Answer equivalence roberta Large
The Answer Equivalence Roberta Large model is designed to evaluate question-answering models and large language models. It offers six evaluation methods, including Normalized Exact Match, Token F1 Score, PEDANTS, Finetuned Neural Matching, Prompting LLM, and Black-box LLM Evaluation. This model is efficient and fast, allowing for quick evaluation of QA models. But what makes it unique? It supports various models, including BERT, DistilRoBERTa, DistilBERT, RoBERTa, Tiny-BERT, and RoBERTa-Large, and can be used for both short and long-form QA. With its ability to provide accurate results and support for multiple models, the Answer Equivalence Roberta Large model is a valuable tool for evaluating QA models. But how does it compare to other models? Its PEDANTS method has been shown to have a high correlation with human judgment, making it a reliable choice for evaluating QA models.
Table of Contents
Model Overview
The QA-Evaluation-Metrics model is a fast and lightweight Python package designed to evaluate question-answering models and prompting of black-box and open-source large language models.
What does it do? It offers six QA evaluation methods with varying strengths, including:
- Normalized Exact Match
- Token F1 Score
- PEDANTS
- Finetuned Neural Matching
- Open Source LLM Evaluation
- Black-box LLM Evaluation
Capabilities
The QA-Evaluation-Metrics model is a powerful tool for evaluating question-answering models. Let’s dive into its capabilities.
Methods
- Normalized Exact Match: Best for short-form QA, this method checks if there are any exact normalized matches between gold and candidate answers.
- Token F1 Score: Suitable for both short and long-form QA, this method calculates the F1 score, precision, and recall between a gold and candidate answer.
- PEDANTS: Effective for both short and medium-form QA, this method uses a similarity score to evaluate the candidate answer.
- Neural Evaluation: Applicable to both short and long-form QA, this method uses a transformer-based approach to evaluate the candidate answer.
- Open Source LLM Evaluation: Suitable for all QA types, this method integrates with open-source models like LLaMA-2-70B-chat and LLaVA-1.5.
- Black-box LLM Evaluation: Also suitable for all QA types, this method uses a paid service to evaluate the candidate answer.
| Method | Best For | Cost | Correlation with Human Judgment |
|---|---|---|---|
| Normalized Exact Match | Short-form QA | Free | Good |
| PEDANTS | Both short & medium-form QA | Free | Very High |
| Neural Evaluation | Both short & long-form QA | Free | High |
| Open Source LLM Evaluation | All QA types | Free | High |
| Black-box LLM Evaluation | All QA types | Paid | Highest |
Performance
The QA-Evaluation-Metrics model showcases remarkable performance in evaluating question-answering models. Let’s dive into its speed, accuracy, and efficiency in various tasks.
Speed
The QA-Evaluation-Metrics model is designed to be fast and lightweight, making it perfect for evaluating large-scale question-answering models. With its efficient architecture, it can quickly process and evaluate multiple models, saving you time and resources.
Accuracy
The package offers six QA evaluation methods, each with its strengths and weaknesses. These methods ensure that the QA-Evaluation-Metrics model provides accurate evaluations, helping you identify the best-performing models for your specific tasks.
Efficiency
The QA-Evaluation-Metrics model is designed to be efficient, with a small model size of 18MB for the tiny-bert model. This makes it perfect for deployment in resource-constrained environments. Additionally, the package supports multiple models, including OpenAI GPT-series and Claude Series models, as well as open-source models like LLaMA-2-70B-chat and LLaVA-1.5.
Use Cases
The QA-Evaluation-Metrics model is a versatile tool that can be used in various scenarios. Here are a few examples:
- Evaluate the performance of question-answering models
- Compare the strengths and weaknesses of different QA evaluation methods
- Use the model to fine-tune and improve the performance of your own QA models
Limitations
While the QA-Evaluation-Metrics model is a powerful tool, it’s not perfect. Let’s take a closer look at some of its limitations.
Evaluation Methods
The QA-Evaluation-Metrics model offers six QA evaluation methods, each with its strengths and weaknesses. For example:
- Normalized Exact Match is great for short-form QA, but it may not perform well for longer answers or more complex questions.
- PEDANTS is a more robust method, but it requires more computational resources and may not be suitable for all use cases.
Model Support
The QA-Evaluation-Metrics model supports a range of models, including OpenAI GPT-series and Claude Series models, as well as open-source models like LLaMA-2-70B-chat and LLaVA-1.5. However:
- Not all models are created equal. Some may perform better than others for specific tasks or datasets.
- The QA-Evaluation-Metrics model may not support the latest or most advanced models, which could impact its performance.
Scoring and Judgement
The QA-Evaluation-Metrics model uses various scoring methods to evaluate answers, including F1 score, PEDANTS score, and transformer score. However:
- These scores are not always perfect and may not accurately reflect the quality of an answer.
- The QA-Evaluation-Metrics model may struggle with nuanced or context-dependent questions, where the answer requires a deeper understanding of the topic.
Format
The QA-Evaluation-Metrics model is a Python package that provides six QA evaluation methods. It supports various input formats and has specific requirements for input and output.
Supported Input Formats
- Tokenized text sequences
- List of strings for reference answers
- String for candidate answer
- String for question
Model Architecture
The package uses a combination of transformer-based models and rule-based approaches for evaluation. The models include:
- Normalized Exact Match: A simple method that checks for exact matches between reference and candidate answers.
- Token F1 Score: A method that calculates the F1 score between reference and candidate answers.
- PEDANTS: A rule-based approach that evaluates the similarity between reference and candidate answers.
- Transformer Neural Evaluation: A transformer-based model that evaluates the similarity between reference and candidate answers.
- LLM Integration: A method that integrates with large language models (LLMs) for evaluation.
Input Requirements
- Reference answers: A list of strings or a single string.
- Candidate answer: A string.
- Question: A string (optional).
Output Requirements
- Boolean value indicating whether the candidate answer matches any reference answer.
- Dictionary containing the F1 score, precision, and recall between reference and candidate answers.
- Dictionary containing the similarity score between reference and candidate answers.
Code Examples
from qa_metrics.em import em_match
reference_answer = ["The Frog Prince", "The Princess and the Frog"]
candidate_answer = "The movie \"The Princess and the Frog\" is loosely based off the Brother Grimm's \"Iron Henry\""
match_result = em_match(reference_answer, candidate_answer)
