bert-base-cased-qa-evaluator
| Property | Value |
|---|---|
| Author | iarfmoose |
| Model Type | Question-Answer Evaluator |
| Base Architecture | BERT-base-cased |
| Hugging Face URL | Model Repository |
What is bert-base-cased-qa-evaluator?
The bert-base-cased-qa-evaluator is a specialized model designed to assess the validity of question-answer pairs. Built on the BERT-base-cased architecture, it includes a sequence classification head that determines whether a given question and its corresponding answer form a semantically coherent pair. This model was specifically developed to work alongside question generation systems, particularly the t5-base-question-generator.
Implementation Details
The model processes input in a specific format: [CLS] question [SEP] answer [SEP]. It leverages BERT's sequence classification capabilities to evaluate the semantic relationship between questions and answers. The training process involved both genuine QA pairs and corrupted samples, with a 50-50 split between intact and manipulated pairs.
- Trained on major datasets: SQuAD, RACE, CoQA, and MSMARCO
- Uses corruption techniques during training (answer swapping, question copying)
- Implements BertForSequenceClassification architecture
- Maintains case sensitivity (cased model)
Core Capabilities
- Evaluates semantic coherence between questions and answers
- Detects mismatched or corrupted QA pairs
- Supports quality assessment of generated questions
- Works with structured input format for question-answer evaluation
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in evaluating the semantic relationship between questions and answers, making it particularly valuable for assessing the quality of automated question generation systems. Its training on diverse datasets and corruption techniques makes it robust for practical applications.
Q: What are the recommended use cases?
The model is specifically designed to work with question generation systems, particularly for evaluating generated questions' quality. It's important to note that while it can assess semantic relationships, it cannot determine the factual correctness of answers.
