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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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tags: |
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- bert |
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- question-classification |
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- trec |
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widget: |
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- text: | |
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Enter your text to classify its content. |
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example_title: "Classify Question Type" |
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--- |
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# BERT-Question-Classifier |
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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 entered (Description, Entity, Expression, Human, Location, Numeric) using the TREC question classification dataset. |
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- **Developed by**: phanerozoic |
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- **Model type**: BertForSequenceClassification |
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- **Source model**: `bert-base-uncased` |
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- **License**: cc-by-nc-4.0 |
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- **Languages**: English |
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## Model Details |
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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. |
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### Configuration |
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- **Attention probs dropout prob**: 0.1 |
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- **Hidden act**: gelu |
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- **Hidden size**: 768 |
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- **Number of attention heads**: 12 |
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- **Number of hidden layers**: 12 |
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## Training and Evaluation Data |
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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. |
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## Training Procedure |
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The training process was systematically automated to evaluate various hyperparameters, ensuring the selection of optimal settings for the best model performance. |
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- **Initial exploratory training**: Various configurations of epochs, batch sizes, and learning rates were tested. |
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- **Focused refinement training**: Post initial testing, the model underwent intensive training with selected hyperparameters to ensure consistent performance and generalization. |
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### Optimal Hyperparameters Identified |
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- **Epochs**: 5 |
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- **Batch size**: 48 |
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- **Learning rate**: 2e-5 |
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### Performance |
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Post-refinement, the model exhibits high efficacy in question type classification: |
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- **Accuracy**: 91% |
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- **F1 Score**: 92% |
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## Usage |
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This model excels in classifying question types in English, ideal for systems needing to interpret and categorize user queries accurately. |
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## Limitations |
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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. |
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## Acknowledgments |
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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. |
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