Roberta Base Squad2 Distilled
Meet Roberta Base Squad2 Distilled, a highly efficient AI model designed for extractive question answering. It's trained on the SQuAD 2.0 dataset and can be used to find answers in documents. What makes this model remarkable is its ability to provide accurate results while being relatively small in size, making it perfect for use cases where speed and efficiency are crucial. With its ability to handle English language queries, it's a great choice for building customizable, production-ready LLM applications. But how does it perform? It achieves an exact score of 79.84% and an F1 score of 83.92%, making it a reliable option for extractive question answering tasks.
Table of Contents
Model Overview
Meet the Roberta-Base Distilled for Extractive QA model! This model is specifically designed for extractive question answering tasks, and it’s here to help you find the answers you need.
The model is trained on the SQuAD 2.0 training set, which is a large dataset of questions and answers. It was also fine-tuned using a technique called distillation, which helps it learn from a more powerful teacher model.
What can it do?
- Answer questions based on the context of a given text
- Identify the most relevant parts of the text to answer your questions
- Work with multiple documents at once
Capabilities
This model excels at extractive question answering and text analysis. It’s designed to find the most relevant answers to your questions within a text.
Primary Tasks
- Extractive Question Answering: It can identify the most relevant answers to your questions within a text.
- Text Analysis: It can analyze text to understand the context and provide accurate answers.
Strengths
- High Accuracy: The model has achieved high accuracy on the SQuAD 2.0 dev set, with an exact score of
79.8366040596311and an F1 score of83.916407079888. - Efficient Training: The model was trained using Haystack’s distillation feature, which allows for efficient training and deployment.
How to Use
You can use this model in Haystack to do extractive question answering on documents. Here’s an example:
from haystack import Document
from haystack.components.readers import ExtractiveReader
docs = [
Document(content="Python is a popular programming language"),
Document(content="python ist eine beliebte Programmiersprache")
]
reader = ExtractiveReader(model="deepset/roberta-base-squad2-distilled")
reader.warm_up()
question = "What is a popular programming language?"
result = reader.run(query=question, documents=docs)
Performance
The model has achieved high accuracy on the SQuAD 2.0 dev set:
| Metric | Score |
|---|---|
| Exact | 79.8366040596311 |
| F1 | 83.916407079888 |
Limitations
This model is a powerful tool for extractive question answering, but it’s not perfect. Let’s talk about some of its limitations.
Language Limitations
- Current Model is trained on English data only, which means it may not perform well on documents or questions in other languages.
Contextual Understanding
- While Current Model is great at extracting answers from text, it may struggle with more complex or nuanced questions that require a deeper understanding of the context.
Limited Domain Knowledge
- Current Model is trained on a specific dataset (SQuAD 2.0) and may not have the same level of knowledge or expertise in other domains.
Overfitting
- Like any machine learning model, Current Model can suffer from overfitting, which means it might become too specialized in recognizing patterns in the training data and not generalize well to new, unseen data.
Technical Limitations
- Current Model requires significant computational resources to run, which can be a challenge for smaller organizations or individuals with limited resources.
