development of this classifier.